Whole genome expression analysis in primary bronchial epithelial cells after exposure to sulphur mustard

Toxicology Letters 230 (2014) 393–401

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Whole genome expression analysis in primary bronchial epithelial cells after exposure to sulphur mustard Paul A. Jowsey *, Peter G. Blain Medical Toxicology Centre, Wolfson Unit, Newscastle University, Newcastle upon Tyne NE 4AA, United Kingdom

H I G H L I G H T S

 Whole genome expression analysis in primary hBEC cells after SM.  More than 400 gene expression changes identified.  Pathways affected include MAPK, p53, DNA repair, cell cycle and mitotic regulation.

A R T I C L E I N F O

A B S T R A C T

Article history: Received 30 June 2014 Received in revised form 31 July 2014 Accepted 1 August 2014 Available online 4 August 2014

Sulphur mustard (SM) is a highly toxic chemical agent and poses a current threat to both civilians and military personnel in the event of a deliberate malicious release. Acute SM toxicity develops over the course of several hours and mainly affects the skin and mucosal surfaces of the eyes and respiratory system. In cases of acute severe exposure, significant lung injury can result in respiratory failure and death. Systemic levels of SM can also be fatal, frequently due to immunodepletion and the subsequent development of secondary infections. Whilst the physical effects associated with SM exposure are well documented, the molecular mechanisms mediating these changes are poorly understood, hindering the development of an effective therapeutic strategy. To gain a better understanding of the mechanism of SM toxicity, this study investigated whole genome transcriptional changes after SM in primary human bronchial epithelial cells, as a model for inhalation exposure. The analysis revealed >400 transcriptional changes associated with SM exposure. Pathways analysis confirmed the findings of previous studies suggesting that DNA damage, cell cycle arrest, cell death and inflammation were important components of SM toxicity. In addition, several other interesting observations were made, suggesting that protein oxidation as well as effects on the mitotic apparatus may contribute to SM toxicity. ã 2014 Elsevier Ireland Ltd. All rights reserved.

Keywords: Sulphur mustard Sulphur mustard toxicity

1. Introduction Sulphur mustard [SM, bis(2-chloroethyl) sulphide)] has been used several times in military conflict during the last 100 years, resulting in mass casualties and fatalities. A large number of civilians were also exposed to SM during the Iran–Iraq conflict in the 1980s, with tens of thousands of people still suffering with the adverse consequences of this exposure (Balali-Mood and Hefazi, 2006). Due to its ease of production and range of debilitating health effects, SM poses a current threat to both the civilian and military population in the event of a deliberate release. The acute toxicity of SM manifests over the course of several hours

* Corresponding author. Tel.: +44 191 2228537; fax: +44 191 2226442. E-mail address: [email protected] (P.A. Jowsey). http://dx.doi.org/10.1016/j.toxlet.2014.08.001 0378-4274/ ã 2014 Elsevier Ireland Ltd. All rights reserved.

post-exposure, resulting in severe skin irritation/blistering, with the mucosal surfaces of the eyes and respiratory system also affected. After high level exposure, SM can become systemic and exert toxic effects on the hematopoietic system, causing immunosuppression and vulnerability to secondary infections (BalaliMood and Hefazi, 2005; Ghabili et al., 2011). The toxicity of SM is driven by the cyclisation of the molecule, accelerated by aqueous conditions, resulting in the formation of a highly reactive sulphonium ion. This positively charged molecule reacts readily with electron-rich macromolecules, including nucleic acids and proteins. Whilst the reactions of SM with DNA and resultant DNA lesions have been well characterised, less is known about RNA or protein adduction and whether this contributes to the overall toxicity of SM. In addition, most studies focus on the acute toxicity of SM. However, there is mounting evidence that a single exposure to SM can cause significant

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long-term health problems after an apparently full recovery from the initial short-term effects. These health problems, documented in the Iranian population exposed to SM in the 1980s, tend to present in areas of the body that were subject to the initial SM exposure, for example the skin, respiratory system and eyes (Balali-Mood et al., 2011; Behravan et al., 2013; Ghasemi et al., 2012; Hosseini-khalili et al., 2009; Khateri et al., 2003; Razavi et al., 2013). The precise mechanisms mediating both the acute and long-term toxicity of SM remain poorly understood. In addition, lack of information on the toxic mechanism of SM and associated cellular response has hindered the development of an effective therapeutic agent. A major component of SM toxicity is thought to involve the formation of highly cytotoxic DNA interstrand and intrastrand crosslinks, which impede essential cellular processes such as DNA replication and transcription. These lesions are processed by the cellular DNA repair machinery, resulting in the generation of double strand breaks (DSBs) after SM (Jowsey et al., 2010), which are both cytotoxic and recombinogenic. Though SM-induced ICLs are the most cytotoxic DNA lesion, the predominant form of DNA damage are DNA monoadducts, targeting DNA bases at multiple positions (Fidder et al., 1994). This myriad of DNA lesions results in the induction of cell death via apoptosis. In addition, the processing of DNA monoadducts and crosslinks by DNA repair proteins results in the generation of strand breaks and subsequent activation of the enzyme poly(ADP-ribose) polymerase-1 (PARP-1). After exposure to high doses of SM, massive amounts of DNA strand breaks are generated, resulting in hyperactivation of PARP-1. This results in depletion of cellular ATP levels, which are required to drive apoptotic cell death, and a subsequent switch to the energy-independent necrotic form of cell death (Kehe et al., 2008; Meier and Millard, 1998; Papirmeister et al., 1985). ATP depletion can also lead to the activation of cellular proteases that have been implicated in the dermal toxicology of SM, via the separation of the dermal–epidermal layer during skin blistering (Greenberg et al., 2006; Hayden et al., 2009; Kan et al., 2003). Necrotic cell death leads to the release of pro-inflammatory mediators, with damage to neighbouring cells and tissues. Some studies have also suggested that SM can increase oxidative stress, with antioxidants offering protection against SM-mediated toxicity (Laskin et al., 2010). From the findings described above, it is likely that the toxic mechanism of SM is complex, involving aspects of DNA damage, cell death (apoptosis/necrosis/autophagy), cell cycle effects, inflammation and oxidative stress. This study aimed to investigate the relevance of the above toxic mechanisms in an appropriate model for the respiratory effects of SM by measuring changes in global gene expression. The data confirmed that exposure to SM induces significant changes in genes involved with DNA repair, cell cycle regulation, inflammation and cell death. In addition, other interesting observations were made, suggesting that protein oxidation as well as effects on the mitotic apparatus may contribute to the mechanism of SM toxicity.

2. Materials and methods 2.1. Cell lines, chemicals and treatments Human primary bronchial epithelial cells (hBEC) were obtained from Promocell and cultured using Airway Epithelial Cell Growth Medium Kit (Promocell) according to the manufacturer’s instructions. SM was obtained from The Defence Science and Technology Laboratories (Dstl, Porton Down, UK) and stored at 4  C. On the day of use, appropriate dilutions were prepared in isopropanol and added to culture medium such that the final isopropanol concentration was 0.2% (v/v) in all samples.

2.2. Cell viability assay Cells were seeded in 24-well plates and allowed to adhere overnight. Cells in normal growth medium (see Section 2.1) were treated with the indicated concentrations of SM (or isopropanol control for ‘untreated’ cells) for 18 h prior to the measurement of cell viability using an MTS-based assay (Promega). Briefly, culture medium was removed and replaced with 300 ml of medium containing 20% (v/v) of MTS reagent. Cells were incubated at 37  C for up to 1 h before the absorbance of each well was measured at 490 nm. Data was plotted on graphs, with cell viability at each dose of SM being calculated as a % relative to untreated cells (untreated cells designated as 100% viability). Isopropanol alone showed no evidence of cytotoxicity at the concentration used (0.2% v/v). The significance of the observed changes in cell viability were investigated using a paired t-test. Levels of significance were defined as follows: p < 0.05 (*), p < 0.01 (**) and p < 0.001 (***). 2.3. Western blotting After the indicated treatments, cells were washed in cold phosphate buffered saline (PBS) before lysis in 1X LDS sample buffer (Invitrogen) containing 5% (v/v) 2-mercaptoethanol. Lysates were boiled for 5 min prior to sonication to shear the cellular DNA. Protein concentrations were determined using Coomassie Bradford Reagent (Pierce Biotechnology) and 20 mg of protein separated on 4–12% bis–tris gels (Invitrogen) using MOPS buffer (Invitrogen) at a constant voltage of 180 V for 1 h. Proteins were transferred to Hybond-C nitrocellulose membrane (GE Healthcare) using an iBlot machine set at 20 V for 13 min (Invitrogen). Gel electrophoresis and transfer were performed at room temperature. Membranes were probed using standard protocols, with the following primary antibodies: Anti-p53 (DO-7, Novocastra), anti-p53 phospho-Ser15, anti-H2AX phospho-Ser139 (Cell Signalling Technologies) and anti-GAPDH (ab9485, Abcam). Membranes were then washed in TBST (50 mM tris pH 7.6, 150 mM NaCl and 0.2% Tween20) followed by incubation with the relevant HRP-conjugated secondary antibody. Membranes were washed thoroughly with TBST and visualised using enhanced chemiluminescence (ECL Plus, GE Biosciences). Images were captured using a Syngene G:Box gel documentation system and bands quantified using ImageJ software (http://imagej.nih.gov/ij/). Data was collected from three independent experiments and expressed graphically as foldchange (relative to isopropanol-treated control). The statistical significance of changes was investigated using a paired t-test (comparing each dose of SM to isopropanol control). Levels of significance were defined as follows: p < 0.05 (*), p < 0.01 (**) and p < 0.001 (***). 2.4. Microarray and data analysis hBEC cells cultured in 6 cm plates (in normal growth medium, see Section 2.1) were exposed to 50 mM SM or 0.2% isopropanol (v/v, solvent control) for 18 h. Each treatment was performed in quadruplicate. Cells were washed in PBS and lysed in 1 ml of TRIzol reagent (Ambion) and RNA purified according to the manufacturer’s instructions. RNA integrity was measured on an Agilent Bioanalyzer 2100 using RNA 6000 Pico assay. Biotinylated cRNA was produced using Illumina1 TotalPrepTM-96 RNA Amplification Kit (Ambion) and hybridised to Illumina HT-12 v3 expression beadchips, according to the manufacturer’s instructions. Microarray datasets were analysed using the Bioconductor package (www.bioconductor.org). Statistical significance of the changes in expression levels between samples was examined using the limma package. The fold change and standard errors were first estimated by fitting a linear model to each gene. This model

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summarises the data from each set of replicates to give a single value per condition, so that these values can be compared between groups, rather than between samples. This was then followed by the application of empirical Bayes smoothing to the standard errors. Pair-wise contrasts were then defined to compare the summarised samples to each other, thus allowing the algorithm to calculate fold change. In this experiment, the adjusted p-value cut-off value of 0.05 was used to detect differentially expressed genes. The RNA purification, chip hybridization and initial data analysis (up to and including the point of calculating fold-change values) were performed by Source Bioscience. The biological relevance of altered gene expression was investigated using Web-based gene set analysis toolkit (Webgestalt; http://bioinfo.vanderbilt.edu/webgestalt/). Entrez gene IDs (see Supplementary Fig. 1) were uploaded and subject to KEGG analysis to reveal the cellular pathways associated with the observed gene expression changes. Data is presented graphically and shows the number of genes from the microarray study which belong to each cellular pathway/process. 3. Results 3.1. Dose-dependent cytotoxicity and p53 induction after exposure of hBEC to SM

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multiple cellular pathways are affected by SM exposure. Consistent with previous reports on the toxic mechanism of SM, pathways involved in the regulation of the cell cycle, DNA replication and DNA damage responses (e.g. p53 signalling) were altered by exposure of hBECs to SM. The KEGG analysis tool provides a hyperlink to the relevant cellular pathway, which then links to a diagram illustrating the composition of these pathways and which specific components are present in the microarray data set. Fig. 2B and C shows examples of gene changes associated with SM exposure (highlighted in grey boxes) in the MAP kinase (MAPK) and p53 signalling pathways. Similar information can be obtained for all of the pathways listed in Fig. 2 simply by uploading the Entrez Gene IDs (Supplementary Fig. 1) into an appropriate KEGG analysis tool. Whilst data analysis of this kind is useful in highlighting important cellular pathways involved in the toxic mechanism, and associated cellular response to SM, it is also important to manually interrogate microarray datasets. This approach can identify genes that are implicated in certain cellular processes which are not highlighted by large-scale KEGG analysis. As an example, methionine sulphoxide reductase A (MSRA), a protein involved in the repair of oxidatively damaged proteins, was not highlighted in the KEGG analysis but was present in the original microarray dataset. 4. Discussion

This study aimed to investigate whole genome expression changes in the acute phase of SM toxicity, which is caused (at least in part) by damage to DNA. We therefore investigated cytotoxicity and the levels of biomarkers of DNA damage (p53 induction/ phosphorylation and H2AX phosphorylation) in hBEC after exposure to different doses of SM for 18 h. As shown in Fig. 1, the levels of DNA damage biomarkers showed a dose-dependent increase after SM, correlating with increased cytotoxicity. Statistical analysis revealed a significant decrease in cell viability after 50 and 100 mM SM, with statistically significant changes in p53, p53 Ser15 and H2AX Ser139 at all doses of SM. Based on these data, we decided to use 50 mM as the dose of SM for the microarray studies. 3.2. Whole genome microarray analysis in hBEC cells exposed to SM Proliferating hBEC were exposed to 50 mM SM for 18 h, with treatments performed in quadruplicate (i.e. four solvent only control and four treated with SM). As described above, this dose caused low level cytotoxicity, which we considered appropriate given that we were interested in gene expression changes associated with the acute toxicity of SM. This dose also caused clear induction of SM-induced DNA damage response pathways, considered to be an important component of SM toxicity. The treatment time of 18 h would also be sufficient to allow transcriptional changes to occur in cells. Bioinformatic analysis of raw microarray data was performed using the ‘bioconductor’ package. Quality control analysis from all arrays were in agreement with the manufacturer’s specifications and hierarchical clustering analysis clearly showed that individual samples (of each quadruplicate) clustered into two groups, untreated and 50 mM SM (data not shown). This led to the identification of 229 up-regulated and 201 down-regulated genes. The identity of each of these genes is described in Tables 1 and 2, which lists up-regulated genes and down-regulated genes, respectively, in order of fold-change (SM-treated compared to untreated). 3.3. Biological function of differentially-regulated genes The biological relevance of altered gene expression was investigated using Web-based gene set analysis toolkit and KEGG analysis. The results are presented in Fig. 2 and showed that

The precise mechanisms mediating SM toxicity are not well understood, thus limiting our understanding of the associated health effects and hindering the development of an effective therapeutic agent. Given the chemical reactivity of SM, it is likely to have pleiotropic effects by adducting to a range of cellular macromolecules. Each of these reactions could impact upon cellular function and contribute to the range of short- and long-term health effects associated with SM exposure. Our previous studies in the field of SM toxicity have focused on its ability to bind DNA, forming a mixture of monoadducts and highly toxic crosslinks. Whilst these DNA adducts undoubtedly contribute to SM toxicity, this study aimed to investigate other potential toxic mechanisms by monitoring whole genome expression changes in human primary bronchial epithelial cells, as a model for inhalation exposure. As well as identifying novel cellular changes associated with SM exposure, the data also corroborated our previous data (as well as that of many other groups) in a model system that is highly relevant to SM toxicity. The two major routes of SM exposure are via the skin and inhalation. Effects on the skin can range from mild irritation to severe blistering (Balali-Mood and Hefazi, 2005; Kehe and Szinicz, 2005). Whilst these lesions can be uncomfortable and slow to heal, they are rarely lethal, unless associated with the development of secondary infections. Inhalation exposure can lead to more severe toxic effects, with SM reactivity and toxicity promoted by the warm and moist lining of the respiratory tract. In addition, lung tissue has an inherent inefficiency in damage repair, tending instead to inflammation and fibrosis in response to major damage. The most serious effects on the respiratory system include oedema and secondary infections, both of which can be fatal (Balali-Mood and Hefazi, 2005; Kehe and Szinicz, 2005). Whilst there have been several gene expression studies after dermal exposure to SM (performed in mice and porcine models), there are no studies using microarray technology in models relevant to inhalation of SM (Dillman et al., 2006; Rogers et al., 2004; Rogers et al., 2008). This study utilised hBEC cells to address this issue, with whole genome expression analysis demonstrating significant changes in the expression of 430 genes. Data analysis highlighted several pathways that were consistent with SM causing DNA damage, including nucleotide excision repair (NER), base excision repair

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Fig. 1. Cellular toxicity and induction of DNA damage responses in hBEC cells after SM. hBEC cells were exposed to the indicated dose of SM for 18 h. (A) Cell viability was measured using an MTS assay. (B) Induction of DNA damage response/DNA damage markers using western blotting with the indicated antibodies. Graphical data represents the mean and standard deviation for three independent experiments. The significance of the observed changes were investigated using a paired t-test. Levels of significance were defined as follows: p < 0.05 (*), p < 0.01 (**) and p < 0.001 (***). NS = not significant.

(BER), p53 signalling and DNA replication. Previous studies have demonstrated that cells lacking BER and NER are more sensitive to SM-induced DNA monoadducts (Jowsey et al., 2009; Matijasevic et al., 2001), with NER and replication-linked repair (homologous recombination) being involved in the repair of SM-induced DNA cross links (Jowsey et al., 2010; Matijasevic et al., 2001). Studies have also demonstrated activation of p53 responses, mediated by the ataxia telangiectasia mutated (ATM) and AT and Rad3-related (ATR) kinases after SM or SM analogues (Jowsey et al., 2009, 2012; Tewari-Singh et al., 2010). Multi-site phosphorylation of p53 blocks

binding of the negative regulator MDM2, protects p53 from proteasome-mediated degradation and initiates p53-dependent regulation of multiple effector genes. KEGG analysis of the microarray data in the present study highlighted the importance of the p53 signalling pathway in the cellular response to SM. A total of ten genes associated with this pathway were significantly altered in SM-exposed cells, including several transcriptional targets of p53 (NOXA, GADD45A, p21 and Wip1). Together, these proteins act to slow cell cycle progression and/or eliminate irreparably damaged cells via apoptosis, thus helping to maintain

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Table 1 Up-regulated genes in hBEC cells after exposure to SM. Gene

Fold-change

Gene

Fold-change

Gene

Fold-change

Gene

Fold-change

Gene

Fold-change

GDF15 IL8 IL1RL1 KRT80 KRT81 C9orf169 IL1F9 ATF3 GPRC5A SERPINB2 IGFL1 C15orf48 DUSP1 RNASE7 PTGES FLNC CSF2 ISG15 RRAD BIRC3 KRT34 CCL20 SPRR2A CLDN1 TNFAIP3 IL1B HIST2H2AC ABHD8 ZP3 CRABP2 ANKRA2 GAMT CA2 GALK1 SUNC1 CYP1A1 CRB3 APH1B ACTA2 RND3 CAPN12 LCP1 HERC5 CRYAB PMAIP1 FAM46A ISYNA1 EFNA1

23.628 11.691 11.165 7.636 6.539 6.212 5.351 5.350 5.302 5.178 5.096 4.985 4.953 4.738 4.622 4.428 4.310 4.259 4.244 4.184 4.123 4.102 3.993 3.929 2.337 2.337 2.334 2.323 2.320 2.316 2.295 2.284 2.278 2.276 2.264 2.243 2.226 2.224 2.223 2.222 2.201 2.194 2.190 2.187 2.187 2.184 2.178 2.176

DDIT3 IL24 MYBPHL SPRR2F LCE1B C1orf74 SPRR2D DHDH CST6 SDCBP2 TRNP1 CPA4 ISG20 MX1 NUPR1 HIST1H2BD AKR1B10 HYAL1 REC8 TGM1 MGC102966 CEACAM1 IL1RN SERPINB7 RPS27L CDKN2B OASL IFIT3 GADD45A RRM2B C7orf10 DUSP14 GJB4 LCE3D HES5 CLDN7 LCE1A CCNA1 AIM1L HCLS1 PHLDA3 IRF6 DKK4 PDLIM7 RARRES3 MR1 SRPX2 RAB24

3.891 3.868 3.863 3.854 3.764 3.761 3.618 3.544 3.533 3.502 3.420 3.335 3.298 3.208 3.179 3.175 3.159 3.142 3.121 3.103 3.097 3.095 3.073 3.070 2.167 2.165 2.155 2.155 2.133 2.126 2.106 2.106 2.104 2.102 2.088 2.075 2.071 2.068 2.067 2.049 2.049 2.049 2.041 2.037 2.032 2.018 2.017 2.005

CGN ALDH3A1 DEFB1 HES2 S100P SLAMF9 IL6 IL1R2 P8 DBNDD1 KRT16 S100A4 HIST2H2AA3 TP53INP1 TMEM125 IFIT1 REEP2 CDKN1A LOC645638 C18orf56 C15orf52 GRHL3 PRDM1 PPP1R15A RFX5 CD68 KPNA7 SLC44A2 IL32 EPPK1 PLA2G3 SNORA12 PRODH FOLR1 STAT2 SERTAD1 LBH GDPD3 CTXN1 DSC2 KRT9 TRIML2 PLA2G10 ELF3 FAM84B HSPC159 FAM46B CCK

3.031 3.017 3.015 3.004 3.001 3.000 2.984 2.981 2.955 2.948 2.942 2.930 2.924 2.897 2.854 2.852 2.850 2.832 2.826 2.825 2.822 2.820 2.815 2.800 2.004 2.000 1.999 1.994 1.990 1.987 1.984 1.984 1.980 1.974 1.968 1.967 1.967 1.956 1.954 1.938 1.937 1.933 1.928 1.927 1.927 1.913 1.913 1.902

SELM CTGF CAPNS2 P4HTM BTG1 TMEM79 LY96 PSG4 TNF KRT6B IFI6 ANKRD1 MAP1LC3A GAST BEX2 GPX2 KRT15 SEMA3B HSD11B1 CLIP3 QPCT LMTK3 CKB IFI27 RAET1G KYNU ALDH1A3 MYO6 UPK2 DHRS3 CXCL2 FUT3 CHMP5 HNMT VASN CYB5R1 KIAA0913 ARHGEF3 ELL3 RAB25 RNF144B LCE1D ARL14 PPP1R14A OSBPL7 LOC647859 DIRC2 SLC2A12

2.786 2.776 2.762 2.760 2.712 2.693 2.690 2.683 2.668 2.662 2.662 2.643 2.633 2.629 2.624 2.578 2.575 2.574 2.574 2.571 2.546 2.543 2.532 2.523 1.901 1.884 1.876 1.875 1.869 1.869 1.864 1.861 1.855 1.853 1.844 1.842 1.840 1.831 1.830 1.824 1.810 1.797 1.796 1.794 1.794 1.789 1.786 1.770

SLC25A24 IFIT2 RGS2 TSPAN10 RAB11FIP1 IL23A CABYR TM7SF2 PROM2 LOC400578 GLIPR1 SDPR IL13RA2 HIST2H2AA4 FBXO32 KRT75 IRF9 FNBP1L CEL EDN1 BEX1 CXCL16 TMEM27 AMTN TUFT1 SESN1 DUSP10 C12orf76 TLCD1 CYP2S1 GBP1 KIAA1370 SECISBP2L UPK1B OXSR1 SLC44A4 SLC46A3

2.520 2.519 2.499 2.493 2.487 2.482 2.473 2.471 2.469 2.464 2.459 2.455 2.445 2.415 2.415 2.400 2.400 2.385 2.382 2.367 2.365 2.362 2.357 2.348 1.769 1.768 1.757 1.756 1.724 1.712 1.697 1.693 1.693 1.686 1.653 1.646 1.646

genetic integrity. A study utilising a human bronchial co-culture model demonstrated changes in protein markers of inflammation, apoptosis and p53 signalling, further corroborating some of the gene expression changes observed in the current study (Pohl et al., 2009). Whilst the KEGG analysis grouped specific genes into specific pathways, it is also important to note that there will be significant overlap and cross-talk between the different genes/proteins and cellular processes. For example, ‘cell cycle’, ‘MAPK signalling’ and ‘p53 signalling’ were all highlighted as being very relevant in the cellular response to SM. Components of both the MAPK and p53 pathways can directly regulate cell proliferation/apoptosis and share common genes/regulatory mechanisms. As an obvious example, p38 is able to directly phosphorylate p53 on Ser 33 after UV radiation. Inhibition of p38 was also shown to markedly reduce UV-induced apoptosis in a p53-dependent manner (Bulavin et al., 1999). Whilst there are studies showing activation of MAPK signalling pathways (e.g. p38, ERK1/2, JNK) after SM (or surrogate compounds), the contribution of MAPK to p53 activation after SM has not been investigated (Black et al., 2010). Despite the MAPK

pathway ranking highly in the KEGG analysis in the present study, there are relatively few publications addressing the functional significance of this pathway in response to SM. Generally, these studies have demonstrated MAPK-dependent increases in proinflammatory markers and MAPK-dependent regulation of antioxidant enzymes after SM/SM surrogates (Black et al., 2010; Ham et al., 2012; Mukhopadhyay et al., 2008; Pal et al., 2009). It will be interesting to investigate how MAPK pathways contribute to other aspects of the cellular response to SM, including p53 activation, cell cycle arrest and apoptosis. The microarray analysis also revealed significant expression changes in a range of genes involved in centromere structure and/ or the regulation of the mitotic checkpoint. These included CENPA (centromere protein A), AurkA (Aurora kinase A), AurkB (Aurora kinase B) and Bub1 (budding uninhibited by benzimidazoles). In dividing cells, sister chromatids are joined by a centromere, which is required for formation of the kinetichore and subsequent attachment to the spindle fibres prior to cytokinesis. Defects in centromere function result in the misalignment of sister chromatids and errors in the separation of the chromatids, resulting in

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Table 2 Down-regulated genes in hBEC cells after exposure to SM. Gene

Foldchange

Gene

Fold-change

Gene

Fold-change

Gene

Fold-change

Gene

Fold-change

TOP2A UBE2C CDC20 CENPA CKAP2L AURKB CDCA3 HJURP CCNA2 HMMR CDKN3 GINS2 DLGAP5 PRC1 CEP55 PSRC1 CCNB2 HIST1H4C AURKA KIF20A CCNB1 PBK NCAPG BUB1 GLT25D1 LOC730534 PPIL5 TROAP MGC40489 UBE2 T SLFN11 BLM C14orf106 MNS1 HNRNPAB WDR4 CDC25B C9orf140 TXNDC5 HMGN2 RAD51AP1 DEPDC1B KIF14 HNRNPA0 PRIM1 CDC7 MND1 KIF20B

7.120 6.980 6.937 6.064 5.932 5.908 5.362 5.255 5.249 4.990 4.704 4.600 4.495 4.477 4.392 4.390 4.135 4.112 3.955 3.916 3.908 3.836 3.774 3.735 2.198 2.195 2.192 2.190 2.188 2.181 2.174 2.161 2.158 2.157 2.133 2.128 2.126 2.124 2.115 2.109 2.104 2.103 2.096 2.087 2.078 2.073 2.073 2.067

ASPM BIRC5 FBXO5 UHRF1 FEN1 MAD2L1 KIFC1 CCNF CDCA5 TACC3 HMGB2 MCM6 KIF11 RRM2 MCM3 FAM64A CDCA8 CDCA7 NTM CCDC58 SKP2 MCM10 MSRA KCNMA1 DKC1 RRM1 AHNAK COMMD10 MSH6 CDC25C VEGFC LOC727803 DSCC1 SPC25 LRP8 RRS1 UNG DIAPH2 DEK GALNTL4 NUSAP1 C3orf14 POLQ HYLS1 PARP1 FABP5 MLF1IP FJX1

3.708 3.695 3.671 3.602 3.555 3.519 3.483 3.467 3.444 3.427 3.408 3.381 3.326 3.249 3.229 3.227 3.191 3.157 3.139 3.128 3.090 3.068 3.060 3.042 2.064 2.057 2.057 2.056 2.055 2.053 2.050 2.031 2.031 2.028 2.024 2.013 2.010 2.007 2.006 2.002 1.995 1.995 1.991 1.980 1.977 1.974 1.956 1.949

GTSE1 GJB2 CDC2 KIF2C PLK4 PLK1 NDC80 NCAPD2 CDCA2 CKS2 RFC5 SMC4 RFC4 FAM72D CDC25A IMMP2L SUV39H1 MCM2 MCM5 C16orf53 TRIP13 COL8A1 SMYD3 RACGAP1 NCAPD3 CLCC1 SND1 CHCHD6 GPSM2 CDCA4 TMEM109 ADCY3 DNAJC9 EXOSC9 TRMT5 FANCD2 LOC399988 TFDP1 FAF1 FIGNL1 C19orf48 EXOSC2 PAICS POLD1 C13orf34 RANBP1 CKS1B H2AFZ

3.002 3.001 2.990 2.976 2.844 2.841 2.824 2.803 2.796 2.775 2.759 2.753 2.746 2.746 2.745 2.737 2.717 2.716 2.715 2.693 2.686 2.682 2.662 2.646 1.947 1.944 1.938 1.929 1.924 1.923 1.912 1.910 1.903 1.902 1.887 1.856 1.846 1.845 1.844 1.844 1.838 1.834 1.824 1.817 1.815 1.813 1.786 1.774

OIP5 GMNN PFAS MCM4 CENPE MELK FAM83D C21orf45 KIF4A TPX2 POLE2 SLC16A9 STIL SLC29A1 RFC3 KPNA2 ANLN TMEM97 PRR11 C10orf11 C10orf59 C12orf48 TTK NEK2 HSPA8 NEIL3 TRAPPC9 SFRS10 PPAT LRBA ACD PPIH KCTD14

2.642 2.640 2.632 2.616 2.589 2.586 2.586 2.567 2.556 2.549 2.537 2.534 2.515 2.509 2.487 2.482 2.470 2.461 2.458 2.431 2.426 2.416 2.413 2.389 1.763 1.746 1.714 1.711 1.685 1.680 1.676 1.638 1.605

VRK1 RAD54L FARS2 LOC653820 ODC1 LYAR C5orf21 SPC24 C3orf26 SKA1 FLJ12684 FOXM1 FST HAUS8 STX8 KIF22 C11orf82 KIF23 SFRS2 DEPDC1 LOC643287 NOP56 DLL1 HTRA1

2.372 2.371 2.364 2.326 2.326 2.311 2.308 2.304 2.301 2.299 2.293 2.289 2.275 2.266 2.261 2.247 2.238 2.237 2.236 2.234 2.220 2.219 2.209 2.199

aneuploidy (Fukasawa, 2005; Nigg and Raff, 2009). At this stage it is unclear whether these proteins are mediating a mitotic/spindle checkpoint in response to SM-induced DNA damage or whether SM is having a more specific effect on centromeric DNA/proteins. It is also possible that SM is able to bind to tubulin, affecting spindle formation and the progression of mitosis. Other chemicals, including organophosphate compounds, have been shown to adduct to tubulin, affecting the structure and function of this protein (Jiang et al., 2010). This should be investigated further as it has important consequences in terms of the ability of SM to induce aneuploidy/mutagenesis/carcinogenesis. Interestingly, a study published during the preparation of this manuscript demonstrated that a monofunctional analogue of SM (2-chloroethyl ethyl sulphide) was able to induce centromere amplification and aneuploidy in human and mouse cells (Bennett et al., 2014). As mentioned earlier, the microarray analysis identified changes in multiple genes involved in cellular replication. This was particularly obvious for the mini chromosome maintenance (MCM) family of genes, with marked decreases in MCM2, MCM3,

MCM4, MCM5, MCM6 and MCM10 being observed. MCM2–7 function as a complex with DNA helicase activity and is required for replication origin firing and to ensure that cells only undergo one round of DNA duplication per cell cycle (Bell and Botchan, 2013). Perhaps the down-regulation of these genes is an active process to prevent replication initiation in the presence of a damaged genome. Other studies have also implicated MCM proteins in the replication checkpoint and shown that interactions occur between MCM proteins and components of the homologous recombination repair pathway (involved in the cellular response to SM) (Bailis et al., 2008; Jowsey et al., 2010). In addition, several members of the MCM family are targeted by the ATM/ATR protein kinases, strongly suggesting that they play an active role in the cellular response to DNA damage (Cortez et al., 2004; Yoo et al., 2004). It will be interesting to investigate the role of MCM proteins in the cellular response to SM using MCM-deficient models. It is also interesting to note that several MCM proteins have diagnostic/ prognostic significance in cancer (Giaginis et al., 2010). For example, it has been suggested that MCM2 can be used as a

A 35

30

25

20

15

10

PTP MKP

ERK

p38

ELK-1 SAP1a c-Myc Proliferation Differentiation

Proliferation Differentiation Inflammation Apoptosis

Sestrins

GADD45

p53R2

P.A. Jowsey, P.G. Blain / Toxicology Letters 230 (2014) 393–401

MEK1 MEK2

MKK3 MKK6 SAP1a GADD153 MAX MEF2C MAPKAPK MSK1/2 CDC25B

BAX

CHK2

ATR

DNA Damage

ATM

p53

B99

DNA Repair and Damage Prevention

Metabolic pathways Cell cycle MAPK signaling pathway DNA replication Progesterone-mediated oocyte maturation Oocyte meiosis Cytokine-cytokine receptor interaction Pathways in cancer p53 signaling pathway Purine metabolism Hepatitis C NOD-like receptor signaling pathway Rheumatoid arthritis Amoebiasis Systemic lupus erythematosus Jak-STAT signaling pathway Base excision repair Pyrimidine metabolism Pancreatic secretion Vascular smooth muscle contraction Toxoplasmosis Chemokine signaling pathway Mismatch repair Nucleotide excision repair Glutathione metabolism Metabolism of xenobiotics by cytochrome P450 Hematopoietic cell lineage Toll-like receptor signaling pathway Tight junction Protein processing in endoplasmic reticulum Malaria Arginine and proline metabolism Arachidonic acid metabolism Fc epsilon RI signaling pathway Small cell lung cancer TGF-beta signaling pathway Chagas disease (American trypanosomiasis) Homologous recombination Histidine metabolism Linoleic acid metabolism African trypanosomiasis Graft-versus-host disease Bladder cancer Fat digestion and absorption Pathogenic Escherichia coli infection Phenylalanine metabolism Steroid biosynthesis alpha-Linolenic acid metabolism

5

0

IL1R

TNFR

B

IL1

ASK1

CHK1

GADD45

NOXA

14-3-3s

CyclinB/CDC2

Apoptosis

Examples of gene changes associated with MAP kinase signalling pathway

TNF

C

p21

CDK2/4/6 Cell Cycle Checkpoints

Examples of gene changes associated with p53 signalling pathway

399

Fig. 2. (A) KEGG pathway analysis of whole genome microarray data after exposure of hBEC cells to SM. hBEC cells were exposed to 50 mM SM for 18 h, followed by whole genome microarray analysis. Significantly altered genes were subject to KEGG pathway analysis using Webgestalt (http://bioinfo.vanderbilt.edu/webgestalt/) and results summarised in a bar chart showing the number of genes associated with each cellular pathway/process. (B) and (C) Examples from KEGG pathway analysis showing MAP kinase and p53 signalling pathways. Specific genes which are altered by SM exposure are highlighted in grey boxes.

Number of Genes

400

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premalignant/malignant marker in the lung (Hashimoto et al., 2004; Tan et al., 2001). Given that the lungs are a major target for SM toxicity and increased incidence of lung cancer has been described after a single exposure to SM (Hosseini-khalili et al., 2009), it might be worth adding MCM2 expression analysis to ongoing clinical studies on individuals previously exposed to SM (e.g. the Iranian Army personnel and civilian populations exposed during the 1980s). As well as direct DNA damage, increased levels of oxidative stress has been proposed as a contributory factor to SM toxicity (Laskin et al., 2010), possibly mediated by the inactivation of glutathione. This oxidative imbalance can cause DNA lesions, for example the pro-mutagenic adduct 8-hydroxy deoxyguanosine (8-OHdG), shown to be increased after exposure of mouse cells to a SM analogue (O'Neill et al., 2010). In addition to DNA damage, reactive oxygen species can also target membranes and proteins. It is therefore interesting to note the gene expression of methionine sulphoxide reductase A (MSRA) is reduced after SM. MSRA is an enzyme involved in the repair of oxidatively damaged proteins, reducing methionine sulphoxide to methionine. Interestingly, protein oxidation has been implicated in a range of age-related diseases, including the neurodegenerative disorders Parkinson’s and Alzheimer’s (Martinez et al., 2010; Sultana and Butterfield, 2013). Individuals exposed to SM develop a range of health defects many years after the original exposure, including chronic problems with the skin, eyes and respiratory system. It is therefore tempting to speculate that protein oxidation may be involved in the development of chronic health effects after exposure to SM. Studies should be performed to investigate the mechanisms mediating the down-regulation of MSRA after SM as well as the role of MSRA in the cellular response to SM. Finally, it would be very informative to measure the expression of MSRA in individuals previously exposed to SM who are now suffering adverse health effects (e.g. the previously mentioned Iranian population). This study is the first to investigate whole genome expression changes associated with acute SM toxicity in a model system relevant to SM inhalation exposure. The results corroborate a range of previous findings relating to SM toxicity as well as identifying multiple other proteins/pathways that warrant further investigation. The results of these studies will improve our understanding of SM toxicity, essential for the development of an effective therapeutic, and may also help identify novel diagnostic/prognostic markers with clinical relevance to SM-exposed individuals. Conflict of interest The authors declare that there are no conflicts of interest. Transparency document The Transparency document associated with this article can be found in the online version. Acknowledgement This research was funded by the UK Home Office. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.toxlet.2014.08.001.

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