Cigarette smoke extract induced cytokine and chemokine gene expression changes in COPD macrophages

www.elsevier.com/locate/issn/10434666 Cytokine 42 (2008) 205–216

Cigarette smoke extract induced cytokine and chemokine gene expression changes in COPD macrophages Lauren Kent a,*, Lucy Smyth a, Chris Clayton b, Laurie Scott b, Ted Cook b, Richard Stephens b, Steve Fox c, Peter Hext c, Stuart Farrow d, Dave Singh a a

Education and Research Centre, South Manchester University Hospitals Trust/University of Manchester, Wythenshawe Hospital, Southmoor Road, Manchester M23 9LT, UK b Molecular Discovery Research, GlaxoSmithKline, Stevenage, Hertfordshire SG1 2NY, UK c Statistical Sciences, GlaxoSmithKline, Stevenage, Hertfordshire SG1 2NY, UK d Target Discovery, GlaxoSmithKline, Stevenage, Hertfordshire SG1 2NY, UK Received 3 August 2007; received in revised form 6 December 2007; accepted 4 February 2008

Abstract Macrophages are key inflammatory cells in chronic obstructive pulmonary disease (COPD). The transcriptional regulation of inflammatory signalling pathways by cigarette smoke (CS) in COPD macrophages is not well understood. We have studied the effects of acute CS exposure on COPD macrophage cytokine, chemokine and signal transduction gene expression profiles. Monocyte derived macrophages (MDMs) from whole blood from patients with COPD (n = 6) were stimulated with 1%, 10% and 25% CS extract (CSE) for 6 h for microarray and quantitative polymerase chain reaction (Q-PCR) analysis. We observed a CSE dose dependant increase in the numbers of significantly regulated genes; 24, 340 and 627 genes at 1%, 10% and 25% CSE, respectively. IL-8 mRNA levels were up-regulated by 10% CSE (2.25-fold increase, 95% CI 1.28–4.00). In contrast a range of other cytokines and chemokines were down-regulated at both 10% and 25% CSE, including IL-1b, -6, -10 and -18, chemokine ligands CCL-2, -3, -4, -5, -8, -15, -20 and CXCL-1, -2 and -10. QPCR and microarray data were highly correlated (r = 0.95, p = 0.0001). NF-jB component p50 and IjBa expression were suppressed by CSE, while there was up-regulation of the AP-1 components c-Jun, FOSL1 and FOSL2. Acute CSE exposure decreased macrophage inflammatory gene expression, with the exception of increased IL-8. There was diverse regulation of key inflammatory signal pathway genes. The effects of acute CS exposure appear to encompass both up-regulation of chemotaxis mechanisms through IL-8, but also downregulation of innate immunity. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: COPD; Macrophages; Cigarette smoke extract; Microarray

1. Background Chronic obstructive pulmonary disease (COPD) is characterised by progressive airflow obstruction and airway

*

Corresponding author. Fax: +44 (0) 161 946 1459. E-mail addresses: [email protected] (L. Kent), [email protected] (L. Smyth), [email protected] (C. Clayton), [email protected] (L. Scott), [email protected] (T. Cook), [email protected] (R. Stephens), [email protected] (S. Fox), [email protected] (P. Hext), [email protected] (S. Farrow), [email protected] (D. Singh). 1043-4666/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.cyto.2008.02.001

inflammation, with the major cause being cigarette smoking. Alveolar macrophage numbers are increased in COPD patients, and play a key role in the pathogenesis of COPD, through the secretion of pro-inflammatory mediators such as chemokines and cytokines [1]. Of particular importance are the neutrophil chemoattractant interleukin (IL)-8, and the pro-inflammatory cytokines tumour necrosis factor (TNF)-a, IL-1b and IL-6. Macrophages also release proteases that cause tissue destruction. The changes in macrophage function caused by cigarette smoke (CS) exposure are therefore of importance to our understanding of inflammation in COPD.

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CS contains high levels of oxygen free radicals, nitric oxide and organic compounds [2] that cause oxidative stress. In vitro studies of human alveolar macrophages have shown that oxidative stress caused by acute cigarette smoke extract (CSE) exposure increases the release of IL-8 [3–5], granulocyte-macrophage colony-stimulating factor (GMCSF) [4,5] and the protease matrix metalloproteinase (MMP)-9 [6]. In contrast, CSE down-regulates the production of pro-inflammatory cytokines in peripheral blood mononuclear cells (PBMCs), including TNF-a and IL-1b [7]. Thus, acute CSE exposure appears to up-regulate the production of some inflammatory mediators, but downregulate others. Different signalling pathways appear to be involved in these changes, as in monocyte derived macrophages (MDMs) it has been shown that the release of IL8 by CSE is dependent on the transcription factor nuclear factor (NF)-jB [8], while in the monocytic THP-1 cell line activator protein 1 (AP-1) signalling was reported to be more important [3]. Most of the studies that have investigated the acute effects of CSE exposure on macrophages have assessed protein expression; either secreted levels of inflammatory mediators [3–5,8] or intracellular levels of signalling proteins [3,8]. CSE regulation of these proteins may occur either at the level of gene transcription or translation, or at the level of protein assembly or secretion. Comprehensive studies of the effects of acute CSE exposure on macrophage gene transcription are lacking. Different study designs are possible to evaluate the effects of CSE exposure on macrophage gene expression. Firstly, it is possible to investigate the short term effects of acute CSE exposure, which is similar to the acute effects of smoking one cigarette. Secondly, it is possible to investigate longer term exposure that is more reflective of chronic cigarette smoking. These are both important but biologically different issues. We chose to study the acute effects of CSE exposure in COPD patients. Our underlying research question was; what are the macrophage gene expression changes caused by a single dose of CSE in patients with COPD? We used MDMs from COPD patients as they are a recognised model for studying macrophage behaviour [3,8]. Microarray technology and real-time quantitative polymerase chain reaction (Q-PCR) were applied to study the acute effects of CSE exposure. Our primary aim was to understand the regulation of cytokine, chemokine and signal transduction genes, although we also studied the regulation of other groups of genes, including anti-oxidant and cell survival genes. Our data shows that acute CSE exposure causes both down- and up-regulation of inflammatory gene expression.

corticosteroids, 2 current smokers) provided blood samples for mRNA and supernatant protein analysis and a further 6 subjects with COPD (all male, mean FEV1 53% predicted, 2 using inhaled corticosteroids, 2 current smokers) provided blood for flow cytometry assessment of cell death. Subjects refrained from smoking for at least 3 h prior to blood donation. Samples were all collected in the morning at approximately 9 am. Subjects provided written informed consent to donate blood for this study. The Local Research Ethics Committee approved this study. 2.2. Cell culture Preparation of MDMs from blood and culture with CSE is described in additional file—methodology. Solutions containing 0%, 1%, 10% and 25% CSE in supplemented RPMI 1640 media were incubated with MDMs in 24-well plates for 6 h at 37 °C and 5% CO2. 2.3. RNA Isolation and Microarray Analysis Cells were lysed in Trizol (Invitrogen, Paisley, UK) and protocols for RNA isolation, quantification and quality assessment are described in the supplement. Samples were randomised prior to Affymetrix microarray analysis (Affymetrix, Santa Clara, USA) as described in the supplement. 2.4. Quantitative PCR The methods for Q-PCR are provided in the supplement. It was decided a priori that IL-1b, IL-6, IL-8, IL-10 and TNF-a would be analysed by TaqMan Q-PCR. After microarray analysis, it was decided that the following 13 highly regulated signalling, cytokine and chemokine genes would be analysed by SybrGreen Q-PCR; Adenosine A2b receptor (ADORA2B), Bcl2 associated anthogene 3 (BAG3), Fas associated death domain (FADD), FOS-like 2 (FOSL2), glutamate-cysteine ligase modifier subunit (GCLM), heme oxygenase 1 (HMOX1), IL-10Ra, chemokine (C–C motif) ligand 8 (CCL8/monocyte chemoattractant protein 2/ MCP-2), metallothionein (MT)1K, NAD(P)H dehydrogenase quinone 2 (NQO2), purinergic receptor P2X (P2RX7) and superoxide dismutase 2 (SOD2). 2.5. IL-8 enzyme-linked immunosorbent assays (ELISAs) Sandwich ELISAs for IL-8 were performed on cell culture supernatants according to the manufacturer’s instructions (R&D Systems Europe Ltd., Abingdon, UK). IL-8 supernatants were diluted 1:4.

2. Methods 2.6. Cell viability 2.1. Patients Six subjects with COPD as defined by current guidelines [9] (all male, mean FEV1 63% predicted, 3 using inhaled

Cell viability was assessed by annexin V/propidium iodide staining and flow cytometric analysis as described in the supplement.

L. Kent et al. / Cytokine 42 (2008) 205–216

2.7. Statistical analysis Gene array data quality was assessed for homogeneity of quality control metrics by principal component analysis (PCA) using SIMCA-P+ software (Umetrics, Windsor, UK). Global analysis of gene expression was initially processed by normalising probe intensity data using Rosetta Resolver (Rosetta, Seattle, USA) [10] prior to loading into SIMCA-P+ (Umetrics, Windsor, UK) for visual assessment of key trends by gene expression PCA. Further analysis was performed on the intensities by mixed-model analysis of variance (ANOVA) using SAS Software (SAS UK, Marlow, UK). The CSE concentration was included as a fixed factor and donor was included as a random factor. A probe set was retained for further analysis if p < 0.05 in at least 3 of the patients. The Dunnett’s post hoc test was used to compare results to control. All probe sets with p 6 0.01 and fold change of 2 were deemed significant. Probe set gene names were obtained from www.affymetrix.com. Functional classification by gene ontology was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://apps1.niaid.nih. gov/david/). For each identified functional group unsupervised hierarchical clustering was performed in SAS software (SAS UK, Marlow, UK) using the Pearson distance and group average linkage of the fold change data. Probe sets with a Pearson distance >0.125 were not allocated to a cluster as this cut off point was decided upon to ensure a low distance between observations. For the primary aim of the study, it was decided a priori to identify cytokine, chemokine and signal transduction genes by combining the results from DAVID with pathway identification using Ingenuity Pathways Analysis (IngenuityÒ Systems, www.ingenuity.com) (from here on referred to as Ingenuity). In addition, the cross reference NCBI database was used to manually search for the functions of genes. A similar approach was used to identify anti-oxidant and cell survival genes. SAS Software (SAS UK, Marlow, UK) was also used to perform a mixed-model ANOVA with Dunnett’s post hoc for Q-PCR results. GraphPad InStat Software (GraphPad Software Inc., San Diego, USA) was used to perform a repeated-measures ANOVA with Tukey’s post hoc test on flow cytometry data, Spearman’s Rank Correlation Coefficient to assess the relationship between array and Q-PCR data, and paired students two-tailed t-test for IL8 protein data. 3. Results Flow cytometry showed no induction of apoptosis or necrosis at any concentration of CSE (data not shown). 3.1. Gene expression microarrays RNA quality was satisfactory for microarray analysis showing no degradation of the 18s and 28s rRNA subunits.

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Two microarrays from different patients (one at 10% and one at 25% CSE) failed the quality control, and were excluded from further analyses. PCA analysis (see additional file—principal component analysis) showed that the contribution of within subject variability to differences between the gene expression profiles at 0%, 1%, 10% and 25% CSE was limited. In contrast, there was significant separation due to CSE effects. There were 24 genes (24 probe sets) with significant changes (>2-fold regulation, p < 0.01) at 1% CSE, 340 genes (401 probe sets) at 10% CSE and 627 genes (778 probe sets) at 25% CSE. 140 probe sets were significantly changed at both 10% and 25% CSE, with 9 probe sets that changed at 1%, 10% and 25% CSE. 3.1.1. Gene function and cluster analysis The DAVID (http://apps1.niaid.nih.gov/david/) database was used to identify the functions of genes significantly regulated at either 1%, 10% or 25% CSE. There were five major groups of genes identified; those involved in cell growth including cell maintenance or proliferation—113 genes (147 probe sets), cell metabolism—100 genes (137 probe sets), signal transduction including transcription—102 genes (133 probe sets), nucleobase, nucleoside, nucleotide and nucleic acid metabolism (NM)—92 genes (129 probe sets) and cell death—35 genes (43 probe sets). Cluster analysis was performed within each of these functional groups (see Fig. 1A–E). Signal transduction and cell death gene clusters were predominantly down-regulated by CSE, although 1 gene cluster showed concentration dependent effects and 1 gene cluster was up-regulated by CSE. Cell growth, cell metabolism and NM genes showed the most evidence of clusters that were up-regulated by CSE. 3.1.2. Cytokine, chemokine, signalling, anti-oxidant and cell survival genes (Tables 1A–1D) Ingenuity and manual search for gene functions were used to refine the DAVID analysis. A number of cytokine and chemokine genes were down-regulated by CSE, but there was no evidence of up-regulation of gene expression. The cytokines down-regulated included those with proinflammatory activity (IL-1b, IL-6 and IL-18) as well as the anti-inflammatory cytokine IL-10. There was evidence of NF-jB pathway up-regulation; as gene expression of the transcriptional activator cAMP response element binding protein (CREBBP) increased and the NF-jB inhibitor IjBa decreased. In contrast, the key NF-jB subunit p50 was down-regulated. There was up-regulation of the dual specificity phosphatases 1 and 6 (MAPK phosphatase-1 and -3, respectively), which are known to inhibit MAPK signalling. The AP-1 subunits cJun and FOS-like antigen 1 and 2 were up-regulated. The Janus activated kinase (JAK)—signal transducer and activators of transcription (STAT) pathway was suppressed; JAK 2 and 3 and STAT1 were down-regulated. Gene expression of the PI3 kinase c catalytic subunit (PIK3CG)

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A

1

2

3

4

Probe set number 4

Mean FC

3 2

Cluster 1

1

Cluster 2 Cluster 3

0 1%

-1

25%

10%

Cluster 4

-2 -3

B

Pearson distance measure

CSE Concentration

Probe set number

Mean FC

4 3

Cluster 1

2

Cluster 2

1

Cluster 3 Cluster 4

0 -1

1%

10%

25%

Cluster 5 Cluster 6

-2 -3

CSE Concentration Fig. 1. Dendograms of Pearson measure of distance between probe sets identified in the following functional gene groups. (A) Cell growth (including cell maintenance or proliferation), (B) cell metabolism, (C) signal transduction (including transcription), (D) nucleobase, nucleoside, nucleotide and nucleic acid metabolism and (E) cell death. Dendograms were constructed in SAS software. Clusters are highlighted and annotations correspond to associated line graphs. Line graphs show geometric mean of the fold change of each cluster across all CSE concentrations.

C

209

Pearson distance measure

L. Kent et al. / Cytokine 42 (2008) 205–216

Probe set number 3

Mean FC

2 1

Cluster 1 Cluster 2

0 1%

10%

25%

Cluster 3 Cluster 4

-1 -2 -3

D

Pearson distance measure

CSE Concentration

Probe set number 4 3

Mean FC

2

Cluster 1 Cluster 2

1

Cluster 3 0 1%

10%

25%

Cluster 4 Cluster 5

-1 -2 -3

CSE Concentration Fig. 1 (continued)

and Toll-like receptor (TLR) 2 were also down-regulated by CSE. A number of anti-oxidant genes were identified that were up-regulated; MT1K, NQO1 and 2, aldo-keto reductase family 1, member C2 (AKR1C2) and HMOX1. In contrast there was significant down-regulation of SOD2.

The following key cell survival and apoptosis genes were down-regulated; B-cell CLL/lymphoma 10 (Bcl-xl), caspase 8, caspase recruitment domain member 12 (CARD12), cathepsin C, FADD and proteasome subunit b type 7 (NEK6). In contrast, the following cell survival and apoptosis genes were up-regulated; BAG3, Bcl2 like 1

210

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Pearson distance measure

E

Probe set number 3

MeanFC

2 1

Cluster 1 Cluster 2

0 1%

10%

Cluster 3

25%

Cluster 4

-1 -2 -3

CSE Concentration Fig. 1 (continued)

Table 1A Gene expression fold change of significantly regulated microarray genes: cytokine and chemokine groupa Group name

Affy ID

Gene

Array fold change 1% CSE

Cytokine and chemokine signalling

a

205067_at 39402_at 207433_at 204912_at 207375_s_at 206295_at 205291_at 205926_at 222062_at 205207_at 210390_s_at 216598_s_at 205476_at 205114_s_at 204103_at 1405_i_at 1555759_a_at

Interleukin 1, b Interleukin 1, b Interleukin 10 Interleukin 10 receptor a Interleukin 15 receptor, a Interleukin 18 (interferon-c-inducing factor) Interleukin 2 receptor, b Interleukin 27 receptor, a Interleukin 27 receptor, a Interleukin 6 (interferon, b 2) Chemokine (C–C motif) ligand 15 Chemokine (C–C motif) ligand 2 Chemokine (C–C motif) ligand 20 Chemokine (C–C motif) ligand 3 Chemokine (C–C motif) ligand 4 Chemokine (C–C motif) ligand 5 Chemokine (C–C motif) ligand 5

204655_at 214038_at 204470_at 204533_at 209774_x_at 207652_s_at

Chemokine (C–C motif) ligand 5 Chemokine (C–C motif) ligand 8 Chemokine (C–X–C motif) ligand 1 Chemokine (C–X–C motif) ligand 10 Chemokine (C–X-C motif) ligand 2 Chemokine-like receptor 1

Genes shown had 2-fold change and p < 0.01. Gene Affymetrix IDs are shown.

10% CSE

25% CSE

IL1b IL1b IL10 IL10RA IL15RA IL18

1.45 1.49 1.59 1.19 1.05 1.29

1.60 1.60 3.55 2.14 1.89 2.17

3.69 4.05 1.93 2.29 2.59 1.76

IL2RB WSX1 WSX1 IL6 CCL15 CCL2 CCL20 CCL3 CCL4 CCL5 RANTES precursor CCL5 CCL8/MCP2 CXCL1 CXCL10 CXCL2 CMKLR1

1.11 1.26 1.08 1.27 1.25 1.11 1.01 1.01 1.07 1.03 1.21

1.50 1.62 1.63 1.77 1.87 1.78 3.40 1.36 2.30 1.59 1.62

2.26 2.22 4.36 3.18 6.84 2.81 9.22 2.14 5.96 2.06 2.34

1.11 1.23 1.06 1.32 1.13 1.41

1.62 4.33 2.10 3.45 1.11 3.61

2.20 9.12 11.07 5.56 3.12 8.05

Table 1B Gene expression fold change of significantly regulated microarray genes: signalling pathways groupa Group name

Affy ID

Gene

Array fold change

MAPK signalling

201041_s_at 201044_x_at 208893_s_at 209664_x_at 202545_at 201328_at

Dual specificity phosphatase 1 Dual specificity phosphatase 1 Dual specificity phosphatase 6 Nuclear factor of activated T-cells1 Protein kinase Cd v-ets erythroblastosis virus E26 oncogene homolog 2

DUSP1 DUSP1 DUSP6 NFATC1 PRKCD ETS2

1.16 1.22 1.23 1.05 1.04 1.06

1.72 1.25 1.03 2.26 1.59 2.06

3.13 3.09 2.08 1.24 2.07 2.14

AP-1 signalling

204420_at 218880_at 201465_s_at 201466_s_at

FOS-like antigen 1 FOS-like antigen 2 v-Jun sarcoma virus 17 oncogene homolog (avian) v-Jun sarcoma virus 17 oncogene homolog (avian)

FOSL1 FOSL2 JUN JUN

1.04 1.52 2.81 1.37

1.67 2.55 4.27 2.33

2.01 2.78 5.90 2.77

NF-jB signalling

228177_at 204549_at 209239_at 201502_s_at 202643_s_at

CREB binding protein (Rubinstein–Taybi syndrome) Inhibitor of jlight polypeptide gene enhancer in B-cells, kinase e Nuclear factor j B1 (p50, p105) Nuclear factor jlight polypeptide gene enhancer in B-cells inhib a TNFa-induced protein 3

CREBBP IKK3 NFKB1 NFKBIA TNFAIP3/A20

1.07 1.09 1.03 1.13 1.07

1.78 1.92 1.15 1.10 1.34

2.24 2.00 2.06 2.03 2.92

Toll-like receptor signalling

231779_at 237342_at 204924_at

Interleukin-1 receptor associated kinase 2 Toll interacting protein Toll-like receptor 2

IRAK2 TOLLIP TLR2

1.23 1.77 1.06

2.35 5.62 1.19

1.43 3.67 2.31

Other signalling

205891_at 1570507_at 208712_at 206369_s_at 239294_at 207091_at 205841_at 205842_s_at 227677_at 232375_at

Adenosine A2b receptor B-catenin Cyclin D1 Phosphoinositide-3-kinase, catalytic, c subunit Phosphoinositide-3-kinase, catalytic, c subunit Purinergic receptor P2X, ligand-gated ion channel, 7 Janus kinase 2 (a protein tyrosine kinase) Janus kinase 2 (a protein tyrosine kinase) Janus kinase 3 (a protein tyrosine kinase, leucocyte) Signal transducer and activator of transcription 1

ADORA2B CTNNB1 CCND1 PIK3CG PIK3CG P2RX7 JAK2 JAK2 JAK3 STAT1

1.26 1.55 1.02 1.08 1.06 2.37 1.14 1.54 1.22 1.05

2.52 2.10 2.25 2.06 1.72 3.98 3.82 2.94 1.50 1.23

3.27 2.99 1.85 2.08 2.03 3.46 2.33 2.84 2.03 2.68

1% CSE

25% CSE

L. Kent et al. / Cytokine 42 (2008) 205–216

a

10% CSE

Genes shown had 2-fold change and p < 0.01. Gene Affymetrix IDs are shown.

211

212

L. Kent et al. / Cytokine 42 (2008) 205–216

Table 1C Gene expression fold change of significantly regulated microarray genes: anti-oxidant groupa Group name

Affy ID

Gene

Array fold change

Anti-oxidants

209699_x_at 211653_x_at 203925_at 234986_at 236140_at 203665_at 217546_at 212185_x_at 237870_at 201467_s_at 210519_s_at 215078_at

Aldo-keto reductase family 1, member C2 Aldo-keto reductase family 1, member C2 Glutamate-cysteine ligase, modifier subunit Glutamate-cysteine ligase, modifier subunit Glutamate-cysteine ligase, modifier subunit Heme oxygenase 1 Metallothionein 1K Metallothionein 2A NAD(P)H dehydrogenase, quinone 2 NAD(P)H dehydrogenase, quinone 1 NAD(P)H dehydrogenase, quinone 1 Superoxide dismutase 2

1% CSE

a

AKR1C2 AKR1C2 GCLM GCLM GCLM HMOX1 MT1K MT2A NQO2 NQO1 NQO1 SOD2

2.00 1.56 1.56 1.61 1.58 1.46 1.73 1.02 1.23 1.57 1.44 1.13

10% CSE

25% CSE

4.56 4.28 3.38 3.65 3.68 3.68 15.46 1.94 1.85 2.33 2.09 1.08

2.17 1.74 3.32 3.74 3.92 3.92 28.18 2.68 2.99 1.71 1.56 2.37

Genes shown had 2-fold change and p < 0.01. Gene Affymetrix IDs are shown.

Table 1D Gene expression fold change of significantly regulated microarray genes: cell survival and apoptosis group, determined using DAVID and ingenuity databases Group name

Affy ID

Gene

Array fold change 1% CSE

Cell survival and apoptosis

*

1557257_at 217911_s_at 212312_at 1570001_at 207686_s_at 213373_s_at 1552553_a_at 225646_at 231234_at 200881_s_at 1554333_at 1554334_a_at 225061_at 200664_s_at 200666_s_at 203810_at 203811_s_at 1554462_a_at 202842_s_at 202843_at 202535_at 200799_at 200800_s_at 202581_at 211538_s_at 219475_at 225214_at 215886_x_at 223289_s_at 239163_at 221962_s_at 222420_s_at 222421_at 223014_at

B-cell CLL/lymphoma 10 Bcl2 anthogene 3 Bcl2 like 1 CASP8 associated protein 2 Caspase 8, apoptosis-related cysteine protease Caspase 8, apoptosis-related cysteine protease Caspase recruitment domain family, member 12 Cathepsin C Cathepsin C DnaJ (Hsp40) homolog, subfamily A, member 1 DnaJ (Hsp40) homolog, subfamily A, member 4 DnaJ (Hsp40) homolog, subfamily A, member 4 DnaJ (Hsp40) homolog, subfamily A, member 4 DnaJ (Hsp40) homolog, subfamily B, member 1 DnaJ (Hsp40) homolog, subfamily B, member 1 DnaJ (Hsp40) homolog, subfamily B, member 4 DnaJ (Hsp40) homolog, subfamily B, member 4 DnaJ (Hsp40) homolog, subfamily B, member 9 DnaJ (Hsp40) homolog, subfamily B, member 9 DnaJ (Hsp40) homolog, subfamily B, member 9 Fas (TNFRSF6) associated via death domain Heat shock 70 kDa protein 1A Heat shock 70 kDa protein 1A Heat shock 70 kDa protein 1A Heat shock 70 kDa protein 2 Pregnancy-induced growth inhibitor Proteasome (prosome, macropain) subunit, b type, 7 Ubiquitin specific protease 12 Ubiquitin specific protease 38 Ubiquitin-conjugating enzyme E2B Ubiquitin-conjugating enzyme E2H Ubiquitin-conjugating enzyme E2H Ubiquitin-conjugating enzyme E2H Ubiquitin-conjugating enzyme E2R 2

Bcl10/Bcl-xl BAG3 BCL2L1 5203092, mRNA CASP8 CASP8 CARD12 CTSC_V1 CTSC_V1 DNAJA1 DNAJA4 DNAJA4 DNAJA4 DNAJB1 DNAJB1 DNAJB4 DNAJB4 DNAJB9 DNAJB9 DNAJB9 FADD HSPA1A HSPA1A HSPA1B HSPA2 OKL38 NEK6 USP12 USP38 AW364833. . . UBE2H UBE2H UBE2H UBE2R2

1.30 1.03 1.01 2.19 1.15 1.08 1.42 1.28 1.65 1.02 1.04 1.05 1.05 1.03 1.03 1.33 1.30 1.04 1.06 1.17 1.05 1.03 1.00 1.17 1.14 2.37 1.30 1.03 1.35 1.34 1.26 1.21 1.32 1.41

10% CSE 1.56 2.05 1.27 1.91 1.55 1.32 3.08 2.26 2.03 1.39 1.54 1.64 1.70 2.30 2.34 4.10 5.67 1.83 1.86 2.44 1.63 2.97 4.30 5.16 2.14 8.60 2.21 1.64 1.62 1.61 2.53 2.13 2.47 1.76

25% CSE 2.29 5.29 2.11 1.94 2.39 2.34 2.32 1.36 1.63 2.04 3.18 3.15 2.95 9.10 8.38 6.71 11.71 2.36 2.24 3.27 2.18 4.71 10.11 16.66 3.09 12.02 2.08 2.13 2.10 2.10 3.11 2.12 2.25 2.46

Genes shown had 2-fold change and p < 0.01. Gene Affymetrix IDs are shown.

(BCL2L1), caspase 8 associated protein 2 and pregnancyinduced growth inhibitor (OKL38). Furthermore, genes

in the heat shock protein (Hsp) and ubiquitin pathways were significantly up-regulated.

L. Kent et al. / Cytokine 42 (2008) 205–216

213

Table 2 Fold change comparison between array and Q-PCR geometric means for 18 genes Gene

Affy ID

Array fold change

IL1b IL6 TNF-a IL10 IL10RA IL8 MCP2 GCLM HMOX1 MTK1 NQO2 SOD2 ADORA2B BAG3 FADD FOSL2 P2RX7

205067_at, 39402_at 205207_at 207113_s_at 207433_at 204912_at 202859_x_at, 211506_s_at 214038_at 236140_at 203665_at 217546_at 237870_at 215078_at 205891_at 217911_at 202535_at 218880_at 207091_at

10% CSE

Q-PCR fold change

25% CSE

1% CSE

1.60 1.77 1.33 3.55 2.14 1.72 4.33 3.68 3.68 15.46 1.85 1.08 2.52 2.05 1.63 2.55 3.98

3.87 3.18 1.79 1.93 2.29 1.08 9.12 3.92 3.92 28.18 2.99 2.37 3.27 5.29 2.18 2.78 3.46

1.47 1.27 1.54 1.59 1.19 1.23 1.23 1.58 1.46 1.73 1.23 1.13 1.26 1.03 1.05 1.52 2.37

10% CSE 1.52 2.40 1.67 1.91 1.13 1.68 1.38 1.53 1.87 1.09 1.28 1.04 2.15 1.17 1.13 1.39 1.85

25% CSE 1.70 1.02 1.13 6.75 1.94 2.25 5.47 3.26 5.86 6.79 1.79 1.34 3.28 2.02 1.45 2.04 4.41

4.35 1.01 1.37 2.66 2.15 1.19 15.02 3.11 6.44 9.36 2.18 2.01 4.38 9.27 2.48 2.80 2.46

Gene Affymetrix IDs are shown.

*

4. Discussion

* IL-8 protein pg/mL

5000 4000 3000 2000 1000 0 Control

1% CSE

10% CSE

25% CSE

Fig. 2. CSE induced IL-8 protein production from COPD MDMs. Individual data and mean (horizontal bar) are shown. *Denotes p < 0.05.

3.2. Q-PCR analysis (Table 2) Q-PCR analysis provided confirmatory evidence of CSE inhibition of cytokine and chemokine gene expression, which was particularly evident for IL-1b and MCP-2. IL8 showed concentration dependent up-regulation at 1% and 10%, but down-regulation at 25%. Inhibition of IL10 expression was observed by Q-PCR, as in the arrays. Q-PCR results were highly correlated to array data (r = 0.95, p < 0.0001). 3.3. IL-8 protein levels Secreted IL-8 protein was significantly up-regulated (p = 0.03) after exposure to 10% CSE and significantly down-regulated (p = 0.02) at 25% CSE (Fig. 2).

The novelty of this study is that we have evaluated the gene expression changes caused by acute CSE exposure in COPD macrophages. Gene expression of a broad range of macrophage cytokines and chemokines was reduced by CSE. Quantitative PCR analysis confirmed the suppressant effect of CSE, although IL-8 mRNA and protein were up-regulated in a concentration dependent manner. While COPD is a disease associated with increased airway inflammation [11,12], our data shows that acute CSE exposure reduces macrophage inflammatory gene transcription. The exception was the neutrophil chemoattractant IL-8. Airway IL-8 levels are known to be increased in COPD patients [4,13,14], and our study further supports a central role for this chemokine in promoting airway inflammation caused by smoking. We observed changes in inflammatory signal transduction pathways that were compatible with these cytokine and chemokine results; there was inhibition (p50, JAK/STAT and MAPK phosphatase-1 and -3) of signal transduction gene expression, but also evidence of up-regulation (CREBBP, IjBa and AP-1 signalling). This study highlights a number of gene expression changes that occur in COPD macrophages in response to acute CSE exposure. Ideally, confirmation of these data using airway macrophages is needed. It would also be valuable to study whether the same or different gene expression changes occur in a control group, such as non-smokers. Additionally, the differences between acute and chronic cigarette smoke exposure is an important topic. Gene array studies are difficult to perform with technical precision, and provide complex data outputs. Given these practical and analytical hurdles, it is difficult to address all of the

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above questions in a single study. The current study is a model for the acute gene expression changes that occur in COPD macrophages after acute smoke exposure. Previous studies in cell lines and primary human macrophages have shown that acute CSE exposure increases IL-8 [4,5] and GM-CSF production [3–5,8,15]. In contrast, in PBMCs CSE has been shown to suppress the production of the inflammatory mediators IL-1b, TNF-a, IL-2 and IFN-c [7]. These observations indicate that CSE may have both pro- and anti-inflammatory effects on immune cells. We observed that acute CSE exposure increased the production of IL-8, but down-regulated the gene expression of other chemokines, including MIP-1a, MIP-1b, MCP-1, MCP-2, RANTES (CCL-3, -4, -2, -8 and -5, respectively) and the CXC-chemokines GROa, GROb and IP10 (CXCL-1, -2 and -10, respectively). CSE also decreased the gene expression of macrophage cytokines, including pro-inflammatory IL-1 and IL-6, and anti-inflammatory IL-10. These findings suggest that acute CSE exposure causes macrophages to increase inflammatory cell chemotaxis through IL-8, but down-regulates the innate immune response. Down-regulation of the innate immune response by cigarette smoke has also been observed in a mouse model [16], and in a study of the effects of cigarette smoke on LPS activation of the human monocytic cell line THP-1 [17]. Interestingly, this study in THP-1 cells also observed up-regulation of IL-8, in contrast to the down-regulation of other cytokines and chemokines. Facchinetti et al. [18] have recently shown that a, bunsaturated aldehydes in cigarette smoke, such as acrolein and crotonaldehyde, are responsible for IL-8 release from macrophages. We speculate that the signalling mechanisms activated by these compounds leads to transcription factor binding specifically to the IL-8 promoter, but a decrease in transcription factor binding at other cytokine and chemokine promoters. This warrants further study. CSE caused down-regulation of TLR2 expression. TLRs function as sensors of pathogenic stimuli, and TLR2 is a sensor for mycobacterial proteins. Our data is compatible with a previous report of TLR2 down-regulation in alveolar macrophages from smokers and COPD patients [19], and suggests a mechanism by which inflammatory mediator production is decreased by CSE. IL-8 gene expression and supernatant protein levels were increased at the lower CSE concentrations, but decreased at 25% CSE. This ‘‘bell shaped curve” for CSE effects on IL-8 is well described [3,20]. Higher concentrations of CSE reduce cell viability [20], either by inducing cell apoptosis or necrosis, which is dependent on concentration [21– 23], cell type [21–24] and exposure duration [21,22,24]. We observed no overall change in cell viability, as flow cytometric analysis did not show increased apoptosis or necrosis. However, we only studied a 6 h time-point, and it is possible that longer exposure would have shown different results. Counting live and dead cells would have been another possible method, although it is unlikely we would have seen increased dead cells within 6 h in the absence of

any flow cytometry changes. Nevertheless, although we failed to observe a decrease in cell viability at 25% CSE, published literature suggests that the reduction in IL-8 production was due to an early reduction in cell function that may have been more measurable had we cultured the cells for a longer time-period [20–22,24]. To investigate the mechanisms responsible for the CSE induced changes in macrophage cytokine and chemokine gene expression, we assessed signalling pathway gene expression regulation. A limitation of these observations is that we did not study intracellular signalling protein expression. Nevertheless there was suppression of NF-jB pathway genes, most notably the subunit p50. In contrast, there was down-regulation of the inhibitor IjBa, and the transcriptional activator CREBBP was up-regulated. In studies using primary cells from COPD airways, increased NF-jB expression in the bronchial epithelium of smokers and COPD patients compared to non-smokers has been reported [25], while in contrast Keatings et al. showed decreased NF-jB:DNA binding in bronchoalveolar lavage leucocytes from smokers compared to non-smokers [14]. These previous publications, and our study, appear to show that CS can cause diverse effects on NF-jB signalling, including both up- and down-regulation. We observed up-regulation of the AP-1 components cJun, FOSL1 and FOSL2. Walters et al. reported that CSE increases activated c-Jun binding to IL-8 promoter motifs in THP-1 cells, showing synergistic effects when co-stimulated with IL-1b [3]. It is possible that the increase in IL-8 that we observed was regulated through AP-1 signalling, and further studies are needed to confirm this possibility. Walters et al. [3] observed no change in NF-jB activity, while in contrast using a different monocytic cell line (MonoMac6 cells) Yang et al. showed that CSE did increase NF-jB activation, and that IKK inhibitors reduced CSE mediated IL-8 release [20]. This suggests that the transcriptional control of IL-8 in CSE experiments may vary with the different monocyte/macrophage model used. In the cell model that we have used, pathway inhibitors in future studies would shed light on the relative importance of NF-jB and AP-1 to the release of IL-8 caused by CSE. MAPK pathways promote inflammation by activation of transcription factors and post-translational mRNA stabilisation [26]. Interestingly, we observed up-regulation of phosphatase enzymes responsible for MAPK deactivation. Protein studies are needed to confirm this observation. CSE increased the expression of a number of anti-oxidant genes, including MT1K, HMOX1, NQO1 and NQO2. There are previous studies using different cell culture models or COPD airway cells showing that oxidative stress regulates these genes [27–31]. Pierrou et al. also reported up-regulation of similar anti-oxidant genes in the bronchial epithelial cells of COPD patients compared to controls [32]. An interesting difference compared to the current study is that we did not find regulation of the glu-

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tathione pathways in our acute model, suggesting that some anti-oxidant defence mechanisms are cell type specific or related to chronic CS exposure. Interestingly, Pierrou et al. reported that NF-jB and AP-1 dependant genes were regulated in COPD patients [32]. Our observations add weight to the importance of changes in NF-jB and AP-1 signalling pathways caused by CS. There were gene expression changes indicative of a complex balance between regulation of pro- and apoptotic genes by acute CSE exposure. There was up-regulation of both pro-apoptotic genes (BAG3 and DEDD2) and antiapoptotic genes (Hsp40 and Hsp70). There was also down-regulation of pro-apoptotic genes (FADD, caspase 8, protein kinase Cd, cathepsin C, granzyme A, death associated transcription factor 1) and the anti-apoptotic genes SYR-box 4 (SOX4) and Bcl-xl. However, it is possible that some of the gene expression changes that we observed are early changes related to apoptosis and/or necrosis. CSE contains a variety of chemical toxins, and the cellular damage caused by these chemicals to the nucleus may include alterations to DNA structure [22]. We identified a group of CSE regulated genes involved in checkpoint control and identification, and those that act directly on the DNA to make repairs. Checkpoint control genes slow the progression of the cell passing through to the next cell cycle phase until DNA repair has been completed at each stage [33]. From previous studies, we expected to find that acute CSE exposure would regulate cytokine, chemokine, inflammatory signalling, anti-oxidant and cell survival genes [3– 8,10–12,14,19,23–25,27–32,34–37]. We have also shown that DNA checkpoint and repair genes are acutely regulated by CSE. In THP-1 cells it has been shown that CSE induced gene expression changes are not observed within the first 4 h [3]. The choice of 6 h as a time-point to capture early gene expression changes is therefore logical, and earlier time-points are probably more suitable for studying protein changes that may occur due to such mechanisms as translational or post-translational effects of CSE. In summary, this study shows that acute CSE exposure down-regulates the expression of a range of inflammatory cytokine and chemokine genes. The exception is IL-8 which is up-regulated. These key signalling pathways regulated by CSE were NF-jB and AP-1. The differential regulation of IL-8 compared to other cytokine and chemokine genes needs further study, and could be a basic mechanism which plays a pivotal role in the airway inflammatory changes caused by smoking.

Acknowledgments Funding for this study was provided by the BBSRC and GlaxoSmithKline. The authors acknowledge Linda Warnock at GSK, Stevenage (UK) for statistical advice.

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