A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

  • Henrik Johansson1,

    Affiliated with

    • Malin Lindstedt1Email author,

      Affiliated with

      • Ann-Sofie Albrekt1 and

        Affiliated with

        • Carl AK Borrebaeck1Email author

          Affiliated with

          BMC Genomics201112:399

          DOI: 10.1186/1471-2164-12-399

          Received: 10 January 2011

          Accepted: 8 August 2011

          Published: 8 August 2011

          Abstract

          Background

          Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power.

          Results

          We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization.

          Conclusions

          A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests.

          Background

          Allergic contact dermatitis (ACD) is a common inflammatory skin disease characterized by eczema and recurrent episodes of itching [1]. The disease affects a significant proportion of the population, with prevalence rates of 7.2% to 18.6% in Europe [2, 3], and the incidence is increasing due to repeated exposure to sensitizing chemicals. ACD is a type IV delayed-type hypersensitivity response caused mainly by reactive T helper 1 (Th1) and interferon (IFN)γ producing CD8+ T cells, at site of contact with small chemical haptens in previously exposed, and immunologically sensitized, individuals [4]. Dendritic cells (DC) in the epidermis initiate the immune reactions by responding to haptens bound to self-molecules subsequently activating T cell-mediated immunity.

          The REACH (Registration, Evaluation, and Authorization of Chemicals) regulation requires that all new and existing chemicals within the European Union, involving approximately 30.000 chemicals, should be tested for hazardous effects [5]. As the identification of potential sensitizers currently requires animal testing, the REACH legislation will have a huge impact on the number of animals needed for testing. Further, the 7th Amendment to the Cosmetics Directive posed a ban on animal tests for the majority of cosmetic ingredients for human use, to be in effect by 2009, with the exceptions of some tests by 2013. Thus, development of reliable in vitro alternatives to experimental animals for the assessment of sensitizing capacity of chemicals is urgent. To date, no non-animal replacements are available for identification of skin sensitizing chemicals, instead the preferred assay is the mouse Local Lymph Node Assay (LLNA) [6], followed by the Guinea pig maximization test (GPMT) [7]. An in vitro alternative to these animal models should exhibit improved reliability, accuracy and importantly correlate to human reactivity.

          DCs play key roles in the immune response by bridging the essential connections between innate and adaptive immunity. Upon stimulation, they can rapidly produce large amounts of mediators that affect chemotaxis and activation of other cells at the site of inflammation, and can selectively respond to various pathogens and environmental factors, by fine-tuning the cellular response through antigen-presentation. Thus, exploring and utilizing the immunological decision-making by DCs during stimulation with sensitizers, could serve as a potent test strategy for the prediction of sensitization.

          Factors that complicate and impede the use of primary DCs as a test platform include adaptable phenotypes and specialized functions of different DC subpopulations, in addition to their wide and sparse distribution. Thus, the development of assays based on the predictability of DC function must rely on alternative cell types or mimics of in vivo DCs. For this purpose, a cell line with DC characteristics would be advantageous, as it constitutes a stable, reproducible and unlimited supply of cells. MUTZ-3 is an unlimited source of CD34+ DC progenitors. Upon differentiation, MUTZ-3 can acquire phenotypes comparable to immature DCs or Langerhans-like DCs [8], present antigens through CD1d, MHC class I and II and induce specific T-cell proliferation [9]. Differentiated MUTZ-3 can also display a mature transcriptional and phenotypic profile upon stimulation with inflammatory cytokines [10].

          In this report, we present a novel test principle for the prediction of skin sensitizers. To simplify the assay procedures and increase reproducibility, we employed progenitor MUTZ-3 cells, without further differentiation, and subjected the cells to stimulation with a large panel of sensitizing chemicals, non-sensitizing chemicals, and controls. The transcriptional response to chemical stimulation was assessed by genome-wide profiling. From data analysis, a biomarker signature of 200 transcripts was identified, which completely separated the response induced by sensitizing chemicals vs. non-sensitizing chemicals and the predictive power of the signature was illustrated, using ROC curves. The biomarker signature includes transcripts involved in relevant biological pathways, such as oxidative stress, DC maturation and cytokine responses, which further could shed light on molecular interactions involved in the process of sensitization. In conclusion, we have identified a biomarker signature with potent predictive power, which we propose as an in vitro assay for the identification of human sensitizing chemicals.

          Results

          The cellular rationale of the in vitro cell culture system

          DCs are essential immunoregulatory cells of the immune system demonstrated by their unique property to recognize antigen for the initiating of T cell responses, and their potent regulatory function in skewing immune responses. This makes them obvious targets for assay development. However, primary DCs constitute a heterogeneous and minor population of cells not suited for screening and the choice would be a human DC-like cell line, with characteristics compared to primary DCs. Since no leukemic cell line with DC-like properties has been reported [11], the generation of human DC-like cell lines relies on available myeloid leukemia cell lines. MUTZ-3 is a human acute myelomonocytic leukemia cell line with a potent ability to mimic primary human DCs [11]. Similar to immature primary DCs, MUTZ-3 progenitors express CD1a, HLA-DR and CD54, as well as low levels of CD80 and CD86 (Figure 1). The MUTZ-3 population also contains three subpopulations of CD14+, CD34+ and double negative cells, previously reported to be transitional differentiation steps from a proliferative CD34+ progenitor into a non-proliferative CD14+ DC precursor [8]. Consequently, constitutively differentiating progenitor MUTZ-3 cells were used as the basis for a test system.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-399/MediaObjects/12864_2011_3567_Fig1_HTML.jpg
          Figure 1

          Phenotype of MUTZ-3 cells prior to stimulation with sensitizing and non-sensitizing chemicals. Cell surface expression levels of CD14, CD1a, CD34, CD54, CD80, CD86 and HLA-DR were assessed with flow cytometry. Gates were set to exclude debris and dead cells, and quadrants were established by comparing with relevant isotype controls. Results are shown from one representative experiment out of six.

          CD86 surface expression in response to sensitizer stimulation

          CD86 is the most extensively studied biomarker for sensitization to date, using e.g. monocyte derived dendritic cells (MoDCs) or human cell lines and their progenitors, such as THP-1, U-937 and KG-1. Thus, as a reference, cell surface expression of CD86 was measured with flow cytometry after 24 h stimulation, using 20 sensitizers and 20 non-sensitizers, as well as vehicle controls (Figure 2). CD86 was significantly up-regulated on cells stimulated with 2-aminophenol, kathon CG, 2-nitro-1,4-phenylendiamine, 2,4-dinitrochlorobenzene, 2-hydroxyethyl acrylate, cinnamic aldehyde, p-phenylendiamine, resorcinol, potassium dichromate, and 2-mercaptobenzothiazole. Hence, an assay based on measurement of a single biomarker, such as CD86, would give a sensitivity of 50% and a specificity of 100%. Consequently, CD86 cannot classify skin sensitizers, using a system based on MUTZ-3 cells.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-399/MediaObjects/12864_2011_3567_Fig2_HTML.jpg
          Figure 2

          Changes in CD86 expression following stimulation with sensitizing and non-sensitizing chemicals. Cell surface expression levels of CD86 were monitored after stimulation with chemicals for 24 h. A). Chemical-induced up regulation of CD86, in terms of changes in frequency of positive cells, were determined by flow cytometry, as exemplified by the comparison of 2-aminophenol-stimulated cells (right dotplot) and unstimulated controls (left dot plot). Results are shown from one representative experiment out of three. Gates were set to exclude debris and dead cells, and quadrants were established by comparing with relevant isotype controls. B) Compilation of frequencies of CD86-positive cells after 24 h of stimulation. Statistical analysis was performed using Student's t test. *p < 0.05, # p < 0.01.

          Analysis of the transcriptional profiles in chemically stimulated MUTZ-3 cells

          The genomic expression arrays were then used to test the same 20 sensitizers and 20 non-sensitizers, in triplicates. The vehicle controls, such as DMSO and distilled water, were included in twelve replicates. In total, a data set was generated based on 144 samples. RMA normalization and quality controls of the samples revealed that the oxazolone and cinnamic aldehyde samples were significant outliers and had to be removed, or they would have dominated the data set prohibiting biomarker identification (data not shown). In addition, one of the replicates of potassium permanganate had to be removed due to a faulty array. This left a data set consisting of 137 samples, each with data from measurements of 29,141 transcripts. In order to mine the data set for information specific for sensitizers vs. non-sensitizers, the software Qlucore Omics Explorer 2.1 was used, which enable real time principal component analysis (PCA) analysis. The input genes were at the same time sorted after desired criteria, i.e. sensitizers and non-sensitizers, based on ANOVA p-value selection. Two different ANOVA analyses were performed (Figure 3). First, Figure 3A and 3B show PCA plots based on 1010 transcripts with a p-value of ≤ 2.0 × 10-6, from a one-way ANOVA analysis, comparing sensitizing vs. non-sensitizing chemicals. As can be seen in Figure 3A, a clear discrimination can be made between the two groups, with non-sensitizers forming a condensed cloud in the lower part of the figure (green), while sensitizers stretch upwards in various directions (red). However, a complete separation is not achieved between the two groups at this level of significance. From Figure 3B, now colored according to stimulating agent, it is evident that one or more replicate of glyoxal, eugenol, hexylcinnamic aldehyde, isoeugenol, resorcinol, penicillin G and ethylendiamine grouped together with the control group. In addition, one replicate or more of the non-sensitizers tween 80, octanoic acid and phenol grouped closely with the sensitizers. Secondly, Figure 3C and 3D show PCA plots based on 1137 genes, with p-values ≤ 7.0 × 10-21, from a multi-group ANOVA analysis, comparing each individual stimulation. Identifying this large number of genes at this level of significance provided strong indications of the power in the data set. In Figure 3D, it is clear that the replicates group together, indicating high quality data. The triplicate samples of potassium dichromate have a discrete profile, which demonstrate a substantial impact of the cells compared to non-sensitizers. Furthermore, 2-hydroxyethyl acrylate, 2-aminophenol, kathon CG, formaldehyde, 2-nitro-1,4-phenylendiamine, 2,4-dinitrochlorobenzoic acid, p-phenylendiamine, 2-mercaptobenzothiazole, cinnamic alcohol and resorcinol have replicates that group together, separate from the negative group. Still, as can be seen in Figure 3C as well as in 3A, complete separation is not achieved with neither of the gene signatures of 1010 and 1137 genes both selected on p-values.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-399/MediaObjects/12864_2011_3567_Fig3_HTML.jpg
          Figure 3

          Principal component analysis of transcripts differentially expressed after chemical stimulation. mRNA levels in MUTZ-3 cells stimulated for 24 h with 20 sensitizing and 20 non-sensitizing chemicals were assessed with transcriptomics, using Affymetrix Human Gene 1.0 ST arrays. Structures and similarities in the gene expression data set were investigated, using principal component analysis (PCA) in the software Qlucore. A) PCA of genes differentially expressed in cells stimulated with sensitizing (red) versus non-sensitizing (green) chemicals (1010 genes identified with one-way ANOVA). B) PCA of genes differentially expressed in cells stimulated with sensitizing versus non-sensitizing chemicals (1010 genes), but now samples are colored by the compound used for stimulation. C) PCA of genes differentially expressed when comparing the different stimulations with 2-way ANOVA (1137 genes). Samples are colored according to sensitizing (red) and non-sensitizing (green) chemicals. D) PCA of genes differentially expressed when comparing the different stimulations with 2-way ANOVA (1137 genes), but now samples are colored by the compound used for stimulation. P, p-value from ANOVA. Q, p-value corrected for multiple hypothesis testing.

          Backward elimination identifies genes with the most discriminatory power

          Even though the data set contains genes with p-values down to 1 × 10-17, lowering the p-value cutoff did not achieve complete separation between sensitizers and non-sensitizers. Gene signatures entirely selected on p-values does not provide the best possible predictive power, since the information is per se not orthogonal. To further reduce the number of transcripts for a predictive biomarker signature, we employed an algorithm for backward elimination (Figure 4A). The algorithm removes genes one by one while taking into account not only the impact of genes individually, but how they perform collectively with the entire selected gene signature. For each gene eliminated, the Kullback-Leibler divergence (KLD) value is lowered, until a breakpoint is reached, at which point 200 genes remained. Continuing eliminating genes at this point causes the KLD to rise again, indicating that information is being lost (Figure 4A). Therefore, the 200 genes with lowest KLD value were selected for further analysis. PCA of the 200 analytes now revealed that they have the ability to completely separate sensitizers from non-sensitizers, indicating that these transcripts can be used as predictors for sensitizing properties of unknown samples (Figure 4B). Importantly, by coloring the samples in the PCA by their potency, according to LLNA, it is clear that potency can also be predicted (Figure 4C), as extreme and strong sensitizers tend to group further from the non-sensitizers, while moderate and extreme sensitizers group closer to non-sensitizers. The 200 genes are termed the "Prediction Signature" and their identities are listed in Table 1. In addition, the transcriptional profiles of the differentially expressed genes are presented in a heatmap (Figure 5).
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-399/MediaObjects/12864_2011_3567_Fig4_HTML.jpg
          Figure 4

          Identification and PCA analysis of Prediction Signature. A) The number of differentially expressed significant genes in cells stimulated with sensitizing versus non-sensitizing chemicals (1010 genes) was reduced, using Backward Elimination. The lowest KLD is observed after elimination of 810 analytes, referred to as the Breakpoint. The remaining 200 genes are considered to be the top predictors in the data set, and are termed Prediction Signature. B) Complete separation between sensitizers (red) and non-sensitizers (green) is observed with PCA of the Prediction Signature. C) Same PCA as in B, now with samples colored according to their potency in LLNA.

          Table 1

          Prediction Signature

          Gene Title

          Gene Symbol

          Entrez Gene ID

          Affymetrix HuGene 1.0 ST ID

          Validation Call frequency (%)

          4-aminobutyrate aminotransferase

          ABAT

          18

          7993126

          30

          abhydrolase domain containing 5

          ABHD5

          51099

          8079153

          85

          alkaline ceramidase 2

          ACER2

          340485

          8154563

          95

          ATP citrate lyase

          ACLY

          47

          8015460

          85

          actin-related protein 10 homolog (S. cerevisiae)

          ACTR10

          55860

          7974587

          75

          ADAM metallopeptidase domain 20

          ADAM20

          8748

          7979927

          35

          aldehyde dehydrogenase 18 fam., member A1

          ALDH18A1

          5832

          7935230

          75

          aldehyde dehydrogenase 1 fam., member B1

          ALDH1B1

          219

          8155327

          70

          anaphase promoting complex subunit 1

          ANAPC1

          64682

          8043349

          55

          anaphase promoting complex subunit 5

          ANAPC5

          51433

          7967149

          25

          ankyrin repeat, fam. A (RFXANK-like), 2

          ANKRA2

          57763

          8112596

          100

          ADP-ribosylation factor GTPase activating protein 3

          ARFGAP3

          26286

          8076515

          55

          Rho GTPase activating protein 9

          ARHGAP9

          64333

          7964436

          75

          ankyrin repeat and SOCS box-containing 7

          ASB7

          140460

          7986433

          65

          ATPase, H+ transporting, lysosomal 38 kDa, V0 subunit d1//ATPase, H+ transporting, lysosomal 38 kDa, V0 subunit d1

          ATP6V0D1//ATP6V0D1

          9114//9114

          8002041

          10

          ATPase, H+ transporting, lysosomal 9 kDa, V0 subunit e1

          ATP6V0E1

          8992

          8110022

          75

          ATPase, H+ transporting, lysosomal 50/57 kDa, V1 subunit H

          ATP6V1H

          51606

          8150797

          100

          B-cell CLL/lymphoma 7A

          BCL7A

          605

          7959354

          85

          bridging integrator 2

          BIN2

          51411

          7963289

          80

          bleomycin hydrolase

          BLMH

          642

          8014008

          15

          brix domain containing 1//ribosome production factor 2 homolog (S. cerevisiae)

          BXDC1//RPF2

          84154//84154

          8062211

          40

          chromosome 11 open reading frame 61

          C11orf61

          79684

          7952445

          55

          chromosome 11 open reading frame 67//integrator complex subunit 4

          C11orf67//INTS4

          28971//92105

          7942783

          50

          chromosome 12 open reading frame 57

          C12orf57

          113246

          7953564

          40

          chromosome 13 open reading frame 18

          C13orf18

          80183

          7971486

          50

          chromosome 15 open reading frame 24

          C15orf24

          56851

          7987172

          50

          chromosome 19 open reading frame 46//alkB, alkylation repair homolog 6 (E. coli)

          C19orf46//ALKBH6

          163183//84964

          8036242

          30

          chromosome 19 open reading frame 54

          C19orf54

          284325

          8036956

          95

          chromosome 1 open reading frame 174

          C1orf174

          339448

          7911897

          40

          chromosome 1 open reading frame 183

          C1orf183

          55924

          7918552

          85

          chromosome 20 open reading frame 111

          C20orf111

          51526

          8066402

          65

          chromosome 20 open reading frame 24

          C20orf24

          55969

          8062326

          20

          chromosome 3 open reading frame 62//ubiquitin specific peptidase 4 (proto-oncogene)

          C3orf62//USP4

          375341//7375

          8087374

          40

          chromosome 9 open reading frame 89

          C9orf89

          84270

          8156404

          100

          coactivator-associated arginine methyltransferase 1

          CARM1

          10498

          8025766

          60

          CD33 molecule

          CD33

          945

          8030804

          45

          CD86 molecule

          CD86

          942

          8082035

          45

          CD93 molecule

          CD93

          22918

          8065359

          50

          cytochrome c oxidase subunit VIIa polypeptide 2 like

          COX7A2L

          9167

          8051777

          45

          corticotropin releasing hormone binding protein

          CRHBP

          1393

          8106418

          45

          chondroitin sulfate N-acetylgalactosaminyltransferase 2

          CSGALNACT2

          55454

          7927146

          90

          cytochrome P450, fam. 51, subfam. A, polypeptide 1

          CYP51A1

          1595

          8140864

          85

          DDRGK domain containing 1

          DDRGK1

          65992

          8064601

          60

          DEAD (Asp-Glu-Ala-As) box polypeptide 19A

          DDX19A

          55308

          7997059

          95

          DEAD (Asp-Glu-Ala-Asp) box polypeptide 21

          DDX21

          9188

          7927936

          60

          24-dehydrocholesterol reductase

          DHCR24

          1718

          7916432

          100

          7-dehydrocholesterol reductase

          DHCR7

          1717

          7950067

          80

          DEAH (Asp-Glu-Ala-His) box polypeptide 33

          DHX33

          56919

          8011861

          100

          DnaJ (Hsp40) homolog, subfam. B, member 4

          DNAJB4

          11080

          7902512

          100

          DnaJ (Hsp40) homolog, subfam. B, member 9

          DNAJB9

          4189

          8135480

          25

          DnaJ (Hsp40) homolog, subfam. C, member 5

          DNAJC5

          80331

          8064208

          10

          DnaJ (Hsp40) homolog, subfam. C, member 9

          DNAJC9

          23234

          7934320

          55

          DNA-damage regulated autophagy modulator 2//choline/ethanolamine phosphotransferase 1

          DRAM2//CEPT1

          128338//10390

          7918474

          100

          D-tyrosyl-tRNA deacylase 1 homolog (S. cerevisiae)

          DTD1

          92675

          8061211

          45

          ER degradation enhancer, mannosidase alpha-like 2

          EDEM2

          55741

          8065855

          80

          ecotropic viral integration site 2B

          EVI2B

          2124

          8014063

          60

          fam. with sequence similarity 36, member A//non-protein coding RNA 201

          FAM36A//NCRNA00201

          116228//284702

          7911085

          15

          fam. with sequence similarity 86, member A

          FAM86A

          196483

          7999304

          25

          Fas (TNF receptor superfam., member 6)

          FAS

          355

          7929032

          70

          fatty acid synthase

          FASN

          2194

          8019392

          100

          F-box protein 10//translocase of outer mitochondrial membrane 5 homolog (yeast)

          FBXO10//TOMM5

          26267//401505

          8161229

          40

          MGC44478

          FDPSL2A

          619190

          8140443

          55

          ferredoxin reductase

          FDXR

          2232

          8018236

          40

          forkhead box O4

          FOXO4

          4303

          8168205

          80

          ferritin, heavy polypeptide-like 5

          FTHL5

          2509

          8126948

          95

          fucosidase, alpha-L- 2, plasma

          FUCA2

          2519

          8129974

          20

          growth arrest-specific 2 like 3

          GAS2L3

          283431

          7957850

          70

          ganglioside induced differentiation associated protein 2

          GDAP2

          54834

          7918955

          80

          growth differentiation factor 11

          GDF11

          10220

          7956026

          65

          glutaredoxin (thioltransferase)

          GLRX

          2745

          8113214

          90

          guanine nucleotide binding protein-like 3 (nucleolar)-like

          GNL3L

          54552

          8167797

          85

          glucosamine-phosphate N-acetyltransferase 1

          GNPNAT1

          64841

          7979196

          90

          glutathione reductase

          GSR

          2936

          8150112

          40

          GTF2I repeat domain containing 2//GTF2I repeat domain containing 2B

          GTF2IRD2//GTF2IRD2B

          84163//389524

          8133549 and 8140170

          50 and 30

          general transcription factor IIIC, polypeptide 2, beta 110 kDa

          GTF3C2

          2976

          8051075

          55

          HMG-box transcription factor 1//component of oligomeric golgi complex 5

          HBP1//COG5

          26959//10466

          8135392

          65

          histone cluster 1, H1c

          HIST1H1C

          3006

          8124397

          45

          histone cluster 1, H1e

          HIST1H1E

          3008

          8117377

          95

          histone cluster 1, H2ae

          HIST1H2AE

          3012

          8117408

          45

          histone cluster 1, H2be

          HIST1H2BE

          8344

          8117389

          15

          histone cluster 1, H3g

          HIST1H3G

          8355

          8124440

          35

          histone cluster 1, H3j

          HIST1H3J

          8356

          8124537

          60

          histone cluster 1, H4a

          HIST1H4A

          8359

          8117334

          10

          histone cluster 2, H2ac//histone cluster 2, H2aa3//histone cluster 2, H2aa4

          HIST-2H2AC//2H2AA3//2H2AA4

          8338//8337//723790

          7905079 and 7919619

          75 and 75

          histone cluster 2, H2bf//histone cluster 2, H2be//histone cluster 2, H2ba

          HIST-2H2BF//2H2BE//2H2BA

          440689//8349//337875

          7919606

          50

          high-mobility group box 3

          HMGB3

          3149

          8170468

          5

          3-hydroxy-3-methylglutaryl-Coenzyme A reductase//3-hydroxy-3-methylglutaryl-CoA reductase

          HMGCR//HMGCR

          3156//3156

          8106280

          90

          3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 (soluble)//3-hydroxy-3-methylglutaryl-CoA synthase 1 (soluble)

          HMGCS1//HMGCS1

          3157//3157

          8111941

          80

          heme oxygenase (decycling) 1

          HMOX1

          3162

          8072678

          10

          heterogeneous nuclear ribonucleoprotein L

          HNRNPL

          3191

          8036613

          30

          insulin receptor substrate 2

          IRS2

          8660

          7972745

          35

          iron-sulfur cluster scaffold homolog (E. coli)

          ISCU

          23479

          7958414

          100

          interferon stimulated exonuclease gene 20 kDa-like 2

          ISG20L2

          81875

          7921110

          45

          potassium voltage-gated channel, Isk-related fam., member 3

          KCNE3

          10008

          7950409

          25

          keratinocyte growth factor-like protein 1//fibroblast growth factor 7 (keratinocyte growth factor)//keratinocyte growth factor-like protein 2//hypothetical protein FLJ20444

          KGFLP1//FGF7//KGFLP2//FLJ20444

          387628//2252//654466//403323

          8155530

          70

          lysophosphatidic acid receptor 1

          LPAR1

          1902

          8163257

          10

          leucine-rich PPR-motif containing

          LRPPRC

          10128

          8051882

          65

          lymphocyte antigen 96

          LY96

          23643

          8146934

          35

          mitogen-activated protein kinase kinase 1//small nuclear RNA activating complex, polypeptide 5, 19 kDa

          MAP2K1//SNAPC5

          5604//10302

          7984319

          30

          mitogen-activated protein kinase 13

          MAPK13

          5603

          8119016

          60

          methyltransferase like 2A

          METTL2A

          339175

          8009008

          45

          microsomal glutathione S-transferase 3

          MGST3

          4259

          7906978

          70

          mitochondrial ribosomal protein L30

          MRPL30

          51263

          8043848

          30

          mitochondrial ribosomal protein L4

          MRPL4

          51073

          8025586

          40

          mitochondrial ribosomal protein S17//glioblastoma amplified sequence//zinc finger protein 713

          MRPS17//GBAS//ZNF713

          51373//2631//349075

          8132922

          60

          mitochondrial poly(A) polymerase//golgi autoantigen, golgin subfam. a, 6 pseudogene

          MTPAP//LOC729668

          55149//729668

          7932834

          45

          5-methyltetrahydrofolate-homocysteine methyltransferase

          MTR

          4548

          7910752

          15

          neighbor of BRCA1 gene 1

          NBR1

          4077

          8007471

          20

          nuclear import 7 homolog (S. cerevisiae)

          NIP7

          51388

          7996934

          75

          NLR fam., pyrin domain containing 12

          NLRP12

          91662

          8039096

          35

          nucleolar protein fam. 6 (RNA-associated)

          NOL6

          65083

          8160682

          95

          NAD(P)H dehydrogenase, quinone 1

          NQO1

          1728

          8002303

          45

          nuclear receptor binding protein 1

          NRBP1

          29959

          8040927

          20

          nucleotide binding protein-like

          NUBPL

          80224

          7973826

          10

          nudix (nucleoside diphosphate linked moiety X)-type motif 14

          NUDT14

          256281

          7981566

          35

          nuclear fragile × mental retardation protein interacting protein 1

          NUFIP1

          26747

          7971361

          60

          nucleoporin 153 kDa

          NUP153

          9972

          8124059

          25

          olfactory receptor, fam. 5, subfam. B, member 21

          OR5B21

          219968

          7948330

          50

          PAS domain containing serine/threonine kinase

          PASK

          23178

          8060205

          55

          PRKC, apoptosis, WT1, regulator

          PAWR

          5074

          7965112

          30

          PDGFA associated protein 1

          PDAP1

          11333

          8141273

          35

          phosphodiesterase 1B, calmodulin-dependent

          PDE1B

          5153

          7955943

          85

          phosphoribosylformylglycinamidine synthase

          PFAS

          5198

          8004804

          60

          pleckstrin homology-like domain, fam. A, member 3

          PHLDA3

          23612

          7923372

          75

          phosphoinositide-3-kinase adaptor protein 1

          PIK3AP1

          118788

          7935337

          20

          PTEN induced putative kinase 1

          PINK1

          65018

          7898663

          70

          phosphomannomutase 2

          PMM2

          5373

          7993148

          65

          partner of NOB1 homolog (S. cerevisiae)

          PNO1

          56902

          8042381

          40

          polymerase (RNA) II (DNA directed) polypeptide E, 25 kDa

          POLR2E

          5434

          8032149

          80

          polymerase (RNA) III (DNA directed) polypeptide E (80 kD)

          POLR3E

          55718

          7993973

          30

          protein phosphatase 1D magnesium-dependent, delta isoform//protein phosphatase, Mg2+/Mn2+ dependent, 1D

          PPM1D//PPM1D

          8493//8493

          8008922

          80

          phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 1

          PREX1

          57580

          8066848

          100

          proline-serine-threonine phosphatase interacting protein 1

          PSTPIP1

          9051

          7985099

          95

          prothymosin, alpha

          PTMA

          5757

          7954006 and 7961022

          20 and 15

          RAB33B, member RAS oncogene fam.

          RAB33B

          83452

          8097507

          40

          renin binding protein

          RENBP

          5973

          8175933

          65

          replication factor C (activator 1) 2, 40 kDa

          RFC2

          5982

          8140151

          30

          ribonuclease H1

          RNASEH1

          246243

          8050079

          90

          ring finger protein 146

          RNF146

          81847

          8121927

          50

          ring finger protein 24

          RNF24

          11237

          8064766

          100

          ring finger protein 26

          RNF26

          79102

          7944510

          95

          ribosomal protein SA//small nucleolar RNA, H/ACA box 62

          RPSA//SNORA62

          3921//6044

          8078918

          75

          RNA pseudouridylate synthase domain containing 2

          RPUSD2

          27079

          7982753

          45

          ribosomal RNA processing 12 homolog (S. cerevisiae)

          RRP12

          23223

          7935425

          75

          retinoid × receptor, alpha

          RXRA

          6256

          8159127

          5

          scavenger receptor class B, member 2

          SCARB2

          950

          8101158

          70

          SERPINE1 mRNA binding protein 1

          SERBP1

          26135

          7916836

          95

          splicing factor proline/glutamine-rich (polypyrimidine tract binding protein associated)//splicing factor proline/glutamine-rich

          SFPQ//SFPQ

          6421//6421

          7914791

          40

          solute carrier fam. 25, member 32//DDB1 and CUL4 associated factor 13

          SLC25A32//DCAF13

          81034//25879

          8152255

          100

          solute carrier fam. 35, member B3

          SLC35B3

          51000

          8123825

          40

          solute carrier fam. 37 (glucose-6-phosphate transporter), member 4

          SLC37A4

          2542

          7952132

          55

          solute carrier fam. 5 (sodium-dependent vitamin transporter), member 6

          SLC5A6

          8884

          8051030

          95

          sphingomyelin phosphodiesterase 4, neutral membrane (neutral sphingomyelinase-3)

          SMPD4

          55627

          8055183

          40

          small nucleolar RNA host gene 1 (non-protein coding)//small nucleolar RNA, C/D box 26

          SNHG1//SNORD26

          23642//9302

          7948908

          20

          small nucleolar RNA host gene 12 (non-protein coding)

          SNHG12

          85028

          7914202

          10

          small nucleolar RNA, H/ACA box 45

          SNORA45

          677826

          7938293

          25

          sorting nexin fam. member 27

          SNX27

          81609

          7905444

          35

          spinster homolog 2 (Drosophila)//MYB binding protein (P160) 1a

          SPNS2//MYBBP1A

          124976//10514

          8011640

          45

          sprouty homolog 2 (Drosophila)

          SPRY2

          10253

          7972217

          75

          squalene epoxidase

          SQLE

          6713

          8148280

          95

          sterol regulatory element binding transcription factor 2

          SREBF2

          6721

          8073522

          45

          ST3 beta-galactoside alpha-2,3-sialyltransferase 6

          ST3GAL6

          10402

          8081219

          100

          serine/threonine kinase 17b

          STK17B

          9262

          8057887

          90

          transmembrane anterior posterior transformation 1

          TAPT1

          202018

          8099506

          65

          taste receptor, type 2, member 5

          TAS2R5

          54429

          8136647

          40

          tubulin folding cofactor E-like

          TBCEL

          219899

          7944623

          55

          tectonic fam. member 2

          TCTN2

          79867

          7959638

          40

          toll-like receptor 6

          TLR6

          10333

          8099841

          30

          transmembrane protein 150B

          TMEM150B

          284417

          8039453

          25

          transmembrane protein 55A

          TMEM55A

          55529

          8151756

          90

          transmembrane protein 59

          TMEM59

          9528

          7916372

          90

          transmembrane protein 97

          TMEM97

          27346

          8005839

          95

          tumor necrosis factor receptor superfam., member 10c, decoy without an intracellular domain

          TNFRSF10C

          8794

          8145244

          75

          translocase of outer mitochondrial membrane 34

          TOMM34

          10953

          8066461

          35

          translocase of outer mitochondrial membrane 40 homolog (yeast)

          TOMM40

          10452

          8029521

          40

          tumor protein p53 inducible protein 3

          TP53I3

          9540

          8050702

          30

          tumor protein p53 inducible nuclear protein 1

          TP53INP1

          94241

          8151890

          100

          twinfilin, actin-binding protein, homolog 2 (Drosophila)//toll-like receptor 9

          TWF2//TLR9

          11344//54106

          8087860

          65

          thioredoxin reductase 1

          TXNRD1

          7296

          7958174

          55

          ubiquitin-fold modifier conjugating enzyme 1

          UFC1

          51506

          7906662

          95

          ubiquitin specific peptidase 10

          USP10

          9100

          7997633

          30

          vesicle-associated membrane protein 3 (cellubrevin)

          VAMP3

          9341

          7897370

          40

          valyl-tRNA synthetase

          VARS

          7407

          8125091 and 8178609

          10 and 10

          vacuolar protein sorting 37 homolog A (S. cerevisiae)

          VPS37A

          137492

          8144774

          60

          zinc finger protein 211

          ZNF211

          10520

          8031792

          45

          zinc finger protein 223

          ZNF223

          7766

          8029360

          65

          zinc finger protein 561

          ZNF561

          93134

          8033795

          60

          zinc finger protein 79

          ZNF79

          7633

          8158022

          100

          ---

          ---

          ---

          7910385

          40

          ---

          ---

          ---

          7946567

          15

          ---

          ---

          ---

          7966223

          45

          ---

          ---

          ---

          7979694

          40

          ---

          ---

          ---

          8130495

          30

          ---

          ---

          ---

          8180237

          60

          ---

          ---

          ---

          8180268

          85

          ---

          ---

          ---

          8180417

          85

          The table shows the biomarker genes found by t-test and Backward Elimination. Genes were annotated, using the NetAffx database from Affymetrix (http://​www.​affymetrix.​com, Santa Clara USA). When found, the Entrez Gene ID http://​www.​ncbi.​nlm.​nih.​gov/​gene was chosen as the gene identifier. The validation call frequency (%) is the occurrence of each gene in the 20 Test Gene Signatures obtained in the validation step.

          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-399/MediaObjects/12864_2011_3567_Fig5_HTML.jpg
          Figure 5

          Transcriptional profiles of sensitizers and non-sensitizers. Hierarchical clustering of the genes in the Prediction Signature. Samples are grouped as sensitizer or non-sensitizer, and all replicates are included. Each row represents one gene, which is scaled to have a mean of zero and standard deviation of one, with colors representing the number of standard deviations from the mean.

          Interrogation of the analysis used to identify the Prediction Signature

          To validate the predictive power of our signature, we used a machine learning method called the Support Vector Machine (SVM) [12], which maps the data from a training set in space in order to maximize the separation of gene expression induced by sensitizing and non-sensitizing chemicals. As training set, 70% of the data set was selected randomly and the entire selection process was repeated. Starting with 29,141 transcripts, the signature was reduced to 200 transcripts, termed "Test Gene Signature", using ANOVA filtering and backward elimination, as described above. The remaining 30% of the data set was used to test each signature. The partitioning of the data set into subsets of 70% training data set and 30% test data set was done in a stratified random manner, while maintaining the relation of sensitizers and non-sensitizers. Thereafter, the Test Gene Signature was used to train an SVM model with the training set, and the predictive power of the model was assessed with the test set. This entire process was iterated 20 times. The frequency by which each gene in the Prediction Signature was included in the Test Gene Signatures is reported in table 1. Figure 6A shows a PCA plot based on the Test Gene Signature from one representative iteration. Clearly, the separation between sensitizers and non-sensitizers resembles the one observed for the Prediction Signature in Figure 4B. In Figure 6A, the samples of the sensitizing and non-sensitizing chemicals in the test set have been colored dark red and dark green respectively, indicating that they are not contributing to the principal components of the plot, but are merely plotted based on their expression values of the selected Test Gene Signature. As can be seen, sensitizers from the test set group with sensitizers from the training set, while non-sensitizers from the test set group with non-sensitizers from the training set. The final outcome of the SVM training and validation can be seen in Figure 6B, where the areas under the ROC curve are plotted for each iteration. The average area under the ROC curve of 0.98 confirms the ability to discriminate sensitizers from control samples. Based on this average, the estimated prediction performance of the assay reveals an accuracy of 99%, sensitivity of 99% and specificity of 99%. While this experiment does not validate the prediction power of the Prediction Signature per se, it does indeed validate the method by which it has been selected, supporting the claim that the Prediction Signature is capable of accurately predicting sensitizing properties of unknown samples.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-399/MediaObjects/12864_2011_3567_Fig6_HTML.jpg
          Figure 6

          Validation of selection procedure of Prediction Signature. The method by which the Prediction Signature was constructed was validated by repeating the process on 70% randomly selected data (training set). The remaining 30% of data was used as a test set for signature validation. The process was repeated for 20 iterations. A) A representative PCA of one of the 20 iterations, which demonstrates that the Test Gene Signature can separate skin sensitizers from non-sensitizers. Only the samples of the 70% training set, displayed in bright colors, were used to build the space of the first three principal components. The test set samples, displayed in dark colors, were plotted into this space based on expression levels of the analytes in the Test Gene Signature. B) An SVM was trained on the 70% training set, and validated with the 30% test set. The areas under the ROC curve from 20 such randomizations are plotted, yielding an average AUC value of 0.98. This indicated that the classification of samples in the test set was correct.

          Interactome, molecular functions and canonical pathways involving the Prediction Signature

          Using Ingenuity Pathways Analysis (IPA, Ingenuity Systems Inc.), 184 of the 200 molecules in the signature were characterized with regard to the interactome, known functions and (canonical) pathways. The remaining 16 molecules could not be mapped to any unique IPA entries. The dominating functions identified were small molecule biochemistry (39 molecules), cell death (33), lipid metabolism (25), hematological system development (18), cell cycle (18), molecular transport (17), cellular growth and proliferation (16), and carbohydrate metabolism (15) (Table 2).
          Table 2

          Dominating functions of the Prediction signature

          Function

          Number of molecules from signature

          Molecule names

          Most prominent sub functions

          small molecule biochemistry

          39

          ABHD5, ACLY, ALDH18A1, BLMH, CD86, CSGALNACT2, CYP51A1, DHCR24, DHCR7, DNAJC5, FAS, FASN, FDXR, FOXO4, GLRX, GNPNAT1, HMGCR, HMOX1, IRS2, LPAR1, LY96, MGST3, MTR, NQO1, PASK, PDE1B, PINK1, PMM2, RENBP, RXRA, SLC25A32, SLC37A4, SLC5A6, SMPD4, SQLE, SREBF2, ST3GAL6, TLR6, TMEM55A

          Metabolism (24), biosynthesis (15), modification (12), synthesis (11)

          cell death

          33

          CD33, DDX19A, DHCR24, DNAJB9, DNAJC5, FAS, FASN, FDXR, FOXO4, GLRX, GNPNAT1, GSR, HIST1H1C, HMGB3, HMOX1, IRS2, LPAR1, MAP2K1, MAPK13, NQO1, PAWR, PDE1B, PHLDA3, PINK1, PPM1D, RXRA, SERBP1, SPRY2, STK17B, TLR6, TNFRSF10C, TP53INP1, TXNRD1

          Apoptosis (30), cell death (13)

          lipid metabolism

          25

          ABHD5, ACLY, CYP51A1, DHCR24, DHCR7, FAS, FASN, FDXR, FOXO4, HMGCR, HMOX1, IRS2, LPAR1, LY96, MGST3, PASK, RENBP, RXRA, SLC37A4, SMPD4, SQLE, SREBF2, ST3GAL6, TLR6, TMEM55A

          Metabolism (18),

          synthesis (11), modification (11)

          hematological system development

          18

          CARM1, CD33, CD86, FAS, FOXO4, HMGB3, HMGCR, HMOX1, IRS2, LY96, NBR1, NQO1, PAWR, PIK3AP1, PPM1D, STK17B, TP53INP1, VAMP3

          Proliferation (10),

          quantity (7)

          cell cycle

          18

          ABHD5, ANAPC5, DNAJB4, DTD1, FAS, FASN, FOXO4, GDF11, HBP1, HMOX1, IRS2, MAP2K1, PAWR, PPM1D, RXRA, SFPQ, SPRY2, TP53INP1

          Cell cycle progression (13), G2 phase (5)

          molecular transport

          17

          ABHD5, DNAJC5, FAS, FOXO4, HMOX1, LPAR1, MTR, NQO1, PASK, PINK1, RENBP, RXRA, SLC25A32, SLC37A4, SLC5A6, SREBF2, TLR6

          Accumulation (9), quantity (5)

          cellular growth and proliferation

          16

          CD33, CD86, FAS, GNPNAT1, HMOX1, IRS2, LPAR1, LY96, MAP2K1, PAWR, PIK3AP1, PPM1D, RXRA, SPRY2, STK17B, TP53INP1

          Proliferation (16), growth (4)

          carbohydrate metabolism

          15

          ABHD5, ACLY, CSGALNACT2, FAS, FASN, FUCA2, GNPNAT1, IRS2, LY96, NQO1, PMM2, RENBP, SLC37A4, ST3GAL6, TMEM55A

          Metabolism (9), biosynthesis (5)

          Dominating functions in the molecular signature. 184 of the 200 molecules were functionally investigated, using IPA. Only functions populated by 15 or more genes were included in the present study.

          Pathways possibly invoked by the molecules in the signature were also investigated using IPA. Those most highly populated involved NRF2-mediated oxidative response (10), xenobiotic metabolism signaling (8), protein ubiquitination pathway (7), LPS/IL-1 mediated inhibition of RXR function (6), aryl hydrocarbon receptor signaling (6) and protein kinase A signaling (6). These pathways are known to take part in reactions provoked by foreign substances, xenobiotics, which supports a relevant biology behind the genomic signature.

          Discussion

          Allergic contact dermatitis (ACD) is an inflammatory skin disease caused by an adaptive immune response to normally innocuous chemicals [13]. Small molecular weight chemicals, so-called haptens, can bind self-proteins in the skin, which enables internalization of the protein-bound allergenic chemical by skin dendritic cell (DC). DCs, under the influence of the local microenvironment, process the protein-hapten complex, migrate to the local lymph nodes and activate naïve T cells. The initiation and development of allergen-specific responses, mainly effector CD8+ T cells and Th1 cells, and production of immunoregulatory proteins, are hallmarks of the immune activation observed in ACD. ACD is also the most common manifestation of immunotoxicity observed in humans [13] and hundreds of chemicals have been shown to cause sensitization in skin [14]. The driving factors and molecular mechanisms involved in sensitization are still unknown even though intense research efforts have been carried out to characterize the immunological responses towards allergenic chemicals. The REACH legislation requires that all chemicals produced over 1 ton/year are tested for hazardous properties such as toxicity and allergenicity [5], which increase the demand for accurate assays with predictive power for hazard identification. Additionally, the 7th Amendment to the Cosmetics Directive (76/768/EEC) poses a complete ban on using animal experimentation for testing cosmetic ingredients by 2013 if a scientifically reliable method is available. Thus, there is a significant need for predictive test methods that are based on human cells. Today, the identification of potential human sensitizers relies on animal experimentation, in particular the murine local lymph node assay (LLNA) [6]. The LLNA is based upon measurements of proliferation induced in draining lymph nodes of mice after chemical exposure [15]. Chemicals are defined as sensitizers if they provoke a three-fold increase in proliferation compared to control, and the amount of chemical required for the increase is the EC3 value. Thus, the LLNA can also be used to categorize the chemicals based on sensitization potency. However, LLNA is, besides the obvious ethical implications, also time consuming and expensive. Human sensitization data often stem from human maximization tests (HMT) [16] and human patch tests (HPT). In an extensive report from the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), the performance characteristics of LLNA were compared to other available animal-based methods and human sensitization data (HMT and HPT) [17]. The LLNA performance in comparison to human data (74 assessments) revealed an accuracy of 72%, a sensitivity of 72% and a specificity of 67%.

          Various human cell lines and primary cells involved in sensitization have been evaluated as predictive test system, such as epithelial cells, dendritic cells and T cells, however, no validated test assay is currently available. THP-1, U937, KG-1 and MUTZ-3, naive or differentiated, are among the human myeloid cell lines most extensively evaluated as platforms for DC-based in vitro assays, as reviewed in [18]. These cells are easy to grow and enable standardization of protocols. U937 and THP-1 are currently being evaluated in pre-validation stage for prediction of skin sensitization. The Human Cell Line Activation Test (hCLAT) is based upon analysis of CD86 and/or CD54 expression on THP-1 cells after chemical stimulation [19, 20]. The Myeloid U937 Skin Sensitization Test (MUSST) also involves analysis of CD86 [21]. These assays are thus very limited in readout. As CD86 is among the markers most extensively studied, we evaluated the expression level of this marker in our assay. We demonstrated its relevance but also its insufficient predictive power (Figure 2), since only 10 out of 20 sensitizing chemicals induced a significant up regulation of CD86. Various other single biomarkers have been suggested to be up regulated upon stimulation with sensitizing chemicals, such as CD40, CD80, CD54, CXCL8, IL-1β, MIP-1β, p38 MAPK, as reviewed in [18], yet single-handedly, none of them have enough predictive power to discriminate between sensitizing and non-sensitizing chemicals. The analysis of biomarker signatures, i.e. combination of biomarkers, has been shown to be superior in molecular diagnostic of cancer and superior to any single biomarker. Consequently, we therefore utilized the power of global transcriptomics and screened the gene regulation induced by a large set of well-defined chemicals and controls in search of predictive biomarker combinations.

          The large number of differentially expressed genes in MUTZ-3 cells stimulated with sensitizing chemicals vs. non-sensitizing controls revealed that MUTZ-3 indeed had a capacity to differentiate between these two groups. Efforts have previously been done to create assays based on genome analysis in various cell systems, such as e.g. CD34+-progenitor cells-derived DCs [2224]. While such assays might provide in vivo like environments, primary cells are not well suited for a high-throughput format considering both donor-dependent variations as well as ethical aspect of such cell sources. Furthermore, previous efforts within in vitro assay development for sensitization that rely on full genome analysis have used a limited set of testing compounds.

          The present study utilized in all 40 compounds and efforts were made to divide these compounds into two subsets, for training and testing respectively. While these experiments have resulted in successful predictions (data not shown), it is our experience that sensitizing compounds differ greatly in their induced gene expression profile, as can be seen in Figure 3D. In this perspective, we strived to include as many training compounds as possible when identifying our Prediction Signature, and did not exclude any compounds for validation. Instead, we validated the method by which the Prediction Signature was identified, by subdividing the samples into training and test sets at random, using unseen data for validation, to avoid overfitting. At present, the Prediction Signature consists of 200 transcripts, based on Figure 4A. Continuing the elimination process beyond 200 transcripts causes loss of information, as seen by the rise of KLD. Experiments have shown that correct classifications are possible even with further reduced signatures, down to 11 genes (data not shown). A reduction of signature size could be assessed in conjunction with validation of the assay, using untested positive and negative compounds in a new test set. By reducing the signature size at this point, the risk of biasing the signature towards this data set increases, making it harder to correctly classify unknown samples. Additional test compounds will also serve to assess the frequency of extreme transcriptional profile outliers, such as Oxazolone and Cinnamic aldehyde, which had to be removed from the analysis performed in this study. A number of reasons may be attributed to the fact that these compounds were not compatible with the assay, such as solubility in the cell media or extreme toxic effects. In those cases, other in vitro alternatives may complement this assay, so that the safety assessment of chemicals for sensitization includes a battery of in vitro assays. Naturally, an additional data set with blinded compounds is essential to validate whether the assay truly performs as estimated by the random subdivisions into training and test sets.

          Of note, our Prediction Signature is able to predict the potency of sensitizing compounds, as defined by the LLNA (Figure 4C). However, the potency predicted by LLNA and that of our classifier do not match for all samples. Notably, the moderate sensitizer 2-hydroxyethyl acrylate showed resemblance to strong and extreme sensitizers with respect to gene expression profile. Similarly, the moderate sensitizers ethylendiamine, hexylcinnamic aldehyde, and glyoxal grouped together with weak sensitizers. These findings support the fact that sensitizing potency, as defined, may need revising.

          By studying the identity of the transcripts and their involvement in intracellular signaling pathways, we were also able to confirm the biological relevance of the Prediction Signature. Using IPA, we found that the most highly populated pathways were nuclear factor-erythroid 2-related factor 2 (NRF2) mediated oxidative response, xenobiotic metabolism signaling, protein ubiquitination pathway, LPS/IL-1 mediated inhibition of Retinoic X receptor (RXR) function, aryl hydrocarbon receptor (AHR) signaling, and protein kinase A (PKA) signaling. These pathways are all known to take part in reactions provoked by xenobiotics, and several were associated with oxidative stress. Furthermore, Toll-like receptor (TLR) signaling is among the top pathways found in IPA. Recent studies on assay development for prediction of sensitization in vitro have to a large extent focused on how danger signals are provided to antigen-presenting cells, inducing pro-inflammatory cytokines and chemokines, as well as co-stimulatory molecules needed for a specific T-cell response. We hypothesize that these signals are provided through the innate immune responses, in analogy with infections, as reviewed in [25].

          The primary pathways found in this study involved NRF2 signaling. This is a pathway activated by Reactive Oxygen Species (ROS), and is a defense mechanism to xenobiotics and response to cellular stress. In the resting cell, NRF2 is bound by kelch-like ECH-associated protein 1 (KEAP1) and located in the cytosol. In the response to ROS activity, KEAP1 is targeted for ubiquitination and protesomal degradation, resulting in the translocation of NRF2 to the nucleus, where it activates transcription of genes containing anti-oxidant response elements (ARE) in their promoter region [26]. The functions of genes transcribed by NRF2 association to ARE include regulation of inflammation, migration of DC and anti-oxidant defense enzymes, such as NADPH quinone oxidoreductase 1 (NQO1) and glutathione S-transferases (GST) [27, 28], genes found in the Prediction Signature. Furthermore, the NRF2/KEAP1/ARE pathway has previously been described as activated in response to skin sensitizers, inducing maturation of dendritic cells [29].

          Similarly, AHR is a transcription factor in the cytosol that is activated by binding to ligands, which includes a wide range of xenobiotic chemicals, such as halogenated aromatic hydrocarbons, polyphenols and a number of pharmaceuticals [30]. In the absence of a ligand, AHR is bound by a complex of chaperon proteins, keeping it in the cytosol. Upon ligand binding, AHR is translocated to the nucleus, where it dimerizes with aryl hydrocarbon receptor nuclear translocator (ARNT) [30]. The ARNT/AHR heterodimer then binds to xenobiotic response elements (XRE) in promoter regions of target genes. The typical target genes for XRE include enzymes for drug metabolism, such as the cytochrome P450 (CYP) superfamily, as well as cytoprotective enzymes mediating defense against oxidative stress, such as NQO1 [31]. Interestingly, while NQO1 is under control of both NRF2 and AHR, with both ARE and XRE in the promoter region, it has also been shown that AHR is among the target genes for the activated NRF2 pathway and vice versa [32]. Thus, a battery of protective enzymes are induced in response to a variety of xenobiotics, possibly through a number of signaling pathways, ultimately leading to the maturation of dendritic cells, as also indicated by the present data. The protein ubiquitination pathway is involved in degradation of short-lived or regulatory proteins involved in many cellular processes, such as the cell cycle, cell proliferation, apoptosis, DNA repair, transcription regulation, cell surface receptors and ion channels regulation, and antigen presentation. Of note, both NRF2 and AHR are in the resting cell bound by proteins that are targeted for ubiquitination upon ligand binding.

          RXR is a nuclear receptor, with retinoic acid as the most prominent natural ligand [33]. It has previously been described as important for xenobiotics recognition and glutathione homeostasis, with cytoprotective enzymes as target genes [34, 35].

          TLR signaling is known to play a major role in dendritic cell maturation, as they activate transcription of a number of pro-inflammatory cytokines, chemokine-receptors for homing to lymph nodes and co-stimulatory molecules [3638]. While TLR6 and TLR9 are present in our Prediction Signature, others have reported TLR4 as a crucial mediator of contact allergy to nickel [39]. As these receptors all signal through nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), it is not surprising that different compounds activate different receptors, considering the chemical diversity of skin sensitizers, as discussed above.

          Lastly, PKA signaling is a vastly versatile pathway activated by numerous stimuli, and, to the best of knowledge, this pathway has not previously been reported in association with skin sensitization. However, individual species of CYPs are known to be phosphorylated by PKA, in response to elevated levels of cyclic adenosine monophosphate (cAMP), triggered by xenobiotics. In addition, cAMP levels influence the nuclear translocation of AHR, connecting these two pathways and their impact on CYP activity [40].

          Conclusion

          In this paper, we have demonstrated the predictive power of a genomic biomarker signature, which correctly classifies sensitizers and non-sensitizers. The biomarker signature was derived from the human DC-like cell line MUTZ-3, which was challenged with a panel of 40 reference chemical compounds. The biomarker genes were shown to be biologically relevant, as demonstrated by their involvement in cytoprotective mechanisms and pathways triggered by xenobiotic substances, supporting their relevance as predictor genes for skin sensitization. The findings reported in this paper might impact the development of in vitro assays for assessment of skin sensitization, which is crucial in order to replace the animal models currently in use.

          Methods

          Chemicals

          A panel of 40 chemical compounds, consisting of 20 sensitizers and 20 non-sensitizers were used for cell stimulations. The sensitizers were 2,4-dinitrochlorobenzene, cinnamaldehyde, resorcinol, oxazolone, glyoxal, 2-mercaptobenzothiazole, eugenol, isoeugenol, cinnamic alcohol, p-phenylendiamine, formaldehyde, ethylendiamine, 2-hydroxyethyl acrylate, hexylcinnamic aldehyde, potassium dichromate, penicillin G, kathon CG (MCI/MI), 2-aminophenol, geraniol and 2-nitro-1,4-phenylendiamine. The non-sensitizers were sodium dodecyl sulphate, salicylic acid, phenol, glycerol, lactic acid, chlorobenzene, p-hydrobenzoic acid, benzaldehyde, diethyl phtalate, octanoic acid, zinc sulphate, 4-aminobenzoic acid, methyl salicylate, ethyl vanillin, isopropanol, dimethyl formamide, 1-butanol, potassium permanganate, propylene glycol and tween 80 (Table 3). All chemicals were from Sigma-Aldrich, St. Louis, MO, USA. Compounds were dissolved in either dimethyl sulfoxide (DMSO) or distilled water. Prior to stimulations, the cytotoxicity of all compounds was monitored, using propidium iodide (PI) (BD Biosciences, San Diego, CA) using protocol provided by the manufacturer. The relative viability of stimulated cells was calculated as
          Table 3

          List of reference chemicals used in assay development

          Compound

          Abbreviation

          Potency

          LLNA

          HMT1

          HPTA1

          Sensitizers

               

          2,4-Dinitrochlorobenzene

          DNCB

          Extreme [15]

          + [15]

            

          Oxazolone

          OXA

          Extreme [15]

          + [15]

            

          Potassium dichromate

          PD

          Extreme [14]

          + [14]

          +

          +

          Kathon CG (MC/MCI)

          KCG

          Extreme [14, 45]

          + [14, 46]

            

          Formaldehyde

          FA

          Strong [15]

          + [15]

          +

          +

          2-Aminophenol

          2AP

          Strong [46]

          + [47]

            

          2-nitro-1,4-Phenylendiamine

          NPDA

          Strong [46]

          + [47]

            

          p-Phenylendiamine

          PPD

          Strong [47]

          + [48]

          +

          +

          Hexylcinnamic aldehyde

          HCA

          Moderate [15]

          + [15]

            

          2-Hydroxyethyl acrylate

          2HA

          Moderate [46]

          + [47]

           

          +

          2-Mercaptobenzothiazole

          MBT

          Moderate [46]

          + [47]

          +

          +

          Glyoxal

          GO

          Moderate [46]

          + [47]

          +

           

          Cinnamaldehyde

          CALD

          Moderate [47]

          + [48]

          +

          +

          Isoeugenol

          IEU

          Moderate [47]

          + [48]

           

          +

          Ethylendiamine

          EDA

          Moderate [14]

          + [14]

            

          Resorcinol

          RC

          Moderate [48]

          + [49]

          -

          +

          Cinnamic alcohol

          CALC

          Weak [46]

          + [48]

            

          Eugenol

          EU

          Weak [47]

          + [48]

           

          +

          Penicillin G

          PEN G

          Weak [47]

          + [48]

          +

           

          Geraniol

          GER

          Weak [14]

          + [14]

          -

          +

          Non-sensitizers

               

          1-Butanol

          BUT

           

          - [50]

            

          4-Aminobenzoic acid

          PABA

           

          - [51]

          -

          +

          Benzaldehyde

          BA

           

          - [52]

            

          Chlorobenzene

          CB

           

          - [14]

            

          Diethyl phthalate

          DP

           

          - [48]

            

          Dimethyl formamide

          DF

           

          - [46]

            

          Ethyl vanillin

          EV

           

          - [52]

            

          Glycerol

          GLY

           

          - [48]

            

          Isopropanol

          IP

           

          - [48]

            

          Lactic acid

          LA

           

          - [14]

            

          Methyl salicylate

          MS

           

          - [14]

          -

           

          Octanoic acid

          OA

           

          - [53]

            

          Propylene glycol

          PG

           

          - [51]

            

          Phenol

          PHE

           

          - [53]

          -

           

          p-Hydroxybenzoic acid

          HBA

           

          - [54]

            

          Potassium permanganate

          PP

           

          -

            

          Salicylic acid

          SA

           

          - [14]

          -

           

          Sodium dodecyl sulphate

          SDS

           

          +2 [14, 53]

          -

           

          Tween 80

          T80

           

          - [20]

           

          +

          Zinc sulphate

          ZS

           

          +2 [55]

            

          List of sensitizers and non-sensitizers used in assay development. 1) HMT, Human Maximization Test; HPTA, Human Patch Test Allergen. Information is derived from [17]. 2) False positives in LLNA.

          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-399/MediaObjects/12864_2011_3567_Equa_HTML.gif
          For toxic compounds, the concentration yielding 90% relative viability (Rv90) was used. For non-toxic compounds, a concentration of 500 μM was used. For non-toxic compounds that were insoluble at 500 μM in medium, the highest soluble concentration was used. For compounds dissolved in DMSO, the final concentration of DMSO in each well was 0.1%. The vehicle and concentrations used for each compound are listed in Table 4.
          Table 4

          Concentrations and vehicles used for each reference chemical

          Compound

          Abbreviation

          Vehicle

          Max solubility

          (μM)

          Rv90

          (μM)

          Concentration

          in culture (μM)

          Sensitizers

               

          2,4-Dinitrochlorobenzene

          DNCB

          DMSO

          -

          4

          4

          Oxazolone

          OXA

          DMSO

          250

          -

          250

          Potassium dichromate

          PD

          Water

          51.02

          1.5

          1.5

          Kathon CG (MC/MCI)1

          KCG

          Water

          -

          0.0035%

          0.0035%

          Formaldehyde

          FA

          Water

          -

          80

          80

          2-Aminophenol

          2AP

          DMSO

          -

          100

          100

          2-nitro-1,4-Phenylendiamine

          NPDA

          DMSO

          -

          300

          300

          p-Phenylendiamine

          PPD

          DMSO

          566

          75

          75

          Hexylcinnamic aldehyde

          HCA

          DMSO

          32.34

          -

          32.24

          2-Hydroxyethyl acrylate

          2HA

          Water

          -

          100

          100

          2-Mercaptobenzothiazole

          MBT

          DMSO

          250

          -

          250

          Glyoxal

          GO

          Water

          -

          300

          300

          Cinnamaldehyde

          CALD

          Water

          -

          120

          120

          Isoeugenol

          IEU

          DMSO

          641

          300

          300

          Ethylendiamine

          EDA

          Water

          -

          -

          500

          Resorcinol

          RC

          Water

          -

          -

          500

          Cinnamic alcohol

          CALC

          DMSO

          500

          -

          500

          Eugenol

          EU

          DMSO

          649

          300

          300

          Penicillin G

          PEN G

          Water

          -

          -

          500

          Geraniol

          GER

          DMSO

          -

          -

          500

          Non-sensitizers

               

          1-Butanol

          BUT

          DMSO

          -

          -

          500

          4-Aminobenzoic acid

          PABA

          DMSO

          -

          -

          500

          Benzaldehyde

          BA

          DMSO

          250

          -

          250

          Chlorobenzene

          CB

          DMSO

          98

          -

          98

          Diethyl phthalate

          DP

          DMSO

          50

          -

          50

          Dimethyl formamide

          DF

          Water

          -

          -

          500

          Ethyl vanillin

          EV

          DMSO

          -

          -

          500

          Glycerol

          GLY

          Water

          -

          -

          500

          Isopropanol

          IP

          Water

          -

          -

          500

          Lactic acid

          LA

          Water

          -

          -

          500

          Methyl salicylate

          MS

          DMSO

          -

          -

          500

          Octanoic acid

          OA

          DMSO

          504

          -

          500

          Propylene glycol

          PG

          Water

          -

          -

          500

          Phenol

          PHE

          Water

          -

          -

          500

          p-Hydroxybenzoic acid

          HBA

          DMSO

          250

          -

          250

          Potassium permanganate

          PP

          Water

          38

          -

          38

          Salicylic acid

          SA

          DMSO

          -

          -

          500

          Sodium dodecyl sulphate

          SDS

          Water

          -

          200

          200

          Tween 80

          T80

          DMSO

          -

          -

          500

          Zinc sulphate

          ZS

          Water

          126

          -

          126

          List of concentrations and vehicles used for each testing compound. 1) Kathon CG is a mixture of the compounds MC and MCI. The concentration of this mixture is given in %.

          Chemical exposure of the cells

          The human myeloid leukemia-derived cell line MUTZ-3 (DSMZ, Braunschweig, Germany) was maintained in α-MEM (Thermo Scientific Hyclone, Logan, UT) supplemented with 20% (volume/volume) fetal calf serum (Invitrogen, Carlsbad, CA) and 40 ng/ml rhGM-CSF (Bayer HealthCare Pharmaceuticals, Seattle, WA), as described [10]. Cultures were maintained at 200.000 cells/ml during expansion, with a media change every 3-4 days. No differentiating steps were performed. Instead, the proliferating progenitor MUTZ-3 was used for stimulations, as delivered by the supplier. Prior to each experiment, the cells were immunophenotyped using flow cytometry as a quality control. Cells were seeded in 6-well plates at 200.000 cells/ml. Stock solutions of each compound were prepared in either DMSO or distilled water, and were subsequently diluted so the in-well concentrations corresponded to the Rv90 value, and in-well concentrations of DMSO were 0.1%. Cells were incubated for 24 h at 37°C and 5% CO2. Thereafter, cells were harvested and analyzed by flow cytometry. In parallel, harvested cells were lysed in TRIzol reagent (Invitrogen) and stored at -20°C until RNA extraction. Stimulations with chemicals were performed in three individual experiments, so that triplicates samples were obtained.

          Phenotypic analysis with flow cytometry

          All cell surface staining and washing steps were performed in PBS containing 1% BSA (w/v). Cells were incubated with specific mouse mAbs for 15 min at 4°C. The following mAbs were used for flow cytometry: FITC-conjugated CD1a (DakoCytomation, Glostrup, Denmark), CD34, CD86, and HLA-DR (BD Biosciences), PE-conjugated CD14 (DakoCytomation), CD54 and CD80 (BD Biosciences). Mouse IgG1, conjugated to FITC or PE were used as isotype controls (BD Biosciences) and PI was used to assess cell viability. FACSDiva software was used for data acquisition with FACSCanto II instrument (BD Bioscience). 10,000 events were acquired and gates were set based on light scatter properties to exclude debris and nonviable cells. Further data analysis was performed using FCS Express V3 (De Novo Software, Los Angeles, CA).

          Preparation of cRNA and gene chip hybridization

          RNA isolation and gene chip hybridization was performed as described [41]. Briefly, RNA from unstimulated and chemical-stimulated MUTZ-3 cells, from triplicate experiments, were extracted and analyzed. The preparation of labeled sense DNA was performed according to Affymetrix GeneChip™ Whole Transcript (WT) Sense Target Labeling Assay (100 ng Total RNA Labeling Protocol) using the recommended kits and controls (Affymetrix, Santa Clara, CA). Hybridization, washing and scanning of the Human Gene 1.0 ST Arrays were performed according to the manufacturer's protocol (Affymetrix). The microarray data have been deposited in the Array Express database http://​www.​ebi.​ac.​uk/​arrayexpress/​ with accession number E-MTAB-670.

          Microarray data analysis and statistical methods

          The microarray data were normalized and quality checked with the RMA algorithm, using Affymetrix Expression Console (Affymetrix). Genes that were significantly regulated when comparing sensitizers with non-sensitizers were identified using one-way ANOVA, with false discovery rate (FDR) as a correction for multiple hypothesis testing. In order to reduce the large number of identified significant genes, we applied an algorithm developed in-house for Backward Elimination of analytes [42]. With this method, we train and test a Support Vector Machine (SVM) model [12] with leave-one out cross-validation, with one analyte left out. This process is iterated until each analyte has been left out once. For each iterative step, a Kullback-Leibler divergence (KLD) is recorded, yielding N KLDs, where N is the number of analytes. The analyte that was left out when the smallest KLD was observed is considered to provide the least information in the data set. Thus, this analyte is eliminated and the iterations proceed, this time with N-1 analytes. In this manner, the analytes are eliminated one by one until a panel of markers remain that have been selected based on the ability of each analyte to contribute with orthogonal information for the discrimination of skin sensitizers vs. non-sensitizers. The selected biomarker profile of 200 transcripts were designated the "Prediction Signature". The scripts for Backwards Elimination and Support Vector Machines were programmed for R [43], with the additional package e1071 [44]. ANOVA analyses and visualization of results with Principal Component Analysis were performed in Qlucore Omics Explorer 2.1 (Qlucore AB, Lund, Sweden). Hierarchical clustering for the heatmap was performed in R.

          Interrogation of the method for identification of the Prediction Signature

          The data set was divided into a training set and a test set, consisting of 70% and 30%, of the chemical compounds, respectively. The division was performed randomly, while maintaining the proportions of sensitizers and non-sensitizers in each subset at the same ratio as in the complete data set. A biomarker signature was identified in the training set, using ANOVA filtering and Backward Elimination, as described above. This test signature was used to train an SVM, using the training set, which was thereafter applied to predict the samples of the test set. The process was repeated 20 times and the distribution of the area under the Receiver Operating Characteristic (ROC) curve [45] was used as a measurement of the performance of the model.

          Assessment of biological functions of Prediction Signature using pathway analysis

          In order to investigate the biological functions the gene profile of the 200 genes derived from the Backward Elimination was analyzed, using the Ingenuity Pathway Analysis software, IPA, (Ingenuity Systems, Inc. Mountain View, USA). The gene profile was analyzed using the 'Build' and 'Path Explorer' functions to build an interactome of the core genes from the Prediction Signature together with connecting molecules, as suggested by IPA. The molecules of the signature were connected using the shortest known paths. In this process only human data from primary cells, cell lines and epidermal tissue was used. Public identifiers were used to map genes in IPA. All molecules except for endogenous and chemical drugs were allowed in the network and all kinds of connections were allowed. Known 'Functions' and 'Canonical Pathways' from IPA were mapped to the signature using the 'Overlay' function. The most densely populated pathways and functions were reported. All were significant, using the built in IPA statistical measures (p-values for functions and -log(p-values) for pathways).

          Abbreviations

          ACD: 

          atopic contact dermatitis

          AML: 

          acute myeloid leukemia cell

          APC: 

          Antigen Presenting Cell

          DC: 

          Dendritic Cell

          GM-CSF: 

          Granulocyte macrophage colony-stimulating factor

          GPMT: 

          Guinea pig maximization test

          HMT: 

          Human Maximation Test

          HPTA: 

          Human Patch Test Allergen

          IL: 

          Interleukin

          LLNA: 

          Local Lymph Node Assay

          PCA: 

          Principal Component Analysis.

          Declarations

          Acknowledgements

          This work was supported by grants from the Swedish Fund for Research Without Animal Experiments, Faculty of Engineering (LTH), the Swedish Research Council (K2010-79X-21371-01-3) and the European Commission as part of the Integrated project 'Novel Testing Strategies for in vitro Assessment of Allergens; Sens-it-iv' (LSHB-CT-2005-018681). We would like to thank Ann-Charlott Olsson for microarray sample preparation and Dr. Anders Carlsson for the backward elimination algorithm.

          Authors’ Affiliations

          (1)
          Department of Immunotechnology, Lund University

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          © Johansson et al; licensee BioMed Central Ltd. 2011

          This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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