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2016 Molecular mechanisms of anti-inflammatory glucocorticoid action: Evidence for a role of glucocorticoid-inducible genes and implications for glucocorticoid insensitivity
Shah, Suharsh Vinaykumar
Shah, S. V. (2016). Molecular mechanisms of anti-inflammatory glucocorticoid action: Evidence for a role of glucocorticoid-inducible genes and implications for glucocorticoid insensitivity (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/26991 http://hdl.handle.net/11023/3002 doctoral thesis
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Molecular mechanisms of anti-inflammatory glucocorticoid action: Evidence for a role of
glucocorticoid-inducible genes and implications for glucocorticoid insensitivity
by
Suharsh Vinaykumar Shah
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
GRADUATE PROGRAM IN CARDIOVASCULAR AND RESPIRATORY SCIENCES
CALGARY, ALBERTA
MAY, 2016
© Suharsh Vinaykumar Shah 2016 Abstract
The anti-inflammatory activity of glucocorticoids, in diseases such as asthma, is attributed to their ability to reduce the expression of multiple inflammatory mediators. While glucocorticoid receptor
(NR3C1)-mediated transcriptional activation, or transactivation, was believed to primarily mediate side-effects, accumulating evidence indicates that transactivation is also important for the inhibition of inflammatory gene expression. This illustrates the need to investigate possible functional roles for specific glucocorticoid-inducible genes.
In this thesis, a repressive role for DUSP1, a phosphatase that inhibits MAPKs, was investigated.
DUSP1 was induced by IL1B and dexamethasone in both human pulmonary A549 and primary
HBE cells. IL1B-induced DUSP1 negatively regulated MAPK activity and in the further presence of dexamethasone, DUSP1 played a transient, typically partial, role in repressing expression of inflammatory genes, including CXCL1, CXCL2 and PTGS2. Thus, additional glucocorticoid- induced gene products are necessary for repression.
Regulation of the mRNA destabilizing protein, ZFP36, by DUSP1 was examined. Following the loss of DUSP1, ZFP36 expression was enhanced and this attenuated IL1B-induced TNF expression. Despite a modest ability of dexamethasone to induce ZFP36, thereby off-setting loss of ZFP36 due to reduced MAPK activity, neither silencing of dexamethasone-induced ZFP36,
DUSP1, nor both together, prevented repression of TNF by dexamethasone.
Finally, while DUSP1 over-expression attenuated IL1B-induced expression of many inflammatory genes (e.g. IL8, CSF2 etc.), others, including, the inflammatory transcription factor, IRF1, and downstream target genes, such as CXCL10, were profoundly enhanced. While MAPK inhibition prolonged IRF1 expression, silencing of IL1B plus dexamethasone-induced DUSP1 reduced IRF1
ii and CXCL10 expression. Since, CXCL10 expression was largely unaffected by dexamethasone, these data suggest a mechanism whereby dexamethasone-induced DUSP1 expression maintains
CXCL10 expression.
In conclusion, this study demonstrates interlinked counterregulatory networks, in which IL1B- induced DUSP1 and ZFP36 regulate MAPK activation and inflammatory gene expression respectively. The transient and partial repressive effect of dexamethasone-induced DUSP1 on only few IL1B-induced inflammatory genes provides a rational for functional screening of glucocorticoid-inducible genes that may show repressive functions. Furthermore, by switching off
MAPKs, DUSP1 maintains IRF1 expression and may contribute to glucocorticoid insensitivity.
iii Acknowledgements
I would like to take this opportunity to thank many people who have travelled with me along this journey of PhD and making it a challenging but a zestful experience right from the very beginning. This thesis wouldn’t have been possible without the guidance and support of my supervisor, Dr. Robert Newton, whose endless inputs and advice throughout the process has led me to where I am today. I must say that when times were hard and the results were not that pleasing, he pushed my limits to success in times of failure. He undoubtfully had faith in the new ideas that I brought to the table and always welcomed my opinions and suggestions.
I would like to also express my great regards to the whole team of the Newton and Giembycz lab, especially to Liz, Neil, and Chris for creating a wonderful training and learning environment for moulding my scientific career. I also express my sincere thanks Mahmoud and Omar for all their help and support during the last few months of my PhD. Also, extra thanks to the members of the Proud and Leigh lab, especially Shahina, Suzanne and Cora for their endless support throughout the course of my PhD.
A very big thanks to the members of my committee, Dr. Proud and Dr. MacNaughton for their constant feedbacks and comments in shaping my career in the right direction. It has been a remarkable experience working alongside such esteemed pioneers of research and their invaluable commitments towards my research has been instrumental.
I would also like to also express my sincere gratitude towards my funding agencies Alberta Lung Association, Alberta Innovates – Health Solutions, the University of Calgary, Allergen etc. namely for the funding they have provided me to nurture my PhD project and scientific career.
Finally, I would like to thank my wife, Lino, my parents, family and friends for their undeviating support during this important phase of my career. This wouldn’t have been possible without you all.
iv Dedication
I would like to dedicate this thesis to my parents and grandparents. I believe that this achievement wouldn’t have been possible without their sincere devotion towards providing me with the best quality education.
v Table of Contents
Abstract ...... ii Acknowledgements ...... iv Dedication ...... v Table of Contents ...... vi List of Tables ...... x List of Figures ...... xi List of Abbreviations ...... xiv
CHAPTER 1: INTRODUCTION ...... 1 1.1 Asthma ...... 1 1.1.1 Burden of asthma ...... 2 1.1.2 Asthma pathogenesis ...... 3 1.1.3 Exacerbation of asthma ...... 8 1.1.4 Pulmonary epithelium and its role in asthma ...... 9 1.1.5 Current asthma therapy ...... 11 1.2 Control of inflammatory gene expression ...... 14 1.2.1 Mitogen activated protein kinase (MAPK) pathways ...... 14 1.2.1.1 Role of MAPK pathways in inflammation and asthma ...... 17 1.2.2 Transcription ...... 19 1.2.3 Inflammatory transcription factors ...... 21 1.2.3.1 NF-B ...... 22 1.2.3.2 IRF1 ...... 25 1.2.4 Post-transcriptional control of gene expression...... 27 1.2.4.1 mRNA stability ...... 27 1.3 Glucocorticoids ...... 29 1.3.1 Endogenous glucocorticoids ...... 29 1.3.2 Molecular mechanisms of glucocorticoid action ...... 30 1.3.3 Transrepression by glucocorticoids ...... 32 1.3.4 Transactivation by glucocorticoids ...... 34 1.3.4.1 DUSP1 ...... 35 1.3.4.2 ZFP36 ...... 37 1.3.5 Insensitivity/resistance to glucocorticoids in asthma ...... 39 1.3.5.1 Mechanisms of reduced glucocorticoid sensitivity ...... 39 1.4 General hypothesis and research aims ...... 42 1.4.1 General hypothesis ...... 42 1.4.2 Research aims: ...... 42
CHAPTER 2 : MATERIAL AND METHODS...... 43 2.1 Materials ...... 43 2.2. Methods ...... 45 2.2.1 Cell Culture methods ...... 45 2.2.1.1 A549 cell culture ...... 45 2.2.1.2 Primary human bronchial epithelial (HBE) cell culture ...... 45
vi 2.2.1.3 Adenoviral infection ...... 46 2.2.1.4 siRNA-mediated gene silencing ...... 46 2.2.2 Western blotting ...... 47 2.2.2.1 Preparation of cell lysates ...... 47 2.2.2.2 SDS polyacrylamide gel electrophoresis ...... 47 2.2.2.3 Protein transfer to nitrocellulose membranes ...... 47 2.2.2.4 Immunodetection of proteins ...... 48 2.2.3 RNA isolation, cDNA synthesis and SYBR Green Real Time PCR ...... 48 2.2.3.1 RNA isolation ...... 48 2.2.3.2 cDNA synthesis ...... 49 2.2.3.3 SYBR green real-time PCR ...... 50 2.2.3.4 Analysis of unspliced nuclear RNA ...... 50 2.2.4 Enzyme-linked immunosorbent assay (ELISA) ...... 51 2.2.5 Chromatin Immunoprecipitation (ChIP) assay ...... 52 2.2.5.1 Cell fixation ...... 52 2.2.5.2 Chromatin shearing by sonication ...... 53 2.2.5.3 Immunoprecipitation of sheared chromatin ...... 54 2.2.5.4 Washing and cross-link reversal of ChIP DNA ...... 55 2.2.5.5 ChIP DNA clean-up ...... 55 2.2.5.6 SYBR green PCR using ChIP DNA ...... 56 2.2.6 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay ...... 56 2.2.7 Data presentation and statistical analyses ...... 57
CHAPTER 3 : FEEDBACK CONTROL OF INFLAMMATORY GENE EXPRESSION BY THE MITOGEN-ACTIVATED PROTEIN KINASE (MAPK) PHOSPHATASE, DUSP1, AND REGULATION BY DEXAMETHASONE ...... 58 3.1 Rationale ...... 59 3.2 Hypothesis ...... 60 3.3 Results ...... 60 3.3.1 Effect of dexamethasone on IL1B-induced MAPK activation and DUSP1 expression ...... 60 3.3.2 Effect of dexamethasone on IL1B-induced gene expression ...... 62 3.3.3 Effect of MAPK inhibitors on IL1B-induced gene expression ...... 64 3.3.4 Effect of DUSP1 over-expression on MAPK activation and inflammatory gene expression ...... 70 3.3.5 Effect of DUSP1 siRNA on MAPK activation by IL1B in the absence and presence of dexamethasone ...... 72 3.3.6 Effect of DUSP1 knock-down on IL1B-induced inflammatory gene mRNA expression in the absence and presence of dexamethasone...... 76 3.3.7 Effect of DUSP1 knock-down on IL1B-induced protein expression in the absence and presence of dexamethasone...... 82 3.4 Discussion ...... 85
CHAPTER 4 : DOWNREGULATION OF THE MITOGEN-ACTIVATED PROTEIN KINASE PHOSPHATASE, DUSP1 LIMITS TNF EXPRESSION THROUGH ENHANCED EXPRESSION OF TRISTETRAPROLIN (TTP; ZFP36) ...... 91
vii 4.1 Rationale ...... 92 4.2 Hypothesis ...... 93 4.3 Results ...... 93 4.3.1 Characterization of TNF expression in the presence of IL1B and dexamethasone.... 93 4.3.2 Analysis of DUSP1 expression in the presence of IL1B and dexamethasone ...... 97 4.3.3 Analysis of ZFP36 expression in the presence of IL1B and dexamethasone ...... 98 4.3.4 Effect of MAPK inhibitors on DUSP1 and ZFP36 expression ...... 98 4.3.5 Analysis of DUSP1 expression in the presence of IL1B and dexamethasone in primary HBE cells ...... 100 4.3.6 Analysis of ZFP36 expression in the presence of IL1B and dexamethasone in primary HBE cells ...... 101 4.3.7 Characterization of TNF expression in the presence of IL1B and dexamethasone in HBE cells ...... 102 4.3.8 Effect of ZFP36 siRNA on IL1B-induced TNF mRNA expression ...... 104 4.3.9 Effect of DUSP1 over-expression on ZFP36 and TNF mRNA expression ...... 107 4.3.10 Effect of MAPK inhibitors on TNF expression...... 108 4.3.11 Effect of DUSP1 knock-down on ZFP36 protein and TNF mRNA expression ..... 111 4.3.12 Role of ZFP36 in the loss of TNF mRNA following DUSP1 knock-down ...... 113 4.3.13 Effect of MAPK inhibitors and DUSP1 over-expression in the regulation of TNF protein expression ...... 116 4.3.14 Effect of DUSP1- and ZFP36-targeting siRNAs in the regulation of TNF protein expression ...... 117 4.4 Discussion ...... 119
CHAPTER 5: MAINTENANCE OF IRF1 BY DUSP1 ENHANCES IRF1-DEPENDENT GENE EXPRESSION: IMPLICATIONS FOR GLUCOCORTICOID THERAPY ...... 128 5.1 Rationale ...... 128 5.2 Hypothesis ...... 129 5.3 Results ...... 129 5.3.1 Effect of DUSP1 over-expression on inflammatory gene expression ...... 129 5.3.2 Kinetics of IL1B-induced inflammatory mRNAs ...... 131 5.3.3 Identification of IRF1-dependent mRNAs induced by IL1B ...... 132 5.3.4 Effect of DUSP1 over-expression on IL1B-induced IRF1 expression...... 133 5.3.5 Effect of MAPK inhibitors on IRF1 expression ...... 134 5.3.6 Effect of MAPK inhibitors on IRF1 transcription and mRNA stability...... 137 5.3.7 Effect of MAPK inhibitors on IRF1 protein stability ...... 138 5.3.8 Characterization of IRF1 expression in the presence of IL1B and dexamethasone . 140 5.3.9 Effect of dexamethasone on IRF1 transcription rate ...... 142 5.3.10 Effect of dexamethasone on IRF1 mRNA and protein stability ...... 144 5.3.11 Effect of dexamethasone on IRF1-dependent gene expression ...... 144 5.3.12 Effect of DUSP1 silencing on MAPKs and IRF1 expression ...... 145 5.3.13 Effect of DUSP1 silencing and MAPK inhibitors on IRF1-dependent gene expression ...... 147 5.3.14 Role of IRF1 in late-phase gene expression in the presence of IL1B and IL1B plus dexamethasone ...... 148
viii 5.3.15 Occupancy of IRF1 at the promoters of IRF1-dependent genes in the presence of IL1B and IL1B plus dexamethasone ...... 151 5.4 Discussion ...... 152
CHAPTER 6 : DISCUSSION ...... 160 6.1 Feedback and feed-forward control of inflammatory gene expression ...... 160 6.2 DUSP1-mediated regulation of inflammatory gene expression ...... 163 6.3 Glucocorticoid-inducible genes and redundancy ...... 167 6.4 A possible role for transrepression in the dexamethasone-induced repression of inflammatory genes ...... 172 6.5 Implication for glucocorticoid therapy and new drug design ...... 174 6.6 Overall conclusion ...... 179
REFERENCES ...... 180
APPENDIX A: ANTIBODIES, SIRNA, PCR AND CHIP PRIMERS ...... 214
APPENDIX B: COPYRIGHT PERMISSION ...... 221
ix List of Tables
Table 3.1 Effect of MAPK inhibitors on cell viability...... 70 Table 3.2 Densitometry analysis for the effect of LMNA targeting siRNA (LsiRNA) on MAPK phosphorylation...... 76 Table 3.3 Effect of LMNA targeting siRNA (LsiRNA) on inflammatory gene expression...... 78 Table 3.4 Effect of LMNA targeting siRNA (LsiRNA) on IL1B-induced inflammatory protein expression...... 83 Table A1. siRNA sequence for knockdown studies ...... 214 Table A2. Antibodies used for western blot analysis ...... 215 Table A3. Primers used for PCR analysis ...... 216 Table A4. Primers used for ChIP PCR analysis ...... 220
x List of Figures
Figure 1.1 MAPK signalling cascade...... 17
Figure 1.2 Schematic representation of the classical, or canonical, NF-B signalling pathway. . 24
Figure 1.3. Transcriptional control by glucocorticoid receptor signalling...... 33
Figure 3.1. Effect of dexamethasone and IL1B on MAPK phosphorylation and DUSP1 expression...... 61
Figure 3.2. Effect of dexamethasone and IL1B on inflammatory gene expression...... 63
Figure 3.3 Effect of JNK inhibitor 8 on IL1B-induced JUN phosphorylation...... 65
Figure 3.4 Effect of MAPK inhibitors on the mRNA expression of inflammatory genes...... 66
Figure 3.5 Effect of MAPK inhibitors on the protein expression of inflammatory genes...... 67
Figure 3.6 Effect of MAPK inhibitors on IL1B-induced inflammatory gene expression...... 69
Figure 3.7 Effect of DUSP1 over-expression on IL1B-induced MAPK phosphorylation and inflammatory gene expression...... 71
Figure 3.8 Effect of LMNA- and DUSP1-targeting siRNAs on IL1B and IL1B plus dexamethasone-induced DUSP1 expression...... 73
Figure 3.9 Effect of LMNA- and DUSP1-targeting siRNA on IL1B-induced MAPK phosphorylation...... 75
Figure 3.10 Effect of DUSP1 targeting siRNAs on IL1B-induced inflammatory gene mRNA expression...... 79
Figure 3.11 Effect of DUSP1 targeting siRNA on IL1B-induced inflammatory gene protein release/expression...... 84
Figure 4.1 Enhanced inflammatory gene expression by IL1B: Feedback control by DUSP1 and feed-forward control by ZFP36...... 93
Figure 4.2 Characterization of TNF expression in the presence of IL1B and dexamethasone. ... 96
Figure 4.3 Analysis of DUSP1 and ZFP36 expression in the presence of IL1B and dexamethasone...... 98
Figure 4.4 Analysis of DUSP1 and ZFP36 expression in the presence of IL1B and MAPK inhibitors...... 100
xi Figure 4.5 Analysis of DUSP1 and ZFP36 expression in primary human bronchial epithelial cells...... 101
Figure 4.6 Analysis of TNF expression in primary human bronchial epithelial cells...... 103
Figure 4.7 Comparison of CT values for TNF expression in A549 and HBE cells generated by RT-PCR...... 104
Figure 4.8 Effect of LMNA siRNA on ZFP36 and TNF expression...... 104
Figure 4.9 Effect of ZFP36 siRNA on IL1B-induced TNF mRNA expression...... 106
Figure 4.10 Effect of DUSP1 over-expression on ZFP36 and TNF mRNA expression...... 108
Figure 4.11 Effect of MAPK inhibitors on TNF expression...... 110
Figure 4.12 Effect of DUSP1 knock-down on ZFP36 and TNF expression...... 112
Figure 4.13 Role of ZFP36 in the enhanced loss of TNF mRNA following DUSP1 knock- down...... 115
Figure 4.14 Effect of MAPK inhibitors and DUSP1 over-expression in the regulation of TNF protein expression...... 117
Figure 4.15 Effect of LMNA siRNA on TNF protein expression...... 117
Figure 4.16 Effect of DUSP1- and ZFP36- targeting siRNAs in the regulation of TNF protein expression...... 118
Figure 4.17 Regulation of TNF gene expression following DUSP1 silencing and glucocorticoid treatment...... 126
Figure 5.1 Effect of DUSP1 over-expression, IL1B and IRF1-targeting siRNA on inflammatory gene expression...... 130
Figure 5.2 Effect of DUSP1 over-expression on IL1B-induced IRF1 expression...... 134
Figure 5.3 Effect of MAPK inhibitors on IRF1 expression...... 136
Figure 5.4 Effect of MAPK inhibitors on IRF1 protein stability...... 139
Figure 5.5 Characterization of IRF1 expression in the presence of IL1B and dexamethasone. . 141
Figure 5.6 Effect of PS1145 on IL1B-induced IRF1 expression...... 143
Figure 5.7 Effect of dexamethasone on IRF1-dependent gene expression...... 145
xii Figure 5.8 Effect of DUSP1-targeting siRNA on IL1B-induced MAPKs and IRF1 expression...... 146
Figure 5.9 Effect of DUSP1-targeting siRNA and MAPK inhibitors on IRF1-dependent gene expression...... 148
Figure 5.10 Effect of IRF1-targeting siRNA on IL1B-induced inflammatory gene expression. 150
Figure 5.11 Characterization of IRF1 occupancy on inflammatory loci in the presence of IL1B and dexamethasone...... 151
Figure 5.12 Effect of PS1145 on IL1B-induced IRF1-dependent inflammatory gene mRNA expression...... 156
Figure 6.1 Regulation of IRF1 and IRF1-dependent late-phase gene expression by IL1B following DUSP1 silencing and glucocorticoid treatment...... 167
xiii
List of Abbreviations
Official Human Genome Organization (HUGO) gene nomenclature committee gene symbols have been used for all genes and gene products.
[Ca2+]i Intracellular Ca2+
ACT D Actinomycin D
ACTH Adrenocorticotropic hormone
Ad Adenovirus
AD5 Adenoviral serotype 5
AF Activation function
AHR Airway hyper-responsiveness
AP-1 Activator protein-1
ARE AU-rich elements
ASK Activation of S-phase kinase
ASM Airway smooth muscle
ATP Adenosine triphosphate
AU Adenine and uridine
AUF1 Adenine-uridine-rich element RNA-binding factor-1
BAL Bronchoalveolar-lavage
BEBM Bronchial epithelial cell basal medium BP Base pairs
BSA Bovine serum albumin
C/EBP CAAT/Enhancer binding protein cAMP Cyclic adenosine monophosphate
CCL (C-C Motif) ligand
xiv CDKN1C/p57KIP2 Cyclin-dependent kinase inhibitor 1C
ChIP Chromatin Immunoprecipitation
CHX Cycloheximide
COX-2 Cyclooxygenase (PTGS2)
CRH Corticotropin releasing hormone
CXCL10 ΙFN-γ-induced chemokine IFN-γ-inducible protein 10
DBD DNA binding domain
Dex Dexamethasone
Dexras Dexamethasone-induced Ras-related protein
DMEM Dulbecco’s modified Eagle’s medium
DoK Downstream of tyrosine kinase ds Double stranded
DSIF DRB (5,6-dichloro-1-β-d-ribofuranosylbenzimidazole) sensitivity-inducing factor
DUSP Dual specificity protein phosphatases
ECL Enhanced chemiluminescence
EDTA Ethylenediaminetetraacetic acid
EGR Early growth response
ERK Extracellular signal regulated kinase
FCS Fetal calf serum
FcR1 High affinity IgE receptor
FEV Forced expiratory volume
FKBP FK506 binding protein
GAPDH Glyceraldehyde-3-phosphate dehydrogenase
xv GFP Green fluorescent protein
GILZ Glucocorticoid-induced leucine zipper (TSC22D3)
GINA Global Initiative for Asthma
GM-CSF Granulocyte-macrophage colony-stimulating factor
GPCR G-protein coupled receptor family
GR Glucocorticoid receptor (NR3C1)
GRE Glucocorticoid response element
GRIP Glucocorticoid receptor interacting protein
H2A Histone 2A
H2B Histone 2B
H3 Histone 3
H4 Histone 4
HAT Histone acetylase
HBE Human bronchial epithelial
HBSS Hank’s balanced salt solution
HDAC Histone deacetylase
HPA Hypothalamic-pituitary-adrenal
HRV Human rhinovirus
HSP Heat shock protein
HuR Human antigen R
ICAM Intracellular adhesion molecule
ICS Inhaled corticosteroids
IFN Interferon
IgE Immunoglobulin E
xvi IKK IBkinase
IL Interleukin iNOS Inducible nitric oxide synthase
IP Immunoprecipitation
IRF Interferon regulatory factor
ISRE Interferon-stimulated response element
IB Inhibitor of B (NFKBIA)
JAK Janus kinase
JNK c-Jun amino terminal kinase
JNK-IN-8 JNK inhibitor 8
KHSRP K-homology domain splicing regulatory protein
LABA Long-acting 2-adrenoceptor agonist
LPS Lipopolysaccharide
MAPK Mitogen activated protein kinase
MHC Major histocompatability complex
MK2 MAPK-activated protein kinases
MKK MAPK kinases
MKKK MAPK kinase kinases
MLCK Myosin light chain kinase
MLK Mixed lineage kinases
MMLV Moloney Murine Leukemia Virus
MOI Multiplicity of infection
MTT 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide
xvii NELF Negative elongation factor
NEMO NF-B essential modulator
NES Nuclear export sequence
NF-AT Nuclear factor of activated T-cells
NFKBIA Inhibitor of B (IB)
NF-B Nuclear factor--light-chain enhancer of activated B cells nGRE Negative GRE
NLS Nuclear localisation sequence
NP40 Nonidet P-40
NR3C1 Nuclear receptor superfamily 3, group C, member 1
ODU Optical density units
PBMCs Peripheral blood mononuclear cells
PBS Phosphate-buffered saline
PDE Phosphodiesterase
PEF Peak expiratory flow
PHA Phytohemagglutinin
PI3K Phosphoinositide 3-kinase
PIC Protease inhibitor cocktail
PKA Protein kinase A
PMA Phorbol myristate acetate
Pol II RNA polymerase II
P-TEFb Positive transcription elongation factor b
PTPs Protein tyrosine phosphatases
xviii RGS Regulator of G-protein signaling
RIPA Radioimmunoprecipitation assay
RT-PCR Real-time polymerase chain reaction
RV Rhinovirus
S Serine
SABA Short-acting 2-adrenoceptor agonist
SAPK1 Stress-activated protein kinase 1
SEGRA Selective GR/NR3C1 agonist siRNA Short interfering RNA
SLAP Src-like adaptor protein snRNAs Small nuclear RNAs
SRC Steroid receptor coactivator
STAT Signal transducers and activators of transcription
SV Simian virus
T Threonine
TACE TNF-converting enzyme
TAK1 Transforming growth factor-β-activated kinase 1
TBP TATA-binding protein
TH T-helper
TLR Toll-like receptor
TNF Tumor necrosis factor α
Tregs T regulatory cells
TSLP Thymic stromal lymphopoietin
TTP Tristetraprolin
xix UBD Ubiquitin D
UTR Untranslated region V Volts
VCAM Vascular cell adhesion molecule
VEGF Vascular endothelial growth factor
ZFP Zinc finger protein
xx Chapter 1: Introduction
Glucocorticoids regulate a variety of processes, including metabolic homeostasis, cell proliferation, immune and inflammatory responses. Due to their potent anti-inflammatory effects, glucocorticoids are used in the treatment of autoimmune, inflammatory and allergic disorders, including asthma, rheumatoid arthritis, ulcerative colitis, inflammatory bowel disease and allergic rhinitis (1). This thesis investigates the molecular mechanisms of inflammatory gene repression by glucocorticoids in the context of the cells relevant to airways inflammation and asthma. In addition, poor clinical responses to glucocorticoids by asthmatics who smoke, or have viral infections, illustrates the need to understand the detailed molecular mechanism(s) of action in order to promote the development of novel glucocorticoid ligands or add-on therapies with an enhanced anti-inflammatory profile.
1.1 Asthma
According to the Global Initiative for Asthma (GINA) 2009, “asthma is a chronic inflammatory disorder of the airways in which many inflammatory cells and cellular events play a role”.
Clinically, the chronic inflammation in asthma is typically associated with symptoms such as wheezing, cough, breathlessness and a variable airflow obstruction that are attributed to non- specific airway hyper-responsiveness (AHR), reversible airway bronchoconstriction and structural changes to the airways (airway remodelling) (GINA, 2009). As per disease severity, asthma can be classified into four categories: intermittent, mild persistent, moderate persistent and severe persistent (2). A number of factors, including viral respiratory infections, host responses, environmental pollutants and tobacco smoke, can not only trigger an asthma attack, but may also influence the risk of initially developing asthma (GINA, 2009). Asthma is one of the most common chronic disease affecting children and young adults (3, 4). However, the prevalence of asthma is
1 more common in children than adults, and susceptibility to developing asthma in childhood is believed be determined by the events that occur in the first three to five years of life (Public Health
Agency of Canada). In addition, exposure to airborne allergens, environmental toxins and tobacco smoke along with frequent respiratory infections in early life are the strongest risk factors associated with the development of childhood asthma (Public Health Agency of Canada).
Although asthma has both genetic and environmental components, a family history of allergic and atopic diseases, such as hay fever and eczema can also influence the risk of developing asthma in children (5).
1.1.1 Burden of asthma
The prevalence, morbidity, mortality and economic burden associated with asthma is increasing in Canada and the rest of the world (6, 7). It is estimated that around 235 million people are affected by asthma worldwide (WHO). Although asthma is more common in high-income countries, some low- and middle-income countries also have high levels of asthma prevalence (8). If this current trend continues, it is projected that by 2025 there will be a 100 million additional asthmatics globally (2). In Canada alone, over 3.2 million of the population have been diagnosed with asthma, and is expected to rise to over 3.9 million by 2030 (statistics Canada, Public Health Agency of
Canada). Even though the number of hospitalizations have been declining over the past 20 years, in 2004, a staggering 10% of the hospitalizations among children was due to asthma (GINA, 2004).
This increasing number of hospitalizations reflects an increase in severe asthma and poor disease management (8). Although the mortality rates have declined significantly in the world, around
180,000 deaths are attributed to asthma every year (WHO). The majority of asthma-related deaths occur in those aged ≥45 years (8). Despite the availability of more efficacious medications, asthma
2 still affects up to 20% of children and causes ~500 deaths per year in Canada (Statistics Canada,
Public Health Agency of Canada).
The cost of asthma creates a considerable economic burden and the patients with severe asthma are responsible for approximately 50% of all direct and indirect costs (GINA, 2004). Direct costs of asthma treatment include hospitalization and medication, and were estimated to exceed $700 million in Canada in 2000 (Public Health Agency of Canada). In view of this, it has been suggested that because unplanned or emergency hospitalization is more expensive than planned treatments, adequate asthma prevention could in fact decrease the overall financial burden of the disease
(GINA, 2009). There are also significant indirect, socio-economic costs associated with asthma, including premature deaths and long-term disability (GINA, 2009). In Canada, these indirect costs were estimated to total $840 million in 2000 (Public Health Agency of Canada) and includes the financial burden related to school absenteeism and days lost from work (GINA, 2009). Asthma accounts for approximately 1% of all disability-adjusted life years, which reflects the high prevalence and severity of the disease (GINA, 2009).
1.1.2 Asthma pathogenesis
The pathophysiology of asthma is complex, but typically involves allergic inflammation of the airways (9). Allergic inflammation in asthma involves increased infiltration of mast cells, macrophages and in particular eosinophils into the airways (10). However, in severe asthma neutrophils are also recruited into the airways (11). Recruitment of inflammatory cells brings about pathological changes through the release of inflammatory mediators, which in turn activate signalling pathways to enhance the expression of multiple inflammatory cytokines, chemokines, adhesion molecules, growth factors, inflammatory enzymes and receptors (10). In particular, the
3 inflammatory response in allergic asthma involves increased synthesis and release of cytokines, such as interleukin (IL) 4, IL5, IL9 and IL13, which, together, are responsible for IgE production from B-cells and eosinophilic inflammation (9, 12).
The allergic response in asthma involves the activation of resident mast cells and dendritic cells by allergen, consequently resulting in the activation of IgE mediated responses (13). These dendritic cells are generally separated from the airway lumen by a layer of epithelium. In order to access the dendritic cells, inhaled allergen either disrupts epithelial tight junctions or increases the permeability of the epithelium (14). For example, the house dust mite allergen, Der p1, possesses intrinsic protease activity and, by cleaving tight junction proteins directly, can access the underlying dendritic cells (15). Allergen-activated dendritic cells migrate to the draining lymph
+ + nodes and present the processed antigen to naïve CD4 TH cells (10). These CD4 TH cells are subsequently activated by binding of the T cell receptor and CD28 on the T cell surface to the antigen-major histocompatability (MHC) complex and CD80/86 on the dendritic cell (16). This co-stimulation serves as a verification signal for T cells, as without it, T cell become anergic, and irresponsive to antigen (16). This mechanism is essential to prevent self-antigen-mediated immune responses (16).
Upon co-stimulation, T cells regulate the production of IL2 and IL4, which induces TH cell proliferation and initiates the polarisation of T cells to a TH2 phenotype respectively (17). TH2 cells predominate in the asthmatic airways and by releasing a number of cytokines, such as IL4,
IL5, IL9 and IL13, TH2 cells play an important role in allergic inflammation associated with asthma (9). The TH2 cells-derived IL5 and IL9 are further involved in the differentiation of eosinophils and mast cells respectively, whereas IL4 and IL13, by binding to their receptors on B
4 cells, initiate the synthesis and release of IgE antibodies (9). These IgE antibodies circulate briefly in the blood before binding to high-affinity IgE receptors, FcRI, on the surface of mast cells (9).
Upon re-exposure, allergen binds to IgE-FcRI and activates the mast cells via the cross-linking of IgE-FcRI (18). Activated mast cells, by releasing multiple inflammatory mediators, contribute to both acute and chronic allergic inflammation (10). The acute phase of allergic inflammation, commonly referred to as the ‘early phase’ of allergic reaction, typically occurs within minutes, or even seconds, following allergen exposure and usually resolves within an hour (10). Mast cell- derived bronchoconstrictors, such as histamine, cysteinyl leukotrienes and prostaglandin D2, initiate the ‘acute/early phase’ of the allergic response (9, 10), and is often associated with mucosal oedema, mucus production and contraction of the airway smooth muscle (10).
The ‘early phase’ of allergic responses are followed by a more sustained chronic inflammation, known as the ‘late phase’ of the allergic response (18). This late allergic response involves increased infiltration of TH2 cells, eosinophils and basophils into the airways via mast cell-derived mediators, including leukotrienes, cytokines and chemokines (9, 18). The ‘late phase’ of the allergic response usually peaks around 8-12 hours after allergen exposure and typically involves increased bronchoconstriction, sustained edema, mucus overproduction and AHR to bronchoconstrictors, such as histamine and methacholine (18). Chemokines, such as chemokine
(C-C Motif) ligand (CCL) 20 and CCL17, recruit TH2 cells into the airways (19). These TH2 cells further enhance the synthesis and release of TH2 cytokines, such as IL4, IL9, IL13, IL3, IL5 and granulocyte-macrophage colony-stimulating factor (GM-CSF; CSF2) (10). Enhanced IL5 stimulates the release of eosinophils and prolongs their survival (9). To participate in the allergic inflammatory response, eosinophils migrate from the blood circulation into the airways up
5 concentration gradients of chemokines such as CCL2, CCL3, CCL5 and CCL11 (20). Upon maturation, eosinophils degranulate and release inflammatory proteins, such as major basic protein, eosinophil neurotoxin, eosinophil cationic protein and eosinophil peroxidase (21).
Eosinophil-derived major basic protein directly damages the airway epithelium, enhances bronchial responsiveness, and initiates degranulation of basophils and mast cells (21). In contrast, cysteinyl leukotriene C4 released by eosinophils contracts airway smooth muscle and increases vascular permeability (22). Even though phenotypically asthmatic inflammation is eosinophilic, a number of studies indicate that virus-induced asthma exacerbations are associated with increased production of the neutrophilic chemokine, IL8, and involves neutrophilic inflammation (23, 24).
The expression of the neutrophil-derived protease, neutrophil elastase, which stimulates mucus production, is also enhanced in neutrophilic asthma and is therefore likely to play a key role in asthma pathogenesis (24).
The allergic inflammation in asthma arises from a relative imbalance between TH1 and TH2 cells
(9). The TH1 cell enhances the production of TH1 cytokine, interferon-γ (ΙFN-γ), which inhibits the synthesis of IgE and the differentiation of precursor cells to TH2 cells (10). Thus, in the absence of ΙFN-γ there may be an enhanced release of TH2 cytokines, which may further induce allergic inflammation (10). In this regard, studies also indicate that TH1 cytokines, ΙFN-γ and IL12, by antagonizing the TH2 responses, inhibit allergic inflammation associated with asthma (25, 26).
Therefore, the TH1 response may be considered protective for asthma, whereas the TH2 response is often linked with severe allergic inflammation (27). However, in vivo studies from asthma show conflicting results and do not fully support this hypothesis (10). For example, the serum levels of
ΙFN-γ were elevated in severe asthmatic patients (28). Similarly, ΙFN-γ levels were also increased
6 in supernatants from phytohemagglutinin (PHA)/phorbol myristate acetate (PMA)-stimulated bronchoalveolar-lavage (BAL)-fluid cell cultures derived from asthma patients (29). Enhanced levels of IFN-γ, by increasing the expression of CD69, HLA-DR and intercellular adhesion molecule (ICAM) 1, contributes to the activation of eosinophils, and thus, is likely to enhance eosinophilic inflammation (10, 30). Therefore, these data point to the possibility that both TH1 and
TH2 cells might contribute to airway inflammation in asthma. Besides TH1-TH2 imbalance, other
TH cells such as TH17, TH22, TH9 and T regulatory cells (Tregs) also play a key role in asthma pathogenesis (31).
If the inflammation in asthma is uncontrolled, it may further lead to structural changes of the airways known as airway remodelling (11). Characteristic changes include airway smooth muscle hypertrophy, basement membrane thickening and angiogenesis (32). In addition, goblet cell metaplasia along with hyperplasia and enlargement of submucosal glands stimulate the secretion of mucus into the airway lumen (10). Airway remodelling may contribute to airflow obstruction and AHR (11). Until recently, it was proposed that airway remodeling may develop late in the disease process as a consequence of uncontrolled or persistent inflammation (33). However, recent findings show that the symptoms associated with airway remodelling, such as basement membrane thickening and epithelial cell disruptions, are detectable very early in the disease pathogenesis especially in children with or without asthma diagnosis (33, 34). Airway remodelling is thereby considered a crucial component of childhood asthma (34). To this end, understanding the underlying mechanisms responsible for structural changes may be helpful in the clinical management of childhood asthma (33).
7 1.1.3 Exacerbation of asthma
Asthma exacerbations can be defined as “….episodes characterized by progressive increase in shortness of breath, cough, wheezing and chest tightness, or some combination of these, and increased airflow obstruction that is manifested by reduction in measurements of lung function such as peak expiratory flow (PEF)” (35). Exacerbations of asthma accelerate disease progression, lower quality of life of asthmatics, increase morbidity, mortality and economic burden of the disease (36, 37). A number of risk factors, including allergen, pollutant and bacterial infections can trigger asthma exacerbation (38). However, infection with respiratory viruses, including human rhinovirus (HRV), influenza virus and respiratory syncytial virus, are major risk factors associated with exacerbation of asthma in both adults and children (39, 40). Studies have indicated that infection with HRV alone is responsible for 60% of asthma exacerbations in children and about 45% of asthma exacerbations in adults (41, 42). Viral-induced asthma exacerbations are mainly characterised by neutrophilic airway inflammation, mucus hypersecretion and bronchial hyper-responsiveness (43). In addition, there is also an increased secretion of a number of cytokines and chemokines, including IL8, IL6, IL1, CCL5 and CXCL10 (44-47), and is mainly attributed to the increased activation of the pro-inflammatory transcription factor, nuclear factor-
-light-chain enhancer of activated B cells (NF-B) (48). Enhanced release of the neutrophilic chemokine, IL8, contribute to neutrophilic inflammation, whereas CXCL10 and CCL5 are responsible for lymphocytic and eosinophilic inflammation respectively (48). Viral infections are also coupled with impaired innate and adaptive immune responses of asthmatics (49, 50). For example, bronchial epithelial cells obtained from asthmatics exhibit enhanced viral replication leading to increased epithelial cell damage and decreased innate immune responses (50). It has also been proposed that impaired production of IFNs during viral infections not only decrease
8 macrophage responses to virus infection, but may also reduce anti-viral immune responses (35,
51). In addition, the synthesis of both type I IFN- and type III IFN- is also deficient in asthmatics compared to healthy individuals following HRV infection and may further correlate with the severity of HRV-induced asthma exacerbations (49, 52, 53). Furthermore, asthmatics who smoke also have a higher frequency of exacerbation with an increased risk of hospitalization during exacerbations (54, 55).
1.1.4 Pulmonary epithelium and its role in asthma
A continuous layer of epithelial cells lines the airways from the trachea to the alveoli, and is the interface between the host and the environment (56). Morphologically, while the airway epithelium in the large airways is pseudostratified, the epithelium in the small airways is columnar and cuboidal (57, 58). The epithelium in the airways is comprised of a variety of cell types, including ciliated, columnar, undifferentiated, secretory, and basal cells. In contrast, the epithelium in the alveoli is composed of type I and type II alveolar cells (57, 58). The ciliated epithelial cells together with basal cells forms a pseudostratified epithelium and play a major role in the removal of foreign particles trapped in the mucus lining the upper airways (mucociliary clearance) (58).
The type I alveolar epithelial cells are thin, flat, squamous cells, covering 95% of the alveolar surface and play a vital role in the gas exchange (59). On the other hand, type II alveolar epithelial cells are cuboidal in appearance and cover 5-10% of the alveolar epithelium. The type II cells secrete surfactant, which reduces alveolar surface tension, and by acting as progenitor cells, the type II cells can repopulate type I cells in the alveolar epithelium (60).
Airway epithelial cells not only act as a first line of defense against inhaled pathogens, allergens and other foreign particles but also possess a wide variety of functions, including repair,
9 regeneration and the regulation of structural and immune cells (57, 58). Airway epithelial cells, by releasing pro-inflammatory cytokines, chemokines, and inflammatory peptides, including IL8,
CXCL5, CXCL10, CCL5, IL1, IL6 and others, contribute to the inflammatory responses in asthma
(61, 62). Epithelial cell-mediated secretion of IL8 and CCL5 increases the recruitment of neutrophils and eosinophils respectively into the airways (9), which in turn, by releasing proteases and cytotoxic mediators in the airways, damages the epithelial cell layer (21). Disruption of airway epithelial integrity is often regarded as an important mechanism in the development of AHR (61).
Enhanced AHR could be attributed to the possibility that the loss of epithelial barrier may allow inhaled allergen to access the mucosa and underlying immune cells. In addition, epithelial cells release large amounts of the cytokine, thymic stromal lymphopoietin (TSLP) that orients dendritic cells towards the TH2 response and triggers allergic inflammation (63, 64). Furthermore, the stem cell factor, produced by airway epithelial cells is involved in the recruitment of mucosal mast cells to the airway surface. These mast cells release bronchoconstrictors, such as histamine and leukotrienes, and further produce bronchoconstriction (9, 65). However, contrary to their role in airway inflammation, epithelial cells, by mediating the release of antimicrobial, complement and other factors, may also contribute to innate immune and host defense responses (66, 67). Equally, airway epithelial cells also produce enzymes, permeabilizing peptides, collectins and protease inhibitors that are involved in the killing of microorganisms, and thereby airway epithelium provides a first line of defense against pathogenic insults (61).
As mentioned above, airway epithelial cells release a number of inflammatory mediators that are involved in the pathogenesis of asthma. In addition, airway epithelium is also the first point of contact for asthma therapy, including inhaled glucocorticoids. Thus, examining the repressive
10 effects of glucocorticoids on airway epithelial cell-derived inflammatory mediators may assist into understanding the molecular mechanism of repression of inflammatory genes by glucocorticoids
(61, 62).
1.1.5 Current asthma therapy
The aim of asthma treatment is not only to reduce or combat the underlying inflammatory process in order to reduce disease progression, improve lung function, reduce symptoms, and exacerbations but also to minimize the side effects associated with asthma therapy (GINA, 2009).
Treatment of asthma consists of a step-wise approach with administration of a reliever treatment, such as short-acting 2-adrenoceptor agonist (SABA), taken “as-needed” for mild asthmatics
(GINA, 2009). SABAs are used as a bronchodilators to relieve symptoms, such as wheezing, chest tightness and cough (GINA, 2009) (68). If the symptoms are not controlled and worsen or become more frequent with reliever medication, addition of one or more preventer medications, including inhaled corticosteroids (ICS), is recommended (GINA, 2009).
ICS is currently the most effective anti-inflammatory therapy available for the treatment of asthma
(68). Despite the widespread use of ICS, many patients with severe asthma and asthmatics who smoke show poor responsiveness to ICS due to severe inflammation. These patients require high, often oral, doses of glucocorticoid over prolonged periods to combat inflammation (69, 70). In such cases, therapeutic usefulness is compromised by systemic contraindications that are, in part, attributed to the metabolic effects of glucocorticoids and to the effects on the hypothalamic- pituitary-adrenal (HPA) axis (71, 72). These side-effects include osteoporosis, muscle wasting, stimulation of gluconeogenesis, increased blood glucose levels, diabetes, cataracts, weight gain, and impaired skeletal muscle growth in children (72, 73). Importantly, these contraindications
11 reduce the usefulness of ICS for the treatment of severe asthma. Moreover, ICSs are also less effective or have no effect on the decline in lung function and they often fail to prevent the progression of asthma in young children (74). Due to this, the dose of ICS may be increased or long-acting 2-adrenoceptor agonist (LABA) may be added to therapy (GINA, 2009). Because long term use of high dose ICS is coupled with increased risk of side-effects (see above), the addition of LABA is preferred over increasing the dose of ICS (GINA, 2009).
LABAs, such as salmeterol and formoterol, act by binding to cell-surface 2-adrenoceptors, which are members of the 7-transmembrane, G-protein coupled receptor (GPCR) family. Upon binding to the 2-receptor, LABA triggers dissociation of Gssubunit of GPCR from the / dimer and further activates adenylate cyclase, which catalyses the conversion of adenosine triphosphate
(ATP) to 3', 5'-cyclic adenosine monophosphate (cAMP), and thereby enhances the intracellular concentration of this second messenger. Consequently, this leads to the activation of the cAMP- dependent pathway where cAMP activates an enzyme protein kinase A (PKA). Activated PKA further phosphorylates a number of downstream targets and produces bronchodilation (75-78). For example, PKA-mediated phosphorylation of myosin light chain kinase (MLCK) reduces the ability of MLCK to phosphorylate myosin and consequently inhibits smooth muscle contraction (76). In addition, cAMP by inhibiting the release of free calcium (Ca2+) from intracellular stores reduces the level of intracellular Ca2+ ([Ca2+]i). Since, increased [Ca2+]i is responsible for the contraction of airway smooth muscle, cAMP-mediated attenuation of [Ca2+]i results in bronchodilation (76).
Together, these mechanisms attenuate smooth muscle contraction to provide relief from bronchoconstriction.
12 Current asthma treatment includes a combination therapy incorporating ICS and LABA to reduce both airway inflammation and bronchoconstriction. Inhalers containing a combination of ICS plus
LABA (GSK: Seretide/Advair (salmeterol/fluticasone propionate) and AstraZeneca: Symbicort
(budesonide/formoterol)) are increasingly prescribed due to their higher clinical efficacy (79-81).
In 1994, Greening et al. first observed the effectiveness of LABA/ICS combination in improving the symptoms and lung functions following the addition of salmeterol to ICS (82). Since then, many clinical studies have demonstrated superior control of asthma with LABA/ICS combination therapy, showing synergistic effect on bronchodilation as well as reduction in the symptoms, improvement in the quality of life and lower frequency of exacerbations (81). In addition, work from Woolcock et al., Pauwels et al. and Wilding et al. show that the addition of LABA to a standard dose of ICS is more effective clinically than increasing the dose of ICS (75). A number of other in vivo and in vitro data are also available explaining the possible mechanisms mediating the increased effectiveness and clinical efficacy of combination therapy (75). In this context, glucocorticoids augment the density of 2-receptor (83), attenuate functional desensitisation of the receptor (84) and increase both Gs expression and coupling to adenylyl cyclase (85).
Glucocorticoids also prevent cytokine (for example IL1B)-mediated hypo-responsiveness of human airway smooth muscle (ASM) cells to 2-receptors (86, 87). In contrast, the mechanism by which LABA increases the activity of glucocorticoids is largely unknown. However, LABA increases the clinical efficacy of ICS to a level that cannot be achieved by ICS alone (75, 88).
Glucocorticoid-mediated histone acetylation (89) and anti-inflammatory gene expression is also enhanced by LABA (75). The translocation of glucocorticoid receptor (GR; NR3C1) into the nucleus upon activation by glucocorticoids is also potentiated by LABA (90).
13 Other preventer medications, such as leukotriene modifiers and/or theophylline, are also recommended as a maintenance therapy for symptomatic control of asthma (GINA, 2009).
Leukotriene modifiers, such as 5’-lipoxygenase inhibitors and leukotriene receptor antagonists, act by blocking the production and receptor binding respectively, of leukotrienes (91). Leukotriene receptor antagonists are particularly used to reverse the bronchoconstriction induced by exercise or smoking (54, 91). In contrast, theophylline is a non-selective inhibitor of phosphodiesterase
(PDE), that increases the intra-cellular concentration of cAMP and produces bronchodilation (71).
Theophylline is generally used to reverse bronchoconstriction in asthmatics who are smokers (54).
However, the utility of theophylline is limited by its very low therapeutic index (68).
1.2 Control of inflammatory gene expression
Inflammatory gene expression can be controlled transcriptionally or post-transcriptionally by processes, such as mRNA stability, translation and protein stability, via the activation of one or more inflammatory transcription factors and/or cellular signalling mechanisms.
1.2.1 Mitogen activated protein kinase (MAPK) pathways
The MAPK pathways are evolutionarily conserved protein kinase cascades, activated by extracellular stimuli such as UV radiation, pro-inflammatory cytokines, including TNF and IL1B, oxidative stress, T- and B-cell receptor activation, and other mitogens (92, 93). Extracellular signal regulated kinase (ERK) 1/2, also known as p44/p42, was the first mammalian MAPK to be characterised (94, 95). Later on, other MAPKs including the c-Jun amino terminal kinase (JNK), also known as stress-activated protein kinase 1 (SAPK1), and p38 MAPK, also known as SAPK2-
4, were characterised (96). The signalling cascade begins with the activation of MAPK kinase kinase (MKKK) at the cell surface receptor by small GTP binding proteins, GTPases (e.g. Ras,
14 cdc42), or other protein kinases (97). These MKKKs phosphorylate and activate a number of
MAPK kinases (MKKs or MEKs), which in turn dual phosphorylate the serine/threonine and tyrosine residues of the MAPKs, ERK, p38 and JNK at a T-x-Y motif, leading to MAPK activation
(Fig. 1.1) (92, 98). Thus, ERK is activated by MKK1/MKK2, p38 by MKK3/MKK6, and JNK by
MKK4/MKK7 (96). In addition, other MKKKs, including MKKK1-4, c-raf, mixed lineage kinases, activation of S-phase kinase (ASK) 1 and 2, transforming growth factor (TGF)-β-activated kinase 1 (TAK1) and tumour progression locus 2 can also activate MAPKs (99). Activated MAPKs by phosphorylating various substrates, including mRNA destabilising proteins, cytoskeletal proteins and transcription factors, regulate the expression of specific genes within the target cells
(94, 100). In this regard, MAPKs exert a number of cellular responses, including proliferation, differentiation, survival, apoptosis, embryogenesis, and gene expression, and some of these responses are mediated via the phosphorylation of transcriptional factors such as c-Jun, ATF-2, p53, Elk-1, NFAT, NF-B and/or AP-1 (94, 95, 97, 101, 102). In addition, p38 MAPK is involved in the phosphorylation of multiple kinases, including MAPK-activated protein kinases (MK2 &
MK3), MSK1, MSK2, MNK1 and MNK2 (102). Conversely, JNK-mediated phosphorylation of the E3 ubiquitin ligase, Itch, leads to the functional activation of Itch and consequently enhanced degradation of c-Jun and JunB (103).
Since MAPKs are activated by phosphorylation of both the threonine and tyrosine residues, dephosphorylation of either of residue is sufficient for complete inactivation (104). This can be achieved by tyrosine-specific protein tyrosine phosphatases (PTPs), serine/threonine-specific phosphatases, or by dual specificity (threonine/tyrosine) protein phosphatases (DUSPs) (104).
Serine/threonine phosphatases, such as PP2A, by dephosphorylating the threonine residue,
15 inactivates both MAPK and MKK (104, 105). In this context, PP2A by dephosphorylating ERK and c-Src, an upstream kinase of ERK signalling, inhibits ERK MAPK pathway (106). In contrast, the serine/threonine phosphatase, PP2C inactivates p38, JNK and MKKs (105). Similarly,
PTPs, such as PTP-SL, haematopoietic PTP and leukocyte PTP, by dephosphorylating the tyrosine residue, targets ERK1/2 and p38 for inactivation (105, 107).
Finally, DUSPs can dephosphorylate both the threonine and tyrosine residues on the same substrate (104, 105). To date, 9 family members of DUSP have been identified. DUSP1, also commonly referred to as MAPK phosphatase 1, was the first DUSP to be discovered in 1991 (108).
All the DUSPs share a common structure and have a different specificity for one or more MAPKs
(104, 105, 109). Thus, DUSP1 has a specificity for ERK, p38 and JNK, whereas DUSP5 has a specificity for ERK and can only dephosphorylate ERK (109). A number of pro-inflammatory stimuli, including TNF and IL1B, that activate MAPK pathways can also induce the expression of
DUSPs (100). The activity of DUSP proteins can be modulated by various post-translational modifications, including acetylation, phosphorylation, ubiquitination and degradation (109). In this context, phosphorylation increases the stability of DUSPs by attenuating the ubiquitination and degradation of DUSPs (109).
16 Stimulus e.g. TNF or IL1B
GTP-binding protein Rac, Cdc42 Ras
MKKK/MEKK TAK1/ASK1 MKKK1/2 Raf
MKK/MEK MKK3/6 MKK4/7 MKK1/2
MAPK p38///d JNK1/2/3 ERK1/2
Targets MAPKAPK c-Jun c-Fos,Elk-1 NF-B ATF-2
Figure 1.1 MAPK signalling cascade. Schematic diagram of the MAPK signalling pathway for the three major MAPKs p38, ERK and JNK (reviewed from (98, 100, 110)).
In addition, the expression of DUSP proteins is often dependent on the activation of MAPKs and as such, DUSPs, by a negative feedback regulatory loop, control the duration and amplitude of
MAPK signalling (111). Thus, p38 MAPK-mediated expression of DUSP1 is involved in a feedback inhibition of p38 MAPK (112). For further details about the role of DUSP1 in a negative regulation of MAPK signalling and inflammation refer to section 1.3.4.1.
1.2.1.1 Role of MAPK pathways in inflammation and asthma
Since activation of MAPKs is associated with immune and inflammatory responses, respiratory burst activity, chemotaxis, granular exocytosis, adherence, apoptosis, or T-cell mediated differentiation, it is believed that the MAPKs may play an important role in asthmatic
17 inflammation (113). In this context, p38 MAPK, by increasing the expression of the neutrophilic chemokine, IL8, enhances the migration and recruitment of neutrophils into the asthmatic airways, and may thereby contribute to neutrophilic inflammation (114). Furthermore, p38 MAPK by affecting the activity of inflammatory transcription factor, NF-B, regulates the expression of a number of pro-inflammatory cytokines (115). In addition, p38 MAPK also modulates the stability and translation of pro-inflammatory cytokines through the regulation of MNK1 (116) and MK2/3
(117). Moreover, deletion of p38 MAPK in epithelial cells was associated with reduced pro- inflammatory gene expression (118). These observations clearly suggest that the activation of p38
MAPK not only increases asthmatic inflammation, but may also worsen the existing inflammation by augmenting the synthesis and release of pro-inflammatory mediators. Conversely, ERK has been shown to promote the differentiation of naive T cells to a TH2 phenotype, and may thereby contribute to allergic inflammation (119). Since both ERK and p38 MAPKs are involved in the migration of mast cells and eosinophils, their role in the development of asthma is implicated (120,
121). In addition, ERK and JNK, by increasing the expression and phosphorylation of c-Fos and c-Jun, control the activity of inflammatory transcription factor AP-1 (98, 122, 123). Similar to NF-
B, AP-1 regulates the expression of a number of inflammatory and immune mediators, including, but not limited to, IL8, CSF2, IL2, TNF and CCL2, that are involved in the pathogenesis of asthma
(124). Equally, JNK has been shown to enhance the recruitment of eosinophils into the asthmatic airways through enhanced expression of IL1B-induced eosinophilic chemokines, CCL11 and
CCL2, and thus, may participate in eosinophilic inflammation (125). These observations clearly support the role of MAPKs in the inflammatory response associated with asthma. However, contrary to this, inhibition of MAPKs can also contribute to asthmatic inflammation. For example, p38 MAPK inhibitors, or genetic disruption of p38 MAPK pathway, potentiate TH1 responses in
18 macrophages and dendritic cells via the increased production of IL12 (126-128). Since the TH1 response contributes to asthma pathogenesis (see section 1.1.2), inhibition of p38 MAPK could also enhance inflammation in asthma. Similarly, inhibition of MEK1, in human airway epithelial cells, enhances the expression of HRV-induced CXCL10 (129). Because CXCL10 is associated with increased airway inflammation and AHR (130), inhibition of ERK MAPK may also, in part, contribute to asthmatic inflammation. In addition, MKK1-/- T cells, upon T-cell receptor engagement, exhibit attenuated JNK activity and elicit enhanced release of TH2 cytokines, including IL4, IL5 and IL13 (103). Thus, loss of JNK activity could contribute to allergic inflammation through augmented TH2 responses.
1.2.2 Transcription
Transcription plays an important role in the regulation of gene expression and occurs in the nucleus, where DNA is tightly packaged into highly organised nucleoprotein structures known as chromatin (131). The structural unit of chromatin is the nucleosome, which consists of 146 bp of
DNA wrapped around a protein core containing two copies of each of the four histone proteins
H2A, H2B, H3 and H4 (132). Packaging of DNA by histone proteins prevents the access of transcription machinery to DNA and thus, unwinding of DNA from histone is essential for transcription to occur (131, 133). In addition, unwinding of DNA also involves the recruitment of histone modifying enzymes and ATP-dependent chromatin remodelling complexes to DNA which catalyze acetylation, phosphorylation and methylation of histones at N-terminal tails (133).
Acetylation and phosphorylation of histone proteins removes the positive charge on histones, thereby decreasing the interaction of histones with negatively charged DNA (134). As a consequence, DNA becomes unwound from histones, and chromatin is transformed into a more
19 relaxed state that is accessible by the transcriptional apparatus (135). This process is collectively referred to as ‘chromatin remodeling’ (135-137).
For transcriptional activation to occur, chromatin remodelling is followed by a subsequent binding of TATA-binding protein (TBP), RNA polymerase II (Pol II) and basal transcription complexes to a gene promoter to initiate transcription (137). This occurs at the TATA box, identified by a consensus 5’-TATAAA-3’ sequence that is generally found 25-30 bp upstream of the transcription start site (138, 139). Subsequently, through a conformational change, 11-15 bp DNA surrounding the transcription start site is unwound and the active site of Pol II accesses the template strand of the promoter to initiate transcription (140). During the initiation of transcription, a large number of short (3 - 10 nucleotides) transcripts are synthesized and released (140). The next step of transcription involves elongation of the transcript and is associated with hyper-phosphorylation of the C-terminal domain of Pol II (137). Following phosphorylation of C-terminal domain, Pol II dissociates from DRB (5,6-dichloro-1-β-d-ribofuranosylbenzimidazole) sensitivity-inducing factor (DSIF) and negative elongation factor (NELF), thereby gets released from the promoter
(promoter escape) (141). This results in the initiation of the elongation phase of transcription.
Elongation proceeds further with the help of positive transcription elongation factor b (P-TEFb), which facilitates efficient movement of Pol II along the DNA by preventing Pol II pausing (142).
In addition, other factors, such as histone disassembly/reassembly factors, transcription and elongation factor SPT6, enhance transcriptional efficiency by facilitating the movement of Pol II through the chromatin (143).
In order to generate a mature mRNA transcript, pre-mRNA undergoes further processing, including 5’ capping, splicing and generation of the 3’ poly(A) tail (144). Modification of the 5’
20 end of pre-mRNA by addition of 7-methylguanosine is called capping (144), and usually occurs very early during the transcription process, even before the mRNA molecules are finished being transcribed by Pol II (131, 144). The 5’ capping of the mRNA is important to protect the mRNA transcript from degradation by exonucleases (144). The next step of the mRNA processing is the removal of introns by splicing, which is catalysed by a complex of proteins and small nuclear
RNAs (snRNAs), called the spliceosome (131, 144). The spliceosome is recruited to the mRNA via an interaction with the Pol II C-terminal domain (131, 144). Finally, upon reaching the end of a gene, Pol II terminates transcription, the newly synthesized immature transcript gets cleaved, and several hundred adenosine residues constituting the poly(A)tail are added at the 3’ end of the transcript by poly(A) polymerase (144). Subsequently, Pol II dissociates from the DNA and gets recycled to initiate further rounds of transcription (144).
1.2.3 Inflammatory transcription factors
Increased expression of inflammatory mediators is a hallmark of inflammation in asthma. The
DNA-binding proteins, commonly referred to as transcription factors, control the process of transcription via the recruitment of Pol II to a gene promoter and further regulate the expression of inflammatory genes (145). Several inflammatory transcription factors such as NF-B, AP-1,
CAAT/enhancer binding protein (C/EBP), signal transducers and activators of transcription
(STAT) and nuclear factor of activated T-cells (NF-AT) have been implicated in mediating inflammation associated with asthma (145). However, in the context of current project only the roles of NF-B and interferon regulatory factor (IRF) 1 will be discussed.
21 1.2.3.1 NF-B
NF-B is a ubiquitous inflammatory transcription factor, discovered in 1986 via its interaction with an 11-base pair sequence in the immunoglobulin light chain enhancer element (146). NF-B belongs to the Rel family of transcription factors and may be composed of hetero or homodimeric combinations of five different subunits; NF-B1 (p50 and its precursor p105), NF-B2 (p52 and its precursor p100), p65 (RelA), RelB and c-Rel (147). Structurally, NF-B consists of a highly conserved DNA-binding/dimerization domain known as the Rel homology domain that regulates the nuclear localisation and DNA binding (148). Furthermore, p105 (NF-B1) and p100 (NF-
B2), contain multiple ankyrin repeats in their C-terminal domain, and are activated via proteolysis, to form the small DNA binding proteins, p50 and p52, respectively (149). In contrast, p65 (RelA), RelB and c-Rel that contain a C-terminal transactivation domain and are therefore capable of activating transcription (149). Conversely, p50 and p52 lack a transactivation domain, and thus, by binding to cRel, RelB, and p65, facilitate the activation of classical or canonical pathway of NF-B activation (150).
A number of stimuli, including pro-inflammatory cytokines, such as TNF and IL1B, mitogens, stress, viruses, double stranded (ds) RNA, phorbol ester, bacteria and bacterial lipopolysaccharide
(LPS) can activate NF-B (151, 152). The activation of NF-B is strongly associated with increased cell survival and inflammatory responses as observed during cancer, chronic inflammatory disorders, including asthma, arthritis and inflammatory bowel disease, and several other neurodegenerative or inflammatory disorders (153). Under resting condition, NF-B is maintained in an inactive form in the cytoplasm through its interaction with an inhibitory protein, inhibitor of B (IB) (NFKBIA) (153). Upon stimulation with an appropriate stimulus, NF-B
22 is activated through a classical (canonical) pathway (Fig. 1.2) (154). This involves the activation of IBkinase (IKK) complex, containing the two catalytic subunits IKK1 (IKK) and IKK2
(IKK), along with a structural subunit referred to as NF-B essential modulator (NEMO; IKK
(155). Among these, IKK2 is essential for the phosphorylation of NFKBIAat serine (S) 32 and
36 residues (151). The phosphorylated NFKBIA is further recognised by ubiquitin ligase machinery, resulting in the poly-ubiquitination and subsequent degradation of NFKBIAby the 26S proteasome. Consequently, p50 and p65 dissociates from NFKBIA and translocates into the nucleus (Fig. 1.2) (151, 155, 156). Once in the nucleus, NF-B binds to specific B sequences, 5’-
GGGRNWYYCC-3’ (R, A/G; N, any nucleotide; W, A/T; Y, C/T), in the promoter or enhancer regions of target genes and activates transcription via the transactivation domain of p65 (155, 157).
Since NFKBIAhas a number of B binding sites in its promoter region, activated NF-B, by binding to these B sites increases the expression of NFKBIA (154)This newly synthesizedNFKBIA translocates into the nucleus and binds to NF-B, resulting in the export of
NF-B from the nucleus into the cytoplasm by means of a nuclear export sequence (NES) present on NFKBIA. This process is important to prevent prolonged activation of NF-B (160), and is further demonstrated in NFKBIA-/- mice, where lack of NFKBIA results in enhanced activation of NF-B and a severe inflammatory phenotype (161). Increased activity of NF-B has been closely associated with augmented transcription of numerous inflammatory genes (145, 152,
162). These include: pro-inflammatory cytokines such as IL1B, TNF, IL6 and CSF2; chemokines such as IL8, CCL2, CCL3, CCL5 and CCL11; inflammatory enzymes such as inducible nitric oxide synthase (iNOS) and cyclooxygenase (COX)-2 (PTGS2); adhesion molecules such as E- selectin, vascular cell adhesion molecule (VCAM)-1 and ICAM-1 (152).
23 Stimulus e.g. IL1B or TNF
IKK A IKK IKK2
B P P
Ub P P Ub Ub C
26S proteosome-mediated CYTOPLASM degradation of IB D NUCLEUS
B TATA GENE
Figure 1.2 Schematic representation of the classical, or canonical, NF-B signalling pathway. (A) Under resting conditions, NF-B, shown here as a heterodimer of p50 and p65, is sequestered in the cytoplasm by the inhibitory protein IBα (NFKBIA). (B) Upon stimulation of the cell, NFKBIA is phosphorylated by an upstream kinase, IKK2 at S32 and S36. (C) Phosphorylated NFKBIA undergoes ubiquitination and 26S proteosome-mediated degradation. (D) Degradation of NFKBIA allows nuclear translocation of NF-B heterodimer, p50 and p65, which may bind to B response elements in the promoter regions of target genes (154, 157).
Thus, NF-B is a major player in almost every aspect of the inflammatory response. In the context of asthma, increased activity of NF-B has been observed in airway epithelial cells, submucosal cells and sputum macrophages (163-165). In addition, peripheral blood mononuclear cells
(PBMCs) from severe uncontrolled asthma patients show increased expression of IKK2 and phosphorylation of NFKBIA and p65 (166). Equally, epithelial cells from asthmatic patients also
24 exhibit enhanced expression and nuclear localisation of p65 (164). Moreover, increased activity of
NF-B is also implicated in asthma exacerbations (see section 1.1.3 for further details) (167, 168).
Overall, these studies indicate that NF-B not only plays an important role in general inflammation, but may also significantly contribute to the inflammatory processes associated with asthma pathogenesis.
1.2.3.2 IRF1
The type I IFN, IFN- and -are a family of cytokines, induced upon viral infections, and are widely involved in antiviral defense, immune activation and cell growth regulation (169). By binding to their homologous receptors, IFNAR1 and IFNAR2, IFN- and - up-regulate the expression of IFN-inducible transcription factors, known as IRFs, via Janus kinase (JAK)/STAT pathway (170). The IRF family consists of 9 family members (IRF1-9) that play important roles in both innate and adaptive immunity (171). All the IRF family members have a conserved N- terminal DNA binding domain (DBD) and a C-terminal regulatory domain (172, 173). Due to the similarity of the DBD of all the IRFs, most members of the IRF family recognize same DNA binding sequence, 5’-GAAANNGAAAG/CT/C-3’ (where N denotes any nucleotide), referred to as interferon-stimulated response element (ISRE) (174). In addition to their interaction with each other as homo/heterodimers, IRFs can also co-operatively interact with other transcription factors, including NF-B and STAT, leading to enhanced gene expression (174-176) .
The first member of the IRF family, IRF-1, was originally identified as a key regulator of type I
IFN gene induction and activity (177). IRF1 mRNA is expressed in a variety of cell types, and its expression is mainly inducible by virus infection, dsRNA (poly (rl) : poly (rc)), ΙFNs (--- or cytokines, such as TNF, IL6 and IL1B (178-182). The induction of IRF1 is mainly depends
25 upon the activation of transcription factors, such as STAT1, STAT2 and NF-B (181, 183). In addition, the transcriptional activity of IRF1 is negatively regulated by its closely related IRF family member, IRF2 (170, 184). Conversely, IRF1, by enhancing the expression of STAT1 via a positive feedback mechanism, up-regulates its own expression (183, 185, 186). Even though IRF1 is a weak transcriptional activator, it induces the expression of a number of genes that are important for antiviral response/antiviral defense, apoptosis, NK cell/T cell development, macrophage function, and regulation of TH1-TH2 responses. These include: IL12p40, iNOS, CXCL10, PTGS2,
IL15, caspase 1, caspase 7, IFN-/guanylate-binding protein and protein kinase R (170, 171,
184, 187, 188).
Since IFN- induces IRF1 and plays an important role in the regulation of TH1 responses (189,
190), IRF1 could also be implicated in the regulation of TH1/TH2 response genes. In this regard, T
-/- cells from IRF1 mice, due to the decreased expression of the TH1 cytokine, IL12, and enhanced production of TH2 cytokines, failed to mount TH1 responses (191, 192). Moreover, IL1B and ΙFN-
γ-induced IRF1 also inhibits the TH2 cytokine, IL4, which further results in the attenuation of TH2 responses (193, 194). Similarly, IRF1 is also involved in Toll-like receptor (TLR)-mediated anti- viral effects (195). In addition, IRF1 plays an important role in the regulation of iNOS (196-198). iNOS catalyzes the production of nitric oxide, which, by inhibiting HRV replication, attenuates
HRV-induced production of a number of cytokines and chemokines in epithelial cells (199-202).
Conversely, since IRF1 is centrally involved in innate immunity, host defense and manipulation of cytokine responses, it may also influence the progression of asthma, where the innate immune response plays a role in the pathology (170, 192, 203, 204). In this context, genetic polymorphisms of IRF1, by inhibiting the binding of transcription factors, NF-B and early growth response
26 (EGR) 1 to IRF1 promoter, altered IRF1 mRNA and IFN- protein expression (205). Attenuated
IRF1 expression and activity was further associated with the regulation of total/specific IgE levels and the development of atopy or allergic diseases (205-208), including asthma in various ethnic groups (204, 206). However, contrary to this, enhanced IRF1 expression has been shown to reduce glucocorticoids responsiveness (209, 210) (see section 1.3.5.1 for further details). Overall, these observations suggest that IRF1 may play a critical role in the pathogenesis of asthma.
1.2.4 Post-transcriptional control of gene expression
In addition to transcriptional control, gene regulation is also controlled by post-transcriptional mechanisms. These include, mRNA stability, translation of mRNA to protein and post- translational modifications. Since these processes can influence gene regulation independently of transcriptional control, it is important to understand the role of these processes in gene expression.
However, in the context of this thesis, only the role of mRNA stability in gene regulation will be discussed below.
1.2.4.1 mRNA stability
Following the mRNA synthesis, capping and polyadenylation protects mature mRNA from being degraded by exonucleases (211). Thus, the mRNA that is spared from degradation gets exported from the nucleus into the cytoplasm, and is further translated into protein (131). The amount of mRNA templates available for translation is mainly dependent on the stability and degradation of mRNA (212). Decapping enzymes and deadenylases remove 5’-cap and 3’-poly(A) tail respectively and makes the mRNA susceptible to degradation by exonucleases (211). Moreover, sequence elements present in the 3’untranslated region (UTR) of many mRNAs can confer instability (213). The most common among these sequences is the adenylate uridylate (AU)-rich
27 sequence, also called as AU-rich element (ARE), consisting of a combination of multiple AUUUA pentamers, UUAUUUAUU nonamers or other clusters of AU pentamers and nonamers (214, 215).
Even though AREs can be found in 5’ UTR, the stabilization of mRNA is predominantly mediated by AREs present in 3’ UTR (216). The AUUUA sequences, located in the 3’ UTR of a number of inflammatory cytokines, chemokines and enzymes, including TNF, CSF2, IL6, IL8 and PTGS2 plays a critical role in the message stability (217, 218). AREs are a target for several RNA binding proteins, including tristetraprolin (TTP; zinc finger protein (ZFP) 36), human antigen R (HuR;
ELAVL1), adenine-uridine-rich element RNA-binding factor-1 (AUF1; HNRNPD) and K- homology domain splicing regulatory protein (KHSRP) (219-223). These proteins, by binding to
AREs, promote deadenylation and subsequent degradation of ARE containing inflammatory transcripts, leading to translational repression (224). For example, ZFP36 binds and destabilizes
TNF mRNA transcript, leading to translational inhibition of TNF (214). In addition, post- translational modifications, such as phosphorylation/dephosphorylation of ARE-binding proteins have also been suggested to regulate the stability of ARE containing inflammatory mRNAs. In this regard, MK2, a downstream substrate of p38 MAPK, by phosphorylating ZFP36, stabilizes inflammatory mRNA transcripts (212, 225-227). Furthermore, the destabilizing activity of AUF1 is also regulated by p38 MAPK-mediated phosphorylation (228, 229). Thus, the stability of inflammatory transcript can be controlled by a balance between stabilising and destabilising factors (212).
28 1.3 Glucocorticoids
1.3.1 Endogenous glucocorticoids
Steroid hormones are synthesized endogenously and are required for maintaining the homeostasis of the human body (230). Different classes of steroid hormones have been identified and these include; glucocorticoids, mineralocorticoids and androgens (230-232). In vivo, these steroids are synthesized from cholesterol by a stepwise series of reactions within the adrenal cortex (231, 233).
The first step of steroid synthesis involves conversion of cholesterol to pregnenolone by the enzyme cytochrome p450 (233). After which, the three groups of steroids are synthesized via distinct pathways (231, 233). Particularly for glucocorticoids, the next step of synthesis pathway involves conversion of pregnenolone to 17 OH-progesterone, which is then metabolized to 11 deoxycortisol (231). Finally, an active cortisol, the major endogenous glucocorticoid hormone, is synthesized via the metabolism of 11 deoxycortisol (231, 233, 234). The synthesis and secretion of the cortisol from the adrenal gland is tightly controlled by the HPA axis (234, 235). A number of stimuli, including infection, inflammation, pain and mental stress, activate HPA axis, leading to the release of corticotropin releasing hormone (CRH) (72). Acting on the anterior pituitary,
CRH releases adrenocorticotropic hormone (ACTH), which in turn stimulates the adrenal cortex to release cortisol (72). Once in the blood, cortisol increases the blood glucose level via the stimulation of gluconeogenesis, and also increases the metabolism of fat, protein and carbohydrates (72). In addition, cortisol also suppresses the immune system and plays an essential role in the resolution of inflammation (72). Thus, cortisol is important for maintaining and restoring the homeostasis following exposure to different stressful stimuli (236, 237). Conversely, glucocorticoids can inhibit the synthesis of CRH and ACTH (72, 236). Thus, by controlling the
29 release of cortisol from the adrenal gland, glucocorticoids participate in a negative feedback loop that regulates the level of cortisol in the blood (72, 236).
1.3.2 Molecular mechanisms of glucocorticoid action
The cellular effects of glucocorticoids are mediated by the glucocorticoid receptor, NR3C1
(nuclear receptor (NR) superfamily 3, group C, member 1), a ligand-activated transcription factor, belonging to the steroid hormone receptor family (238). Alternative mRNA splicing of human
NR3C1 gene generates two isoforms of NR3C1, termed and 2. NR3C1 is a ‘classical’ ligand-activator receptor, whereas NR3C1a dominant negative inhibitor of NR3C1, is defective in steroid binding and does not bind to any known steroid agonist (240, 241). Similar to other members of the NR family, NR3C1 comprises of N-terminal transactivation domain, termed as activation function (AF) 1, a conserved DBD, and a C-terminal domain that is important for ligand binding (238, 242). The well conserved DBD of NR3C1 contains two zinc finger structural motifs and mediates DNA binding (242, 243). In addition, the DBD also comprises of the nuclear localisation sequence (NLS) and a linker region. The NLS is involved in nuclear translocation and dimerization of NR3C1 (72, 237, 243). The ligand binding domain of NR3C1 mediates the binding of glucocorticoids to NR3C1, and thus, is important in the ligand-induced activation of NR3C1
(237, 243). Furthermore, NR3C1 also contains an additional NLS2 and a second transactivation domain termed as AF2 (237, 243). Both, the constitutively active AF1 and the ligand dependent
AF2, due to their ability to interact with general transcription machinery, coactivators and chromatin remodelling complexes, regulates the transcriptional activity of NR3C1 (237, 243).
In the absence of ligand, NR3C1 is predominantly present as an inactive complex in the cytoplasm with chaperones and co-chaperone molecules (244, 245). The heat shock proteins (hsp)90 and
30 hsp70 are the two most important chaperones involved in the regulation of NR3C1 activity (244).
The binding of hsp90 to ligand binding domain of NR3C1 exposes the ligand-binding site, but masks the two NLSs and confines NR3C1 into the cytoplasm (243). The hsp90 also inhibits the binding of glucocorticoids to NR3C1 and regulates the function of the receptor in the nucleus
(246). Upon entering the cytoplasm of a cell, glucocorticoids binds to the cytoplasmic NR3C1 (72,
247), which then undergoes a conformational change with the exchange of FK506 binding protein
(FKBP)51 for FKBP52 (248). Subsequently, NR3C1 translocates into the nucleus due to the recruitment of the transport protein, dynein, by FKBP52 (248). Once in the nucleus, FKBP52 and chaperone molecule, hsp90, dissociates, leaving NR3C1 free to exert effects on transcription (Fig.
1.3) (72, 247). Activated NR3C1 binds as a homodimer to glucocorticoid response elements
(GREs) (consensus sequence; 5’-GGTACAnnnTGTTCT-3’) and further activates transcription
(transactivation) (249) (Fig. 1.3). In addition, transcriptional activation by NR3C1 requires recruitment of a number of coactivators, including the steroid receptor coactivator (SRC)1, SRC2,
SRC3, CBP/p300, PCAF and ATP-dependent chromatin remodelling complexes, to the transcriptional machinery through their interaction with AF1 and AF2 (243, 250). Binding of
NR3C1 to GREs up-regulates the transcription of GRE-dependent metabolic genes, such as tyrosine aminotransferase and phosphoenolpyruvate carboxykinase, involved in gluconeogenesis
(72). Moreover, a number of genes, including lipocortin I, secretory leukocyte protease inhibitor and the decoy IL1 type II receptor, are also up-regulated by glucocorticoids and could play significant roles in the resolution of inflammation (251, 252). However, because these proteins were induced very slowly, over 24-28 h, they cannot explain the rapid anti-inflammatory effects produced by glucocorticoids (72). Hence, up-regulation of gene transcription, or transactivation,
31 was generally believed to be associated with many of the metabolic side-effects of glucocorticoids
(72).
Following on from the characterisation of the classical GRE, very few genes, such as pro- opiomelanocortin and ACTH, were described as having a negative GRE (nGRE) sites (72). These sites were lacking some of the conserved nucleotides required for the transcriptional activation found at the classical GRE sites (72, 253, 254). Thus, initially, nGRE sites were suggested as a possible mechanism for the anti-inflammatory, glucocorticoid-dependent repression of gene transcription (62). This concept of nGRE-mediated repression is further supported by Surjit et al. suggesting that glucocorticoids can induce repression of inflammatory genes via the binding of activated NR3C1 to nGREs (255). However, this is unlikely as pro-inflammatory genes, such as
CSF2, PTGS2 and IL8, do not appear to contain nGREs in their promoters (72, 256).
1.3.3 Transrepression by glucocorticoids
An alternate mechanism that has been widely proposed for the anti-inflammatory effects of glucocorticoids is the tethering nGRE (62, 253, 254, 257). In this case, NR3C1 interacts with other transcription factors, such as NF-B and AP-1, to prevent inflammatory gene transcription
(transrepression) (Fig. 1.3) (254). Interaction of NR3C1 with these transcription factors directly represses gene transcription without involving the binding of NR3C1 to DNA (72).
Transrepression by NR3C1 is also believed to inhibit inflammatory gene transcription via the effects on DNA chromatin structure known as chromatin remodelling (see section 1.2.2 for further details) (62, 247, 254). In this context, activated NR3C1 is thought to reduce or reverse histone acetylation by the recruitment of histone deacetylases (HDACs) (254). For example, recruitment of HDAC2 by NR3C1 to NF-B leads to deacetylation of histones and subsequent inhibition of
32 Glucocorticoid
CYTOPLASM hsp90 FKBP52 + dynein
hsp90
NR3C1 FKBP51
hsp70 FKBP51 hsp70
hsp90
hsp90 dynein
NR3C1 FKBP52
NUCLEUS
NR3C1 hsp90
FKBP52 hsp90
NR3C1 NR3C1 NR3C1 TF TF
GRE TATA GENE TF-RE TATA GENE TRANSACTIVATION TRANSREPRESSION (simple GRE) (tethering nGRE) Figure 1.3. Transcriptional control by glucocorticoid receptor signalling. In the absence of glucocorticoid, glucocorticoid receptor (NR3C1) is held in the cytoplasm by chaperone molecules heat shock protein (hsp) 70, a dimer of hsp90, and FK506-binding protein (FKBP) 51. Binding of glucocorticoid to NR3C1 initiates exchange of FKBP51 for FKBP52, dissociation of hsp70 and the binding of transport protein, dynein. Subsequently, the NR3C1 complex translocates to the nuclear membrane and into the nucleus. In the nucleus, hsp 90, FKBP52 and dynein dissociate from NR3C1 to allow dimerization of NR3C1 on simple glucocorticoid response element (GRE) to initiate transcriptional activation of target genes (transactivation). Alternatively, NR3C1 may also bind to inflammatory transcription factors, such as NF-B and AP-1, and inhibit gene transcription (transrepression) (258, 259).
NF-B-mediated transcriptional responses (260, 261). Additionally, it has also been reported that
NR3C1, by inhibiting the P-TEFb-mediated phosphorylation of Pol II C-terminal domain or via the recruitment of glucocorticoid receptor interacting protein (GRIP1), represses gene transcription
33 (262, 263). These data, therefore suggest that tethering nGREs could play an important role in mediating the anti-inflammatory effects of glucocorticoids.
1.3.4 Transactivation by glucocorticoids
Accumulating evidence suggests that the up-regulation of anti-inflammatory genes by glucocorticoids can also be regarded as an important mechanism in the repression of inflammatory gene expression by glucocorticoids (114, 254, 264, 265). In this context, IL1B induced expression of IL8 and PTGS2, in A549 cells, is NF-B dependent (266, 267), and is totally repressed by dexamethasone (268). Classically, this would be explained by stating that dexamethasone represses IL8 and PTGS2 by transrepression of NF-B. However, analysis of NF-B activity indicates otherwise. In NF-B-dependent luciferase reporter cells, there was only 30-40% repression of NF-B activity by dexamethasone (268, 269). Furthermore, nuclear run-on analysis also showed a similar degree of repression for IL8 and PTGS2 by dexamethasone (268, 270).
These data therefore suggest a key role for post-transcriptional mechanism(s) in the repression of
IL8 and PTGS2. In addition to this, the repression of IL8 and PTGS2 by dexamethasone is prevented by the inhibition of transcription and translation (271, 272). This clearly suggests a role for new gene synthesis, i.e. transactivation in the repression of pro-inflammatory genes, such as
IL8 and PTGS2. Likewise, King et.al., showed that >50% of the IL1B-induced acute phase inflammatory mRNAs (peak of mRNA expression at 1 or 2 h following IL1B treatment) were repressed by dexamethasone in a manner that requires protein synthesis (273). This also suggests that transactivation by NR3C1 plays a major role in the repressive effects of dexamethasone.
To further understand the possible anti-inflammatory roles of transactivation, microarray analysis was carried out in A549 cells (271). This analysis revealed that hundreds of genes, including
34 DUSP1, ZFP36, NFKBIA, glucocorticoid-induced leucine zipper (GILZ; TSC22D3) and regulator of G-protein signaling 2 (RGS2), many with potential anti-inflammatory/bronchoprotective effects, were up-regulated by dexamethasone (271). In this context, TSC22D3 represses NF-B and AP-1-dependent transcription, whereas NFKBIA is an endogenous inhibitor of NF-B (264,
274). In addition, up-regulation of downstream of tyrosine kinase (Dok)-1, Src-like adaptor protein
(SLAP) and dexamethasone-induced Ras-related protein (Dexras)1 by glucocorticoids inhibit the activation of signalling transduction cascade through a variety of different mechanisms (275-277).
Moreover, glucocorticoid-induced expression of Clara cell secretory 10-kDa protein and thymosin
4 sulfoxide plays a protective role in allergic inflammation and neutrophilic responses respectively (278, 279). Similarly, glucocorticoid-induced RGS2, a GTPase-activating protein that attenuates G(q) signaling, is bronchoprotective (280). Thus, multiple glucocorticoid-inducible genes can regulate various aspects of the regulation of inflammatory gene expression and may therefore be expected to play redundant roles (254) (Refer to chapter 6 (section 6.3) for more details about the possible redundancy between glucocorticoid-inducible genes). In addition, glucocorticoid-induced DUSP1 and ZFP36 also play an essential role in mediating the anti- inflammatory actions of glucocorticoids and will be further discussed below.
1.3.4.1 DUSP1
DUSP1 is a nuclear phosphatase with a molecular weight of 40 kDa, induced by a number of stimuli, including pro-inflammatory cytokines, such as TNF and IL1B, LPS, glucocorticoids, and
β2-adrenoceptor agonists, such as salmeterol (114, 281-285). Inflammatory stimuli-induced
DUSP1 expression is largely MAPK dependent, and specifically, a role of ERK MAPK in the expression of DUSP1 has been demonstrated (111, 286). In addition, post-transcriptional
35 processing and post-translational modifications, including mRNA stability, phosphorylation, ubiquitination, degradation and acetylation play a crucial role in the regulation of DUSP1 activity
(287-289). In this regard, ERK-mediated phosphorylation of DUSP1 at S359 and S364 residues enhances DUSP1 stability, whereas phosphorylation at S296 and S323 residues targets DUSP1 for proteasomal degradation (287, 289, 290). In contrast, glucocorticoids, by inhibiting ERK, attenuates the proteasomal degradation of DUSP1, leading to sustained expression of DUSP1
(291). In addition, acetylation of DUSP1 does not affect protein stability or the phosphatase activity, but enhances the interaction of DUSP1 with p38 MAPK, leading to enhanced dephosphorylation of p38 MAPK and subsequently reduced expression of inflammatory genes
(288, 292). Similarly, the stability of DUSP1 mRNA in airway smooth muscle cells is enhanced by TNF in a p38 MAPK-dependent manner (293). Since DUSP1 expression is enhanced in a
MAPK-dependent manner, and enhanced expression of DUSP1 is involved in a negative regulation of MAPK signalling, DUSP1 forms a negative feedback regulatory loop to limit MAPK activity and the expression of inflammatory mediators (254, 294-297).
Inflammatory gene regulation by MAPKs often involves transcriptional and/or post-transcriptional mechanisms. In this regard, the p38 MAPK pathway is known to regulate inflammatory gene expression at the post-transcriptional level (227, 298). Thus, DUSP1-mediated inhibition of p38 is believed to predominantly contribute to post-transcriptional regulation of inflammatory gene expression by glucocorticoids (254). However, recent data also suggest that p38 also increases the transcriptional activity and/or the expression of inflammatory transcription factors including;
ATF-1, ATF-2, AP-1 and NF-B (254, 299-301). Thus, enhanced expression of DUSP1 by glucocorticoids could also regulate the transcriptional repression of inflammatory gene expression
36 by glucocorticoids (254). Similarly, DUSP1 attenuates JNK-mediated transcriptional activity of
AP-1, indicating a further role for DUSP1 in transcriptional inhibition (302). In addition, by inhibiting the synthesis of proinflammatory cytokines, DUSP1 limits the inflammatory responses in vivo (282;304;305). In this regard, DUSP1 knock-out mice exhibit enhanced MAPK activity with exaggerated inflammatory responses in response to LPS or zymosan (281, 303, 304).
However, the repressive effect of glucocorticoids in DUSP1-/- mice was gene specific. For example, while glucocorticoid-induced repression of CSF2 and IL1A was strongly attenuated, the repression of TNF, IL1B and PTGS2 was only partially attenuated (281). These data, therefore imply that additional, DUSP1-independent mechanism of repression by dexamethasone must exist for the inhibition of inflammatory genes (281, 295).
1.3.4.2 ZFP36
ZFP36 is an mRNA destabilizing protein, belonging to the family of tandem CCCH zinc finger proteins, and its expression is enhanced in T cells, macrophages, fibroblasts and pulmonary A549 cells by a number of stimuli, including IL1B, LPS, phorbol esters, growth factors and β2- adrenoceptor agonists, such as salbutamol (305-308). Pro-inflammatory stimuli-induced ZFP36 protein expression is highly dependent on MAPK activation (308, 309). ZFP36 protein appears initially as a ~40 kDa protein, which becomes phosphorylated and migrates at ~45-kDa on SDS-
PAGE (308, 310). Phosphorylation of ZFP36 at S52 and S173 residues is mediated by a p38- dependent kinase, MK2, and is suggested to enhance ZFP36 stability (310). This stabilisation of
ZFP36 is mediated via the binding of the adaptor protein, 14-3-3, to ZFP36 (310), which in turn inhibits the destabilizing activity of ZFP36 (311). In contrast, dephosphorylation of ZFP36 by
PP2A increases the activity of ZFP36 (312). Thus, a regulatory loop exists whereby an initial
37 MK2-mediated phosphorylation of ZFP36 in the presence of inflammatory stimuli results in
ZFP36 with low activity to allow the up-regulation of inflammatory transcripts by MAPKs.
However, the subsequent dephosphorylation of ZFP36 by PP2A activates mRNA-bound ZFP36, leading to a rapid degradation of target mRNA, thus preventing prolonged or exaggerated inflammatory responses (226).
Given the ability of ZFP36 to reduce the expression of ARE-containing mRNAs (see section
1.2.4.1 for more details), it is often suggested that ZFP36 is a negative feed-forward regulator of inflammatory gene expression (305). In this regard, ZFP36 is an established negative regulator of
ARE-containing mRNAs, including TNF, PTGS2, CSF2, IL6 and IL8, and mice lacking ZFP36 develop severe and chronic inflammation (215, 222, 313). The mRNA destabilizing effect of
ZFP36 is mainly attributed to its interaction with the mRNA decay machinery, such as decapping enzymes, the exonuclease, XRN1, and the subunits of the exosome (314, 315). Conversely, the deadenylation of poly(A) tail by ZFP36 is mainly due to the activation of the deadenylase, poly(A)- specific ribonuclease (314-316).
The expression of ZFP36 is also modestly up-regulated by glucocorticoids in primary human airway epithelial cells, pulmonary A549 cells, bronchial BEAS-2B epithelial cells and in the airways following glucocorticoid inhalation (Leigh et. al. unpublished data) (306, 308, 317).
Furthermore, the role of ZFP36 in glucocorticoid-induced repression of inflammatory gene expression is also indicated (295, 317). In this regard, glucocorticoid-induced repression of chemokine gene expression was impaired in ZFP36 knockout mice (317). Moreover, siRNA- mediated knock-down of ZFP36 attenuated dexamethasone-induced repression of TNF and IL8
(306, 318). Conversely, LPS and IL1B-induced ZFP36 expression is partially repressed by
38 dexamethasone (308, 319). In this context, recent data point to the possibility that the dexamethasone-mediated repression of ZFP36 could be due to the enhanced expression of DUSP1 in the presence of dexamethasone, which, by inhibiting p38 MAPK, reduces the expression of
ZFP36 (320). However, this hypothesis warrants further investigation and is further tested in this thesis.
1.3.5 Insensitivity/resistance to glucocorticoids in asthma
Although glucocorticoids are widely used for treating asthma, symptoms related to severe asthma are difficult to control due to poor sensitivity or resistance to the anti-inflammatory effects of glucocorticoids (71). Glucocorticoid resistance may refer to a decrease in the maximum response to glucocorticoids, a rightward shift in the dose response curve or potentially both of these effects
(321, 322). Clinically, glucocorticoid resistance is defined as the presence of unresolved airway obstruction or no improvement in lung functions, such as forced expiratory volume (FEV1) or PEF, after two weeks of treatment with high dose oral glucocorticoids (62, 322). Overall, resistance to glucocorticoids complicates the clinical management of asthma and results in a significant financial burden (71, 247). Further understanding of the molecular mechanisms of glucocorticoid resistance may help in the development of an effective anti-inflammatory therapy for the treatment of severe asthma (322).
1.3.5.1 Mechanisms of reduced glucocorticoid sensitivity
A number of mechanisms may contribute towards the decreased responsiveness of severe asthmatics to glucocorticoids (247, 321, 322). In this context, TNF plays an important role in asthma pathogenesis and enhanced level of TNF is often associated with reduced sensitivity to glucocorticoids (323, 324). The expression of TNF is increased in BAL and bronchial biopsy
39 specimens from severe asthma patients, and was further linked with glucocorticoid resistance
(325). In addition, TNF reduces glucocorticoid responsiveness in monocytes (326) and activates pathways that are involved in chronic airway remodeling and sub-epithelial fibrosis (327).
Moreover, enhanced activation of ERK and p38 MAPK pathway by TNF enhances the expression of IL8, a neutrophil chemoattractant (328). Activation of ERK MAPK in airway smooth muscle cells is insensitive to the repressive actions of glucocorticoids, and is further involved in the recruitment of neutrophils contributing to airway inflammation (328). Additionally, cytokines associated with TH1 immunity may also contribute to the pathogenesis of severe glucocorticoid resistant asthma (329). For example, IFN-/TLR4-MyD88-dependent AHR is resistance to glucocorticoid therapy (330). Similarly, treatment of murine macrophages with IFN-inhibits nuclear translocation of NR3C1 through the activation of MyD88 pathway (331). Furthermore, increased exposure to cytokines and activation of MAPK have also been found to induce glucocorticoid insensitivity through attenuating NR3C1 ligand binding (322). For example, the
TH2 cytokines, IL2, IL4 and IL13, have been found to decrease the binding of glucocorticoids to
NR3C1 (321). In addition, cytokines, such as TNF and TGF-, by decreasing the expression of
NR3C1, reduces glucocorticoid responses (326, 332). Equally, p38 and JNK MAPK-mediated phosphorylation of NR3C1 reduces the translocation of NR3C1 into the nucleus and suggested to contribute to glucocorticoid insensitivity (333). In this respect, cytokines, such as IL2 and IL4, by activating p38 MAPK, decreases nuclear translocation of NR3C1 (247). In addition, enhanced activity of inflammatory transcription factors, such as NF-B, STAT5, IRF1 and AP-1, have all been implicated in reduced response to glucocorticoids (322). STAT5 has been found to bind
NR3C1 and further associated with defective nuclear translocation and DNA binding of NR3C1
(334). AP1 is thought to interact with monomeric NR3C1 and thereby inhibiting the interaction of
40 NR3C1 with GRE or other transcription factors (335). The expression of NF-B was enhanced in
PBMCs from asthmatics and has been shown to correlate with reduced glucocorticoid responsiveness (336). IFNs and TNF-mediated activation of IRF1 may also contribute to reduce response to glucocorticoids in airway smooth muscle cells through sequestration of the transcriptional co-activator of NR3C1, GRIP1 (209, 210). In this context, depleting the amount of
GRIP1 available to NR3C1 in the presence of high level of IRF1 would decrease the transcriptional function of NR3C1 (209, 210).
The detailed nature of the asthmatic phenotype may also play a crucial role in determining the responsiveness of different asthmatics to glucocorticoid therapy. Thus, even though glucocorticoids are highly effective in reducing eosinophilic inflammation, as seen in mild to moderate asthmatics, severe asthmatics, with neutrophilic inflammation, respond poorly to glucocorticoids (321, 337). Furthermore, enhanced activity of phosphoinositide 3-kinase (PI3K), especially in asthmatics who smoke, by attenuating HDAC activity, may contribute to reduced glucocorticoid sensitivity (338). In this context, smokers also show a marked reduction in HDAC2 activity (339, 340). Since, HDAC2 is important for the repression of NF-B by glucocorticoids
(260, 261), attenuation of HDAC2 activity may further result in reduced responses to glucocorticoids. However, contrary to this, recent work by Wang et al. demonstrated that budesonide-mediated inhibition of cytokine release in patients with severe smoking history was independent of HDAC2 activity, and thus, inhibition of HDAC2 activity may not always corresponds to reduced response to glucocorticoids (209, 341). Further, increased expression of
NR3C1β, a dominant negative inhibitor of NR3C1, by interfering with DNA binding ability of
NR3C1, reduces glucocorticoid responsiveness (342). NR3C1β by binding to GRE may out-
41 compete NR3C1 binding to GRE (322, 343). NR3C1β has also been found to form heterodimers with NR3C1, which may inhibit NR3C1 interaction with GRE (241). However, the data supporting the role of NR3C1in reduced glucocorticoid response are not clear and currently inconclusive. Overall, insensitivity to glucocorticoids may involves a number of cellular and molecular mechanisms, and an improved understanding of these mechanisms may help to identify new targets for therapeutic interventions.
1.4 General hypothesis and research aims
1.4.1 General hypothesis
The overriding hypothesis for this thesis is that glucocorticoids induce the expression of genes, which then play critical roles in the repression of inflammatory gene expression. In addition, this thesis also seeks to test the hypothesis that some glucocorticoid-induced genes may also play an opposing role by promoting resistance/insensitivity to the repressive effects of glucocorticoids.
1.4.2 Research aims:
The overall aims of this thesis are: (I) to investigate the roles of glucocorticoid-induced DUSP1 in the repression of inflammatory gene expression by glucocorticoids; (II) to examine the feed- forward inhibitory role of ZFP36 on the regulation of ARE-containing inflammatory transcripts in
DUSP1 inhibited cells and implication of such effector mechanisms on glucocorticoid-induced repression of inflammatory genes; and (III) to study glucocorticoid-induced DUSP1 as a mechanism of glucocorticoid insensitivity.
42 Chapter 2 : Material and Methods 2.1 Materials
AbD Serotec, Raleigh, NS, USA: Primary antibodies (see Table A1)
Abcam, Toronto, Canada: Primary antibodies (see Table A1)
American Type Culture Collection, Rockville, MD, USA: A549 cells
Applied Biosystems Inc, Foster City, CA, USA: MicroAmp Optical 96 well reaction plate
Biotium Inc, Hayward, CA, USA: Luciferase assay buffer
Calbiochem, Ontario, Canada: SB203580, UO126, JNK inhibitor 8
Cell Signalling, Danvers, MA, USA: Primary antibodies (see Table A1)
Dako, Mississauga, ON, Canada: Goat anti-rabbit, goat anti-mouse and rabbit anti-goat immunoglobulins
Diagenode, Denville, USA: Bioruptor/sonicator
Fisher Scientific, Nepean, ON, Canada: Methanol, ethanol, sulphuric acid (H2SO4), glacial acetic acid, hydrochloric acid (HCI), sterile tissue culture reagents
Invitrogen, Burlington, ON, Canada: Dulbecco's Modified Eagle's Medium (DMEM),
Dulbecco's Modified Eagle's Medium (DMEM) F-12, fetal calf serum (FCS), NuPage western blotting system, Lipofectamine RNAiMAX, Lipofectamine 2000, trypsin ethylenediaminetetraacetic acid (EDTA), penicillin-streptomycin, SYBR greenER mastermix,
Fast SYBR green master mix, protein G magnetic Dynabeads, proteinase K
43 Thermo Scientific - Pierce Protein Research Products, Rockford, IL, USA: ECL western blotting substrate, 16% formaldehyde, protease inhibitor cocktail
Promega, Madison, WI, USA: Reporter lysis buffer, magnetic separation stand (twelve-position) for ChIP
Qiagen Ltd, Mississauga, ON, Canada: QlAshredder, RNeasy Mini Kit, DNase set including
RDD buffer, siRNAs (see Table A2)
R&D Systems, Minneapolis, MN, USA: Human recombinant IL1B, human recombinant TNF, duoSet CSF2, CXCL1, CXCL10, IL6, IL8 and TNF ELISA kits
Roche Diagnostics, Laval, QC, Canada: Complete protease inhibitor tablets for western blot analysis
Santa Cruz Biotechnology Inc., Santa Cruz, CA, USA: Primary antibodies (see Table A3)
Sigma-Aldrich Company, Oakville, ON, Canada: TRIZMA-base, L-glutamine, phosphate- buffered saline (PBS), Hanks balanced salt solution (HBSS), phenylmethylsulphonyl fluoride
(PMSF), sodium orthovanadate (Na3VO4), sodium fluoride (NaF), sodium pyrophosphate
(Na4P207), bovine serum albumin (BSA), orange G, ethidium bromide, dexamethasone, sodium bicarbonate (NaHC03), polyoxyethylene-sorbitan (Tween 20), glycine, Ponceau S, bromophenol blue, β-mercaptoethanol, select agar, acrylamide-bis, TEMED, 1,4-Piperazinediethanesulfonic acid, Piperazine-1,4-bis(2-ethanesulfonic acid) (PIPES), Corning Spin-X® UF 500 Concentrator
5000 columns, sodium chloride (NaCl), sodium deoxycholate, sodium dodecyl sulphate (SDS),
Nonidet P-40 (NP-40)
44 2.2. Methods
2.2.1 Cell Culture methods
2.2.1.1 A549 cell culture
A549 cells are adenocarcinoma human alveolar basal epithelial cells derived via culturing of cancerous lung tissue in the explanted tumor of a 58-year-old Caucasian male (344). The cells were grown in 175 cm2 culture flasks in a media containing Dulbecco’s modified Eagle’s medium
(DMEM) supplemented with 10% v/v foetal calf serum (FCS) and 2 mM L-glutamine. Cells were
o incubated at 37 C, 5 % CO2/95% air and passaged when 90-95% confluent. For experiments, cells were cultured either in 6, 12 or 24 well plates and incubated with serum-free medium, to arrest the cell growth, overnight prior to experiments.
2.2.1.2 Primary human bronchial epithelial (HBE) cell culture
Primary human bronchial epithelial (HBE) cells were collected and cultured in the laboratory of
Dr. David Proud (University of Calgary, Canada). HBE cells isolated from non-transplanted normal human lungs obtained using the tissue retrieval service at the International Institute for the
Advancement of Medicine, Edison, NJ, USA, were cultured in bronchial epithelial cell growth medium (BEGM) supplemented with 5% FCS for the first 72 h of culture at 37°C in 5% CO2/95% air. After 72 h, cells were washed with Hank’s balanced salt solution (HBSS) and fed with BEGM for further 14 days. The medium was changed every two days. After ~14 days, once the cells had reached >85% confluence, they were used for experiments. For experiments, cells were cultured in 6 well plates in bronchial epithelial cell basal medium (BEBM) and incubated with BEBM, to arrest the cell growth, overnight prior to experiments.
45 2.2.1.3 Adenoviral infection
A549 cells were grown in 6 or 12 well plates to approximately 70% confluence. Cells were then infected with multiplicity of infection (MOI) of 10 of either an empty adenoviral serotype 5 (AD5) green fluorescent protein (GFP)-expressing vector (Ad5-GFP) or Ad5-DUSP1, containing a
DUSP1 cDNA expression cassette (345) for 24 h in serum containing media. After 24 h cells had reached full confluence and were then incubated in serum-free media overnight, to arrest the cell growth, prior to experiments.
2.2.1.4 siRNA-mediated gene silencing
Gene silencing was carried out by using DUSP1-, ZFP36-, IRF1- or lamin A/C (LMNA)-specific siRNAs at a final concentration of 25 nM (DUSP1 & ZFP36 siRNA) or 50 nM (IRF1 siRNA).
Concentration of control LMNA siRNA was same as that for the targeting siRNAs. Each siRNA was mixed with lipofectamineTM RNAiMAX (1 μl of 1 μg/μl) (Invitrogen) in 100 μl of serum-free
DMEM and incubated at room temperature for 30 min prior to dilution to 1 ml and addition to cells. A549 cells were grown to approximately 60-70% confluence in 12 well plates before being incubated for 24 h at 37oC in serum free DMEM containing siRNA and lipid. Cells were then treated with specific stimuli for an experiment. LMNA siRNA was used as a control siRNA and was subjected to an identical protocol to the siRNA of interest. The LMNA-targeting siRNA- mediated knockdown of LMNA was confirmed previously in our laboratory (308). See Table A1 for siRNA sequences.
46 2.2.2 Western blotting
2.2.2.1 Preparation of cell lysates
Cells were lysed in 1 × Laemlli buffer (10% v/v glycerol, 5% v/v β-mercaptoethanol, 0.625 M
SDS, 0.25 M TRIZMA-HCL pH 6.8 and 0.75 mM bromophenol blue) containing 1 × completeTM protease inhibitor mixture and phosphatase inhibitors (50 mM NaF, 2 mM Na3VO4 and 20 mM
Na4O7P2). To achieve complete cell lysis, cells were frozen overnight at -20°C. After defrosting, cells were scraped from plates on ice and transferred to a fresh 1.5 ml Eppendorf tube. The cell lysates were then heated to 100oC for 10 min and subjected to sonication for 30 min at 4oC. After sonication, the samples were again heated to 100oC for 2 min before loading into gels.
2.2.2.2 SDS polyacrylamide gel electrophoresis
Gel electrophoresis of proteins was carried out by using the Novex NuPAGE western blotting system (Invitrogen, Paisley, U.K.) or Bio-Rad Mini-PROTEAN gel electrophoresis system. Cell lysates (25 to 30 µl) and rainbow protein ladder (5 µl) were loaded on to the gel. Total proteins were size-separated at 150 V for 90 min on 10-12% polyacrylamide gel by using 1 × running buffer
(25 mM TRIZMA base, 191.8 mM glycine, 0.1% w/v SDS).
2.2.2.3 Protein transfer to nitrocellulose membranes
Proteins were then electro-transferred onto nitrocellulose membranes using 1 × transfer buffer (25 mM TRIZMA base, 191.8 mM glycine, 20 % v/v methanol) at 0.4 mA for 90 min on ice. Following the transfer of proteins onto the membranes, equal loading of protein was confirmed by staining the membranes with Ponceau S staining dye (1.3 mM Ponceau S dissolved in 5% v/v acetic acid) for 1 min at room temperature. Destaining to visualise protein bands was achieved by washing the
47 membranes with 1 × washing buffer (1 × TBS-Tween, 0.5 M TRIZMA base, 1.5 M NaCl, 0.1 % v/v Tween 20) for 5 to 10 min with agitation on an orbital shaker.
2.2.2.4 Immunodetection of proteins
After staining, the membranes were blocked in 1 × TBS-Tween containing 5% skimmed milk for
1 h at room temperature with continuous agitation on an orbital shaker. The membranes were then washed for 5 min with 1 × washing buffer and incubated with appropriate polyclonal primary antibodies according to the manufacturer’s guidelines, between few hours to overnight, either at room temperature or at 4oC in TBS-Tween plus 5% BSA or skimmed milk. The membranes were then again washed for 30 min with 1 × washing buffer with gentle agitation on an orbital shaker.
Washing buffer was changed after every 10 min. The membranes were then incubated with anti- rabbit, anti-mouse or anti-goat immunoglobulins linked to horseradish peroxidase diluted in TBS-
Tween plus 3% skimmed milk for 1 h at room temperature. Following 30 min of further washing
(3 x 10 min each wash), immune complexes were detected using enhanced chemiluminescence
(ECL) and visualised by autoradiography. The saturation of immune complexes was avoided by taking multiple exposures of x-ray films. Autoradiographic bands were quantified by densitometry using Total LabTM software (Nonlinear Dynamics, Newcastle, UK). See Table A2 for details of antibodies used.
2.2.3 RNA isolation, cDNA synthesis and SYBR Green Real Time PCR
2.2.3.1 RNA isolation
Total RNA was extracted using the RNeasy mini kit (Qiagen) according to manufacturer’s guidelines. Cells were lysed in 350 L of RLT buffer containing 1% v/v -mercaptoethanol and frozen at -80oC. Cells were thawed and scrapped from plates on ice. The lysates were then
48 transferred into a QIAshredder spin column and centrifuged at maximum speed for 2 min. The homogenized lysates were then mixed with an equal volume (350 L) of 70 % ethanol, applied to
Rneasy mini column and centrifuged at 9279 g (10000 rpm) for 30 sec. The flow through was discarded and the mini columns were then washed with 350 L of buffer RW1. DNA contamination was removed by treating the spin columns with 80 l of 1 × RDD buffer containing
18 units of DNase I and incubation at room temperature for 30 min. The columns were then washed once with 350 L RW1 and twice with 500 L buffer RPE for 30 sec at 9279 g. For the second wash with buffer RPE, the samples were centrifuged for 2 min at 9279 g. The flow through was discarded and the spin columns were further centrifuged at full speed for 1 min to remove residual
RPE. The columns were then transferred to 1.5 ml collection tube. To elute the RNA, 30 l of
RNase-free water was added directly onto silica gel membrane and incubated at room temperature for 1-2 min before centrifugation at ≥ 9279 g (10000 rpm) for 1 min. Eluted RNA was quantified by measuring the absorbance at 260 nm using a NanoDrop instrument and stored at -80oC.
2.2.3.2 cDNA synthesis cDNA synthesis was carried out using Quanta bioscience qscript cDNA synthesis kit. 0.5 g RNA was reverse transcribed into cDNA in 20 l reaction mixture containing 1 × Moloney Murine
Leukemia Virus (MMLV) reverse transcriptase, 1 mM each dNTPs [dATP, dCTP, dTTP, dGTP],
1 U RNase inhibitor and 5 ng random hexamers plus oligo (dT). Reaction cycling parameters for
Eppendorf thermocycler were 70oC for 5 min, 22oC for 5 min, 42oC for 30 min and 90oC for 4 min. The resultant cDNA was then diluted 1:4 in Rnase free water and stored at -80oC.
49 2.2.3.3 SYBR green real-time PCR
Real-time PCR (RT-PCR) was carried out using ABI 7900HT or StepOnePlus™ instruments. Each
RT-PCR reaction contained 2.5 µl of cDNA plus 5 l of SYBR greenER MasterMix or Fast SYBR green MasterMix, 2.3 l of water and 2.5 ng of each of the appropriate forward and reverse primers in a 10 l of final reaction volume. Relative cDNA concentrations were obtained from standard curves generated by a serial dilution of stimulated cDNA sample analysed at the same time as experimental samples. Amplification conditions were: 50oC for 2 min, 95oC for 10 min then 40 cycles of 95oC for 15 s and 60 or 70oC for 1 min. Primer specificity was assessed using dissociation
(melt) curve analysis: 95oC for 15 s, 60oC for 20 s followed by ramping to 95oC over 20 min. The specificity of primers was indicated by a single peak in the change of fluorescence with temperature plot. See Table A3 for primer sequences.
2.2.3.4 Analysis of unspliced nuclear RNA
Unspliced nuclear RNA, or nascent transcript, accumulates transiently in the nucleus following transcriptional activation and may be measured as a surrogate of transcription rate (346, 347).
Unspliced nuclear RNA was analyzed using SYBRgreen primers that crossed the exon 3/intron 4 junction for TNF and exon 1/intron 1 junction for IRF1. Since the primer sets detect both unspliced
RNA and genomic DNA, the signal due to the contaminating genomic DNA was assessed in each sample. Each RNA sample was subjected to reverse transcription, both in the absence and the presence of reverse transcriptase. The presence of an amplification product in the reverse transcription-negative samples was attributed to genomic DNA contamination and samples with greater than 10% genomic contamination for U6 were excluded from further analysis. RNA
50 extraction, cDNA synthesis, and SYBR green RT-PCR were carried out as described above. See
Table A3 for primer sequences.
Since unspliced GAPDH RNA was down-regulated by TNF treatment (Newton R. unpublished data), small nuclear RNA U6 was used as the housekeeping gene for nuclear RNA normalisation.
Unspliced U6 RNA expression did not change upon stimulation of cells (Newton R. unpublished data). U6 is RNA polymerase III dependent small non-coding RNA (snRNA), and a component of the spliceosome that is expressed in the nucleus (348).
2.2.4 Enzyme-linked immunosorbent assay (ELISA)
Cell supernatants were collected and the cellular debris were removed by centrifugation at 2320 g
(5000 rpm) for 2 min at 4oC. The supernatants were then transferred to fresh 1.5 ml Eppendorf tubes and stored at -20oC. ELISA for CSF2, CXCL1, IL6, IL8, TNF and CXCL10 was carried out using R&D systems DuoSet ELISA kits according to the manufacturer’s guidelines. Flat bottomed
96-well ELISA plate was coated using 100 l of capture antibody. The plate was then incubated overnight at room temperature and washed 3 times with washing buffer (PBS-0.1% v/v Tween
20). After blocking with reagent diluent (PBS containing 1 % w/v BSA) for 1 h, the plate was again washed 3 times. 100 l of cell supernatants or standards was then loaded to the plate and incubated for 2 h at room temperature. Following further 3 washes, 100 l of biotinylated detection antibody was added to the plate and incubated for another 2 h. The plate was then washed 3 times and 100 l of streptavidin horseradish peroxidise was added. After incubating for further 20 min, the plate was washed again 3 times and 100 l of H2O2/tetramethylbenzidine substrate solution was added. The colour reaction was developed by incubating the plate at 37oC in the dark. The development reaction was stopped by adding 50 l of 2N H2SO4 to each well and sample
51 absorbance was read at 450 nm on a FLUOstar optima plate reader. Protein concentrations in the samples were determined through interpolation of a linear standard curve of known protein concentrations.
For HBE cells, TNF release was determined following concentration of 900 μl of cell supernatant to less than 100 μl using Corning Spin-X® UF 500 Concentrator 5000 columns. Concentrated supernatants were adjusted to final volume of 110 l using 1% w/v BSA/PBS, and ELISA for TNF was performed using 100 l of concentrated supernatant as described above. Cell-associated TNF in A549 cells was detected by lysing the cells in 100 l of 1 × firefly luciferase assay buffer containing 1 × CompleteTM protease inhibitor mixture and phosphatase inhibitors (50 mM NaF, 2 mM Na3VO4 and 20 mM Na4P2O7). ELISA for TNF was carried out using 50 l of cell lysates diluted with 50 l of 1% w/v BSA/PBS. Standard curves were generated using the same firefly luciferase assay buffer and ELISA was carried out as described above.
2.2.5 Chromatin Immunoprecipitation (ChIP) assay
ChIP was performed to study the interaction between the transcription factor, IRF1, binding to inflammatory gene promoter sites (protein/DNA interaction) following differential cell treatment.
2.2.5.1 Cell fixation
Following desired treatment of A549 cells, grown to confluence in 100 mm dishes, the cells were fixed to cross-link and preserve protein/DNA interactions. Cross-linking was achieved by adding
500 l of fixation solution (0.75% v/v formaldehyde: 375 l 16% formaldehyde + 125 l of double distil water) directly to the culture medium and incubating for 10 min at room temperature with gentle rocking on an orbital shaker. Formaldehyde was quenched by adding 405 l of 2.5 M
52 glycine (final concentration 125 mM in total of 8 ml solution) and incubating for 5 min at room temperature with gentle rocking on an orbital shaker. The fixation solution was poured off and the cells were further washed with 5 ml of ice-cold 1 × HBBS for 5 min at room temperature with gentle rocking on an orbital shaker. HBSS was aspirated completely from the dish and 4 ml of ice- cold immunoprecipitation (IP) lysis buffer (5 mM PIPES at pH 8.0, 1 mM EDTA, 85 mM KCl,
5% v/v glycerol, 0.5% v/v NP-40) supplemented with protease inhibitor cocktail (PIC) was added.
The cells were scraped from the dish, collected in a pre-chilled 15 ml conical tube and lysed by nutating for 2 h at 4°C. The nuclei were collected by centrifugation (600 g for 5 min at 4°C) and resuspended in 2 ml of IP lysis buffer supplemented with PIC. The lysed cells were then passed through a 5 ml syringe with 30G needle to release the nuclei completely and collected in the same chilled 15 ml conical tubes. The cells were further centrifuged (600 g for 5 min at 4°C), supernatants were discarded and nuclei were resuspended in 300 l of ice-cold radioimmunoprecipitation assay (RIPA) buffer (1 × PBS, 1 mM EDTA, 150 mM NaCl, 5% v/v glycerol, 0.5% w/v sodium deoxycholate, 0.1% w/v SDS, 1% v/v NP-40) supplemented with PIC.
The nuclei were either stored at -80°C or the protocol was continued.
2.2.5.2 Chromatin shearing by sonication
Chromatin sheared to a size of 200-500 bp was used for ChIP experiments. Frozen nuclei samples were thawed on ice and sonicated with a Diagenode Bioruptor at high power in 30 sec bursts separated by 30 sec incubations in ice-cold water maintained at 4°C for a total of 30 min. Lysates were cleared by centrifugation at maximum speed for 15 min at 4°C on a table-top centrifuge. Of
300 l sheared nuclei supernatants, 270 l of supernatants were transferred to a fresh pre-chilled
1.5 ml Eppendorf tube and used for immunoprecipitation. The remaining 30 l of supernatants
53 was transferred to a separate pre-chilled 0.2 ml PCR tube, stored at -80oC and used as input DNA to check the sonication efficiency and for the validation of ChIP primers.
In order to check the sonication efficiency, 90 l of cross-link reversal solution (TE (Tris-EDTA) containing 0.7% w/v SDS, 200 g/ml proteinase K and 5 M NaCL) was added to 10 l of input
DNA and cross-links were reversed for 3 h at 55°C followed by 16 h at 65°C using an Eppendorf thermal cycler. Input DNA was then purified using ChIP DNA clean and concentrator kit (Zymo
Research) using 7 volumes of DNA binding buffer, followed by elution in 50 l elution buffer.
Cleaned input DNA was then run on 1 % agarose gel for 45 min-1 h at 80 V and sizes of sheared
DNA fragments were visualised using a gel imager.
2.2.5.3 Immunoprecipitation of sheared chromatin
After the samples had been subject to shearing by sonication, the immunoprecipitation reaction was set up. Protein G magnetic Dynabeads were used for immunoprecipitation. Beads were mixed thoroughly by vortexing for 30 s on the vortex and sufficient quantities of beads (100 l/ IP reaction) were transferred to a pre-chilled 1.5 ml Eppendorf tube. Beads were then pelleted by placing the tubes on magnetic stand. The liquid was removed by pipetting without disturbing the beads and the beads were then washed quickly two times with ice-cold 500-600 l of RIPA buffer, with resuspending the beads each time. After the final wash, the beads were resuspended in RIPA containing 1 × PIC and BSA (5 mg/ml) at original volume (i.e. if you have started with 100 μl beads, resuspend them in 100 μl). To this, 12 g of IRF1 (C-20, SC-497) antibody was added and nutated overnight at 4°C.
54 Antibody-bound beads were pelleted with magnetic stand and washed quickly two times with ice- cold 500-600 l of RIPA buffer, with resuspending the beads each time. After the final wash, the beads were resuspended in RIPA containing 1 × PIC and BSA (1 mg/ml) at original volume. 100
l of antibody-bound beads were then transferred to each tube containing 270 l of sheared chromatin DNA, thawed earlier on ice, and nutated overnight at 4°C.
2.2.5.4 Washing and cross-link reversal of ChIP DNA
Beads were collected using a magnetic stand and subjected to four washes with ice-cold RIPA buffer containing 500 mM NaCl, followed by 4 washes with ice-cold LiCl buffer (20 mM Tris at pH 8.0, 1 mM EDTA, 250 mM LiCl, 0.5% v/v NP-40 and 0.5% w/v sodium deoxycholate). After the final wash, beads were pelleted using magnetic stand and the liquid was removed from each tube. The cross-link was then reversed using Eppendorf thermal cycler. In order to reverse the cross-link, 100 l of cross-link reversal solution (TE (Tris-EDTA) containing 0.7% w/v SDS, 200
g/ml proteinase K and RIPA w/500 mM NaCL) was added to antibody-bound beads at room temperature and the samples were transferred to a fresh 0.2 ml PCR tube. The conditions for reversing the cross-link were: 3 h at 55°C followed by 16 h at 65°C.
2.2.5.5 ChIP DNA clean-up
After reversing of the cross-links, ChIP DNA was purified using ChIP DNA clean and concentrator kit (Zymo Research). Cross-linked reversed antibody-bound beads were pelleted and the supernatants were transferred to a fresh 1.5 ml Eppendorf tube. To this, 7 volumes of DNA binding buffer was added, mixed well before transferring to Zymo-Spin columns and centrifuged at ≥ 9279 g (10000 rpm) for 30 s. The flow through was discarded and the columns were then washed twice using 250 l of washing buffer by centrifugation at ≥ 9279 g (10000 rpm) for 30 s. To elute the
55 ChIP DNA, 50 l of elution buffer was added directly to the columns matrix and incubated at room temperature for 1-2 min before centrifugation at ≥ 9279 g (10000 rpm) for 30 s. Eluted ChIP DNA was then further used for PCR or stored at -20°C.
2.2.5.6 SYBR green PCR using ChIP DNA qPCR amplification and SYBR green detection method was employed to analyse the DNA obtained by ChIP. IRF1 occupancy for a given region under a specific experimental condition was measured as the difference between the CT value for the specific region relative to the geometric mean of the CT values for three negative control regions (hMYOD1, hOLIG3, hMYOG) not predicted to occupy IRF1. Amplification of diluted input DNA generated similar CT values for control and test regions primers. Assays were generally performed in biological replicate and repeated at least 4 times with qualitatively similar results. Primer sequences are shown in Table
A4. PCR conditions were same as described earlier in section 2.2.3.3.
2.2.6 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay
MTT assay was used to assess the viability of cells following various treatments. This assay is based on the ability of a mitochondrial dehydrogenase enzyme, present in viable/proliferating cells, succinate dehydrogenase, to cleave the tetrazolium rings of MTT. MTT is pale yellow in color and is converted to dark blue/purple formazan crystals upon cleavage by the mitochondrial dehydrogenase enzyme. The number of viable cells is directly proportional to the amount of formazan product/blue purple color in the wells (349).
Following the desired treatments of A549 cells in a 12-well plate, the supernatant was aspirated,
200 μl/well of MTT reagent (1 mg/ml in HBSS) was added and incubated at 37°C for 30 min. The
MTT reagent was aspirated from the wells and the formazan crystals were then solubilised by the
56 addition of DMSO (200 μl/well). 100 μl of each sample was then transferred into a 96 well plate and the colour was quantified by determining the optical density units (ODU) at 584 nm using a
FLUOstar optima plate reader. Percent viability of the cells was calculated by comparing to medium control using the following formula:
% viability = ODU (sample) × 100 ODU (medium control)
2.2.7 Data presentation and statistical analyses
GraphPad Prism 5 software was used for all statistical analyses. All graphical data are plotted as mean ± S.E. and normalised to either GAPDH (protein and cytoplasmic RNA) or U6 (unspliced nuclear RNA) expression as indicated. One-way ANOVA with a Bonferroni post-test was used for comparing 5 or fewer comparisons. Since the Bonferroni post-test gives high and increasingly inappropriate false negative rates (i.e. type II or β error) for greater than five comparisons, ANOVA with Newman-Keuls multiple comparison test was used for greater than five comparisons, as is recommended for greater power in hypothesis testing (Prism 5, Graphpad Software). ANOVA with a Dunnett's post-test was used for comparisons against a single control column. Two-tailed, paired Student t test was used for comparing two treatment groups. ChIP data were analyzed by
Friedman test with Dunn’s post-test. Significance between groups was assumed as follows, P <
0.05 (*), P < 0.01 (**), P < 0.001 (***).
57 Chapter 3 : Feedback control of inflammatory gene expression by the mitogen-activated protein kinase (MAPK) phosphatase, DUSP1, and regulation by dexamethasone
Data presented in this chapter have been published:
Shah S, King EM, Chandrasekhar A, Newton R. Roles for the mitogen-activated protein kinase (MAPK) phosphatase, DUSP1, in feedback control of inflammatory gene expression and repression by dexamethasone. J. Biol. Chem. 2014;289(19): 13667-13679
Copyright Journal of Biological Chemistry.
This work was reprinted with permission based on the J. Biol. Chem. copyright permission policy.
King EM contributed to Fig. 3.1 and 3.2
Chandrasekhar A contributed to Fig. 3.2
58 3.1 Rationale
In a previous study, King et al. identified 11 IL1B-induced inflammatory mRNAs whose expression was not reduced by the translational blocker, cycloheximide, yet their repression by dexamethasone was cycloheximide-sensitive (273). These data may indicate that glucocorticoid- dependent gene expression is important in the repression of these inflammatory genes. In addition, a NR3C1 receptor antagonist and siRNA-mediated knock-down of NR3C1 attenuated the dexamethasone-dependent repression of these 11 IL1B-induced inflammatory mRNAs (273), further suggesting that the repression of these 11 IL1B-induced inflammatory mRNAs by glucocorticoid is NR3C1 dependent. In this regard, glucocorticoid-induced expression of DUSP1 plays an important role in the repression of MAPK signalling by glucocorticoids and may explain the gene expression-dependent repression by glucocorticoids (114, 291, 350). Indeed, DUSP1 expression is strongly, but transiently, induced by inflammatory stimuli and a clear role for DUSP1 in feedback inhibition of MAPKs and the expression of many inflammatory genes is established
(303, 304, 351-353). Similarly, DUSP1 is implicated in the glucocorticoid-dependent repression of CXCL1 and IL6 from human airway smooth muscle cells, and IL8 from human airway epithelial cells (354-356). However, glucocorticoid-dependent repression of inflammatory gene expression in DUSP1-/- mice was gene specific (281). For example, glucocorticoid-induced repression of
CSF2 and IL1A was strongly attenuated, but the repression of TNF, IL1B and PTGS2 was only partially attenuated (281). In addition, even though the expression of inflammatory gene expression often depends on MAPKs, a role for glucocorticoid-induced DUSP1 in the repression of inflammatory gene expression can be difficult to demonstrate (114, 283). Therefore, in the current chapter, possible repressive roles of DUSP1 that is induced by IL1B and IL1B plus the synthetic glucocorticoid, dexamethasone, is examined. Since pulmonary A549 cells, like primary
59 bronchial epithelial cells, show glucocorticoid-dependent repression of inflammatory gene expression, they have been selected for this study (273).
3.2 Hypothesis
The hypothesis being tested in this chapter is that glucocorticoid-induced DUSP1 may play an important role in the repression of inflammatory gene expression by glucocorticoids.
3.3 Results 3.3.1 Effect of dexamethasone on IL1B-induced MAPK activation and DUSP1 expression
Effect of IL1B, in the presence and absence of the synthetic glucocorticoid, dexamethasone, on the activation of p38, ERK and JNK MAPK pathways was examined at different time points. The phosphorylation, as assessed by T-x-Y motif, of the p38, ERK and JNK MAPK cascades was robustly activated by IL1B. Co-treatment with dexamethasone repressed all three MAPK pathways at 1, 2 and 6 h post-treatment (Fig. 3.1A). Even though the repression by dexamethasone mainly occurred at, and after, 1 h, the peak of IL1B-induced MAPK activity, at 30 min, was weakly affected by dexamethasone, and in fact the repressive effect of dexamethasone was variable at this time, with no repression in some analyses and partial repression in others. Thereafter, the repression of IL1B-induced MAPK activation by dexamethasone enhanced gradually with time and the repression was maximum at 6 h post-treatment.
Dexamethasone-induced repression of MAPKs was correlated with the enhanced expression of
DUSP1 protein. DUSP1 was strongly induced by IL1B that reached a peak at 1 h, before declining at 2 h, and reaching basal levels by 6 h post-treatment (Fig. 3.1B). Following IL1B plus dexamethasone-treatment, DUSP1 protein was induced within 30 min. In the case of dexamethasone alone, DUSP1 was induced at 1 h and this was further enhanced by co-treatment
60 with IL1B. A similar, but lesser, enhancing effect of IL1B on dexamethasone-induced DUSP1 expression was observed at 2 h. At 6 h, DUSP1 expression was predominantly dexamethasone- dependent with no significant enhancement by IL1B (Fig. 3.1B).
A time (h) ¼ ½ 1 2 6 Dex + + + + + + + + + + IL1B - - + + - - + + - - + + - - + + - - + + P-ERK P-p38 P-JNK
GAPDH B time (h) ¼ ½ 1 2 6 Dex + + + + + + + + + + IL1B - - + + - - + + - - + + - - + + - - + + DUSP1 GAPDH 2.0 *** 1.5 *** * 1.0 *** ** ** ** *** *** DUSP1 ***
/GAPDH 0.5 * *** * 0.0
Figure 3.1. Effect of dexamethasone and IL1B on MAPK phosphorylation and DUSP1 expression. A, A549 cells were either not stimulated or stimulated with IL1B (1 ng/ml), dexamethasone (Dex 1 μM) or a combination of the two as indicated. Cells were harvested after 0.15, 0.5, 1, 2 or 6 h and total proteins were prepared for western blot analysis of phospho-ERK (P-ERK), phospho-p38 (P-p38), phospho-JNK (P-JNK) and GAPDH. Blots representative of at least 4 such experiments are shown. B, As in A, cells were harvested at the times indicated for western blot analysis of DUSP1 and GAPDH. Following densitometric analysis, data (N = 9 - 11) were normalized to GAPDH and plotted as means ± S.E. Significance, using ANOVA with a Bonferroni's multiple comparison test is indicated. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
61 3.3.2 Effect of dexamethasone on IL1B-induced gene expression
Treatment with IL1B enhanced the expression of 11 inflammatory mRNAs (official human genome nomenclature committee symbol with other commonly used gene symbols in brackets);
CCL2 (MCP1), CCL20 (MIP3α), CSF2 (GM-CSF), CXCL1 (GROα), CXCL2 (GROβ, MIP2α),
CXCL3 (GRO, MIP2β), IL6 (IL-6), IL8 (IL-8, CXCL8), ISG20 (CD25), OLR1 (LOX1) and
PTGS2 (COX-2), and these inflammatory genes were repressed by dexamethasone in a cycloheximide-sensitive manner (Fig. 3.2A & B) (273). IL1B-induced expression of CXCL1, IL8 and IL6 increased gradually over 6 h. Conversely, CXCL2 mRNA expression reached a peak at 1 h and thereafter declined by 6 h. In respect of CCL2, CCL20, CXCL3 and PTGS2 mRNAs, the expression was enhanced from 1 h to 2 h and maintained at this level for up to 6 h. conversely,
CSF2 mRNA was highly induced at 1 h, reached a maximum level at 2 h, and reduced by 6 h (Fig.
3.2A). Since these kinetics are consistent with a rapid induction of mRNA expression, these genes are considered immediate/early response genes whose expression was induced by IL1B and not affected by the translational blocker, cycloheximide (273).
In marked contrast, ISG20 and OLR1 mRNAs were not induced by IL1B at 1 h. At 2 h, there was only a modest increase, and at 6 h, the mRNA expression was substantially enhanced by IL1B
(Fig. 3.2B). Even though these kinetics were more consistent with late phase/secondary response genes, the expression of these mRNAs was insensitive to cycloheximide in the presence of IL1B
(114). Nevertheless, because the timing of induction for these two mRNAs was different from the nine genes showing classical primary response kinetics, they were simply referred to as delayed or secondary response genes.
62 A 2 2 4 CCL2 CCL20 CSF2 1 1 2 *** ** ** *** *** 0 * 0 0 * *** 2 1.5 1.5 CXCL1 CXCL2 CXCL3 NS 1.0 1.0 1 IL1B 0.5 0.5 *** *** *** ** IL1B + Dex ** ** *** 0 0.0 0.0
gene/GAPDH 1.5 2 4 IL6 IL8 PTGS2 1.0 2 1 0.5 *** *** **** *** *** 0 *** 0.0 ** 0 *** 0 2 4 6 0 2 4 6 0 2 4 6 time (h) time (h) time (h) B C 6 ISG20 100 N = 9 genes CCL2 4 100 CCL20 *** 2 CSF2 *** ** *** CXCL1
+Dex 0 +Dex
at each time) at eachtime)
CXCL2 50 ***
50 3 OLR1 CXCL3
IL-1 IL6 IL-1 2 gene/GAPDH IL8 *** 1 PTGS2 IL-1 of %
(% of(% IL-1 *** ( ** 0 0 0 0 2 4 6 0 2 4 6 1 2 6 time (h) time (h) time (h) D 200 30000 CSF2 CXCL1 150 20000 100 time (h) 1 2 6 10000 *** Dex + + + + + + 50 *** *** *** IL1B - - + + - - + + - - + + 0 0 1000 4000 PTGS2
(pg/ml) Release IL6 IL8 750 3000 GAPDH 500 2000 250 1000 * *** *** 0 *** 0 *** *** 0 2 4 6 0 2 4 6 time (h) time (h)
Figure 3.2. Effect of dexamethasone and IL1B on inflammatory gene expression. A & B, A549 cells were either not stimulated (NS) () or stimulated with IL1B (1 ng/ml) () or IL1B plus dexamethasone (1 μM) (IL1B + Dex) () as indicated. Cells were harvested after 1, 2 or 6 h for real-time PCR analysis of the indicated genes and GAPDH. Data (N = 11) were normalized to GAPDH and are plotted as means ± S.E. Significance was tested relative to time-matched IL1B- treated samples using ANOVA with a Bonferroni post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. Genes were grouped according to expression pattern with apparent ‘early-phase’ mRNAs in A and ‘delayed response’ mRNAs in B. C, The effect of IL1B + dexamethasone for each inflammatory mRNA in panel A is plotted as a percentage of IL1B for each time (left panel). The overall effect of dexamethasone in the presence of IL1B for the 9 genes combined is plotted as means ± S.E. (right panel). D, Cells were treated as in A and the supernatants harvested after 1, 2 or 6 h for cytokine/chemokine release measurement. Data (N = 6) expressed as pg/ml are plotted as means ± S.E. (left panel). Significance, relative to IL1B-treated samples was tested using ANOVA with a Bonferroni post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. Cells were harvested at the times indicated for western blot analysis of PTGS2 and GAPDH (right panel). Blots representative of at least 4 such experiments are shown.
63 The mRNA expression of all IL1B-induced 11 inflammatory genes was significantly repressed by dexamethasone at 6 h (Fig. 3.2A & B). This effect was more modest at 1 and 2 h, and CCL20 showed no repression at 1 h. Dexamethasone-dependent repression of the 9 primary response genes (Fig. 3.2A) increased progressively with time (Fig. 3.2C). However, this was not the case with respect to ISG20 and OLR1, where there was a little mRNA accumulation at 1 or 2 h. These findings were consistent with the previous data showing that the repression of these 11 mRNAs was prevented or reduced by cycloheximide, and the fact that time is necessary for the synthesis of the glucocorticoid-induced gene-products mediating repression (114).
The expression of CSF2, CXCL1, IL6 and IL8 released into the supernatant along with the cellular expression of PTGS2 was also analysed. In each case, there was a time-dependent enhancement by IL1B and dexamethasone co-treatment produced a significant repression at all times (Fig.
3.2D).
3.3.3 Effect of MAPK inhibitors on IL1B-induced gene expression
To explore the role of p38, ERK and JNK MAPK pathways in the expression of IL1B-induced 11 inflammatory mRNAs, the p38 inhibitor, SB203580, and the MAPK/ERK kinase (MEK) 1/2 inhibitor, U0126, were tested along with JNK inhibitor, JNK inhibitor 8. JNK inhibitor 8 is a recently identified, highly selective, cell permeable inhibitor, of the JNK MAPK pathway with a potency of ~1 µM on cellular JUN phosphorylation (357). A549 cells were treated with increasing concentration of JNK inhibitor 8 prior to stimulation with IL1B and western blot analysis for phosphorylation of cellular JUN was carried out. Treatment with IL1B significantly increased the phosphorylation of JUN and this was prevented by JNK inhibitor 8 (EC50 = 0.8 µM). Near maximal
64 (83%) repression was achieved at 10 µM, and hence, this concentration was used for subsequent analyses (Fig. 3.3).
time (h) ½ JNK-IN-8 IL1B - + + + + + + + P-JUN JUN
100
IL1B) 50
JUN/JUN (%
- ***
P *** 0 *** -7 -6 -5 -4 NSIL-1 Log [JNK-IN-8 (M)]
Figure 3.3 Effect of JNK inhibitor 8 on IL1B-induced JUN phosphorylation. A549 cells were either not stimulated (NS), treated with IL1B (1 ng/ml) or pre-treated with increasing concentrations of JNK inhibitor 8 (JNK-IN-8) for 30 min prior to IL1B stimulation. Cells were harvested after 0.5 h and total proteins prepared for western blot analysis of phospho-JUN (P-JUN) and JUN. Representative blots are shown. Following densitometric analysis, data (N = 5), normalized to JUN, were expressed as a percentage of IL1B and are plotted as mean ± S.E. Significance, using ANOVA with a Dunnett's post test is indicated. ***, p < 0.001.
Similar to JNK inhibitor 8, SB203580 and UO126 were also used at a maximally effective concentration (10 µM) as determined previously by analysis of substrate phosphorylation and functional responses in A549 cells (358, 359). SB203580 produced a partial and time-dependent inhibition of most of the IL1B-induced mRNAs (Fig. 3.4). However, while IL1B-induced CXCL2 and ISG20 were unaffected, IL6 was strongly repressed following SB203580 treatment. In contrast, UO126 and JNK inhibitor 8 produced moderate to weak repressive effects (Fig. 3.4).
65 time (h) 1 2 6 1 2 6 1 2 6 JNK-IN-8 + + + + + + + + + UO126 + + + + + + + + + SB203580 + + + + + + + + + IL1B -++++ -++++ -++++ -++++ -++++ -++++ -++++ -++++ -++++ 8 8 CCL2 CCL20 2.0 CSF2 6 6 1.5 * * 4 * 4 * 1.0 * 2 2 0.5 * * * * 0 0 * 0.0
CXCL1 4 CXCL2 3 CXCL3 4 3 2 * * * 2 * 2 * * * 1 1 * gene/GAPDH 0 0 0 IL6 1.5 IL8 2.0 PTGS2 2 * * 1.5 1.0 * * ** * * ** * ** ** * * 1 * * ** * 1.0 ** * * ** 0.5 * * * * * * ** ** 0.5 * *** * * * 0 * 0.0 0.0
time (h) 1 2 6 1 2 6 JNK-IN-8 + + + + + + UO126 + + + + + + SB203580 + + + + + + IL1B -++++ -++++ -++++ -++++ -++++ -++++ 8 8 ISG20 OLR1 6 6 4 4 ** gene/ * ** GAPDH 2 * 2 * * ** * ** 0 0 *** Figure 3.4 Effect of MAPK inhibitors on the mRNA expression of inflammatory genes. A549 cells were either not stimulated, treated with IL1B (1 ng/ml) or pre-treated with UO126, SB203580 or JNK inhibitor 8 (JNK-IN-8) each at 10 µM for 30 min prior to IL1B stimulation. Cells were harvested after 1, 2 or 6 h for real-time PCR analysis of the indicated genes and GAPDH. Data (N = 8) were normalized to GAPDH and plotted as means ± S.E. ‘Early-phase’ genes are shown in top panel and ‘delayed response’ genes in bottom panel. Significance, relative to time-matched IL1B-treated samples, was tested by ANOVA with a Dunnett's post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
66 At the level of cytokine production, IL1B-induced release of CSF2, CXCL1, IL6 and IL8 was significantly reduced by SB203580 at 2 and 6 h post-treatment (Fig. 3.5). Even though the release of CSF2 and CXCL1 at 2 and 6 h was robustly inhibited, the release of IL6 and IL8 was only partially affected by UO126 (Fig. 3.5). JNK inhibitor 8 also produced a significant attenuation of
CSF2 and CXCL1 release, but showed a partial, non-significant, inhibition of IL6 and IL8 at either time point (Fig. 3.5).
time (h) 2 6 JNK-IN-8 + + UO126 + + SB203580 + + IL1B -++++ -++++ 90 10 60 5
***
**
*** CSF2 30
***
***
*** 0 0 5000 100 2500
*** 50 ***
***
***
***
***
CXCL1 0 0
(pg/ml) 15 450 Release Release
10 300 *
IL6 5 ** 150
*** 0 *** 0 1500 10000
**
1000 *
**
IL8 5000 500 * 0 0 Figure 3.5 Effect of MAPK inhibitors on the protein expression of inflammatory genes. A549 cells were either not stimulated, treated with IL1B (1 ng/ml) or pre-treated with UO126, SB203580 or JNK inhibitor 8 (JNK-IN-8) each at 10 µM for 30 min prior to IL1B stimulation. Supernatants were harvested after 2 or 6 h for measurement of cytokine/chemokine release. Data (N = 8) expressed in pg/ml are plotted as means ± S.E. Significance, relative to time-matched IL1B-treated samples, was tested by ANOVA with a Dunnett's post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001
Inflammatory stimuli-induced DUSP1 expression is, in part, dependent on MAPK pathways, and inhibition of a single MAPK pathway may reduce or prevent DUSP1 expression (112, 286, 360).
Since DUSP1 inhibits all three MAPK pathways, inhibition of any one MAPK pathway could
67 correspondingly enhance the activation of the remaining two MAPK pathways. Equally, cross- feedback control by p38 MAPK down-regulates the ERK pathway, which may therefore be enhanced in the presence of a p38 inhibitor (361). Furthermore, dexamethasone also produces the repression of all three MAPKs together (Fig. 3.1A). Thus, the effect of simultaneous inhibition of three MAPK pathways was investigated on inflammatory gene expression (Fig. 3.6). The expression of the 9 primary response genes (shown in Fig. 3.2A) was strongly induced by IL1B and, in each case, this was significantly and substantially inhibited by the combined MAPK inhibitors (Fig. 3.6A). A similar, almost complete, repression was also observed for ISG20 and
OLR1 (Fig. 3.6B). Equally, IL1B-induced release of CSF2, CXCL1, IL6 and IL8 was also completely repressed by MAPK inhibitors in combination (Fig. 3.6C & D). In the case of PTGS2 protein expression, it was weakly induced by IL1B at 2 h and moderately reduced by the individual kinase inhibitors (Fig. 3.6D). By 6 h, there was robust PTGS2 expression and this was significantly attenuated by each MAPK inhibitor. In contrast, IL1B-induced PTGS2 expression, at 2 and 6 h, was almost completely blocked by the combined MAPK inhibitors (Fig. 3.6D). The MAPK inhibitors, alone or in combination, had no effect on cell viability as measured by MTT assay
(Table 3.1).
68 A 15 1.5 3 CCL2 CCL20 CSF2 10 1.0 2 5 *** 0.5 1 *** *** ** 0 0.0 0 ** * 6 CXCL1 1.2 CXCL2 1.2 CXCL3 NS 4 0.8 0.8 IL1B 2 0.4 0.4 *** IL1B + UO *** *** *** *** ** *** 0 0.0 0.0 + SB + J8
gene/GAPDH 1.5 1.5 2.0 IL6 IL8 PTGS2 1.5 1.0 1.0 1.0 0.5 0.5 0.5 * ** ** ** *** 0.0 ** *** 0.0 0.0 0 2 4 6 0 2 4 6 0 2 4 6 time (h) time (h) time (h)
B 10 15 8 ISG20 OLR1 6 10 4 5 gene/ 2 *** GAPDH *** 0 0 0 2 4 6 0 2 4 6 time (h) time (h) C D time (h) 2 6 150 5000 CSF2 CXCL1 JNK-IN-8 + + + + 100 UO126 + + + + 2500 50 SB203580 + + + + *** *** - + + + + + - + + + + + 0 ** 0 IL1B 200 15000 PTGS2
(pg/ml) Release 150 IL6 IL8 10000 GAPDH 100 50 5000 0.3 *** 0 0 *** 0.2 * 0.1 ** 0 2 4 6 0 2 4 6 *** ** ** time (h) time (h) PTGS2 0.0 *** /GAPDH Figure 3.6 Effect of MAPK inhibitors on IL1B-induced inflammatory gene expression. A & B, A549 cells were either not stimulated (NS) (), treated with IL1B (1 ng/ml) () or pre-treated with a combination of UO126, SB203580 plus JNK inhibitor 8 each at 10 µM (UO+SB+J8) for 30 min prior to IL1B stimulation (). Cells were harvested after 1, 2 or 6 h for real-time PCR analysis of the indicated genes and GAPDH. Data (N = 4) were normalized to GAPDH and plotted as means ± S.E. ‘Early-phase’ genes are shown in A and ‘delayed response’ genes in B. C, Cells were treated as in A and the supernatants harvested after 1, 2 or 6 h for the measurement of cytokine/chemokine release. Data (N = 4) expressed in pg/ml are plotted as means ± S.E. D, Cells were treated as in A and harvested at the times indicated for western blot analysis of PTGS2 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 4), normalized to GAPDH, are plotted as means ± S.E. Significance, relative to time-matched IL1B- treated samples, was tested by ANOVA with a Dunnett's post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
69 Table 3.1 Effect of MAPK inhibitors on cell viability.
A549 cells were either not treated, treated with IL1B (1 ng/ml), or pre-treated with 10 μM of either SB203580, UO126, JNK inhibitor 8 or a combination of SB203580, UO126 plus JNK inhibitor 8 each at 10 µM (UO+SB+J8) for 30 min prior to IL1B stimulation for 1, 2 or 6 h. Cell viability was determined by MTT assay. Data (N = 4), expressed as OD584, are showed as means ± S.E. Significance, versus IL1B, using ANOVA with a Dunnett's post test is indicated. However, significance was not achieved for any of the tested samples.
Time (h) Treatment OD value ± S.E. No treatment 0.597 ± 0.1260 IL1B 0.684 ± 0.1738 IL1B + SB203580 0.589 ± 0.1275 1 h IL1B + UO126 0.622 ± 0.1387 IL1B + JNK8 0.614 ± 0.0939 IL1B + UO + SB + J8 0.556 ± 0.1287 No treatment 0.533 ± 0.1111 IL1B 0.531 ± 0.0892 IL1B + SB203580 0.595 ± 0.1263 2 h IL1B + UO126 0.628 ± 0.1483 IL1B + JNK8 0.637 ± 0.1733 IL1B + UO + SB + J8 0.498 ± 0.1108 No treatment 0.485 ± 0.1238 IL1B 0.604 ± 0.1606 IL1B + SB203580 0.525 ± 0.0980 6 h IL1B + UO126 0.484 ± 0.1295 IL1B + JNK8 0.528 ± 0.1084 IL1B + UO + SB + J8 0.523 ± 0.0918
3.3.4 Effect of DUSP1 over-expression on MAPK activation and inflammatory gene expression
DUSP1 over-expression was confirmed by western blotting. Control GFP adenovirus had no effect on DUSP1 expression. IL1B-induced phosphorylation of ERK and p38 was substantially reduced following adenoviral over-expression of DUSP1 (Fig. 3.7A), and was consistent with previous data (114, 283). IL1B-induced JNK phosphorylation was also inhibited following DUSP1 over- expression and, in each case, GFP expressing adenovirus had no effect (Fig. 3.7A).
70 A time (h) 1 Ad-DUSP1 + + Ad-GFP + + IL1B - - - + + + DUSP1 P-ERK P-p38
P-JNK
GAPDH
B time (h) 1 2 6 C time (h) 1 2 6 Ad-DUSP1 + + + + + + Ad-DUSP1 + + + + + + Ad-GFP + + + + + + Ad-GFP + + + + + + IL1B - - - + + + - - - + + + - - - + + + IL1B - - - + + + - - - + + + - - - + + + 1.0 1.0 1.0 * 8 8 8 * * 6 6 6 0.5 0.5 0.5 4 4 4
CCL2 ISG20 2 2 2 0.0 0.0 0.0 0 0 0 * * 1.0 1.0 * 1.0 8 8 8 * 6 6 6
0.5 0.5 0.5 Gene/GAPDH 4 4 4
OLR1 CCL20 2 2 2 0.0 0.0 0.0 0 0 0 1.5 1.5 * 1.5 * 1.0 1.0 1.0 D time (h) 2 6
CSF2 0.5 0.5 0.5 * * Ad-DUSP1 + + + + 0.0 0.0 0.0 Ad-GFP + + + + 2 2 2 ** * ** IL1B - - - + + + - - - + + + * *** 1 1 1 60 100 *** *** CXCL1 ** 40 * 0 0 0 * 50 CSF2 20 ** ** 0 0 1.0 ** 1.0 1.0 * *** ** 400 *** 0.5 0.5 0.5 ** 4000 CXCL2 300 **
0.0 0.0 0.0 200 ** 2000 Gene/GAPDH * CXCL1 100 1.0 * 1.0 0 0 1.0 ** ** ** 60 1000 *** 0.5 * 0.5 0.5 ***
CXCL3 40 Release (pg/ml)Release 0.0 0.0 0.0 IL6 500 *** 20 ** 2 2 *** *** *** 2 0 0
IL6 1 ** 1 1 1500 6000 ** * ** 1000 4000 0 0 0 ***
IL8 *** 1.5 1.5 * 1.5 500 2000 *** 1.0 ** 1.0 1.0 *** 0 0 IL8 ** 0.5 0.5 0.5 time (h) 2 6 0.0 0.0 0.0 Ad-DUSP1 + + + + ** Ad-GFP + + + + ** 2 2 2 * - - - + + + * IL1B - - - + + + 1 * 1 1 PTGS2
PTGS2 0 0 0 GAPDH
Figure 3.7 Effect of DUSP1 over-expression on IL1B-induced MAPK phosphorylation and inflammatory gene expression.
71 Figure 3.7 continued. A, A549 cells were either not infected or infected with Ad5-DUSP1, or Ad5-GFP at a MOI of 10 for 24 h before IL1B treatment (1 ng/ml). After 1 h, the cells were harvested for western blot analysis of DUSP1, phospho-ERK (P-ERK), phospho-p38 (P-p38), phospho-JNK (P-JNK) and GAPDH. Blots representative of at least 4 such experiments are shown. B & C, Cells were treated as in A and harvested at 1, 2 or 6 h for real-time PCR analysis of the indicated genes and GAPDH. Data (N = 4) were normalized to GAPDH and plotted as means ± S.E. Significance relative to time-matched IL1B and Ad5-GFP-treated samples, was tested by ANOVA with a Bonferroni post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. ‘Early-phase’ genes are shown in B and ‘delayed response’ genes in C. D, Cells were treated as in A and the supernatants harvested after 2 or 6 h for cytokine/chemokine release measurement. Data (N = 4) expressed in pg/ml are plotted as means ± S.E. (top panel). Significance, relative to time-matched IL1B and Ad5-GFP-treated samples, was tested by ANOVA with a Bonferroni post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. Cells were also harvested at the times indicated for western blot analysis of PTGS2 and GAPDH (lower panel). Blots representative of at least 4 such experiments are shown.
In respect of the 11 IL1B-induced genes, DUSP1 over-expression produced a significant inhibition
(Fig. 3.7B&C). Equally, the protein expression for CSF2, CXCL1, IL6, IL8 and PTGS2 was also significantly attenuated following DUSP1 over-expression (Fig. 3.7D). The control GFP adenovirus had no effect. Thus, like dexamethasone, DUSP1 over-expression also produced a significant inhibition of all three MAPK pathways and IL1B-induced inflammatory gene expression.
3.3.5 Effect of DUSP1 siRNA on MAPK activation by IL1B in the absence and presence of dexamethasone
The control siRNA targeted to LMNA had no effect on DUSP1 expression at any time (Fig. 3.8A).
Conversely, IL1B or IL1B plus dexamethasone-induced DUSP1 was substantially and significantly inhibited by two separate DUSP1 targeting siRNAs at all times tested (Fig. 3.8B). As described above, IL1B activated all three MAPK pathways and dexamethasone produced a significant repression of MAPKs at 1, 2 and 6 (Fig. 3.9). LMNA siRNA had no effect on MAPK activation induced by IL1B or IL1B plus dexamethasone (Fig. 3.9A, Table 3.2).
72 A time (h) 1 2 6 LMNA siRNA + + + + + + Dex + + + + + + IL1B - + + + + - + + + + - + + + + DUSP1 GAPDH 2 * ** *
1 ***
DUSP1 /GAPDH 0 B time (h) 1 2 6 DUSP1 siRNA 2 + + + + + + DUSP1 siRNA 1 + + + + + + LMNA siRNA + + + + + + Dex + + + + + + + + + IL1B - + + + + + + - + + + + + + - + + + + + + DUSP1 GAPDH 600 *** *** 400 *** *** * *** *** ** * **
200 * * * * (% IL1B IL1B (%+
lamin siRNA) lamin 0
DUSP1/GAPDH Figure 3.8 Effect of LMNA- and DUSP1-targeting siRNAs on IL1B and IL1B plus dexamethasone-induced DUSP1 expression. A549 cells were: A, incubated without or with LMNA-specific siRNA; or B, incubated with LMNA (control) or DUSP1-specific siRNAs. After 24 h, cells were treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Cells were harvested at 1, 2 or 6 h for western blot analysis of DUSP1 and GAPDH. Representative blots are shown. Following densitometric analysis, data in A (N = 7) were normalized to GAPDH and are plotted as mean ± S.E. In B, data (N = 10), normalized to GAPDH, were expressed as a percentage of LMNA siRNA plus IL1B-stimulated cells for each time and plotted as means ± S.E. In each case, significance was tested using ANOVA with a Bonferroni's multiple comparison test is indicated. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
However, in the presence of DUSP1 targeting siRNAs, 1 h post-IL1B treatment, the phosphorylation of all three MAPKs was significantly enhanced relative to LMNA control (Fig.
3.9 B & C). This effect was not seen at 2 or 6 h post-IL1B treatment. Thus, DUSP1 plays a significant role in the feedback control of MAPKs at 1 h, but not at later times.
73 IL1B-induced phosphorylation of ERK, p38 and JNK was significantly reduced by dexamethasone co-treatment at each time (Fig. 3.9A-C). In the presence of two DUSP1 targeting siRNAs the phosphorylation of MAPKs was significantly enhanced at 1 h relative to the IL1B plus dexamethasone-treated control (Fig. 3.9B & C). Thus, IL1B plus dexamethasone-induced DUSP1 reduced MAPK activation. However, while evaluating the role of DUSP1 in dexamethasone- induced repression, it is essential to consider the inhibitory feedback role of DUSP1. Thus, in Fig.
3.9D, the effect of IL1B plus dexamethasone on MAPK phosphorylation was expressed as a percentage of the IL1B-induced MAPK phosphorylation for each siRNA treatment. In this way the repressive effect of dexamethasone can be assessed and compared in the presence and absence of DUSP1.
In the presence of dexamethasone, the phosphorylation of each MAPK was reduced to ~50% of the IL1B plus LMNA control siRNA-treated level at 1 h (Fig. 3.9C & D). However, this repressive effect was significantly and substantially attenuated by the DUSP1-targeting siRNAs (Fig. 3.9D)
(i.e. the percentage MAPK phosphorylation for IL1B plus dexamethasone/IL1B was close to
100%), and further suggested that dexamethasone-dependent repression of IL1B-induced MAPK phosphorylation was prevented by DUSP1 knock-down. Therefore, these data confirm that DUSP1 not only plays a key role in the feedback control of ERK, p38 and JNK MAPKs, but is also responsible for their repression by dexamethasone at 1 h.
74 A B time (h) 1 2 6 DUSP1 siRNA 2 + + + + + + time (h) 1 2 6 DUSP1 siRNA 1 + + + + + + LMNA siRNA + + + + + + LMNA siRNA + + + + + + Dex + + + + + + Dex + + + + + + + + + IL1B - + + + + - + + + + - + + + + IL1B - + + + + + + - + + + + + + - + + + + + + P-ERK P-ERK P-p38 P-p38
P-JNK P-JNK
GAPDH GAPDH
C time (h) 1 2 6 D DUSP1 siRNA 2 + + + + + + time (h) 1 2 6 DUSP1 siRNA 1 + + + + + + DUSP1 siRNA 2 + + + LMNA siRNA + + + + + + DUSP1 siRNA 1 + + + Dex + + + + + + + + + LMNA siRNA + + + IL1B - + + + + + + - + + + + + + - + + + + + + IL1B+Dex + + + + + + + + + 300 * ** ** 200 *** 100 * 100 100 *** * * 100 50 50 50
P-ERK
P-ERK * * * A) 0 0 0 0 150 200 * * * * ** 100 100 *** ** 100 * 100 50 50 P-p38 P-p38 50 * * * 0 0 0 0 P-MAPK/GAPDH 300 * 200 * 150 * **
(% IL1B + LMNA siRNA) + LMNA (% IL1B
* IL1Brelevant + % siRN * Effect ofon Dex P-MAPK 150 100 200 ** *** ( *** 100 100
P-JNK 100 50 * * P-JNK 50 50 ** 0 0 0 0
Figure 3.9 Effect of LMNA- and DUSP1-targeting siRNA on IL1B-induced MAPK phosphorylation. A549 cells were; A, incubated without or with LMNA-specific siRNA, or B, incubated with LMNA (control) or DUSP1-specific siRNAs. After 24 h, cells were treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Cells were then harvested at 1, 2 or 6 h and total proteins were prepared for western blot analysis of phospho-ERK (P-ERK), phospho-p38 (P-p38), phospho-JNK (P-JNK) and GAPDH. Representative blots are shown. Blots representative of at least 6 - 9 such experiments are shown. Densitometric data from A appear in Table 3.2 shown below. C, Following densitometric analysis, data from B were normalized to GAPDH, expressed as a percentage of LMNA siRNA plus IL1B-stimulated for each time and plotted as means ± S.E. Significance was tested using ANOVA with a Newman-Keul multiple comparison test. Significance between: LMNA control siRNA plus IL1B and each of the DUSP1 targeting siRNAs plus IL1B, and the LMNA control plus IL1B plus Dex is shown. Other comparisons are specifically indicated. *, p < 0.05; **, p < 0.01; ***, p < 0.001. D, For each P- MAPK at each time, the effect of IL1B plus dexamethasone expressed as a percentage of IL1B for each of the three individual siRNAs is plotted as a mean ± S.E. The percent IL1B plus dexamethasone/IL1B for the LMNA siRNA is compared with that for each DUSP1-specific siRNAs using ANOVA with a Dunnett's post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
75 These data contrast significantly with effects of IL1B treatment observed at 6 h (Fig. 3.9B-D).
Even though IL1B-induced MAPK activation was reduced relative to earlier times, dexamethasone still produced a significant repression at 6 h (Fig. 3.9A & B). However, this was not affected by the DUSP1-targeting siRNAs, suggesting that the repressive effects of dexamethasone observed at 6 h were not due to DUSP1 (Fig. 3.9B-D). Conversely, the effect of the two DUSP1 siRNAs at
2 h post-IL1B plus dexamethasone treatment was intermediate between the effects at 1 and 6 h.
The phosphorylation of MAPKs induced by IL1B at 2 h was increased, but did not reach significance (Fig. 3.9C). Similarly, there was non-significant losses of dexamethasone-dependent repression following DUSP1 knock-down at 2 h (Fig. 3.9D).
Table 3.2 Densitometry analysis for the effect of LMNA targeting siRNA (LsiRNA) on MAPK phosphorylation.
MAPKs/GAPDH Time Gene Symbol NS IL1B IL1B + LSiRNA IL1B + Dex IL1B + Dex + LsiRNA P-ERK 0.47 ± 0.15 0.83 ± 0.21* 0.81 ± 0.20 0.44 ± 0.13** 0.49 ± 0.12* 1h P-p38 0.086 ± 0.028 0.61 ± 0.11*** 0.69 ± 0.14 0.33 ± 0.086** 0.42 ± 0.10* P-JNK 0.16 ± 0.067 0.52 ± 0.10*** 0.52 ± 0.069 0.25 ± 0.053*** 0.32 ± 0.046** P-ERK 0.39 ± 0.087 0.99 ± 0.24** 1.1 ± 0.29 0.44 ± 0.088* 0.55 ± 0.12* 2h P-p38 0.29 ± 0.14 0.90 ± 0.20** 0.85 ± 0.22 0.41 ± 0.093** 0.45 ± 0.098* P-JNK 0.16 ± 0.029 0.57 ± 0.20** 0.50 ± 0.12 0.24 ± 0.045*** 0.17 ± 0.030** P-ERK 0.60 ± 0.19 1.3 ± 0.49* 1.4 ± 0.45 0.59 ± 0.15* 0.72 ± 0.18* 6h P-p38 0.19 ± 0.11 0.71 ± 0.24*** 0.73 ± 0.25 0.21 ± 0.060** 0.29 ± 0.15 P-JNK 0.24 ± 0.11 0.44 ± 0.056** 0.43 ± 0.083 0.18 ± 0.052*** 0.24 ± 0.061**
3.3.6 Effect of DUSP1 knock-down on IL1B-induced inflammatory gene mRNA expression in the absence and presence of dexamethasone.
The expression of all 11 inflammatory mRNAs was induced by IL1B and this was significantly repressed by dexamethasone. The LMNA siRNA had no effect on IL1B and IL1B plus dexamethasone-induced gene expression at any time (Table 3.3). Therefore, in subsequent analyses, the effects of DUSP1-targeting siRNAs were compared solely to the LMNA control siRNA. The expression of all 9 acute phase mRNAs (shown in Fig. 3.2A) was increased by DUSP1
76 knock-down at 1 h following IL1B treatment (Fig. 3.10A). This effect was significant in respect of one or both DUSP1-targeting siRNAs for CCL2, CSF2, CXCL3, IL6, IL8 and PTGS2.
However, by 2 h, with the exception of IL6, this trend was markedly reduced or not seen post-
IL1B treatment. Conversely, the expression of all 9 acute phase mRNAs produced a general trend toward attenuated expression by DUSP1 knock-down at 6 h post-IL1B treatment. This was significant for CCL2, CCL20, CSF2, CXCL1, CXCL2, CXCL3 and PTGS2 in respect of one or both DUSP1 siRNAs (Fig. 3.10A). IL1B-induced expression of the two delayed response genes,
ISG20 and OLR1, was also enhanced by DUSP1 knock-down (Fig. 3.10C). This was significant for OLR1 at 1 h for both siRNAs and for one siRNA at 2 h. There was no effect of the DUSP1 siRNAs on either gene at 6 h (Fig. 3.10C).
Dexamethasone co-treatment produced a significant repression of IL1B-induced expression of all mRNAs at 2 and 6 h (Fig. 3.10A). IL1B-induced all the mRNAs, with the exception of CCL20, were also repressed by dexamethasone at 1 h post-treatment (Fig. 3.10A). Although this was not significant for CSF2, these data were consistent with Fig. 3.2. In respect of DUSP1-targeting siRNAs, there was a general trend towards higher levels of inflammatory gene expression at 1 h post-treatment. Indeed, the mRNA expression of CCL2, CCL20, CSF2, CXCL1, CXCL2, CXCL3,
IL8 and PTGS2 was enhanced, with at least one DUSP1 siRNA, relative to IL1B plus dexamethasone (plus LMNA siRNA) treated cells (Fig. 3.10A). Even though, ISG20 was not induced by IL1B at 1 h, dexamethasone produced a significant repression of ISG20 and this was inhibited by the DUSP1 siRNAs (Fig. 3.10C). IL1B-induced OLR1 expression was also repressed by dexamethasone, but this was unaffected by DUSP1 knock-down (Fig. 3.10C).
77 Table 3.3 Effect of LMNA targeting siRNA (LsiRNA) on inflammatory gene expression. A549 cells were incubated with LMNA-specific siRNA for 24 h before being treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Cells were harvested after 1, 2 or 6 h for real-time PCR analysis of the indicated genes and GAPDH. Data (N = 11) were normalized to GAPDH and presented as means ± S.E. Significance, relative to time-matched IL1B and IL1B plus LMNA siRNA-treated samples, was tested by ANOVA with a Bonferroni's post test *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Gene Symbol Gene/GAPDH Time NS IL1B IL1B + LSiRNA IL1B + Dex IL1B + Dex + LsiRNA CCL2 0.012 ± 0.0025 0.20 ± 0.042 0.14 ± 0.027 0.11 ± 0.018*** 0.086 ± 0.014*** CCL20 0.0013 ± 0.00041 0.31 ± 0.093 0.26 ± 0.072 0.30 ± 0.074 0.22 ± 0.045 CSF2 0.0014 ± 0.00040 0.46 ± 0.23 0.43 ± 0.21 0.18 ± 0.065 0.24 ± 0.10 CXCL1 0.0072 ± 0.0022 0.27 ± 0.049 0.65 ± 0.060 0.13 ± 0.017*** 0.47 ± 0.083*** CXCL2 0.019 ± 0.0050 0.97 ± 0.20 0.90 ± 0.18 0.85 ± 0.19 0.74 ± 0.18*** 1h CXCL3 0.012 ± 0.0031 0.51 ± 0.079 0.41 ± 0.068 0.32 ± 0.051*** 0.28 ± 0.041*** IL6 0.0033 ± 0.0017 0.78 ± 0.17 0.74 ± 0.16 0.34 ± 0.11*** 0.28 ± 0.090*** IL8 0.0083 ± 0.0029 0.50 ± 0.067 0.45 ± 0.062 0.29 ± 0.042*** 0.26 ± 0.035*** ISG20 0.46 ± 0.089 0.50 ± 0.094 0.48 ± 0.091 0.37 ± 0.070* 0.37 ± 0.082** OLR1 0.016 ± 0.0033 0.082 ± 0.016 0.058 ± 0.0098 0.039 ± 0.0075*** 0.034 ± 0.0060*** PTGS2 0.11 ± 0.023 0.55 ± 0.087 0.45 ± 0.074 0.31 ± 0.048*** 0.23 ± 0.044*** CCL2 0.013 ± 0.0025 1.0 ± 0.14 0.94 ± 0.11 0.46 ± 0.095*** 0.32 ± 0.025*** CCL20 0.044 ± 0.041 0.97 ± 0.12 0.98 ± 0.10 0.68 ± 0.11*** 0.68 ± 0.065*** CSF2 0.0039 ± 0.0023 3.0 ± 0.82 3.3 ± 0.94 1.1 ± 0.31*** 1.2 ± 0.35*** CXCL1 0.0061 ± 0.0016 0.72 ± 0.12 0.57 ± 0.087 0.30 ± 0.050*** 0.38 ± 0.085*** CXCL2 0.013 ± 0.0054 0.91 ± 0.19 0.85 ± 0.18 0.67 ± 0.18 0.62 ± 0.16 2h CXCL3 0.011 ± 0.0058 1.1 ± 0.13 0.95 ± 0.085 0.43 ± 0.068*** 0.35 ± 0.059*** IL6 0.0050 ± 0.0017 1.6 ± 0.45 1.4 ± 0.42 0.39 ± 0.15*** 0.27 ± 0.11*** IL8 0.0074 ± 0.0044 0.96 ± 0.12 0.81 ± 0.091 0.37 ± 0.055*** 0.37 ± 0.050*** ISG20 0.48 ± 0.096 0.78 ± 0.17 0.78+ ± 0.17 0.55 ± 0.15*** 0.50 ± 0.096*** OLR1 0.024 ± 0.0051 0.72 ± 0.12 0.55 ± 0.099 0.19 ± 0.037*** 0.15 ± 0.028*** PTGS2 0.081 ± 0.018 1.1 ± 0.16 0.99 ± 0.14 0.44 ± 0.081*** 0.29 ± 0.030*** CCL2 0.0084 ± 0.0012 1.3 ± 0.12 1.3 ± 0.20 0.099 ± 0.014*** 0.067 ± 0.011*** CCL20 0.00083 ± 0.000421.1 ± 0.23 1.1 ± 0.24 0.18 ± 0.037*** 0.19 ± 0.036*** CSF2 0.0017 ± 0.00040 0.78 ± 0.14 0.92 ± 0.23 0.027 ± 0.0065*** 0.048 ± 0.011*** CXCL1 0.0031 ± 0.00067 1.5 ± 0.32 0.64 ± 0.16 0.25 ± 0.054*** 0.15 ± 0.024*** CXCL2 0.0079 ± 0.0025 0.61 ± 0.15 0.63 ± 0.16 0.18 ± 0.045*** 0.18 ± 0.038*** 6h CXCL3 0.0053 ± 0.0014 0.91 ± 0.12 0.82 ± 0.096 0.078 ± 0.017*** 0.067 ± 0.016*** IL6 0.0029 ± 0.00098 3.9 ± 0.80 3.9 ± 0.76 0.042 ± 0.017*** 0.060 ± 0.025*** IL8 0.0035 ± 0.0016 1.4 ± 0.29 1.2 ± 0.26 0.13 ± 0.027*** 0.15 ± 0.026*** ISG20 0.44 ± 0.098 5.1 ± 1.3 6.4 ± 2.3 0.67 ± 0.12*** 0.80 ± 0.12*** OLR1 0.019 ± 0.0044 3.1 ± 0.48 3.3 ± 0.63 0.37 ± 0.075*** 0.23 ± 0.059*** PTGS2 0.053 ± 0.0089 1.1 ± 0.16 0.96 ± 0.12 0.069 ± 0.0087*** 0.055 ± 0.0074***
78 A time (h) 1 2 6 B DUSP1 siRNA 2 + + + + + + time (h) 1 2 6 DUSP1 siRNA 1 + + + + + + DUSP1 siRNA 2 + + + LMNA siRNA + + + + + + DUSP1 siRNA 1 + + + Dex + + + + + + + + + LMNA siRNA + + + IL1B - + + + + + + - + + + + + + - + + + + + + IL1B+Dex + + + + + + + + + 300 *** 100 *** 200 ** ** ** ** * *** *** * 100 50
CCL2 ** *** *** *** CCL2 0 *** 0 200 *** 100 ** ** *** * 100 * ** 50 **
CCL20
CCL20 0 *** 0 * * 200 ** *** *** 100 ** 100 50
CSF2 *** * CSF2 0 *** 0 150 *** *** *** *** *** *** *** 100 100 ** * 50 * ** 50
CXCL1 *** CXCL1
siRNA) 0 0 200 ** *** 150 * ** *** ** *
*** 100 on gene/GAPDHon
LMNA 100 * *** * relevant siRNA)
50
CXCL2
CXCL2
*** Dex
B B 0 0
B B
Gene/GAPDH 1
* - IL1 200 *** *** *** *** IL 100 *** *** *** *** (% 100 * * ** (% 50
CXCL3 *** CXCL3 ** 0 *** Effectof 0 300 *** ** *** 100 200 ** ** **
IL6
** IL6 100 50 ** ** * 0 *** *** 0 ** 200 ** *** * 100 * *** ** *** **
IL8 100 IL8 50 *** *** * 0 *** 0 *** * *** * *** 200 *** *** 100 * ** *** *** * 100 ** *** *** ** * 50 ** PTGS2 ** *** PTGS2 0 *** 0
C time (h) 1 2 6 D DUSP1 siRNA 2 + + + + + + time (h) 1 2 6 DUSP1 siRNA 1 + + + + + + DUSP1 siRNA 2 + + + LMNA siRNA + + + + + + DUSP1 siRNA 1 + + + Dex + + + + + + + + + LMNA siRNA + + + IL1B - + + + + + + - + + + + + + - + + + + + + IL1B+Dex + + + + + + + + +
200 ** ** 100
* ** 100 * ** 50
ISG20
ISG20
* B
1 -
1B 0
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*** IL IL 300 *** *** *** *** 100 200 *** (%
(% ***
* * gene/GAPDH
LMNA 50 **
relevant relevant siRNA) Gene/GAPDH
OLR1
OLR1 100 * EffectDexonof * 0 *** *** 0
Figure 3.10 Effect of DUSP1 targeting siRNAs on IL1B-induced inflammatory gene mRNA expression.
79 Figure 3.10 continued. A & C, A549 cells were incubated with LMNA (control) or DUSP1- specific siRNAs for 24 h before being treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Cells were harvested after 1, 2 or 6 h for real-time PCR analysis of the indicated genes and GAPDH. Data (N = 5 - 13) normalized to GAPDH, were expressed as a percentage of LMNA siRNA plus IL1B-stimulated for each time and plotted as means ± S.E. Significance was tested using ANOVA with a Newman-Keul multiple comparison test. Significance between: LMNA control siRNA plus IL1B and each of the DUSP1 targeting siRNAs plus IL1B, and the LMNA control plus IL1B plus Dex is shown. Other comparisons are specifically indicated. *, p < 0.05; **, p < 0.01; ***, p < 0.001. ‘Early-phase’ genes are shown in A and ‘delayed response’ genes in C. B & D, For each inflammatory gene at each time, the effect of IL1B plus Dex expressed as a percentage of IL1B for each of the three individual siRNAs is plotted as a mean ± S.E. The percent IL1B plus dexamethasone/IL1B for the LMNA siRNA is compared with that for each of the DUSP1-specific siRNAs using ANOVA with a Dunnett's post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. ‘Early-phase’ genes are shown in B and ‘delayed response’ genes in D.
Thus, with the exception of OLR1, IL1B plus dexamethasone-induced DUSP1 expression exerts negative regulation of these inflammatory genes. However, this effect had completely waned by 6 h and none of the genes showed any significant effects of DUSP1 silencing when compared to
IL1B plus dexamethasone (plus LMNA siRNA) (Fig. 3.10A & C). At 2 h, the effect of DUSP1 knock-down was rather variable and intermediate with the effects at 1 and 6 h.
In order to correctly analyse the role of DUSP1 in the dexamethasone-dependent repression of inflammatory genes, it is necessary to consider the feedback control exerted by IL1B-induced
DUSP1. For example, DUSP1 exerts significant feedback inhibition of IL1B-induced CCL2 mRNA at 1 h post-treatment (Fig. 3.10A). At this time, IL1B-induced CCL2 expression was significantly repressed by dexamethasone and this was modestly reversed by the DUSP1 siRNAs
(Fig. 3.10A).
However, the repressive effect of dexamethasone was not changed when IL1B plus dexamethasone-induced expression of CCL2 was expressed as a percentage of the IL1B-induced
CCL2 for the LMNA and DUSP1 targeting siRNAs (Fig. 3.10B, top left panel). This effect was
80 more evident in respect of CXCL3, IL6 and IL8 at 1 h. In each case, dexamethasone produced a significant repression and the DUSP1-targeting siRNAs increased this mRNA expression relative to IL1B plus dexamethasone (plus LMNA siRNA)-treated cells (Fig. 3.10A). However, when the effect of dexamethasone in the presence of each siRNA (LMNA vs DUSP1 siRNA1 and DUSP1 siRNA2) was compared, there was no difference in the overall repression (Fig. 10B). Thus, despite a clear feedback inhibitory role in the mRNA expression of these genes, DUSP1 had no additional role in the repression of CCL2, CXCL3, IL6 and IL8 mRNAs by dexamethasone at 1 h. A similar result was observed for OLR1 (Fig. 3.10D).
However, the effect of DUSP1 silencing was contrasting in respect of CXCL1, CXCL2, PTGS2 and CSF2. For each of these genes, IL1B-induced gene expression at 1 h was significantly attenuated by dexamethasone, and in each case, DUSP1 knock-down enhanced their mRNA expression (Fig. 3.10A). Thus, DUSP1 exerts a negative regulatory effect on these mRNAs in the presence of IL1B plus dexamethasone. The relative repressive effect of dexamethasone was next considered in the context of each siRNA treatment (Fig. 3.10B). For CXCL1 and CXCL2, the level of repression produced by dexamethasone plus LMNA siRNA was significantly higher than with the two DUSP1 targeting siRNAs (Fig. 3.10B). Indeed, in the presence of the DUSP1 targeting siRNAs dexamethasone did not produce any repression. A similar trend was observed for CSF2.
Thus, the repressive effect of dexamethasone on CXCL1 and CXCL2, and possibly CSF2, at 1 h, is mostly dependent on DUSP1. Similarly, ISG20, was also repressed by dexamethasone at 1 h and in the presence of the DUSP1-targeting siRNAs there was no repression (Fig. 3.10C & D). In respect of PTGS2 mRNA, dexamethasone-induced repression was significantly reversed by the two DUSP1-targeting siRNA at 1 h (Fig. 3.10A & B). However, even in the presence of DUSP1
81 siRNAs, there was still significant repression by dexamethasone (Fig. 3.10A & B). Thus, DUSP1 does not account for the full repressive effect of dexamethasone on PTGS2 mRNA at 1 h.
All 9 primary response genes and the two delayed response genes induced by IL1B were profoundly repressed by dexamethasone at 6 h (Fig. 3.10A & C). However, in all cases DUSP1 knock-down did not produce any effect. This contrasts with 2 h, where intermediate effects, between 1 and 6 h, were observed (Fig. 3.10A). It is important to note that the percent repressive effect of dexamethasone for the 9 primary response genes at 6 h was apparently reduced with
DUSP1 knock-down relative to the repression achieved in the presence of LMNA siRNA (Fig.
3.10B). However, this effect was mainly attributed to the lower expression of each mRNA that is produced following DUSP1 knock-down in the presence of IL1B (Fig. 3.10A). As a consequence, these data do not support a role for DUSP1 in the dexamethasone-dependent repression of the 9 primary response genes at 6 h. Conversely, while IL1B plus dexamethasone-induced ISG20 mRNA expression was unaffected by DUSP1 knock-down (Fig. 3.10C & D), the dexamethasone- dependent repression of OLR1 was modestly reduced by the DUSP1 targeting siRNA (Fig. 3.10D).
3.3.7 Effect of DUSP1 knock-down on IL1B-induced protein expression in the absence and presence of dexamethasone.
Supernatants from the experiments in Figs. 3.8 - 3.10 were analyzed for cytokine release. The release of CSF2, CXCL1, IL6 and IL8 was significantly enhanced following IL1B treatment at 6 h and this was significantly repressed by dexamethasone in a manner that was unaffected by the
LMNA control siRNA (Table 3.4). However, because of the need to use sub-confluent cells to achieve optimal siRNA transfection, the release of CSF2 at 2 h was below the detection limit of the assay and hence could not be analysed (Fig. 3.11A). IL1B-induced release of CSF2 and CXCL1 was not affected by the two DUSP1-targeting siRNAs, whereas the release of IL6 and IL8 showed
82 a trend toward enhanced expression (Fig. 3.11), and reached significance in respect of IL6 at 6 h with one DUSP1 siRNA. In the case of PTGS2 protein, while LMNA siRNA produced no effect,
IL1B-induced PTGS2 expression was enhanced by DUSP1 targeting siRNAs at 2 h (Fig. 3.11C).
However, at 6 h, IL1B-induced PTGS2 expression was unaffected by DUSP1 knock-down.
Table 3.4 Effect of LMNA targeting siRNA (LsiRNA) on IL1B-induced inflammatory protein expression. A549 cells were incubated with LMNA-specific siRNA for 24 h before being treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Cell supernatants were also harvested after 2 or 6 h for measurement of cytokine/chemokine release. Data (N = 11 - 13) expressed in pg/ml and presented as means ± S.E. Note: release of CSF2 at 2 h was below the detection limit of the assay and hence not analysed further. Significance, relative to time-matched IL1B and IL1B plus LMNA siRNA-treated samples, was tested by ANOVA with a Bonferroni post test *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Protein Release (pg/ml) Time Gene Symbol NS IL1B IL1B + LSiRNA IL1B + Dex IL1B + Dex + LsiRNA CXCL1 16 ± 3.9 490 ± 130 460 ± 120 270 ± 80*** 280 ± 77*** 2h IL6 0.27 ± 0.13 4.0 ± 0.94 3.7 ± 0.88 0.90 ± 0.34*** 0.78 ± 0.20*** IL8 17 ± 5.3 430 ± 130 410 ± 120 150 ± 37** 170 ± 43* CSF2 0.96 ± 0.27 21 ± 3.03 19 ± 2.6 3.1 ± 0.35*** 3.1 ± 0.34*** CXCL1 100 ± 23 9400 ± 1600 8900 ± 1600 2500 ± 740*** 2400 ± 660*** 6h IL6 0.38 ± 0.25 68 ± 13 48 ± 7.1 1.5 ± 0.79*** 2.4 ± 1.0*** IL8 130 ± 43 5800 ± 1000 4800 ± 920 770 ± 170*** 840 ± 170***
In terms of repression by dexamethasone, the release of each cytokine/chemokine was significantly repressed by dexamethasone. However, DUSP1-targeting siRNAs generally increased the release of each cytokine/chemokine in the presence of dexamethasone, and this was significant for CXCL1 at 2 and 6 h and IL8 at 2 h (Fig. 3.11A). When expressed as a percentage of IL1B for each siRNA
(Fig. 3.11B), the repression produced by dexamethasone at 6 h was significantly attenuated by both DUSP1 targeting siRNAs for CSF2, CXCL1 and IL8, and by one DUSP1-targeting siRNA for IL6.
83 A time (h) 2 6 B DUSP1 siRNA 2 + + + + time (h) 2 6 DUSP1 siRNA 1 + + + + DUSP1 siRNA 2 + + LMNA siRNA + + + + DUSP1 siRNA 1 + + Dex + + + + + + LMNA siRNA + + IL1B - + + + + + + - + + + + + + IL1B+Dex + + + + + + 40 *** *** 100 20 50 ***
CSF2 * CSF2 0 *** 0 1500 *** 15000 ** 150 *** 1000 ** * *** * 10000 *** 100 * ** *** 500 5000 50
CXCL1 *** CXCL1 0 0 0
12 ** 150 *** 150
+relevant siRNA (pg/ml)
Release 8 *** 100 ** *** 100 EffectDex of
IL6
4 50 IL6 50 * IL1B IL1B 0 ** 0 *** 0 1000 *** *** (% 150 *** 10000 ** *** 100
IL8 IL8 500 *** *** 5000 50 ** 0 0 *** 0
C time (h) 2 6 time (h) 2 6 DUSP1 siRNA 2 + + + + LMNA siRNA + + + + DUSP1 siRNA 1 + + + + Dex + + + + LMNA siRNA + + + + IL1B - + + + + - + + + + Dex + + + + + + IL1B - + + + + + + - + + + + + + PTGS2 PTGS2 GAPDH GAPDH
Figure 3.11 Effect of DUSP1 targeting siRNA on IL1B-induced inflammatory gene protein release/expression. A, A549 cells were incubated with LMNA (control) or DUSP1-specific siRNAs for 24 h before being treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Supernatants were harvested after 2 or 6 h for cytokine/chemokine release measurement. Data (N = 9 - 15) expressed as pg/ml are plotted as means ± S.E. Note: release of CSF2 at 2 h was below the detection limit of the assay and hence not shown. Significance was tested between the LMNA control siRNA plus IL1B and each of the DUSP1 targeting siRNAs plus IL1B, and LMNA control plus IL1B plus Dex using ANOVA with a Newman-Keul multiple comparison test. Other comparisons are specifically indicated. *, p < 0.05; **, p < 0.01; ***, p < 0.001. B, For each cytokine/chemokine at each time, the effect of IL1B plus Dex was expressed as a percentage of IL1B for each of the three individual siRNAs and is plotted as a mean ± S.E. The percent IL1B plus dexamethasone/IL1B for the LMNA siRNA is compared with that for each DUSP1-specific siRNAs using ANOVA with a Dunnett's post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. C, Cells were treated as in A and harvested for western blot analysis of PTGS2 and GAPDH. Blots representative of at least 4 such experiments are shown.
Thus, a role for DUSP1 in the dexamethasone-dependent repression of CSF2, CXCL1, IL6 and
IL8 protein release was indicated. However, because this reversal of repression was very partial at
84 6 h, the existence of non DUSP1-dependent mechanisms of repression is predicted (Fig. 3.11B).
Conversely, the reversal of repression produced by the DUSP1 siRNAs at 2 h was complete for
CXCL1 and near complete for IL8 at 2 h (Fig. 3.11A & B). Thus, DUSP1 provides all, or the majority, of the repressive effect of dexamethasone on CXCL1 and IL8 at 2 h, but may have a lesser role at 6 h. The effect on PTGS2 protein was similar to that for IL6 release. IL1B plus dexamethasone-induced PTGS2 expression was not affected by LMNA siRNA (Fig. 3.11C).
However, despite a clear feedback role, DUSP1 knock-down had no effect on dexamethasone- dependent repression of PTGS2 protein at any time (Fig. 3.11C).
3.4 Discussion
In this chapter the feedback inhibitory and repressive role of IL1B and IL1B plus dexamethasone- induced DUSP1 was investigated on inflammatory gene expression. The mRNA expression of 11
IL1B-induced inflammatory genes, whose repression by dexamethasone was sensitive to translational blockade, was only modestly repressed by p38, MEK1/2 and JNK inhibitors when used alone. However, combined inhibition of all the three MAPKs together produced almost complete inhibition of the mRNA and, where tested, protein expression of all 11 genes. This observed difference between the effects of individual and combined MAPK inhibition could be attributed to the pathway redundancy in the induction of these 11 IL1B-induced inflammatory genes. Alternatively, cross-regulatory control between MAPK pathways may diminish the apparent role of any single MAPK pathway following its inhibition (362, 363). Thus, simultaneous inhibition of all three MAPK pathways may overcome this issue and also represents a highly effective means of inhibiting inflammatory gene expression, one that is also adopted by glucocorticoids. Likewise, MAPK activation and inflammatory gene expression were significantly inhibited by DUSP1 over-expression. Since, expression of the 11 IL1B-induced inflammatory
85 genes was inhibited by MAPK inhibitors and DUSP1 over-expression, the current data suggest that the expression of these 11 IL1B-induced inflammatory genes is MAPK-dependent. Thus, these genes should be valid targets to explore the function of DUSP1 that is induced by both IL1B and dexamethasone.
To understand the mechanism(s) of glucocorticoid-mediated inflammatory gene repression by
DUSP1, it is essential to first consider the feedback regulatory role of DUSP1 (362, 363).
Following IL1B treatment, loss of DUSP1 enhanced the phosphorylation of MAPK at 1 h.
However, there was no obvious effect at 2 or 6 h due to the loss of DUSP1. These data therefore suggest that IL1B-induced ERK, p38 and JNK MAPKs are only transiently regulated by DUSP1 via negative feedback processes. Similarly, siRNA-mediated loss of DUSP1 also enhanced the expression of 9 primary response genes shown in Fig. 3.2 A, plus ISG20 and OLR1, at 1 h and this was significant for at least 7 genes. This was in line with previous observations indicating the feedback regulatory role of DUSP1 in the regulation of multiple inflammatory genes, including
CSF2, IL6, IL8 and PTGS2 in A549 cells (284, 304, 353). However, this enhancement of inflammatory mRNA by DUSP1 loss was not seen at, or after, 2 h of IL1B treatment. In contrast,
IL1B-induced expression of many of the primary response genes, but not the two delayed response genes, was significantly reduced relative to IL1B plus LMNA treated cells by DUSP1 knock-down at 6 h post-IL1B treatment. These data therefore point to the possibility that enhanced phosphorylation of MAPKs (at 1 h) in the presence of DUSP1 siRNA may further enhance negative feed-forward regulation, for example, the expression of an inhibitory protein (possibly at
2 or 4 h), which may consequently produce enhanced loss of IL1B-induced inflammatory mRNAs
(at 6 h) in DUSP1 inhibited cells. Indeed, additional layers of MAPK-dependent regulatory control
86 exist in the form of ZFP36, which binds to AUUUA-containing mRNAs, e.g. CSF2, IL8 or PTGS2, to promote mRNA destabilization and translational silencing (364, 365). ZFP36 is strongly and transiently induced by pro-inflammatory stimuli (365), including IL1B, in A549 cells, where the expression is MAPK-dependent (366). Furthermore, enhanced MAPK activation following
DUSP1 loss is associated with enhanced ZFP36 expression (320). This in turn may lead to enhanced feed-forward control of ARE-containing inflammatory genes by ZFP36. Since the
3’UTR of all 9 of the primary response genes shown in Fig. 3.2A have one or more AUUUA motifs, which would be expected to bind ZFP36, it may explain the observed attenuation of inflammatory mRNA expression at 6 h post-IL1B treatment in DUSP1 inhibited cells. These data also offer a mechanistic explanation for the effect of glucocorticoids on ZFP36 expression. While
ZFP36 is induced by pro-inflammatory stimuli, such as IL1B (366), glucocorticoids, by reducing
MAPK activity, modestly reduce inflammatory stimuli-induced expression of ZFP36 (366, 367).
This glucocorticoid-induced attenuation of ZFP36 may seem detrimental in the context of anti- inflammatory action of glucocorticoids. However, ZFP36 expression is also modestly induced by glucocorticoids (366, 368, 369). Thus, by balancing the potential losses in ZFP36 expression due to the repression of MAPKs, glucocorticoids maintain the expression, or activity, of key effectors involved in regulatory control. Without the maintenance of such regulatory circuits, glucocorticoids could enhance inflammatory responses.
In terms of the repression of MAPKs by dexamethasone, DUSP1 plays a non-redundant role as the major effector in the early onset of repression of all three MAPK pathways following IL1B plus dexamethasone co-treatment. This was correlated with enhanced expression of DUSP1 in the presence of IL1B plus dexamethasone. However, the role of DUSP1 in MAPK repression was
87 diminished by 2 h, and, at 6 h, there was no obvious effect of DUSP1 knock-down. This suggests that there must be additional DUSP1-independent mechanisms by which dexamethasone exerts repression of MAPKs. In this regard, glucocorticoid-induced effector genes, TSC22D3 and
CDKN1C, which may inhibit Ras-Raf activation and JNK signalling, respectively, are both induced by dexamethasone in A549 cells and may play an important role in dexamethasone- induced repression of MAPKs (282, 350, 363, 370-373). In addition, dexamethasone-mediated enhancement of Dok-1, SLAP and Dexras1 inhibit the activation of signalling transduction cascades through a variety of different mechanisms and may contribute to MAPK repression by dexamethasone (275-277). Furthermore, the data presented here are also supported by a study showing similar results where MAPK phosphorylation was only partially affected by DUSP1 knock-down following dexamethasone pre-treatment (114).
In the case of inflammatory gene repression by dexamethasone, the repression of IL1B-induced
CXCL1 and CXCL2 mRNAs was almost completely prevented by DUSP1 knock-down at 1 h, with a similar trend for CSF2. In contrast, dexamethasone-induced repression of CCL2, CXCL3,
IL6, IL8 and OLR1 at 1 h was not affected by the loss of DUSP1. This may be due to insufficient knock-down of DUSP1. However, this may seems unlikely due to the fact that DUSP1-targeting siRNAs prevented the dexamethasone-dependent inhibition of MAPK phosphorylation and the repression of CXCL1, CXCL2 mRNA at 1 h. Thus, DUSP1-independent mechanisms may play a role in dexamethasone-induced repression of CXCL3, IL6, IL8 and OLR1. Moreover, even though
DUSP1 was involved in the feedback control of IL1B-induced PTGS2 at 1 h, dexamethasone- dependent repression was only moderately prevented in the presence of DUSP1 siRNAs, and thus, suggests a role for both DUSP1-dependent and DUSP1-independent mechanisms of repression by
88 dexamethasone. In respect of dexamethasone-dependent repression at 6 h, the repression of all the mRNAs was enhanced relative to earlier times. However, DUSP1 knock-down did not affect the repression of any of these genes and therefore these data do not support a role for DUSP1 in dexamethasone-induced repression occurring at 6 h. Thus, following the early phase of DUSP1- dependent repression, additional effector processes must be induced by dexamethasone, in addition to DUSP1, that either play a predominant role or act redundantly with DUSP1 to produce inflammatory gene repression. For example, while DUSP1 protein expression was induced within
30 min-1 h post-IL1B plus dexamethasone treatment, the expression of TSC22D3, which negatively regulates the activation of NF-κB, AP-1 and possibly MAPKs, is also induced within
1-2 h following IL1B plus dexamethasone treatment (370, 372). Thus, in addition to DUSP1,
TSC22D3 may also play a role in dexamethasone-dependent repression of inflammatory genes at and after 1 h.
For IL6, IL8 and PTGS2 protein expression, DUSP1 knock-down showed a trend towards increased expression and this was in line with the previously described feedback inhibitory role for DUSP1 in the regulation of these genes (284). At the level of CXCL1 protein release, dexamethasone-dependent repression was totally prevented by the loss of DUSP1 at 2 post-IL1B treatment. This confirms that DUSP1 is a predominant effector of repression at this time. However, by 6 h post-IL1B, dexamethasone-dependent repression of CXCL1 release was only partially reversed by the loss of DUSP1. Similarly, a role for DUSP1 in the dexamethasone-dependent repression of IL8 release at 2 h was clearly evident. By 6 h post-IL1B plus dexamethasone treatment, both IL8 and CSF2 release showed a very modest inhibition of repression following
DUSP1 knock-down. Thus, a role for DUSP1 in the repression by dexamethasone was clearly
89 confirmed for CXCL1 and IL8 release at 2 h, but only a modest role can be established at 6 h.
Conversely, dexamethasone-induced repression of IL6 and PTGS2 at both 2 and 6 h may be predominantly mediated by DUSP1-independent mechanisms of repression.
In conclusion, the data presented in this chapter demonstrate that, in A549 cells, glucocorticoid- induced DUSP1 plays a major role in the repression of MAPKs, selected inflammatory mRNAs and proteins at early times. However, this role is only transient, and by 6 h, DUSP1-independent mechanisms of repression may dominate with respect to the repression of MAPKs and inflammatory genes by dexamethasone. In summary, these current data, contrary to evolving dogma that DUSP1 is a major effector of glucocorticoid action, suggest that DUSP1 only has a transient, more minor, role compared to DUSP1-independent mechanisms of repression.
Therefore, these data emphasize the need to continue the characterization of glucocorticoid- induced effector responses that are important in the repression of inflammatory genes and to explore their roles relative to known effectors such as DUSP1. Only by understanding the detailed mechanism(s) of the anti-inflammatory response of glucocorticoids, the anti-inflammatory NR3C1 agonist therapies for the treatment of asthma can be improved.
90 Chapter 4 : Downregulation of the mitogen-activated protein kinase phosphatase, DUSP1 limits TNF expression through enhanced expression of tristetraprolin (TTP; ZFP36)
Data presented in this chapter have been published:
Shah S, Mostafa MM, McWhae A, Traves SL, Newton R. Incoherent feed-forward control of TNF by tristetraprolin (ZFP36) is limited by the mitogen-activated protein kinase phosphatase, DUSP1: Implications for regulation by glucocorticoids. J. Biol. Chem. 2016;291(1):110-25
Copyright Journal of Biological Chemistry.
This work was reprinted with permission based on the J. Biol. Chem. copyright permission policy.
Mostafa MM contributed to Fig. 4.2, 4.5 and 4.6
McWhae A contributed to Fig. 4.2
Traves SL contributed to 4.5 and 4.6
91 4.1 Rationale
In the previous chapter, knock-down of IL1B-induced DUSP1 expression transiently enhanced the appearance of phosphorylated MAPKs and this was correlated with increased expression of inflammatory mRNAs at 1 h post-IL1B treatment. However, the expression of multiple inflammatory mRNAs at 6 h post-IL1B treatment was decreased by the loss of DUSP1. This observation, while initially unexpected, is consistent with the concept that MAPKs may increase
ZFP36 expression to subsequently down-regulate ARE-containing mRNAs. This is tested in the current chapter (Fig. 4.1). Indeed, in A549 cells, ZFP36 is strongly and transiently induced by
IL1B in a MAPK-dependent manner (366). Additionally, enhanced activation of MAPKs following DUSP1 loss is associated with augmented expression of ZFP36 (320). Thus, enhanced
ZFP36 expression may further induce the feed-forward control of the 9 primary response ARE- containing inflammatory mRNAs (CCL2, CCL20, CSF2, CXCL1, CXCL2, CXCL3, IL6, IL8 and
PTGS2) (shown in the previous chapter). ZFP36-mediated feed-forward control, in DUSP1 inhibited cells, may consequently produce enhanced loss of primary response inflammatory mRNAs at 6 h post-IL1B treatment. In this context, post-transcriptional regulation of the pro- inflammatory cytokine, TNF, is conferred via multiple AREs located in the 3’-UTR of the TNF mRNA (214, 374). This region is critical for regulating message stability and is targeted by several
RNA binding proteins, including ZFP36 (219). ZFP36 negatively controls TNF expression by promoting mRNA deadenylation and degradation with consequent reductions in TNF biosynthesis
(375-377). Thus, TNF was used as a model, ZFP36 target gene, to explore the relationship(s) between the regulation of MAPK activation by DUSP1 with the expression of ZFP36 and the subsequent effects on downstream gene expression in A549 cells
92 IL1B treatment IL1B
DUSP1 MAPK Feed-forward control by ZFP36 TNF gene expression
Figure 4.1 Enhanced inflammatory gene expression by IL1B: Feedback control by DUSP1 and feed-forward control by ZFP36. IL1B treatment results in the activation of MAPKs. This, along with the activation of other signalling pathway and inflammatory transcription factors, e.g. NF-κB and AP-1 (not shown), enhances the expression of inflammatory genes (e.g. TNF), as well as the negative feedback regulator, DUSP1, and the feed-forward regulator, ZFP36. By binding and promoting mRNA decay, and/or translational arrest, of ARE-containing mRNAs, ZPF36 is a negative regulator of inflammatory gene expression. Following MAPK activation, the increased expression of DUSP1 is one mechanism by which MAPK activity is restored to basal. Expression of ZFP36 depends on MAPK activation and this limits the expression of ARE-containing inflammatory genes, such as TNF.
4.2 Hypothesis
Since, the loss of DUSP1 in chapter three was associated with increased loss of IL1B-induced inflammatory mRNAs at 6 h, the hypothesis for this chapter is that siRNA-mediated loss of
DUSP1, by augmenting MAPK phosphorylation at 1 h, increases ZFP36 expression, leading to the enhanced loss of ARE-containing mRNAs at 6 h. The implications of these programme are also investigated in the context of glucocorticoid treatment.
4.3 Results
4.3.1 Characterization of TNF expression in the presence of IL1B and dexamethasone
TNF mRNA was rapidly induced by IL1B (Fig. 4.2A), reached a peak at 2 h post-stimulation, and then declined sharply towards basal levels over the following 4 h. In addition, the expression of unspliced nuclear TNF RNA was also transiently augmented by IL1B at 1 and 2 h and suggests
93 that rapid enhancement of TNF transcription may contribute to the increase in TNF mRNA (Fig.
4.2B). To assess the role of post-transcriptional mechanisms in IL1B-induced TNF mRNA expression, the stability of TNF was assessed by the actinomycin D chase method. IL1B-induced
TNF mRNA was very stable at 30 min post-stimulation (Fig. 4.2C). However, treatment with IL1B for 90, 120 and 180 min, resulted in reduced TNF mRNA expression (a loss of ~50% of initial levels (t = 0)) within 30 - 40 min of actinomycin D treatment (Fig. 4.2C). Conversely, at short, 30 min, IL1B treatment, the mature (spliced) TNF mRNA levels continued to increase following actinomycin D treatment. This observed enhancement in TNF mRNA could be due to the fact that while actinomycin D inhibits RNA Pol II-dependent transcription, the production of TNF mRNA still continues for a very short period of time following actinomycin D treatment. This brief production of TNF mRNA could be mainly attributed to the accumulation of immature/unspliced
TNF RNA (Fig. 4.2B). This explanation is also in line with the fact that splicing and processing of immature mRNAs can take several minutes, or often longer, to produce mature mRNAs (378).
Thus, the actinomycin D chase experiments may be considered as reflecting post-transcription
RNA processing and maturation as well as mRNA degradation. Moving to TNF protein, TNF was not released into the supernatant under basal conditions, and only very low levels of TNF protein release were detected at 2 h following IL1B treatment (Fig. 4.2D, left panel). TNF release reached a peak around 4 h post-IL1B treatment and maintained at this level for up to 18 h. In order to analyse the expression of TNF protein inside or associated with the cells, following the removal of supernatants, cells were washed with PBS, and then lysed in a soft-lysis buffer (containing protease and phosphatase inhibitors) that was compatible with the ELISA. Following ELISA analysis of cell lysates, cell-associated TNF protein was first detected at 1 h post-IL1B treatment
(Fig. 4.2D, right panel). This was further enhanced at 2 h, before reaching a peak at 4 h, and then
94 declining towards basal levels by 6 h. Since, TNF is initially expressed as a membrane bound uncleaved form that then gets cleaved by the metalloproteinase, TNF-converting enzyme (TACE), the expression of uncleaved TNF was also examined by western blotting (Fig. 4.2E, left panel)
(379). Uncleaved TNF was detected very rapidly within 1 h post-IL1B treatment, reached a maximum level by 2 h, and returning to basal levels by 6 h (Fig. 4.2E, left panel).
IL1B-induced TNF mRNA was modestly repressed at all times by dexamethasone (Fig. 4.2A).
Since dexamethasone also produced a partial attenuation of unspliced nuclear TNF RNA, these data suggest that the repressive effect of dexamethasone on TNF mRNA is predominantly attributed to transcriptional repression (Fig. 4.2B). Equally, the t½ of TNF mRNA, following 90 min of IL1B treatment, was around 30 - 40 min, and this was further decreased by dexamethasone post-actinomycin D treatment (Fig. 4.2C, right panels). In the case of TNF protein, dexamethasone almost completely repressed IL1B-induced cell-associated, uncleaved and secreted TNF at all times (Fig. 4.2D & E). Thus, in addition to transcriptional and post-transcriptional repressive mechanisms, repression of TNF by dexamethasone may also involve translational, or possibly post-translational, mechanisms of repression.
95 A B 50 1.5 100 100 NT 1.0 IL1B * 25 50 * * * 50 IL1B+Dex RNA 0.5 *
mRNA *
IL1B+Dex each time) each
unTNF/U6
IL1B+Dex time) each
(% ofat(% IL1B (% ofat (% IL1B TNF/GAPDH 0 0 0.0 0 0 2 4 6 1 2 4 6 0 2 4 6 1 2 4 6 time (h) time (h) time (h) time (h) C 300 30 min 150 90 min IL1B time post Act D (min) 45 100 200 IL1B+Dex Dex + 50 150 IL1B + + 100 * 0 0 100 30 150 120 min 150 180 min
mRNA 50 15
mRNA
(% t =t 0) (%
mRNA (% t =t 0) (%
100 100 =t (%0)
TNF/GAPDH TNF/GAPDH 0 * * * TNF/GAPDH 0 50 50 0 20 40 60 0 0 time (min) 0 15 30 45 0 15 30 45 time (min) time (min) D E time (h) 2 time (h) 1 1 2 6 Dex + 200 IL1B - + + + IL1B - + + Supernatant 30 Cell associated Uncleaved TNF Uncleaved TNF 20
100 GAPDH GAPDH TNF
TNF 10
(pg/ml)
(pg/well) release release
***
** * *** *** *** ** * * *** * 0 1.5 100 0 ** 0 5 10 15 20 0 2 4 6 1.0 50
time (h) TNF
0.5 TNF
protein
protein
/GAPDH (% IL1B) (% NT IL1B IL1B+Dex /GAPDH 0.0 0 Figure 4.2 Characterization of TNF expression in the presence of IL1B and dexamethasone. A549 cells were either not treated (NT) or stimulated with IL1B (1 ng/ml) or a combination of IL1B and dexamethasone (Dex, 1 μM) as indicated. Cells were harvested at the indicated times for real-time PCR analysis of (A) TNF and GAPDH mRNA or (B) unspliced nuclear (un) TNF RNA and U6 RNA. Data (N = 4), normalized to GAPDH or U6, are plotted as means ± S.E. The effect of IL1B + dexamethasone for TNF mRNA (A right panel) (N = 12) and unspliced nuclear (un) TNF RNA (B right panel) (N = 4) is plotted as a percentage of IL1B for indicated times. Significance, relative to time matched IL1B-treated samples was tested using a paired t-test. *, p < 0.05. C, A549 cells were treated with IL1B (1 ng/ml) for 30, 90, 120 and 180 min. Actinomycin D (Act D, 10 μg/ml) was then added (t = 0) and the cells were harvested as indicated. RNA was extracted for real-time PCR analysis of TNF and GAPDH. Data (N = 3) were normalized to GAPDH and are plotted as a percentage of t = 0 for each treatment as means ± S.E. (left panels). A549 cells were also treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (Dex, 1 μM) for 90 min. After 90 min, actinomycin D (Act D 10 μg/ml) was added (t = 0) and the cells were harvested at the indicated times for real-time PCR analysis of TNF and GAPDH. Data (n = 4) were normalized to GAPDH and are plotted as a percentage of t = 0 for each treatment as means ± S.E. (middle panel). The effect of IL1B plus dexamethasone at 45 min post Act D treatment is plotted as a percentage of t = 0 for 45 min. Significance, relative to IL1B-treated samples was tested using a paired t-test. *, p < 0.05 (right panel). D, Supernatants (1 ml), from the cells in A, were harvested for TNF release measurement (left panel). Alternatively, cells were harvested after 1, 2, 4 or 6 h in soft lysis buffer and total protein was prepared for the detection of cell-associated TNF via ELISA (right panel). Data (N = 4), expressed in pg/ml (for supernatant) and pg/well (cont.…)
96 Figure 4.2 continued. (for cell associated TNF), are plotted as means ± S.E. Significance, relative to time matched IL1B-treatment, was tested using a paired t-test. *, p < 0.05; **, p< 0.01; ***, p< 0.001. E, A549 cells were either not treated or stimulated with IL1B (1 ng/ml) for 1, 2 or 6 h (left panel) or with IL1B (1 ng/ml) or a combination of IL1B and dexamethasone (Dex, 1 μM) for 2 h (right panel) and total protein was prepared for western blot analysis of uncleaved (~25 kDa) TNF and GAPDH. Blots representative of 4 such experiments are shown. Following densitometric analysis, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. Significance, relative to non-treated (left panel) or IL1B-treated (right panel) samples was tested using ANOVA with a Dunnett's post test (left panel) or a paired t-test (right panel) is indicated.*, p < 0.05;**, p< 0.01; ***, p< 0.001.
4.3.2 Analysis of DUSP1 expression in the presence of IL1B and dexamethasone
Following treatment of cells with IL1B in the absence and presence of dexamethasone, DUSP1 protein expression was analysed by western blotting. IL1B-induced DUSP1 reached a maximum level within 1 h, then declined steeply by 2 h, reaching basal levels by 6 h post-treatment (Fig.
4.3A). Conversely, dexamethasone alone produced a modest enhancement in DUSP1 protein at 1 h, and this was significantly enhanced by 2 h. In the case of IL1B plus dexamethasone co- treatment, DUSP1 protein was robustly induced at 1 h, and declined by 2 h post-treatment.
However, at 6 h, while IL1B did not induce any DUSP1 protein expression, dexamethasone alone significantly enhanced DUSP1 expression and this was maintained in the presence of IL1B. These results are in line with the data shown in the previous chapter.
97 A B time (h) ½ 1 2 6 time (h) 1 2 6 Dex + + + + + + + + Dex + + + + + + IL1B - - + + - - + + - - + + - - + + - - + + - - + + - - + + NS IL1B DUSP1 NS ZFP36 GAPDH 3 GAPDH *** 3.0 * * *** 2 * *** ** ** *** 1.5
protein 1
DUSP1 *** ***
ZFP36 protein /GAPDH *** *** * ** /GAPDH 0 0.0 Figure 4.3 Analysis of DUSP1 and ZFP36 expression in the presence of IL1B and dexamethasone. A & B, A549 cells were either not treated or stimulated with IL1B (1 ng/ml), dexamethasone (Dex, 1 μM) or a combination of the two. Cells were harvested at the times indicated prior to western blot analysis of DUSP1, ZFP36 and GAPDH. Blots representative of at least 4 such experiments are shown. Following densitometric analysis, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. For A, Significance, using ANOVA with a Bonferroni's multiple comparison test is indicated. For B, Significance, using ANOVA with a Dunnett's post test is indicated. *, p < 0.05; **, p< 0.01; ***, p< 0.001. NS = nonspecific band.
4.3.3 Analysis of ZFP36 expression in the presence of IL1B and dexamethasone
ZFP36 was undetected in untreated cells, but the expression, comprised of a protein doublet, was significantly induced within 1 h of IL1B treatment (Fig. 4.3B). IL1B-induced ZFP36 expression was maintained at 2 h, and detected as a slowly migrating protein doublet, presumably due to the phosphorylation of ZFP36 (310, 380). The expression of IL1B-induced ZFP36 was considerably reduced by 6 h. Conversely, ZFP36 protein expression was not induced by dexamethasone at either
1 or 2 h (Fig. 4.3B). In fact, co-treatment with IL1B, dexamethasone significantly attenuated IL1B- induced ZFP36 expression at 1 and 2 h (Fig. 4.3B). However, at 6 h, ZFP36 was modestly induced by dexamethasone, and at this time there was no repression of IL1B-induced ZFP36 expression by dexamethasone (Fig. 4.3B).
4.3.4 Effect of MAPK inhibitors on DUSP1 and ZFP36 expression
A549 cells were treated with maximally effective concentrations of SB203580 (10 µM), U0126
(10 µM) or JNK inhibitor 8 (JNK-IN-8) (10 µM), which inhibit the p38, MEK1/2 (to inhibit ERK)
98 and JNK MAPK pathways respectively. SB203580 and JNK-IN-8 did not produce any clear effect on IL1B-induced DUSP1 expression at 1 h (Fig. 4.4A). However, UO126 produced a partial inhibition of DUSP1. In contrast, IL1B-induced DUSP1 protein expression was almost completely prevented in the presence of a combination of all three MAPK inhibitors, suggesting that IL1B- induced DUSP1 expression is MAPK-dependent.
In the case of IL1B-induced ZFP36 expression, while SB203580 produced a complete loss of
ZFP36 expression, U0126 and JNK-IN-8 both produced a modest, non-significant inhibition of
ZFP36 expression at each time (Fig. 4.4B). IL1B-induced ZFP36 expression was completely attenuated by the combined MAPK inhibitors at all times (Fig. 4.4B). Since IL1B-induced ZFP36 expression was MAPK dependent, these data suggest that reduced activity of MAPKs, by reducing the expression of ZFP36, could enhance the stability and expression of ARE-containing mRNAs, such as TNF (Fig. 4.1).
99 A B time (h) ½ 1 2 6 time (h) 1 2 6 Dex + + + + + + + + Dex + + + + + + IL1B - - + + - - + + - - + + - - + + IL1B - - + + - - + + - - + + DUSP1 NS NS ZFP36 GAPDH 3 GAPDH *** 3.0 * * *** 2 * *** ** ** *** 1.5
protein 1
DUSP1 *** ***
ZFP36 protein /GAPDH *** *** * ** /GAPDH 0 0.0
A B time (h) 1 2 6 JNK-IN-8 + + + + + + UO126 + + + + + + time (h) 1 SB203580 + + + + + + IL1B - + + + + + - + + + + + - + + + + + JNK-IN-8 + + NS UO126 + + ZFP36 SB203580 + + IL1B - + + + + + GAPDH NS DUSP1 ** 0.8 * ** GAPDH **
0.4
ZFP36
protein /GAPDH 0.0 Figure 4.4 Analysis of DUSP1 and ZFP36 expression in the presence of IL1B and MAPK inhibitors. A & B, A549 cells were either not treated, treated with IL1B (1 ng/ml) or pre-treated with either UO126, SB203580, JNK inhibitor 8 (JNK-IN-8) or a combination of UO126, SB203580 plus JNK-IN-8 each at 10 µM for 30 min prior to IL1B stimulation. Cells were harvested at the times indicated prior to western blot analysis of DUSP1 or ZFP36 and GAPDH. Blots representative of at least 4 such experiments are shown. Following densitometric analysis, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. Significance, using ANOVA with a Dunnett's post test is indicated. *, p < 0.05; **, p< 0.01. NS = nonspecific band.
4.3.5 Analysis of DUSP1 expression in the presence of IL1B and dexamethasone in primary HBE cells
In order to further validate the findings obtained in A549 cells, the mRNA and protein expression of DUSP1 was examined in primary HBE cells. DUSP1 mRNA was robustly induced by IL1B and dexamethasone at 1 h, following which the mRNA expression declined rapidly at 2 h post- treatment (Fig. 4.5A, left panel). Whereas IL1B-induced DUSP1 mRNA had returned to basal levels at 18 h, DUSP1 mRNA was strongly induced by dexamethasone at 18 h (Fig. 4.5A, left panel). Thus, at 18 h, the mRNA expression of DUSP1 was mostly driven by dexamethasone, with no effect of IL1B. Equally, DUSP1 protein was also strongly induced by IL1B and IL1B plus dexamethasone (Fig. 4.5B, top panels). In the case of dexamethasone treatment alone, DUSP1 protein was induced at 6 and 18 h post-treatment and there was a little or no further effect of IL1B on dexamethasone-induced DUSP1 expression (Fig. 4.5B, top panels). Conversely, the expression
100 of DUSP1 protein was rapidly induced by IL1B, reached a peak within 1 h post-stimulation, and then declined slowly towards basal levels over the following 18 h.
A
*** 5.0 * NT 3 *** ** ** *** *** ** Dex *** *** * 2 * ** ** ** 2.5 * ** IL1B
* mRNA
ZFP36 * mRNA ** IL1B+Dex
DUSP1 1 * /GAPDH
/GAPDH 0 0.0 ½ 1 2 6 18 ½ 1 2 6 18 time (h) time (h) B time (h) ½ 1 2 6 18 Dex + + + + + + + + + + IL1B - - + + - - + + - - + + - - + + - - + + DUSP1 GAPDH ZFP36 NS GAPDH Figure 4.5 Analysis of DUSP1 and ZFP36 expression in primary human bronchial epithelial cells. A, HBE cells were either not treated (NT) or stimulated with IL1B (1 ng/ml), dexamethasone (Dex, 1 μM) or a combination of the two as indicated. Cells were harvested at the indicated times for real-time PCR analysis of DUSP1, ZFP36 and GAPDH. Data (N = 4), normalized to GAPDH, are plotted as means ± S.E. Significance, using ANOVA with a Bonferroni's multiple comparison test is indicated. *, p < 0.05; **, p< 0.01; ***, p< 0.001. B, HBE cells were treated as in A and harvested at the indicated times for western blot analysis of DUSP1, ZFP36 and GAPDH. Blots representative of at least 4 such experiments are shown. NS = nonspecific band.
4.3.6 Analysis of ZFP36 expression in the presence of IL1B and dexamethasone in primary HBE cells
ZFP36 mRNA was strongly induced by IL1B in HBE cells, and reached a peak at 1 h post- stimulation, before declining rapidly to basal levels by 2 h (Fig. 4.5A, right panel). Dexamethasone alone did not change ZFP36 mRNA expression until 18 h and also had no effect on IL1B-induced
ZFP36 mRNA expression at this time (Fig. 4.5A, right panel). In the case of IL1B plus dexamethasone co-treatment, ZFP36 mRNA was strongly induced at 1 h and declined steeply over the following 18 h post-treatment (Fig. 4.5A, right panel). The early induction of ZFP36 mRNA was predominantly mediated by IL1B, whereas the late phase expression of ZFP36 mRNA at 18
101 h was mainly dexamethasone-dependent. ZFP36 protein expression was rapidly induced by IL1B at 1 and 2 h post-treatment, thereafter IL1B-induced ZFP36 protein declined rapidly over the following 18 h (Fig. 4.5B, bottom panel). However, dexamethasone-induced ZFP36 expression was variable at 18 h. Dexamethasone either alone or in the presence of IL1B, did not produce any obvious effect on ZFP36 protein expression (Fig. 4.5B, bottom panel).
4.3.7 Characterization of TNF expression in the presence of IL1B and dexamethasone in HBE cells
Similar to A549 cells, TNF mRNA was rapidly enhanced by IL1B in HBE cells (Fig. 4.6A, left panel), reached a peak at 2 h and then declining sharply over the following 4 h. Since the CT values for TNF mRNA expression by real-time PCR was same (22 - 24 cycles) in both A549 and HBE cells (Fig. 4.7), these data suggest that TNF mRNA expression is similar in both cell types. In the case of protein expression, there was no TNF release under basal conditions. Treatment with IL1B produced low levels of TNF protein at 18 h (Fig. 4.6B). These data are in line with previous findings in HBE cells, where treatment of HBE cells with virus produced a very low release of
TNF (381). In addition, the expression of uncleaved ~25 kDa TNF was also examined by western blotting (Fig. 4.6C). Uncleaved TNF was rapidly induced by IL1B and first detected within 1 h, reached a peak at 2 h and was only marginally detectable by 6 h. Since all the blotting and detection conditions, including exposure time, were identical with the blots for A549 cells, presented in Fig.
4.2E, it can be concluded that, like TNF mRNA, uncleaved ~25 kDa TNF protein expression is also similar in A549 and HBE cells. Conversely, dexamethasone produced a modest repression of
IL1B-induced TNF mRNA at all times (Fig. 4.6A right panel). Similarly, uncleaved and soluble
TNF protein expression was also profoundly repressed by dexamethasone at 2 and 18 h respectively (Fig. 4.6B & C, right panel).
102 A B time (h) 18 NT Dex Dex + + IL1B IL1B+Dex IL1B - - + + 0.9 200 0.6 4 100
* * TNF
mRNA 0.3 2
(pg/ml)
release release
IL1B+Dex each time) each
TNF/GAPDH 0.0 ofat(% IL1B 0 0 1 2 6 18 0 5 10 15 time (h) time (h) time (h)
C time (h) 2 time (h) 1 1 2 6 Dex + IL1B - + + + IL1B - + + Uncleaved TNF Uncleaved TNF GAPDH GAPDH
4 *** 100 ** **
2 50
TNF
TNF
protein
/GAPDH
protein (% IL1B) (% 0 /GAPDH 0 Figure 4.6 Analysis of TNF expression in primary human bronchial epithelial cells. A, HBE cells were either not treated (NT) or stimulated with IL1B (1 ng/ml), dexamethasone (Dex, 1 μM) or a combination of the two as indicated. Cells were harvested for real-time PCR analysis of TNF and GAPDH. Data (N = 4), normalized to GAPDH, are plotted as means ± S.E. (left panel). The effect of IL1B + dexamethasone for TNF mRNA (N = 4) is plotted as a percentage of IL1B for indicated times (right panel). Significance, relative to time matched IL1B-treated samples was tested using a paired t-test. *, p < 0.05. B, Supernatants, from the cells in A were harvested for TNF release measurement. C, HBE cells were either not treated or stimulated with IL1B (1 ng/ml) for 1, 2 or 6 h (left panel). Alternatively, cells were not treated or stimulated with IL1B (1 ng/ml) or a combination of IL1B and dexamethasone (Dex, 1 μM) for 2 h (right panel). Total protein was prepared for western blot analysis of uncleaved (~25kDa) TNF and GAPDH. Blots representative of at least 4 such experiments are shown. Following densitometric analysis, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. Significance, relative to non-treated (left panel) samples was tested using ANOVA with a Dunnett's post test or IL1B-treated (right panel) samples was tested using a paired t-test.*, p < 0.05;**, p< 0.01; ***, p< 0.001.
103
IL1B treatedIL1B stimulatedNot IL1B treatedIL1B A stimulatedNot B 1.0 E+1 1.0 E+1 TNF A549 cells TNF HBE cells 1.0 1.0 30 1.0 E-1 1.0 E-1 1.0 E-2 1.0 E-2 20 1.0 E-3 1.0 E-3
1.0 E-4 10
∆Reaction ∆Reaction 1.0 E-4 (Ct) Cycle 1.0 E-5 1.0 E-5 0 1.0 E-6 1.0 E-6
2 6 10 14 18 22 26 30 34 38 42 2 6 10 14 18 22 26 30 34 38 42
HBE A549 A549 Cycle (Ct) Cycle (Ct) Figure 4.7 Comparison of CT values for TNF expression in A549 and HBE cells generated by RT-PCR. A, SYBR green RT-PCR for TNF mRNA was performed in A549 (left panel) and HBE cells (right panel) for non-stimulated and IL1B-treated cells in duplicates. In each case, the samples were analysed for TNF mRNA using primers specific for TNF. The RT-PCR conditions were similar for both A549 and HBE cells. Plots of ∆Reaction against CT are shown. The positions of reaction profiles corresponding to non-stimulated and IL1B-treated samples are indicated. B, CT values for TNF mRNA expression in both A549 and HBE cells, obtained from A, representative of at least 4 RT-PCR results, is plotted as means ± S.E. 4.3.8 Effect of ZFP36 siRNA on IL1B-induced TNF mRNA expression
The role of ZFP36 in the regulation of TNF mRNA expression was examined by using ZFP36- targeting siRNAs. Control siRNA targeted to LMNA had no effect on IL1B and IL1B plus dexamethasone-induced ZFP36 and TNF expression at any time (Fig. 4.8A & B).
A B time (h) 1 2 6 LMNA siRNA + + + + + + time (h) 1 2 6 Dex + + + + + + LMNA siRNA + + + + + + IL1B - + + + + - + + + + - + + + + Dex + + + + + + 150 *** IL1B - + + + + - + + + + - + + + + *** ** *** *** ** ZFP36 NS 100
mRNA 50 GAPDH (%IL1B) TNF/GAPDH 0 Figure 4.8 Effect of LMNA siRNA on ZFP36 and TNF expression. A, A549 cells were incubated with or without LMNA-specific siRNA. After 24 h, cells were treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Cells were harvested at 1, 2 or 6 h for western blot analysis of ZFP36 and GAPDH. Blots representative of at least 4 such experiments are shown. B, Cells treated as in A and harvested for real-time PCR analysis of TNF and GAPDH. Data (N = 4), normalized to GAPDH, are plotted as means ± S.E. Significance, using ANOVA with a Bonferroni's multiple comparison test is indicated. **, p< 0.01; ***, p< 0.001. NS = nonspecific band.
104 IL1B-induced ZFP36 expression was significantly inhibited by the two independent ZFP36- targeting siRNAs at all times tested (Fig. 4.9A). In respect of IL1B-induced TNF mRNA, ZFP36 knock-down did not produce any effect at 1 and 6 h, but significantly increased TNF mRNA expression at 2 h (Fig. 4.9A middle panel). This result further confirms that TNF mRNA is negatively regulated by ZFP36. Furthermore, IL1B-induced unspliced nuclear TNF RNA was also unaffected by ZFP36 silencing, and is consistent with post-transcriptional role for ZFP36 in the regulation of TNF expression (Fig. 4.9A, lower panels), and was further explored using the actinomycin D chase methodology. Thus, A549 cells were treated with LMNA or ZFP36-targeting siRNAs followed by treatment with IL1B for 90 min prior to actinomycin D chase (Fig. 4.9B).
IL1B-induced TNF mRNA decayed with a t½ of 30-40 min, and this was not affected by LMNA siRNA. However, silencing of ZFP36 significantly attenuated the loss of TNF mRNA (Fig. 4.9B).
This supports a post-transcriptional role for ZFP36 in the regulation of IL1B-induced TNF mRNA in A549 cells.
The effect of ZFP36 knock-down was also tested on IL1B plus dexamethasone-induced TNF mRNA and unspliced nuclear TNF RNA expression (Fig. 4.9A). Dexamethasone produced a significant attenuation of IL1B-induced TNF mRNA expression at all times. However, IL1B plus dexamethasone-induced TNF mRNA expression was not changed by ZPF36 knock-down.
Because the expression of IL1B-induced TNF mRNA was enhanced following ZFP36 silencing at
2 h, the actual percentage repression by dexamethasone was also increased at 2 h (Fig. 4.9A, right panels). Conversely, ZFP36 silencing did not produce any significant effect on unspliced nuclear
TNF RNA in the presence IL1B plus dexamethasone (Fig. 4.9A, lower panels).
105 A time (h) 1 2 6 ZFP36 siRNA 2 + + + + + + ZFP36 siRNA 1 + + + + + + LMNA siRNA + + + + + + Dex + + + + + + + + + time (h) 1 2 6 IL1B - + + + + + + - + + + + + + - + + + + + + NS ZFP36 siRNA 2 + + + ZFP36 ZFP36 siRNA 1 + + + LMNA siRNA + + + GAPDH IL1B+Dex + + + + + + + + + 300 *** *** *** *** 100 200 *** ** *** ***
*** * * *** 50
LMNA
siRNA)
siRNA) mRNA
100 Dexon
(%IL1B+
Relevant Effectof
(% IL1B (% TNF/GAPDH TNF/GAPDH 0 0 150 100 100 /U6
RNA 50
LMNA
siRNA) siRNA)
50 Dexon
(%IL1B+
Relevant Effectof
unTNF/U6 unTNF 0 IL1B (% 0
time post Act D (min) 45 B ZFP36 siRNA 2 + LMNA siRNA ZFP36 siRNA1 ZFP36 siRNA2 150 150 150 ZFP36 siRNA 1 + IL1B LMNA siRNA + 100 100 100 IL1B+siRNA IL1B + + + * 75 ** 50 50 * ** 50 *** **
mRNA 50 (% t =t (% 0) 0 TNF/GAPDH 0 0 25
0 20 40 60 0 20 40 60 0 20 40 60 mRNA (% t =t (%0) time (min) time (min) time (min) 0 TNF/GAPDH Figure 4.9 Effect of ZFP36 siRNA on IL1B-induced TNF mRNA expression. A, A549 cells were incubated with LMNA (control) or ZFP36-specific siRNAs for 24 h before treatment with IL1B (1 ng/ml) or IL1B plus dexamethasone (Dex, 1 μM) as indicated. Cells were harvested at 1, 2 or 6 h and total protein was prepared for western blot analysis of ZFP36 and GAPDH. Blots representative of at least 4 such experiments are shown. NS = nonspecific band. Cells were also harvested for real-time PCR analysis of TNF and GAPDH (middle panel) or unspliced nuclear (un) TNF RNA and U6 (lower panel). Data (N = 4) normalized to GAPDH or U6, were expressed as a percentage of LMNA siRNA plus IL1B-stimulated for each time and are plotted as means ± S.E. Significance was tested using ANOVA with a Newman-Keul multiple comparison test. Significance for specific comparisons are indicated. *, p < 0.05; **, p< 0.01; ***, p< 0.001. The effect of IL1B plus Dex expressed as a percentage of IL1B for each of the three individual siRNAs is plotted as a mean ± S.E. (right panels). B, A549 cells were incubated with LMNA or ZFP36- specific siRNAs for 24 h before being treated with IL1B (1 ng/ml) for 90 min. Actinomycin D (Act D, 10 μg/ml) was then added (t = 0) and the cells were harvested at the indicated times. RNA was extracted for real-time PCR analysis of TNF and GAPDH. Data (N = 4), normalized to GAPDH, are plotted as a percentage of t = 0 for each treatment as means ± S.E. Significance, relative to time matched IL1B-treated samples was tested using a paired t-test. *, p < 0.05; **, p < 0.01; ***, p < 0.001, (left panel). The effect of IL1B plus LMNA siRNA and IL1B plus ZFP36 siRNAs at 45 min post Act D treatment is plotted as a percentage of t = 0 for 45 min. Significance, relative to IL1B-treated samples was tested using ANOVA with a Dunnett's post test. **, p < 0.01, (right panel).
106 4.3.9 Effect of DUSP1 over-expression on ZFP36 and TNF mRNA expression
Since dexamethasone-induced DUSP1 represses IL1B-induced activation of MAPKs, and, as shown in this chapter, IL1B-induced ZFP36 is MAPK-dependent (Fig. 4.4B), the effect of DUSP1 over-expression on ZFP36 protein expression was also tested (382). The over-expression of
DUSP1 was confirmed by western blotting (Fig. 4.10). Control Ad-GFP virus was without any effect on DUSP1 expression. Inhibition of p38, JNK and ERK MAPK phosphorylation, and therefore activity, following DUSP1 over-expression are shown in the previous chapter and therefore were not reassessed in this chapter. ZFP36 expression was undetected in untreated cells and was not affected by DUSP1 or control GFP adenovirus (Fig. 4.10). IL1B-induced ZPF36 protein expression was unaffected by Ad-GFP, but Ad-DUSP1 almost completely inhibited ZFP36 expression at all times (Fig. 4.10).
In the case of TNF mRNA, both Ad-DUSP1 and control Ad-GFP had no effect on basal TNF mRNA expression (Fig. 4.10, lower panels). IL1B-induced TNF expression was not affected by
Ad-GFP. However, IL1B-induced TNF mRNA expression was significantly repressed by Ad-
DUSP1 at 1 h (Fig. 4.10, lower panels). This repression of TNF mRNA by DUSP1 over-expression was reduced at 2 h, and, surprisingly, by 6 h, there was no repression by Ad-DUSP1. These data suggest that even though the early induction of IL1B-induced TNF mRNA is MAPK-dependent, at longer time points, enhanced expression of DUSP1 by switching off MAPKs may overcome this effect on TNF mRNA expression.
107 time (h) 1 2 6 Ad-DUSP1 + + + + + + Ad-GFP + + + + + + IL1B - - - + + + - - - + + + - - - + + + DUSP1
GAPDH NS ZFP36 GAPDH 1.5 * 1.0 *
ZFP36 0.5
protein
GAPDH / 0.0 150 *** *** **
100 *** TNF
mRNA 50
GAPDH
/ (% of(% IL1B at each each at time) 0 Figure 4.10 Effect of DUSP1 over-expression on ZFP36 and TNF mRNA expression. A549 cells were either not infected or infected with Ad5-DUSP1 or Ad5-GFP (control) at a MOI of 10 for 24 h before IL1B treatment (1 ng/ml). Cells were harvested after 1, 2 or 6 h and total protein was prepared for western blot analysis of DUSP1, ZFP36 and GAPDH. Blots representative of at least 4 such experiments are shown. NS = nonspecific band. Following densitometric analysis for ZFP36, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. (middle panels). Significance, using ANOVA with a Dunnett's post test is indicated. *, p < 0.05. Cells were also harvested for real-time PCR analysis of TNF and GAPDH (lower panels). Data (N = 4), normalized to GAPDH, are plotted as means ± S.E. Significance relative to time-matched IL1B and Ad5-GFP- treated samples, was tested using ANOVA with a Bonferroni post test. **, p< 0.01; ***, p< 0.001.
4.3.10 Effect of MAPK inhibitors on TNF expression
A549 cells were treated with the maximally effective concentrations of SB203580 (10 µM), U0126
(10 µM) or JNK inhibitor 8 (JNK-IN-8) (10 µM). TNF mRNA was strongly induced by IL1B at all times, and was not affected by any of the MAPK inhibitors at either 1 or 2 h post-IL1B treatment
(Fig. 4.11A). Conversely, IL1B-induced TNF mRNA expression was significantly enhanced by
SB203580 at 6 h (Fig. 4.11A). Since MAPK pathways are inhibited by both dexamethasone and
DUSP1 over-expression, the effect of the three MAPK pathways inhibitors in combination was also assessed. Combined inhibition of MAPKs produced a substantial and significant 81.2 ± 2.4%
108 loss of TNF mRNA following 1 h of IL1B treatment (Fig. 4.11A, right panel). However, by 2 h post-IL1B treatment, TNF expression was 54.7 ± 6.2% of IL1B control, and, at 6 h, there was an enhancement to 183.2 ± 22.2 % relative to IL1B treatment alone. Thus, even though TNF mRNA expression was attenuated by the combined inhibition of MAPKs at early time points, at later times, this repressive effect of MAPK inhibitors was both reversed and overcome.
The role of transcriptional and post-transcriptional mechanisms in the regulation of TNF by
MAPKs was assessed by measuring the accumulation of unspliced nuclear TNF RNA and TNF mRNA stability respectively. Accumulation of unspliced nuclear TNF RNA was not affected by individual MAPK inhibitors at 1 or 2 h (Fig. 4.11B). However, at 4 or 6 h post-IL1B treatment, while JNK-IN-8 showed a very modest effect, SB203580 and U0126 produced quite substantial, but not significant, enhancements of unspliced nuclear TNF RNA. In respect of combined MAPK inhibitors, IL1B-induced unspliced nuclear TNF RNA was significantly reduced at 1 h. Even though, there was no effect at 2 h, the accumulation of unspliced nuclear TNF RNA was enhanced by combined MAPK inhibitors at 4 and 6 h (Fig. 4.11B). These data suggest that the transcription of TNF at early times is MAPK-dependent. However, activation of MAPKs results in repression of TNF mRNA at later times.
In terms of the mRNA stability, TNF mRNA was very stable following 30 min IL1B treatment
(Fig. 4.2C). This effect was re-confirmed in Fig. 4.11C, where there was no overall loss of TNF mRNA over 60 min following actinomycin D chase. However, SB203580 produced an overall
~50% reduction in TNF mRNA post-actinomycin D treatment over this period. Pilot actinomycin
D chase experiments showed that SB203580 had no effect on the loss of TNF mRNA at either 2 or 4 h post-IL1B treatment (data not shown).
109 A 1.0 *** NT SB203580 UO126 JNK-IN-8 SB+UO+J8 IL1B 200 * 200 200 200 * 0.5
100 100 100 100
mRNA mRNA
** **
*** time) each **
TNF/GAPDH TNF/GAPDH 0.0 (% ofat IL1B 0 0 0 0 0 2 4 6 1 2 6 1 2 6 1 2 6 1 2 6 time (h) time (h) time (h) time (h) time (h)
B 1.5 NT SB203580 UO126 JNK-IN-8 SB+UO+J8 *
1.0 *** IL1B 400 400 400 400 RNA RNA 0.5 200 200 200 200
*** * *
unTNF/U6
unTNF/U6 each time) each
0.0 (% ofat IL1B 0 0 0 0 0 2 4 6 1 2 4 6 1 2 4 6 1 2 4 6 1 2 4 6 time (h) time (h) time (h) time (h) time (h)
C IL1B treatment time (min) 30 90 120 180 240 300 SB203580 + + + + + IL1B IL1B + + + + + + + + + + 200 IL1B + * SB203580 150 50 50 50 50 * mRNA 100 *
(% t =t 0) (% * 100
25 25 25 25 mRNA TNF/GAPDH * * 0 =t (%0) 50
0 20 40 60 TNF/GAPDH 0 0 0 0 0 time (min) Figure 4.11 Effect of MAPK inhibitors on TNF expression. A, A549 cells were either not treated (NT), treated with IL1B (1 ng/ml) or pre-treated with either UO126, SB203580, JNK inhibitor 8 (JNK-IN-8) or a combination of UO126, SB203580 plus JNK-IN-8 each at 10 µM for 30 min prior to IL1B stimulation. Cells were harvested after 1, 2 or 6 h for real-time PCR analysis of TNF and GAPDH. Data (N = 4) were normalized to GAPDH and are plotted as means ± S.E. The effect of IL1B + MAPK inhibitors for TNF mRNA is plotted as a percentage of IL1B at the indicated times. B, RNA from the experiments in A were subjected to real-time PCR analysis of unspliced nuclear (un) TNF RNA and U6. Data (N = 4) were normalized to U6 and are plotted as means ± S.E. The effect of IL1B + MAPK inhibitors for unspliced nuclear (un) TNF mRNA is plotted as a percentage of IL1B at the indicated times. For both A & B, significance, relative to either non-treated or IL1B-treated samples was tested using ANOVA with a Bonferroni post test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. C, A549 cells were treated with IL1B (1 ng/ml) or pre- treated for 30 min with SB203580 at 10 µM prior to IL1B stimulation for 30 min. Following this (t = 0), actinomycin D (Act D, 10 μg/ml) was added and cells were harvested at the indicated times. RNA was extracted for real-time PCR analysis of TNF and GAPDH. Data (N = 4) normalized to GAPDH, are plotted as a percentage of t = 0 for each treatment as means ± S.E. (left panel). Data following 45 min of Act D treatment are shown (right panel). A549 cells were also treated with IL1B or IL1B + SB203580 at 10 µM for 90, 120, 180 and 240 min, prior to the addition of Act D (t = 0) for 45 min and analyzed as above (Right panels). Significance, relative to time matched IL1B-treated samples was tested using a paired t-test. *, p < 0.05.
Therefore, the loss of TNF mRNA was assessed following IL1B and IL1B plus SB203580 treatments for 90, 120, 180 and 240 min. Similar to 30 min of IL1B treatment, SB203580 significantly attenuated IL1B-induced TNF mRNA loss following 90 min IL1B stimulation.
110 However, SB203580 had no effect on IL1B-induced TNF mRNA loss following 120, 180 or 240 min treatments (Fig. 4.11C, right panel). These data suggest that at shorter IL1B treatment times, inhibition of p38 can destabilize and/or reduce nuclear RNA processing of TNF mRNA, but at longer treatment times there was no obvious stabilization of TNF mRNA. This could be attributed to the fact that in the absence of prior p38-dependent mRNA stabilization (occurring at 30 - 90 min post IL1B), there is no possibility of any subsequent p38-dependent mRNA destabilisation.
These data therefore support the possibility of existence of MAPK-dependent feed-forward mechanisms of TNF mRNA inhibition.
4.3.11 Effect of DUSP1 knock-down on ZFP36 protein and TNF mRNA expression
In the previous chapter, siRNA-mediated loss of DUSP1 enhanced the expression of IL1B-induced phosphorylated MAPKs at 1 h. Since IL1B-induced expression of both ZFP36 and TNF was
MAPK-dependent, the effect of DUSP1 loss on TNF and ZFP36 expression was also assessed.
DUSP1 protein was robustly induced by IL1B at 1 h, and this was substantially inhibited by the two DUSP1-targeting siRNAs (Fig. 4.12A). DUSP1 knock-down had no effect on TNF mRNA at either 1 or 2 h post-IL1B treatment (Fig. 4.12B, upper panel). However, DUSP1-targeting siRNAs produced a significant loss of IL1B-induced TNF mRNA expression at 6 h (Fig. 4.12B, upper panel). In respect of unspliced nuclear TNF RNA, DUSP1 knock-down had no effect at 1 or 2 h, and, at 6 h, a trend towards reduced expression was observed (Fig. 4.12B, lower panel). In the case of IL1B-induced ZFP36 expression, DUSP1 silencing produced a marginal or no effect at 1 h post- treatment (Fig. 4.12A). However, at 2 h following IL1B treatment, the expression of the upper
ZFP36 band was markedly enhanced by the two DUSP1-targeting siRNAs (Fig. 4.12A).
111 A time (h) 1 2 6 DUSP1 siRNA 2 + + + + + + DUSP1 siRNA 1 + + + + + + LMNA siRNA + + + + + + Dex + + + + + + + + + IL1B - + + + + + + - + + + + + + - + + + + + + NS DUSP1 GAPDH NS ZFP36 GAPDH B time (h) 1 2 6 DUSP1 siRNA 2 + + + + + + time (h) 1 2 6 + + + + + + DUSP1 siRNA 1 DUSP1siRNA 2 + + + LMNA siRNA + + + + + + + + Dex + + + + + + + + + DUSP1siRNA 1 + + + + - + + + + + + - + + + + + + - + + + + + + LMNA siRNA IL1B IL1B+Dex + + + + + + + + + 200 *** *** ** *** *** ** ** 100 ** *** 100 ** 50
LMNA *
mRNA siRNA)
siRNA)
Dexon
(%IL1B+
Effectof Relevant
(% IL1B (% TNF/GAPDH 0 TNF/GAPDH 0 150 ** ** 100 100 /U6
RNA 50
LMNA
siRNA) siRNA)
50 Dexon
(%IL1B+
Effectof Relevant
unTNF/U6 unTNF (% IL1B (% 0 0 Figure 4.12 Effect of DUSP1 knock-down on ZFP36 and TNF expression. A, A549 cells were incubated with LMNA (control) or DUSP1-specific siRNAs for 24 h before treatment with IL1B (1 ng/ml) or IL1B plus dexamethasone (Dex, 1 μM) as indicated. Cells were harvested at 1, 2 or 6 h and total protein was prepared for western blot analysis of DUSP1, ZFP36 and GAPDH. Blots representative of at least 7 – 9 such experiments are shown. NS = nonspecific band. B, Cells were treated as in A and harvested after 1, 2 or 6 h for real-time PCR analysis of TNF and GAPDH (upper left panel) or unspliced nuclear (un) TNF and U6 (lower left panel). Data (N = 4 - 8), normalized to GAPDH (for TNF mRNA) or U6 (for unspliced nuclear (un) TNF RNA), were expressed as a percentage of LMNA siRNA plus IL1B-stimulated for each time and are plotted as means ± S.E. Significance was tested using ANOVA with a Newman-Keul multiple comparison test. Significance for specific comparisons is indicated. **, p< 0.01; ***, p< 0.001. The percent IL1B plus dexamethasone/IL1B for the LMNA siRNA was also plotted and compared with that for each of the DUSP1-specific siRNAs using ANOVA with a Dunnett's post test. **, p< 0.01 (right panel).
The effect of DUSP1 knock-down in the presence of IL1B plus dexamethasone was also examined.
Co-treatment with dexamethasone attenuated IL1B-induced ZFP36 expression (Fig. 4.12A).
Conversely, the silencing of DUSP1, at 2 h, enhanced the expression of ZFP36 in the presence of
IL1B plus dexamethasone (Fig. 4.12A). In the case of TNF mRNA, while IL1B-induced TNF mRNA was repressed significantly by dexamethasone at all times, DUSP1-targeting siRNAs
112 produced no effect (Fig. 4.12B, upper panel). Nevertheless, the substantial loss of IL1B-induced
TNF mRNA that occurred at 6 h following DUSP1-targeting siRNAs resulted in a reduced percentage repression by dexamethasone at 6 h (Fig. 4.12B, upper right panel). Furthermore, the knock-down of DUSP1 revealed no significant effects on unspliced nuclear TNF RNA, suggesting that TNF transcription rate was not affected by DUSP1 loss in the presence of IL1B plus dexamethasone (Fig. 4.12B, lower panel). Since ZFP36 reduces TNF mRNA expression and stability (Fig. 4.9), the data presented in this section suggest that enhanced expression of ZFP36, at 2 h, following DUSP1 knock-down may potentiate the loss of TNF mRNA, at 6 h, in the presence of IL1B. Thus, the effect of ZFP36 silencing on TNF mRNA expression and stability was assessed in the presence of DUSP1 siRNAs.
4.3.12 Role of ZFP36 in the loss of TNF mRNA following DUSP1 knock-down
The role of ZPF36 was evaluated by transfecting the cells with DUSP1 and ZFP36-targeting siRNAs either alone or in combination. In each case, there was a significant knock-down of both
DUSP1 and ZFP36 (Fig. 4.13A). As shown in Fig. 4.9A, TNF mRNA expression was only marginally affected by the loss of IL1B-induced ZFP36 at 6 h (Fig. 4.13A, lower panel). In marked contrast, knock-down of DUSP1 alone produced a significant loss of TNF mRNA at 6 h (Fig.
4.13A, lower panel). This observation is in line with the data shown in Fig. 4.12B. However, additional loss of ZFP36, in DUSP1 inhibited cells, significantly reduced the repressive effect of
DUSP1 siRNAs (Fig. 4.13A, lower panel). These data confirm a role for ZFP36 in the enhanced loss of TNF mRNA following DUSP1 knock-down.
Since ZFP36 is an mRNA destabilizing protein, the stability of TNF mRNA was also assessed in the presence of DUSP1-targeting siRNAs. As shown in Fig. 4.2, IL1B-induced TNF mRNA was
113 decayed to ~50% following 45 min of actinomycin D treatment (Fig. 4.13B). DUSP1-targeting siRNAs significantly increased the loss of TNF mRNA (Fig. 4.13B) suggesting that in addition to enhancing ZFP36 expression at 2 h (Fig. 4.12A), DUSP1-targeting siRNAs also attenuate TNF mRNA stability. Thus, the effect of ZFP36 knock-down on TNF mRNA stability was tested in the presence and absence of DUSP1 siRNAs. The loss of TNF mRNA stability was significantly attenuated by ZFP36 siRNAs (Fig. 4.13B) and was consistent with previously shown effect of
ZFP36 knock-down on TNF mRNA stability in Fig. 4.9B. Thus, these data confirm that the stability of TNF mRNA is negatively regulated by ZFP36. In addition, the repressive effect of
DUSP1 siRNAs on TNF mRNA stability was significantly inhibited by the additional loss of
ZFP36 (Fig. 4.13B). In fact, the decay of TNF mRNA was similar to ZFP36 knock-down (Fig.
4.13B). These data therefore confirm that the enhanced decay of TNF mRNA following DUSP1 knock-down was ZFP36-dependent.
The effect of simultaneous knocking-down of DUSP1 and ZFP36 was assessed in the context of
IL1B and IL1B plus dexamethasone. In each case, the expression of both DUSP1 and ZFP36 was robustly inhibited by DUSP1- and ZFP36-targeting siRNAs (Fig. 4.13C). In respect of IL1B- induced TNF mRNA, combined knock-down of DUSP1 and ZFP36 produced a modest, but significant, repression at 1 h, no effect at 2 h, and a significant repression at 6 h (Fig. 4.13D, left panel). The effect of combined knock-down at 6 h was consistent with the data shown in Fig.
4.13A. IL1B-induced TNF mRNA was significantly repressed by dexamethasone at all times (Fig.
4.13D, left panel) and is similar to the data shown in Fig. 4.2A. There was no effect of combined
DUSP1 plus ZFP36 siRNAs on IL1B plus dexamethasone-induced TNF mRNA at 1 and 2 h (Fig.
4.13D, left panel). However, at 6 h, as a percentage of IL1B+dexamethasone+LMNA siRNA
114 A time (h) 1 C time (h) 1 ZFP36 siRNA 2 + + DUSP1 + ZFP36 siRNA 2 + + DUSP1 siRNA 2 + + ZFP36 siRNA 1 + + DUSP1 + ZPF36 siRNA 1 + + DUSP1 siRNA 1 + + LMNA siRNA + + Dex + + + IL1B - + + + + + + + IL1B - + + + + + + DUSP1 NS DUSP1 NS GAPDH GAPDH NS NS ZFP36 ZFP36 GAPDH GAPDH
150 6 h D * DUSP1 + ZFP36 siRNA 2 + + 100 * DUSP1 + ZPF36 siRNA 1 + + DUSP1 + ZFP36 siRNA 2 + ### ## + + ### ### LMNA siRNA DUSP1 + ZFP36 siRNA 1 +
mRNA 50
(%IL1B siRNA) +LMNA Dex + + + LMNA siRNA +
TNF/GAPDH 0 IL1B - + + + + + + IL1B+Dex + + + 150 *** 1 h * *** 100 B ** *** time post Act D (min) 45 100 * 50 ZFP36 siRNA 2 + + 50 ZFP36 siRNA 1 + + DUSP1 siRNA 2 + + 0 0 DUSP1 siRNA 1 + + 150 *** 2 h IL1B + + + + + + + *** 100 150 *** 100 *** ***
Relevant Relevant siRNA) 50
### mRNA
100 ### ### ### 50
TNF/GAPDH
TNF/GAPDH 0 0 mRNA
50 EffectDexonof
# #
(% t =t 0) (% (%IL1B+LMNA (%IL1B+LMNA siRNA) TNF/GAPDH 150 *** 0 *** 6 h 100 100 ** *** IL1B (% *** 50 50 *** ** 0 0 Figure 4.13 Role of ZFP36 in the enhanced loss of TNF mRNA following DUSP1 knock- down. A, A549 cells were incubated with LMNA (control) or DUSP1- and/or ZFP36-specific siRNAs for 24 h before being treated with IL1B (1 ng/ml). While not indicated, LMNA siRNA was added to all the treatment groups except for the combination siRNA treatments to maintain a constant concentration of siRNAs across all the treatments. Cells were harvested after 1 h and total proteins were prepared for western blot analysis of DUSP1, ZFP36 and GAPDH. Blots representative of at least 4 such experiments are shown (top panel). NS = nonspecific band. Cells were also harvested after 6 h for real-time PCR analysis of TNF and GAPDH. Data (N = 4) normalized to GAPDH were expressed as a percentage of LMNA siRNA plus IL1B and plotted as means ± S.E. Significance was tested using ANOVA with a Newman-Keul multiple comparison test (lower panel). B, A549 cells were treated as in A before being treated with IL1B (1 ng/ml) for 2 h. Following this (t = 0), actinomycin D (Act D, 10 μg/ml) was added and cells were harvested after 45 min. RNA was extracted for real-time PCR analysis of TNF and GAPDH. Data (n = 6) normalized to GAPDH, are plotted as a percentage of t = 0 for each treatment as means ± S.E. For both A & B, significance between: LMNA control siRNA plus IL1B and each of the DUSP1 and/or ZFP36 targeting siRNAs plus IL1B is shown by #. Other comparisons are specifically indicated. */#, p < 0.05; ##, p< 0.01; ***/###, p< 0.001. C, A549 cells were incubated with LMNA (control) or DUSP1 and ZFP36-specific siRNAs for 24 h before being treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (Dex, 1 μM) as indicated. Cells were harvested after 1 h and total proteins were prepared for western blot analysis of DUSP1, ZFP36 and GAPDH. Blots representative of at least 4 such experiments are shown. NS = nonspecific band. D, A549 cells were treated as in C and harvested after 1, 2 or 6 h for real-time PCR analysis of TNF and GAPDH. Data (N = 4) normalized to GAPDH were expressed as a percentage of LMNA siRNA plus (cont.…)
115 Figure 4.13 Continued. IL1B-stimulated for each time and are plotted as means ± S.E. Significance was tested using ANOVA with a Newman-Keul multiple comparison test. Significance for specific comparisons are indicated.*, p < 0.05; **, p< 0.01; ***, p< 0.001 (left panel).The percent IL1B plus dexamethasone/IL1B for the LMNA siRNA was also plotted and compared with that for each of the DUSP1 and ZFP36-specific siRNAs using ANOVA with a Dunnett's post test.*, p < 0.05 (right panel).
(16.7 ± 1.0 %), the expression of IL1B plus dexamethasone-induced TNF mRNA was significantly reduced (9.7 ± 0.6 & 8.3 ± 1.2 %) in the presence of the DUSP1 plus ZFP36 combined siRNAs
(Fig. 4.13D, left panel). Conversely, because each siRNA produced a significant loss of IL1B- induced TNF expression at 1 h, there was a reduction in the percentage repression by dexamethasone at 1 h (Fig. 4.13D, right panels). However, the percentage repression by dexamethasone at either 2 or 6 h was not changed (Fig. 4.13D, right panels).
4.3.13 Effect of MAPK inhibitors and DUSP1 over-expression in the regulation of TNF protein expression
IL1B-induced release of TNF into the supernatants was almost totally prevented by SB203580 and was significantly inhibited by both U0126 (74.0 ± 8.8%) and JNK-IN-8 (69.0 ± 20.2%) (Fig.
4.14A). Similarly, the combined inhibition of MAPKs also completely inhibited TNF release.
While the control, Ad-GFP virus, had no effect, DUSP1 over-expression significantly attenuated
TNF release into the supernatants (Fig. 4.14B). Thus, these data suggest that IL1B-induced TNF protein release is MAPK-dependent.
116 A time (h) 6 B JNK-IN-8 + + time (h) 6 UO126 + + Ad-DUSP1 + + SB203580 + + Ad-GFP + + IL1B - + + + + + IL-1B - - - + + + 200 *** 100 ** *** * ***
100 *** 50
TNF
TNF
(pg/ml)
(pg/ml)
release release 0 0 Figure 4.14 Effect of MAPK inhibitors and DUSP1 over-expression in the regulation of TNF protein expression. A, A549 cells were either not treated, treated with IL1B (1 ng/ml) or pre- treated with either UO126, SB203580, JNK inhibitor 8 (JNK-IN-8) or a combination of UO126, SB203580 plus JNK-IN-8 each at 10 µM for 30 min prior to IL1B stimulation. B, A549 cells were either not infected or infected with Ad5-DUSP1 or Ad5-GFP (control) at a MOI of 10 for 24 h before IL1B treatment (1 ng/ml). For both A & B, cells supernatants were harvested after 6 h for TNF release measurement. Data (N = 4) expressed in pg/ml are plotted as means ± S.E. Significance, using ANOVA with a Dunnett's post test (for A) or with a Bonferroni's multiple comparison test (for B) is indicated. *, p < 0.05; **, p< 0.01; ***, p< 0.001.
4.3.14 Effect of DUSP1- and ZFP36-targeting siRNAs in the regulation of TNF protein expression
To assess the role of ZFP36 and DUSP1 in regulating TNF protein expression, TNF release was measured from the experiments shown in Figs. 4.9 and 4.12 respectively. Whereas a control siRNA targeted to LMNA had no effect on IL1B-induced TNF release at 6 (Fig. 4.15), knock-down of
ZFP36 significantly enhanced the release of TNF following IL1B treatment (Fig. 4.16A) and is consistent with the enhanced expression of TNF mRNA observed at 2 h in Fig. 4.9.
time (h) 6 Figure 4.15 Effect of LMNA siRNA on TNF protein LMNA siRNA + + expression. A549 cells were incubated with or without Dex + + LMNA-specific siRNA. After 24 h, cells were treated with IL1B - + + + + IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) 150 *** *** ) as indicated. Cells supernatants were harvested after 6 h for
100 TNF release measurement. Data (N = 4) expressed in pg/ml TNF
pg/ml 50 (%IL1B
release release are plotted as means ± S.E. Significance, using ANOVA 0 with a Bonferroni's multiple comparison test is indicated. *, p < 0.05; **, p< 0.01; ***, p< 0.001.
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A B time (h) 6 time (h) 6 ZFP36 siRNA 2 + + time (h) 6 DUSP1 siRNA 2 + + time (h) 6 ZFP36 siRNA 1 + + ZFP36 siRNA 2 + DUSP1 siRNA 1 + + DUSP1 siRNA 2 + LMNA siRNA + + LMNA siRNA + + ZFP36 siRNA 1 + Dex + + + DUSP1 siRNA 1 + Dex + + + LMNA siRNA + LMNA siRNA + IL1B - + + + + + + IL1B+Dex + + + IL1B - + + + + + + IL1B+Dex + + + 60 * 12 **
** *** 100 40 *** *** 8 ** 100
*** TNF
50 TNF 4 50
(pg/ml) (pg/ml)
20 release
release release
Dexon siRNA)
Dexon siRNA)
release
release release
Relevant Effectof
Effectof Relevant (% IL1B (% 0 IL1B (% 0 0 0 C time (h) 6 time (h) 6 DUSP1 siRNA 2 + + + + DUSP1 siRNA 1 + + DUSP1 + ZFP36 siRNA 2 time (h) 6 ZFP36 siRNA 2 + + DUSP1 + ZPF36 siRNA 1 + + DUSP1 + ZFP36 siRNA 2 + ZFP36 siRNA 1 + + LMNA siRNA + + DUSP1 + ZFP36 siRNA 1 + + + + IL1B - + + + + + + + Dex LMNA siRNA + IL1B - + + + + + + 40 IL1B+Dex + + + * *** *** 80 ** *** *** 100 20 ** ***
TNF 40 50
TNF
siRNA)
release (pg/ml)
Dexon
release (pg/ml)
release release
Relevant Effectof 0 0 IL1B (% 0
Figure 4.16 Effect of DUSP1- and ZFP36- targeting siRNAs in the regulation of TNF protein expression. A, B & C, cells were incubated with LMNA (control) or DUSP1-and/or ZFP36- specific siRNAs for 24 h before being treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (Dex, 1 μM) as indicated. Cells supernatants were harvested after 6 h for TNF release measurement. Data (N = 4) expressed in pg/ml are plotted as means ± S.E. Significance, using ANOVA with a Newman-Keul multiple comparison test is indicated. *, p < 0.05; **, p< 0.01; ***, p< 0.001 (left panels). In each case, the percent IL1B plus dexamethasone/IL1B for the LMNA siRNA was also plotted and compared with that for each of the DUSP1- and ZFP36-specific siRNAs using ANOVA with a Dunnett's post test (right panels). For C (left panel): LMNA siRNA was added in all the treatment groups except for the combination siRNA treatments in order to maintain a constant siRNA concentration across all the treatments.
However, DUSP1 siRNAs were without any obvious effect on TNF release (Fig. 4.16B). The effects of combined knock-down of ZFP36 and DUSP1 were also examined (Fig. 4.16C). Thus, as shown in Fig. 4.16A, IL1B-induced TNF release was augmented by ZFP36 siRNA, whereas
DUSP1 siRNA was without any marked effect (Fig. 4.16C). In the presence of combined ZFP36 plus DUSP1 siRNA, TNF release was higher than following DUSP1 knock-down alone, but in each case this was not significantly different from the effect of ZFP36-targeting siRNAs alone
(Fig. 4.16C, left panel). In the presence of IL1B plus dexamethasone, TNF release was not
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significantly changed by the knock-down of ZFP36 and DUSP1 either individually (Fig. 4.16A &
B) or in combination (Fig. 4.16C, middle panel). Furthermore, the percentage repression by dexamethasone was also unaltered by either treatment (Fig. 4.16A, B & C, right panels).
4.4 Discussion
In the current chapter, the role of phosphatase, DUSP1, and the mRNA destabilizing protein,
ZFP36, in the regulation of TNF mRNA, as a model ARE-containing transcript, was examined. In this context, IL1B-induced ZFP36 reduces TNF mRNA stability and exerts feed-forward inhibition on TNF mRNA and protein expression in A549 cells. Thus, the data presented here clearly explain why mechanistically simple explanations, not taking into account overall network function, are unlikely to provide a sufficient explanation for repression by glucocorticoids.
In A549 cells, IL1B-induced ZFP36 expression was MAPK-dependent, and primarily mediated by the p38 MAPK. In contrast, inhibition of MAPKs, in particular p38 MAPK, enhanced IL1B- induced TNF mRNA expression and was appeared to be primarily associated with enhanced transcription. These data therefore suggest the existence of, as yet uncharacterised, MAPK- dependent mechanisms that negatively regulate TNF transcription. In terms of the stability of TNF mRNA, following 30 min of IL1B stimulation, TNF mRNA appeared to be stable within the 45 min of actinomycin D chase. This contrasts with longer IL1B treatment times, where ZFP36 is expressed and TNF mRNA decayed to 50% within 30 - 40 min of actinomycin D treatment. In the presence of the p38 MAPK inhibitor, SB203580, the apparent mRNA stabilisation, observed following 30 min of IL1B treatment, did not occur and at longer times there was no destabilisation of what was presumably an already unstable transcript. These data and interpretation are consistent
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with previous findings in macrophages where TNF mRNA was stabilized transiently following 30
- 60 min of LPS treatment, but this stabilisation was lost within 2 h post-LPS treatment (383).
Similarly, a role for p38 MAPK in the stabilisation of other ARE-containing mRNAs, including
IL8, PTGS2 and TNF has also been indicated (227, 376, 384-386).
In the previous chapter, IL1B-induced MAPK phosphorylation was enhanced following DUSP1 knock-down at 1 h (Fig. 4.17A). Equally, as shown in this chapter, silencing of DUSP1, by enhancing the expression of ZFP36 protein at 2 h, subsequently increased the loss of the ARE- containing TNF mRNA at 6 h (Fig. 4.17A). This regulatory loop is therefore consistent with a previously indicated role for p38 MAPK in inducing the expression and activity of ZFP36 to destabilize ARE-containing mRNAs, such as TNF (309, 387). In addition, the observed attenuation of IL1B-induced TNF mRNA following DUSP1 silencing was not primarily associated with reduced TNF transcription, although some loss of transcription was evident at 6 h. Rather, the enhanced loss of TNF mRNA following DUSP1 knock-down was inhibited by further knock-down of ZFP36. This was mainly attributed to reduced TNF mRNA stability, an effect that was also blocked by ZFP36 silencing. Thus, these data directly confirm that loss of DUSP1 enhances ZFP36 expression to increase negative feed-forward control and attenuate TNF mRNA stability (Fig.
4.17A). Since the loss of ZFP36 did not completely prevent all the effects of DUSP1 knock-down on TNF mRNA expression, the possibility of additional non-ZFP36-dependent, but MAPK- dependent, regulation of TNF mRNA expression is suggested. In this regard, AREs are targeted by a number of RNA binding proteins, including HuR (ELAVL1), AUF1 (HNRNPD) and KHSRP
(220, 221, 223). These factors, by competing for ARE binding, inhibit the biosynthesis of
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inflammatory cytokines and chemokines (214, 215). In this regard, mice deficient in AUF1 show exaggerated inflammatory responses following endotoxin activation due to the enhanced expression of TNF and IL1B (388). Similarly, the translation of TNF in macrophages was profoundly inhibited by the over-expression of HuR (389). Thus, TNF expression can also be negatively regulated by non ZFP36-dependent mechanisms.
The above findings highlight a number of key points in respect of the regulation of TNF mRNA expression by glucocorticoids. The IL1B-induced accumulation of unspliced nuclear TNF RNA was partially attenuated by dexamethasone suggesting that dexamethasone targets TNF transcription. This effect was largely reflected in TNF mRNA, which was modestly, but significantly, repressed by dexamethasone at all times. While transcriptional repression was clearly important, actinomycin D chase experiments showed that the loss of TNF mRNA was also increased by dexamethasone. Thus, as has been observed for other inflammatory genes, transcriptional and post-transcriptional processes may both contribute to the repressive effects of glucocorticoids on TNF mRNA (254, 390). Since MAPK pathways play an important role in transcriptional and post-transcriptional processes, and are strongly repressed by dexamethasone, the implication of MAPK pathways in these processes points to possible repressive mechanisms
(227, 281, 291, 293, 298, 300). However, with co-treatment, there is little repression of MAPK phosphorylation by dexamethasone at 30 min post-IL1B treatment (Fig. 4.17B) (114, 283, 382).
Therefore, this is unlikely to be responsible for the observed repression of TNF mRNA by dexamethasone at 30 min. In this context, since dexamethasone-induced repression of TNF mRNA was unaffected in the presence of the translational blocker, cycloheximide, effects due to
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glucocorticoid-induced gene expression can be ruled out, and this therefore supports a role for direct transrepression by NR3C1 (273) (see chapter six, section 6.4 for further details).
In the case of ZFP36, since ZFP36 expression was MAPK-dependent, dexamethasone produced a modest inhibition of IL1B-induced ZFP36 expression at 1 and 2 h (Fig. 4.17B). This was consistent with previously shown repressive effect of dexamethasone on IL1B- and LPS-induced ZFP36 in
A549 cells and macrophages, respectively (308, 391). Contrary to this, a number of studies report that ZFP36 can also be induced by glucocorticoids (306, 308, 317, 318). This also holds true for the data shown in this chapter. However, the inducibility of ZFP36 by glucocorticoids in A549 cells was relatively modest. In any case, this glucocorticoid-mediated induction of ZFP36 may allow the expression of ZFP36 to be restored or maintained at longer times (i.e. 6 h and thereafter) following IL1B plus dexamethasone treatment (Fig. 4.17B). Nevertheless, loss of MAPK activity by dexamethasone-dependent effector mechanisms will reduce ZFP36-mediated negative feed- forward control (Fig. 4.17B). While this should enhance TNF mRNA expression at longer times, this does not happen in the presence of glucocorticoids. In fact, there was more than 50% repression of TNF mRNA by dexamethasone at 6 h post-IL1B treatment (Fig. 4.2A). In addition, silencing of DUSP1 or ZFP36, either alone or in combination, did not affect the repression of TNF mRNA by dexamethasone at any time. Therefore, a role for additional, non-DUSP1/non-ZFP36- dependent effector mechanisms of repression by glucocorticoids is predicted (254, 392) (Fig.
4.17B).
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The above data document the existence of the regulatory mechanisms illustrated in Fig. 4.1 in respect of TNF mRNA expression and a similar scenario was also evident for protein release. In
A549 cells, TNF protein was released rapidly into the supernatants following peak mRNA expression. This reached ~100 pg/ml (~50 pg/106 cells), a level that is within the range (30 – 900 pg/106 cells) that are produced by mast cells, which are a physiologically relevant source of TNF
(393-397). Furthermore, in human airway epithelial cells, low nanomolar levels of TNF were sufficient to produce maximal responses, including cytokine release and activation of NF-κB
(398). In addition, low picomolar levels (~10 nM) of TNF were also capable of producing responses, particular in the context of co-stimulants such as histamine (398). Thus, the low amounts of TNF produced by A549 and primary HBE cells could be sufficient to elicit a biological response. In addition, since membrane-tethered TNF is biologically active, the expression of the membrane tethered uncleaved, ~25 kDa, TNF was also examined (399-401). Uncleaved TNF expression induced by IL1B was similar in both A549 and primary HBE cells, and in each case, dexamethasone produced a significant repression of IL1B-induced ~25 kDa TNF. Furthermore, in
A549 cells, while the expression of the IL1B-induced uncleaved TNF had largely diminished by
6 h, cell-associated TNF was maintained at a relatively higher level. This membrane tethered and/or cell-associated TNF may play key roles in the rapid activation of resident or infiltrating cells. However, the analysis of the functional relevance are beyond the scope of the current chapter.
Even though inhibition of MAPK significantly inhibited TNF protein expression, DUSP1 silencing had no effect on TNF release. In contrast, TNF release was significantly increased by ZFP36 silencing and indicates a key role for ZFP36 in the negative regulation of TNF translation. While
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the exact mechanism of this repression is unclear, it may involve ZFP36-dependent loss of the polyA tail, which, by decreasing the translation efficiency and mRNA stability can reduce TNF biosynthesis (215, 377, 402). Thus, while transient increases in MAPK activation following
DUSP1 silencing promoted MAPK-dependent TNF synthesis, the corresponding enhancement of
ZFP36 expression following DUSP1 loss could counterbalance this effect resulting in no net effect of DUSP1 silencing on TNF protein release (Fig. 4.17A). Because there was enhanced TNF protein release following ZPF36 loss, the scheme depicted in Fig. 4.1 holds true for TNF protein synthesis.
In the case of repression by dexamethasone, IL1B-induced TNF protein was almost completely repressed by dexamethasone. Since TNF mRNA was partially repressed, this implies that translational and/or post-translational mechanisms may play an important role in the inhibition of
TNF expression by glucocorticoids, and such mechanisms have been previously suggested (403).
Furthermore, knock-down of ZFP36 or DUSP1 alone had no effect on the level of TNF released by IL1B plus dexamethasone. These data suggest that neither DUSP1 nor ZFP36 plays any overriding role in glucocorticoid-mediated repression of TNF protein in this model. Moreover, the observed enhancement in the percentage repression of TNF release by dexamethasone was mainly attributed to the fact that loss of ZFP36 enhanced the level of IL1B-induced TNF release (Fig.
4.16A). Similarly, combined knock-down of both ZFP36 and DUSP1 did not affect IL1B plus dexamethasone-induced TNF release (Fig. 4.16C middle panel). This suggests that additional glucocorticoid-induced repressive mechanisms must account for the repression of TNF release by dexamethasone (Fig. 4.17B).
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The findings shown in this chapter, in respect of the effects of IL1B and dexamethasone on
DUSP1, ZFP36 and TNF expression, were similar in both A549 and primary HBE cells. Thus, the regulatory mechanisms that are proposed for A549 cells are likely to be relevant to primary human airways epithelial cells. In addition, recent data in other cell lines, including primary human airway smooth muscle cells, as well as in mouse models also support that ZFP36 plays an important role in the inhibition of ARE-containing inflammatory transcripts, such as TNF (Fig. 4.1) (404, 405).
In particular, the interaction between DUSP1-mediated feedback control and ZFP36-mediated feed-forward control shows how modulation of a single signalling component can lead to opposing effects on gene expression at different times. Finally, ZFP36 may not completely explain the effects of DUSP1 silencing. Thus, ARE-containing inflammatory gene expression could be negatively regulated by a number of additional non ZFP36-dependent (but MAPK-dependent) mechanisms. Such data add to the complexity of the system and confirm the need for detailed modelling to assess therapeutic strategies (406). In respect of the anti-inflammatory effects of glucocorticoids, these data suggest that neither DUSP1 nor ZFP36 play a dominant repressive role on TNF expression. Thus, while negative feed-forward control may be down-regulated by the early effects of glucocorticoids, at longer times, additional repressive mechanisms by glucocorticoids must also exist in order to maintain the repression of TNF.
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A B IL1B treatment (No DUSP1) IL1B + glucocorticoid IL1B IL1B Other GC effectors MAPK GC DUSP1 MAPK Feed-forward Feed-forward GC control by ZFP36 control (ZFP36) TNF gene TNF gene Other GC expression expression effectors 150 DUSP1 siRNA 700 Phosho-p38 Phospho-p38 200 Phospho-p38 100 150 350 50 100 50 0 0 200 500 DUSP1 protein 0 DUSP1 protein ZFP36 protein
200 100 250
Gene/GAPDH % 1 h h IL1B+Lsi 1 % 100 0 0 ¼ ½ 1 2 6 150 150 0 ZFP36 protein ZFP36 protein 150
100 100 IL1B+Dex TNF mRNA h IL1B 1 % 100 Gene/GAPDH 50 50
50 0 ofat (% each time) IL1B 0 1 2 6
300 % IL1B+Lsi % TNF/GAPDH 0 TNF mRNA 100 0 2 4 6 200 TNF mRNA 50 time (h) 100 NT IL1B IL1B+siRNA 0 0 0 2 4 6 1 2 4 6 time (h) time (h) NT IL1B IL1B+Dex Figure 4.17 Regulation of TNF gene expression following DUSP1 silencing and glucocorticoid treatment. Schematics representing possible regulatory networks are shown along with summarized data from the current and a prior chapter (chapter three). A, With reduced DUSP1 expression there is a reduced negative feedback control of MAPKs leading to enhanced MAPK activity (at 1 h) (bold) at the times where DUSP1 expression would have been elevated. This could promote enhanced TNF expression. However, enhanced MAPK activity enhances expression of ZFP36 (at 2 h) (bold), which may decrease TNF expression (at 6 h). Actual expression of TNF is dependent on the temporal interplay of these competing regulatory processes. B, In the presence of glucocorticoid co-treatment, DUSP1 expression is enhanced (bold) and promotes inactivation of MAPKs (grey). Loss of MAPK activity reduces the expression of feed-forward (cont...)
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Figure 4.17 Continued. negative control genes, such as ZFP36. However, ZFP36 is also modestly induced by glucocorticoid alone (in brackets) and this may help maintain feed-forward control. Given the net loss of IL1B-induced ZFP36 expression in the presence of glucocorticoids (at 1 and 2 h), additional glucocorticoid effectors must exist to maintain the repression of TNF (at later times). Repression of MAPKs is also maintained by non-DUSP1-dependent mechanisms. Positive signalling/expression (blue) is represented by arrows. Negative effects are indicated by (red) lines ending in a T-bar. Time course expression of phospho-p38, DUSP1 and ZFP36 protein and TNF mRNA in the presence of IL1B and IL1B plus DUSP1 siRNA (A) or IL1B plus dexamethasone (B) is shown. The protein expression data for DUSP1 and ZFP36 were generated following densitometric analysis of western blots showed in Fig. 4.3. For phospho-p38, western blots were taken from chapter three. For phospho-p38, DUSP1, ZFP36 and TNF, at each time, the effect of IL1B plus Dex expressed as a percentage of IL1B is plotted as a mean (B, right panels).
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Chapter 5: Maintenance of IRF1 by DUSP1 enhances IRF1-dependent gene expression: Implications for glucocorticoid therapy
5.1 Rationale
In previous work by King et. al., the effect of dexamethasone on 39 IL1B-induced inflammatory genes was analysed in A549 cells (273). Based on the peak of their mRNA induction these genes were placed into 3 different groups. Genes with a peak in mRNA expression occurring at 1 or 2 h post-IL1B treatment were called ‘early-phase’ genes. In contrast, genes that had a peak of mRNA expression at 6 h and less than 50% of this peak at 1 or 2 h were defined as ‘late-phase’ genes.
Finally, genes that had a peak of mRNA expression at 2, 6 or 18 h post-IL1B treatment, and did not fall into neither of the above two categories, were referred to as ‘intermediate’ genes (273). In addition, in chapter three, while investigating the role of DUSP1 as a regulator of dexamethasone- induced repression of inflammatory genes, DUSP1 was over-expressed. As expected, this led to the repression of many inflammatory genes, including CSF2 (GM-CSF), IL8 and PTGS2. In contrast, in this chapter, many other IL1B-induced inflammatory genes, including IRF1, ICAM1,
CFB, CXCL10, IFIT1, MX1, UBD etc. were substantially enhanced by DUSP1 over-expression.
With the exception of IRF1 and ICAM1, the majority of these, IL1B-induced, DUSP1-enhanced genes were late-phase genes (273), and may therefore require the prior expression of other gene products, for example a transcription factor, for their expression. This concept was further confirmed by the effect of protein synthesis inhibitor, cycloheximide, where inhibition of de novo protein synthesis led to the inhibition of these genes by IL1B (273). Thus, the enhanced expression of IRF1, a transcription factor, by DUSP1 could be responsible for the increased expression of late-phase genes. Furthermore, the DUSP1-mediated increase in IRF1 expression could contribute
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to a lack of glucocorticoid sensitivity for such genes. Indeed IRF1 has been previously implicated in playing a role in glucocorticoid insensitivity (210, 407).
5.2 Hypothesis
The hypothesis that up-regulation of DUSP1 by dexamethasone helps to maintain IL1B-induced
IRF1 expression and thereby sustain the expression of downstream IRF1-dependent genes is tested.
5.3 Results
5.3.1 Effect of DUSP1 over-expression on inflammatory gene expression
A549 cells were treated with IL1B, for 6 h, in the presence of a DUSP1-expressing adenovirus,
Ad-DUSP1, or a control virus. While control virus had little or no effect on IL1B-induced inflammatory gene expression, many inflammatory genes, including CCL4, CCL20, CSF2,
CXCL1, CXCL3, CXCL5, IL8, PTGS2 and others showed a significant repression by Ad-DUSP1
(Fig. 5.1A). This is consistent with the prior observations in chapter three. Conversely, IL1B- induced BIRC3, CCL2, CCL5, IFNGR1, IL6, LAMB3, MAP3K8, NFKB2, NFKBIZ and
TNFAIP3 mRNAs were partially inhibited by DUSP1 over-expression (Fig. 5.1A). However, a number of mRNAs, including APOL6, CFB, CMPK2, CXCL10, HELZ2, ICAM1, IFIT1, IFIT3 isoforms 1 & 2, IRF1, MX1, STAT5A, TLR2 and UBD revealed significantly enhanced IL1B- induced expression following DUSP1 over-expression (Fig. 5.1A). Additionally, EFNA1, GOS2 and TNF mRNAs were also enhanced by 1.34 ± 1.4, 1.47 ± 0.8 and 2.22 ± 1.7 fold respectively by DUSP1 over-expression, but this effect did not attain significance (Fig. 5.1A).
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Gene p Gene A NS IL1B IL1B+Ad5-DUSP1IL1B+Ad5-Null B D F NS IL1B+LMNAsiIL1B+IRF1si1IL1B+IRF1si2 p PI3 *** time (h) 18 ICAM1 CCL5 *** *** TFF1 *** STAT5A CXCL5 *** *** CSF2 *** Ad-GFP + + CCL20 CXCL3 *** Ad-DUSP1 + + PTGS2 *** 1.5 CSF2 CXCL5 *** IL1B - - - + + + TFF1 IL8 *** 1.0 TNF 2000 *** *** CXCL1 *** EFNA1 CCL4 ** mRNA 0.5 TLR2 BCL2A1 * 1000 FAM129A *** 0.0 ICAM1
Gene /GAPDH
CCL20 ** (pg/ml) BIRC3 Release Release
CXCL10 1 2 6 18 IL1B *** 0 TNFAIP3 OLR1 *** 8 GOS2 CSF3 *** ISG20 ** C MAP3K8 SOD2 *** 1000 4 PI3 NFKBIA *** IRF1 CCL2 IL32 * mRNA IL6 MAP3K8 500 BIRC3 0 STAT5A IFNGR1 Gene /GAPDH PTGS2 mRNA 1 2 6 18 CCL2 0 CFB * * CCL5
IRF1/GAPDH time (h) UBD * * LAMB3 1 2 6 18 IFIT3iso1 * * IL6 time (h) TNFAIP3 APOL6 IFIT3iso2 HELZ2 * ** NFKB2 TLR2 IFIT3iso1 APOL6 *** *** NFKBIZ NT IL1B CFB UBD IFIT1 *** *** EFNA1 CMPK2 HELZ2 MX1 *** *** ICAM1 ** IFIT1 MX1 CXCL10 * ** GOS2 CXCL10 IFIT3iso1 ** time (h) 1 1 2 6 18 18 CMPK2 *** *** HELZ2 * IFIT3iso2 *** *** APOL6 *** IL1B - + + + + - CFB *** E TNF IRF1 time (h) 2 MX1 ** IRF1 siRNA 2 + 0 100 400 STAT5A ** GAPDH UBD *** IRF1 siRNA 1 + CMPK2 *** LMNA siRNA + % IL1B IRF1 *** 4 *** IL1B - + + + + IFIT1 *** TLR2 *** 2 IRF1 IFIT3iso2 *** CXCL10 IRF1 *** protein /GAPDH 0 GAPDH
0 100 400 % IL1B Figure 5.1 Effect of DUSP1 over-expression, IL1B and IRF1-targeting siRNA on inflammatory gene expression. A, A549 cells were either not infected or infected with Ad5- DUSP1 or Ad5-Null/Ad5-GFP (control) at a MOI of 10 for 24 h before IL1B treatment (1 ng/ml). Cells were harvested after 6 h for real-time PCR analysis of the indicated genes and GAPDH. Data (N = 4), normalized to GAPDH, were expressed as a percentage of IL1B-stimulated cells and plotted as means ± S.E. Significance relative to IL1B-treated samples, was tested by ANOVA with a Dunnett's post-test. *, p < 0.05; **, p< 0.01; ***, p< 0.001. B, Cells were treated as in A and the supernatants harvested after 18 h for CXCL10 release measurement. Data (N = 4) expressed in pg/ml are plotted as means ± S.E. Significance, relative to time-matched IL-1B and Ad5-GFP- treated samples, was tested by ANOVA with a Bonferroni post test. *, p < 0.05; **, p< 0.01; ***, p< 0.001. C, A549 cells were either not treated (NT) () or stimulated with IL1B (1 ng/ml) () as indicated. Cells were harvested after 1, 2, 6 or 18 h for real-time PCR analysis of IRF1 and GAPDH. Data (N = 4) were normalized to GAPDH and plotted as means ± S.E. (upper panel). Cells were also harvested at the times indicated for western blot analysis of IRF1 and GAPDH (lower panel). Representative blots are shown. Following densitometric analysis, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. Significance, using ANOVA with a Dunnett's post test is indicated. ***, p< 0.001. D, Cells were treated as in C and harvested after 1, 2, 6 or 18 h for real-time PCR analysis of indicated genes and GAPDH. Data (N = 4) were normalized to GAPDH and plotted as means. E, A549 cells were incubated with LMNA (control) or (cont.…)
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Figure 5.1 Continued. IRF1-targeting siRNAs. After 24 h, cells were treated with IL1B (1 ng/ml) as indicated. Cells were harvested after 2 h for western blot analysis of IRF1 and GAPDH. Blots representative of at least 4 such experiments are shown. F, A549 cells were treated as in E and harvested after 6 h for real-time PCR analysis of indicated genes and GAPDH. Data (N = 4), normalized to GAPDH, were expressed as a percentage of IL1B-stimulated cells and plotted as means ± S.E. For each IRF1-targeting siRNA, significance relative to IL1B+LMNA siRNA- treated samples, was tested by ANOVA with a Dunnett's post test. *, p < 0.05; **, p< 0.01; ***, p< 0.001.
While adenoviral over-expression of DUSP1 significantly enhanced IL1B-induced CXCL10 release, the control, Ad-GFP virus, was without any effect (Fig. 5.1B).
5.3.2 Kinetics of IL1B-induced inflammatory mRNAs
Following IL1B treatment, IRF1 mRNA was highly induced at 1 h, reached a peak 2 h post- stimulation, and then declined steeply towards basal levels over the following 4 h (Fig. 5.1C, upper panel). This expression kinetic correlated closely with IRF1 protein expression, which also peaked at 2 h before declining rapidly by 6 h following IL1B treatment (Fig. 5.1C, lower panels). The mRNA expression of inflammatory genes, showing significant enhancement by DUSP1 over- expression, was also tested. The expression of ICAM1 and STAT5A was modestly induced at 1 h with a peak in expression at 2 - 6 h, prior to declining at later times (Fig. 5.1D, upper panel). These data contrast with the effects on APOL6 and other inflammatory genes, which were all modestly induced by IL1B at 1 and 2 h, but showed a peak in expression at 6 h (Fig. 5.1D, lower panel).
CFB mRNA was not induced until 6 h and revealed a further increase in expression at 18 h post-
IL1B treatment (Fig. 5.1D, lower panel). Thus, with the exception of IRF1, STAT5A and ICAM-
1, the majority of these IL1B-induced, DUSP1-enhanced genes fell into the "late" group of genes that were induced by IL1B. This suggests that their mRNA expression requires, or depends on, the prior expression of another gene product, most likely a transcription factor. Since IRF1 has an
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“early” kinetic of expression, and is a transcription factor, a role in the induction of downstream genes was tested.
5.3.3 Identification of IRF1-dependent mRNAs induced by IL1B
A549 cells were treated with two independent IRF1-targeting or LMNA (control)-targeting siRNAs prior to stimulation with IL1B. IL1B-induced IRF1 protein expression was robustly inhibited by both of the IRF1-targeting siRNAs (Fig. 5.1E). The control LMNA siRNA, was without effect, (Fig. 5.1E). After harvesting at 6 h, qPCR was carried out for the IL1B-induced genes that were significantly increased by DUSP1 over-expression, plus a number of other genes showing no effect, or repression, following DUSP1 over-expression. All the late-phase mRNAs,
(Fig. 5.1D, lower panel) except TLR2, that had previously shown significant enhancement by
DUSP1-over expression were significantly reduced by IRF1 silencing (Fig. 5.1F). In contrast, the early response genes, STAT5A and ICAM1, were not down-regulated by IRF1 silencing and therefore were not IRF1-dependent (Fig. 5.1F). Additionally, the expression of those genes that were either not affected, or repressed, by DUSP1 over-expression, such as CSF2, TFF1, EFNA1,
BIRC3, TNFAIP3 etc., was also not changed by IRF1 knock-down (Fig. 5.1F). However, the
IL1B-induced expression of CCL5 and CXCL5 showed significant 52.08 ± 5.6 and 20.65 ± 8.2 fold enhancements respectively compared to IL1B plus LMNA siRNA treated cells following
IRF1 silencing (Fig. 5.1F). This suggests that the expression of these mRNAs may be negatively regulated by IRF1, or an IRF1-dependent process. Given a role for IRF1 in the expression of IL1B- induced late-phases genes that were enhanced by DUSP1 over-expression, the effects of DUSP1 over-expression and/or MAPKs inhibition on IRF1 expression were also examined.
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5.3.4 Effect of DUSP1 over-expression on IL1B-induced IRF1 expression
A549 cells were treated with Ad-DUSP1 or control Ad-GFP adenovirus in the presence and absence of IL1B. The over-expression of DUSP1 was confirmed by western blotting (Fig. 5.2A).
IRF1 protein was only modestly detectable at 1 h post-IL1B treatment. At 2 and 4 h, IRF1 was highly induced by IL1B, but there was no effect of DUSP1 over-expression. Conversely, while
IL1B-induced IRF1 expression had declined by 6 h, over-expression of DUSP1 appeared to maintain IL1B-induced IRF1 expression. Control Ad-GFP virus had no effects on IL1B-induced
IRF1 expression (Fig. 5.2A).
As described in Fig. 5.1C, IRF1 mRNA was induced by IL1B at 1 h, reached a peak at 2 h, and declined sharply by 6 h (Fig. 5.2B). While IL1B-induced IRF1 mRNA was unaffected by the control GFP virus, Ad-DUSP1 significantly (45.9 ± 8.8%) attenuated IL1B-indcued IRF1 mRNA at 1 h (Fig. 5.2B). However, this repressive effect of the DUSP1 adenovirus was lost by 2 h, and at 6 h, there was a significant enhancement (2.6 ± 0.4 fold) of IRF1 expression over that obtained with IL1B alone (Fig. 5.2B). Analysis of the effect of DUSP1 over-expression on unspliced nuclear
IRF1 using RNA from the experiments depicted in Fig. 5.1A revealed that expression of unspliced nuclear IRF1 RNA to be significantly enhanced following DUSP1 over-expression (Fig. 5.2C).
Thus, the DUSP1-mediated enhancement of IRF1 mRNA may involve enhanced IRF1 transcription.
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A time (h) 1 2 4 6 Ad-GFP + + + + Ad-DUSP1 + + + + IL1B - + + + - + + + - + + + - + + + DUSP1 NS IRF1 GAPDH
1.0 *
0.5
IRF1
protein /GAPDH 0.0 B C time (h) 1 2 6 time (h) 6 Ad-GFP + + + + + + Ad-GFP + Ad-DUSP1 + + + + + + Ad-DUSP1 + IL1B - - - + + + - - - + + + - - - + + + IL1B - + + + 1.0 400 ***
0.5 200
*** *** RNA IRF1
mRNA *** ***
(%IL1B) /GAPDH 0.0 unIRF1/U6 0 Figure 5.2 Effect of DUSP1 over-expression on IL1B-induced IRF1 expression. A, A549 cells were either not infected or infected with Ad5-DUSP1, or Ad5-GFP at a MOI of 10 for 24 h before IL1B treatment (1 ng/ml). Cells were harvested after 1, 2, 4 or 6 h for western blot analysis of DUSP1, IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. B, Cells were treated as in A and harvested at 1, 2 or 6 h for real-time PCR analysis of IRF1 and GAPDH. Data (N = 4) were normalized to GAPDH and plotted as means ± S.E. For A & B, significance relative to time- matched IL1B and/orAd5-GFP-treated samples was tested by ANOVA with a Bonferroni multiple comparison test. *, p < 0.05; ***, p< 0.001. C, Cells were treated as in A and harvested after 6 h for real-time PCR analysis of unspliced nuclear (un) IRF1 RNA and U6 RNA. Data (N = 4), normalized to U6, were expressed as a percentage of IL1B-stimulated cells and plotted as means ± S.E. Significance, relative to IL1B-treated samples was tested by ANOVA with a Dunnett's post- test.***, p< 0.001. NS = nonspecific band
5.3.5 Effect of MAPK inhibitors on IRF1 expression
Since DUSP1 over-expression attenuates IL1B-induced MAPK activation in A549 cells (chapter three), the effect of MAPK inhibition on IL1B-induced IRF1 was also tested. MAPK pathways
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were inhibited using maximally effective concentrations of the p38 inhibitor, SB203580 (10 µM), the MAPK/ERK kinase (MEK) 1/2 inhibitor, U0126 (10 µM) or the JNK inhibitor, JNK inhibitor
8 (JNK-IN-8) (10 µM), individually and in combination to inhibit all three MAPK pathways simultaneously in the presence of IL1B (Fig. 5.3A). IL1B-induced IRF1 protein expression was not affected by the MAPK inhibitors either alone or in combination at 2 h. However, by 4 h post-
IL1B treatment, when IL1B-induced IRF1 protein expression was starting to decline, SB203580, or three inhibitors combined, maintained IRF1 protein expression to significantly higher levels compared to IL1B-induced IRF1 protein. At 6 h, IL1B-induced IRF1 protein had further declined, but SB203580 and the combination inhibitor treatment significantly attenuated this loss (Fig.
5.3A).
To explore the mechanistic basis of IRF1 regulation by MAPKs, A549 cells were treated with
IL1B for various times in the presence of the MAPK pathway inhibitors, as in Fig. 5.3A, and IRF1 mRNA expression was analyzed by qPCR. Following 1 h of IL1B treatment, IRF1 mRNA was rapidly induced, reached a maximum level at 2 h, and declined at 4 and 6 h (Fig. 5.3B, left panel).
The MAPK pathway inhibitors, alone or in combination, had no significant effect on IL1B-induced
IRF1 mRNA at 1 or 2, although the combination of MAPK inhibitors produced a modest attenuation at 1 h (Fig. 5.3B, right panel). By 4 h, IRF1 mRNA expression was significantly enhanced by SB203580 and the three inhibitors together relative to IL1B-treated and this effect was more pronounced at 6 h (Fig. 5.3B, right panel).
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B A time (h) 2 4 6 JNK-IN-8 + + + + + + 300 SB203580 300 UO126 UO126 + + + + + + + + + + + + 1.5 200 ** 200 SB203580 NT ** IL1B - + + + + + - + + + + + - + + + + + *** 1.0 IL1B 100 100 IRF1 0 0 0.5
mRNA JNK-IN-8 SB+UO+J8 ** 300 300
GAPDH *** *** *** each time)each
IRF1/GAPDH 0.0 (% of IL1B at at IL1B (%of
IRF1/GAPDH 200 200 6 0 2 4 6 *** ** time (h) 100 100 4 ** * *** 0 0 1 2 4 6 1 2 4 6 protein 2 *** * time (h) time (h)
IRF1/GAPDH 0 C D 400 SB203580 400 UO126 300 30 min 150 90 min 300 300 1.5 *** 200 100 NT 200 200 * * * 50 1.0 *** IL1B 100 100 100 *** 0 0 0 0
RNA 0.5 400 400 JNK-IN-8 SB+UO+J8 150 120 min 150 180 min *** ** mRNA
unIRF1/U6 *** (% t = (%= t 0)
each time)each 300 300
0.0 unIRF1/U6 100 100 (% of IL1B at at IL1B (%of 0 2 4 6 200 200 * IRF1/GAPDH time (h) 100 100 50 50 0 0 0 0 1 2 4 6 1 2 4 6 0 15 30 45 0 15 30 45 time (h) time (h) time (h) time (h) E 300 IL1B treatment time (min) 30 90 120 180 240 IL1B 200 IL1B + SB203580 + + + + + IL1B + + + + + + + + + + SB203580 100 *
mRNA * *** (% t = (%= t 0) 200 50 50 50 50
IRF1/GAPDH 0
0 20 40 60 100 25 25 25 25 mRNA
time (min) (%= t 0)
IRF1/GAPDH 0 0 0 0 0 Figure 5.3 Effect of MAPK inhibitors on IRF1 expression. A, A549 cells were either not treated, treated with IL1B (1 ng/ml) or pre-treated with either UO126, SB203580, JNK inhibitor 8 (JNK-IN-8) or a combination of UO126, SB203580 plus JNK-IN-8 each at 10 µM for 30 min prior to IL1B stimulation. Cells were harvested at the indicated times prior to western blot analysis of IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. B & C, Cells were treated as in A and harvested at 1, 2, 4 or 6 h for real-time PCR analysis of (B) IRF1 and GAPDH or (C) unspliced nuclear (un) IRF1 RNA and U6. Data (N = 4) were normalized to GAPDH or U6 and are plotted as means ± S.E. The effect of IL1B + MAPK inhibitors for IRF1 mRNA or unspliced nuclear (un) IRF1 RNA is plotted as a percentage of IL1B at the indicated times. For A, B & C, significance, relative to time-matched non-treated or IL1B-treated samples was tested using ANOVA with a Bonferroni multiple comparison test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. D, A549 cells were treated with IL1B (1 ng/ml) for 30, 90, 120 and 180 min. Actinomycin D (Act D, 10 μg/ml) was then added (t = 0) and the cells were harvested as indicated. RNA was extracted for real-time PCR analysis of IRF1 and GAPDH. Data (N = 3) were normalized to GAPDH and are plotted as a percentage of t = 0 for each treatment as means ± S.E. E, A549 cells were treated with IL1B (1 ng/ml) or pre-treated for 30 min with SB203580 at 10 µM prior to IL1B stimulation for 30 min. Following this (t = 0), actinomycin D (Act D, 10 μg/ml) was added and cells were harvested at the indicated times. RNA was extracted for real-time PCR analysis of IRF1 and GAPDH. (Cont.…)
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Figure 5.3 Continued. Data (N = 4) normalized to GAPDH, are plotted as a percentage of t = 0 for each treatment as means ± S.E. (left panel). Data following 45 min of Act D treatment are also shown (right panel). A549 cells were also treated with IL1B or IL1B + SB203580 at 10 µM for 90, 120, 180 and 240 min, prior to the addition of Act D (t = 0) for 45 min and analyzed as above (right panels). Significance, relative to time matched IL1B-treated samples was tested using a paired t-test. *, p < 0.05; ***, p < 0.001.
5.3.6 Effect of MAPK inhibitors on IRF1 transcription and mRNA stability
Analysis of unspliced nuclear IRF1 RNA revealed that the peak in transcription rate occurred at or before 1 h post-IL1B treatment, and then declined rapidly to reach near basal levels by 6 h (Fig.
5.3C, left panel). At 1 and 2 h, IRF1 transcription was high and the MAPK pathway inhibitors had no effect (Fig. 5.3C, right panel). However, the unspliced nuclear IRF1 RNA induced by IL1B was significantly enhanced at 4 h by SB203580, UO126 and all three inhibitors together (Fig.
5.3C, right panel). Similar, but lesser effects were observed at 6 h (Fig. 5.3C, right panel). Taken together, these data suggest that the induction phase of IRF1 mRNA expression was only marginally affected by MAPK inhibitors. However, inhibition of MAPKs reduced the rapid loss of IRF1 mRNA occurring after the peak in mRNA expression at 2 h. This effect involved a failure to attenuate IRF1 transcription at the later time points.
The stability of IRF1 mRNA following MAPK inhibition was also assessed using actinomycin D chase methodology. Following short, 30 min, of IL1B treatment, IRF1 mRNA appeared to be highly stable and did not decline below the 100% level over the 60 min of the actinomycin D chase period (Fig. 5.3D). Instead, IRF1 mRNA levels continued to rise immediately post-actinomycin D treatment. As suggested for TNF mRNA stability in chapter four, this may also be due to the fact that while transcription is blocked by actinomycin D, the prior accumulation of pre-formed unspliced/unprocessed-mRNA may allow the production of mature IRF1 mRNA to continue
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briefly following actinomycin D treatment (378). However, this apparent rise in IRF1 mRNA was not observed at longer IL1B treatment times (90, 120 or 180 min post IL1B treatment) (Fig. 5.3D).
Rather, there was a loss of IRF1 mRNA to ~50% of starting levels (t = 0) over the course of the actinomycin D chase following longer (90, 120 and 180 min) IL1B treatment times (Fig. 5.3D).
Since the p38 MAPK was a major regulator of IL1B-induced IRF1 mRNA and protein expression
(see above), the effect of SB203580 was also examined on IRF1 mRNA stability. At 30 min, there was no loss of IL1B-induced IRF1 mRNA by SB203580 over the course of the actinomycin D chase (Fig. 5.3E, left panel). However, the continued increase in IRF1 mRNA post-actinomycin D treatment was inhibited by SB203580. This suggests that p38 MAPK may play a role in the post- transcriptional processing of unprocessed-RNA to mature IRF1 mRNA. This needs further investigation. Conversely, SB203580 had no effect on IRF1 mRNA stability following 90 min of
IL1B treatment, and at all later times (120, 180 and 240 min), IRF1 mRNA stability was modestly enhanced by SB203580, with significance being achieved only at 120 min post-treatment (Fig.
5.3E, right panels).
5.3.7 Effect of MAPK inhibitors on IRF1 protein stability
IRF1 protein is a short-lived protein with a t½ of around 30 min (408, 409). To determine the effect of MAPK inhibition on IRF1 protein stability, a chase methodology was adopted using the translational inhibitor, cycloheximide. Following 2 h of IL1B treatment, IL1B-induced IRF1 protein expression was profoundly inhibited by the addition of cycloheximide (Fig. 5.4A).
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CHX A IL1B CHX B IL1B MG132 time (h) -2 0 ¼ ½ 1 time (h) -2 0 1
Harvest Harvest
time (h) 0 ¼ ½ 1 CHX + + CHX + + + MG132 + + IL1B - + + + + IL1B - + + + + IRF1 IRF1
GAPDH GAPDH 150 *** 300 * ** ** 100 200 * ***
50 IRF1
IRF1 100
protein
protein
/GAPDH
(%IL1B) /GAPDH 0 (%IL1B) 0
CHX C IL1B MG132 D SB+UO+J8 IL1B CHX time (h) -2 0 ¼ ½ 1 time (h) -2½ -2 0 ¼ ½ 1
Harvest Harvest
time (h) 0 ¼ ½ 1 0 ¼ ½ 1 time (h) 0 ¼ ½ 1 0 ¼ ½ 1 MG132 + + + SB/UO/J8 + + + + CHX + + + + + + CHX + + + + + + IL1B - + + + + + + + + IL1B - + + + + + + + + IRF1 IRF1 GAPDH GAPDH
100 + CHX 100 + CHX + MG132 + SB+UO +J8
50 * 50 *
protein (%IL1B) protein 0
IRF1/GAPDH 0 (% of control) (%of 0 20 40 60 IRF1/GAPDH 0 20 40 60 time (min) time (min) Figure 5.4 Effect of MAPK inhibitors on IRF1 protein stability. A, A549 cells were pre-treated with IL1B (1 ng/ml) for 2 h. Following this (t = 0), cycloheximide (CHX, 10 μg/ml) was added and cells were harvested at the indicated times for western blot analysis of IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 4), normalized to GAPDH, were expressed as a percentage of IL1B-stimulated cells and plotted as means ± S.E. Significance relative to IL1B-treated samples, was tested by ANOVA with a Dunnett's post-test. **, p< 0.01; ***, p< 0.001. B, A549 cells were pre-treated with IL1B (1 ng/ml) for 2 h. Following this (t = 0), cycloheximide (CHX, 10 μg/ml) and/or MG132 (10 μg/ml) was added and cells were harvested after 1 h prior to western blot analysis of IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 4), normalized to GAPDH, were (cont.…)
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Figure 5.4 Continued. expressed as a percentage of IL1B-stimulated cells and plotted as means ± S.E. Significance, relative to either IL1B or IL1B+MG132-treated samples was tested using ANOVA with a Bonferroni multiple comparison test. *, p < 0.05; **, p < 0.01; ***, p < 0.001. C, Cells were treated as in B and harvested at the indicated times prior to western blot analysis of IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 4), normalized to GAPDH were expressed as a percentage of IL1B and plotted as means ± S.E. Significance, relative to IL1B+CHX-treated samples was tested using a paired t-test. *, p < 0.05. D, A549 cells were either not treated, treated with IL1B (1 ng/ml) or pre-treated with a combination of UO126, SB203580 plus JNK inhibitor 8 (JNK-IN-8) each at 10 µM for 30 min prior to IL1B stimulation for 2 h. Following this (t = 0), cycloheximide (CHX, 10 μg/ml) was added and cells were harvested at the indicated times prior to western blot analysis of IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 4), normalized to GAPDH were expressed as a percentage of IL1B or IL1B+MAPK inhibitors and plotted as means ± S.E. Significance, relative to IL1B+CHX-treated samples was tested using a paired t-test. *, p < 0.05.
Within 30 min of cycloheximide treatment IL1B-induced IRF1 was significantly decreased, and by 60 min post-cycloheximide treatment, IRF1 protein was almost completely lost (t½ = ~30 mins).
Conversely, IL1B-induced IRF1 protein expression was significantly enhanced by the proteasome inhibitor, MG132 (Fig. 5.4B). In the presence of cycloheximide, the stability of IRF1 protein was modestly increased by MG132 (Fig. 5.4B & C). Like MG132, combined MAPK inhibitors also produced a partial stabilisation of IL1B-induced IRF1 protein (Fig. 5.4D).
5.3.8 Characterization of IRF1 expression in the presence of IL1B and dexamethasone
Since MAPK activity is reduced by glucocorticoids in A549 cells (114, 283, 382), the effect of the synthetic glucocorticoid, dexamethasone, was examined on IL1B-induced IRF1 expression. IRF1 protein was rapidly induced by IL1B, reached a peak at 2 h, before declining at 4 and 6 h (Fig.
5.5A & B). IL1B-induced IRF1 protein was modestly (32.5 ± 5.6%), but significantly, repressed by co-treatment with dexamethasone at 2 h, but this repressive effect was reversed by 4 h, and at
6 h there was no repression of IRF1 protein by dexamethasone (Fig. 5.5A & B).
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A B C 2 6 18 time (h) 1 time (h) 1 2 4 6 time (h) 1 2 6 Dex + + + + + + + + Dex + + + + Dex + + + + + + IL1B - - + + - - + + - - + + - - + + IL1B - + + + + + + + + IL1B - - + + - - + + - - + + NS IRF1 IRF1 IRF1 GAPDH GAPDH GAPDH 150 *** 150 *** *** ** * 0.6 100 100 0.4
50 *** protein
protein 50
*** IRF1 0.2
protein
(% IL1B 2 h) 2 (%IL1B
/GAPDH IRF1/GAPDH
0 h) 2 (%IL1B IRF1/GAPDH 0 0.0
IL1B D E G Dex CHX 45 1.5 *** 100 * NT * * 1.0 time (h) -4 0 ¼ ½ 1 30 * * Dex *** 50 IL1B
15 0.5 * mRNA
mRNA ** IL1B+Dex each time)each *** ** IL1B+Dex ** * Harvest (% of IL1B at at IL1B (%of 0.0 0 0 IRF1/GAPDH IRF1/GAPDH 0 ¼ ½ 1 0 ¼ ½ 1
(Fold of 1 h NT) h 1 of (Fold time (h) 0 2 4 6 1 2 4 6 0.5 1 2 6 18 time (h) CHX + + + + + + 1.0 *** 100 F Dex + + + + ** IL1B IL1B - + + + + + + + + 0.5 50 *** IL1B+Dex RNA IRF1
*** 150 each time)each
** IL1B+Dex unIRF1/U6
0.0 at IL1B (%of 0 100 GAPDH 0 2 4 6 1 2 4 6 time (h) time (h) 50 100 Control
mRNA
(% t = 0) +Dex 0
NT IL1B IRF1/GAPDH 0 20 40 60 50
time (min) * protein
0 (% of control) (%of IRF1/GAPDH 0 20 40 60 time (min)
Figure 5.5 Characterization of IRF1 expression in the presence of IL1B and dexamethasone. A&B, A549 cells were either not treated or stimulated with IL1B (1 ng/ml), dexamethasone (Dex, 1 μM) or a combination of the two as indicated. Cells were harvested at the indicated times prior to western blot analysis of IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 6), normalized to GAPDH were expressed as a percentage of 2 h IL1B-stimulated cells and plotted as means ± S.E. For A, significance, using ANOVA with a Bonferroni's multiple comparison test is indicated. For B, significance relative to IL1B-treated samples was tested by ANOVA with a Dunnett's post-test.*, p < 0.05; **, p< 0.01; ***, p< 0.001. C, HBE cells were not treated (NT) or treated as in A and harvested at the indicated times for western blot analysis of IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N = 4) were normalized to GAPDH and plotted as means ± S.E. Significance, using ANOVA with a Dunnett's post-test is indicated. ***, p< 0.001. D, A549 cells were treated as in A and harvested at the indicated times for real-time PCR analysis of IRF1 and GAPDH mRNA (upper left panel) or unspliced nuclear (un) IRF1 RNA and U6 RNA (lower left panel). Data (N = 4 - 11), normalized to GAPDH were expressed as a fold of 1 h NT (upper left panel) or were normalized to U6 (lower left panel), and plotted as means ± S.E. The effect of IL1B + dexamethasone for IRF1 mRNA (upper right panel) (N = 11) and unspliced nuclear (un) TNF RNA (lower right panel) (N = 4) is plotted as a percentage of IL1B for indicated times. Significance, relative to time matched NT or IL1B-treated samples was tested using a Bonferroni's multiple comparison test. *, p < 0.05; **, p< 0.01; ***, p< 0.001. E, HBE cells were (cont....)
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Figure 5.5 Continued. treated as in A and harvested at the indicated times for real-time PCR analysis of IRF1 and GAPDH mRNA. Data (N = 4), normalized to GAPDH and plotted as means ± S.E. Significance, using ANOVA with a Bonferroni's multiple comparison test is indicated. *, p < 0.05; **, p< 0.01. F, A549 cells were treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (Dex, 1 μM) for 90 min. Following this (t = 0), actinomycin D (Act D 10 μg/ml) was added and the cells were harvested at the indicated times for real-time PCR analysis of IRF1 and GAPDH. Data (N = 4) were normalized to GAPDH and plotted as a percentage of t = 0 for each treatment as means ± S.E. G, A549 cells were treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (Dex, 1 μM) for 4 h. Following this (t = 0), cycloheximide (CHX, 10 μg/ml) was added and cells were harvested at the indicated times prior to western blot analysis of IRF1 and GAPDH. Representative blots are shown. Following densitometric analysis, data (N=4), normalized to GAPDH were expressed as a percentage of IL1B or IL1B+Dex and plotted as means ± S.E. Significance, relative to IL1B+CHX-treated samples was tested using a paired t-test. *, p < 0.05. NS = nonspecific band.
In primary HBE cells, IRF1 protein was not induced by IL1B at 1 h post-treatment. However, at 2 h, IRF1 protein expression was significantly increased by IL1B and was almost completely diminished by 6 h (Fig, 5.5C). There was no effect of dexamethasone on IRF1 protein expression.
In A549 cells, IRF1 mRNA was rapidly induced by IL1B at 1 h, and this continued to increase, reaching a peak at 2 h, before declining at 4 and 6 h (Fig. 5.5D, upper left). At each time, IRF1 mRNA was only 30 - 40% repressed by dexamethasone (Fig. 5.5D, upper right). In primary HBE cells, similar modest, but non-significant, repressive effects of dexamethasone were observed on
IL1B-induced IRF1 mRNA (Fig. 5.5E).
5.3.9 Effect of dexamethasone on IRF1 transcription rate
Analysis of unspliced nuclear RNA revealed that IL1B-induced IRF1 transcription was significantly (~50% loss) inhibited by dexamethasone at 1 h post-treatment (Fig. 5.5D, lower right). Since IL1B-induced IRF1 mRNA expression is blocked by the dominant inhibitor of NF-
κB, IκBα∆N, and repression of IRF1 mRNA by dexamethasone is unaffected by cycloheximide
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(273), dexamethasone-mediated loss of IRF1 transcription could be attributed to classical NR3C1 transrepression. Indeed, TNF-induced IRF1 expression was only modestly repressed by dexamethasone in bronchial epithelial BEAS-2B cells (Shah S. unpublished data). Furthermore, binding of p65 (RELA) to a site upstream of IRF1 transcription start is also attenuated by dexamethasone (Dr. Anthony Gerber – personal communication). Thus, the effect of the maximum effective concentration of selective IKK2 inhibitor, PS1145 (10 M), on IRF1 expression was tested. IL1B-induced IRF1 mRNA was significantly, 71.4 ± 5.3 %, repressed by PS1145 at 2 h
(Fig. 5.6). This confirms a role for NF-κB in IRF1 expression and supports the concept of direct
NR3C1 transrepression in the dexamethasone-mediated repression of IRF1. Regardless, the repressive effect of dexamethasone on IL1B-induced unspliced IRF1 nuclear RNA was completely lost at 2, 4 and 6 h post-IL1B treatment (Fig. 5.5D, lower right). Thus, the net effect of dexamethasone on IRF1 mRNA expression was relatively minor.
time (h) 2 Figure 5.6 Effect of PS1145 on IL1B- PS1145 + induced IRF1 expression. A549 cells were IL1B - + + either not stimulated, treated with IL1B (1 150 IRF1 ng/ml) or pre-treated with PS1145 (10 µM) * 100 for 30 min prior to IL1B stimulation. Cells
50 were harvested after 2 h for real-time PCR mRNA (%IL1B) analysis of IRF1 and GAPDH. Data (N = 4),
IRF1/GAPDH 0 normalized to GAPDH were expressed as a percentage of IL1B-stimulated cells and plotted as means ± S.E. Significance, relative to IL1B-treated samples was tested using a
paired t-test. *, p < 0.05.
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5.3.10 Effect of dexamethasone on IRF1 mRNA and protein stability
Following 90 min of IL1B treatment, IRF1 mRNA was reduced to ~50% of initial levels (t = 0) within 45 min of actinomycin D addition (Fig. 5.5F). This was unaffected by dexamethasone co- treatment. The effect of dexamethasone co-treatment on IRF1 protein stability was also analyzed.
IL1B-induced IRF1 protein was rapidly lost following the addition of cycloheximide. This loss of
IRF1 protein was modestly, but significantly, reduced in the presence of dexamethasone (Fig.
5.5G).
5.3.11 Effect of dexamethasone on IRF1-dependent gene expression
Expression of the IRF1-dependent IL1B-induced mRNAs identified in Fig. 5.1F, was analysed in the presence of dexamethasone at 6 h. Co-treatment with dexamethasone produced a variable repression of IL1B-induced 10 IRF1-dependent mRNAs (Fig. 5.7A). While CMPK2, IFIT1, MX1,
IFIT3 isoform 2 and HELZ2 were highly (>88 %) repressed, UBD, APOL6, CFB and IFIT3 isoform 1 were only partially (50 – 60 %) attenuated by dexamethasone (Fig. 5.7A). IL1B-induced
CXCL10 mRNA was unaffected by dexamethasone co-treatment (Fig. 5.7A).
In the case of protein expression, IL1B-indued CXCL10, released into the supernatants, was undetectable up to 4 h post-IL1B treatment. Following this, CXCL10 release was gradually enhanced by IL1B and reached a plateau at ~18 h (Fig. 5.7B). Co-treatment with dexamethasone for up to 8 h did not affect IL1B-induced CXCL10 release. Thereafter, dexamethasone produced a partial, but non-significant, repression of CXCL10 release (Fig. 5.7B).
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A NS Dex IL1B IL1B+DexGene p B CXCL10 200 IFIT3iso1 * 150 APOL6 ** 100 CFB **
(pg/ml) 50 Release Release UBD * CXCL10 HELZ2 *** 0 IFIT3iso2 ** 0 5 10 15 20 MX1 * time (h) IFIT1 *** CMPK2 *** NT IL1B Dex IL1B+Dex
0 100 % IL1B
Figure 5.7 Effect of dexamethasone on IRF1-dependent gene expression. A, A549 cells were either not treated (NT) or stimulated with IL1B (1 ng/ml), dexamethasone (Dex, 1 μM) or a combination of the two as indicated. Cells were harvested at 6 h for real-time PCR analysis of indicated genes and GAPDH. Data (N = 4), normalized to GAPDH were expressed as a percentage of IL1B and plotted as means ± S.E. Significance, relative to IL1B-treated samples was tested using a paired t-test. *, p < 0.05; **, p< 0.01; ***, p< 0.001. B, Cells were treated as in A and the supernatants harvested at indicated times for CXCL10 release measurement. Data (N = 4) expressed in pg/ml are plotted as means ± S.E.
5.3.12 Effect of DUSP1 silencing on MAPKs and IRF1 expression
Since dexamethasone reduces MAPK activity by inducing the expression of DUSP1 (chapter three), the effect of DUSP1 knock-down on IRF1 protein expression was also examined. DUSP1 expression was induced by IL1B at 1 h and this was enhanced by co-treatment with dexamethasone
(Fig. 5.8A). As shown in chapter three, DUSP1-targeting siRNAs profoundly reduced DUSP1 expression and produced a marked increase in the phosphorylation of p38, ERK and JNK MAPKs in the presence of both IL1B and IL1B plus dexamethasone (Fig. 5.8A). LMNA siRNA was used as a control siRNA and had no effect on IL1B or IL1B plus dexamethasone-induced DUSP1 or on
MAPK activity (Fig. 5.8A). These data confirm that IL1B-induced MAPKs are negatively
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regulated by DUSP1 and enhanced expression of DUSP1 by dexamethasone is involved in the repression of MAPKs by glucocorticoids at 1 h.
Western blot analysis for IRF1 was performed at 4 h, the time at which the loss of IL1B-induced
IRF1 was just starting to occur. IL1B-induced IRF1 protein expression was significantly reduced by the two DUSP1 siRNAs relative to LMNA control siRNA (Fig. 5.8B). The expression of IRF1 protein induced by IL1B plus dexamethasone was comparable to that with IL1B alone and is consistent with Fig. 5.5B. However, knock-down of DUSP1 produced a significant reduction in the expression of IRF1 in the presence of IL1B plus dexamethasone (Fig. 5.8B). These data support a role for DUSP1 in maintaining IRF1 expression in the presence of both IL1B and IL1B plus dexamethasone. A B time (h) 1 time (h) 4 DUSP1 siRNA 2 + + DUSP1 siRNA 2 + + DUSP1 siRNA 1 + + DUSP1 siRNA 1 + + LMNA siRNA + + LMNA siRNA + + Dex + + + + Dex + + + IL1B - + + + + + + + + IL1B - + + + + + + DUSP1 IRF1 P-p38 GAPDH P-ERK *** 150 * ** *
P-JNK 100 siRNA)
protein 50 GAPDH
IRF1/GAPDH 0 (%IL1B+LMNA C time (h) 6 6 Figure 5.8 Effect of DUSP1DUSP1-targeting siRNA siRNA 2 on+ IL1B + -induced MAPKs+ + and IRF1 expression. A&B, A549 cells were incubatedDUSP1 withsiRNA LMNA 1 + (control) + or DUSP1+ - specific + siRNAs. After 24 h, LMNA siRNA + + + + cells were treated with IL1B (1 ng/ml)Dex or IL1B plus+ + dexamethasone+ +(1 + μ +M) (Dex) as indicated. Cells were then harvested at 1 and 4 IL1Bh and- total+ + + proteins + + + were- prepared+ + + + +for + western blot analysis 200 200 of DUSP1, phospho-ERK (P-ERK), phospho-p38CXCL10 (P-p38), phosphoIFIT3iso2-JNK (P-JNK), IRF1 and * GAPDH. Blots representative of at least100 6 - 9 such* experiments100 are shown.* For B, following
*
mRNA
siRNA) (%IL1B densitometric analysis, data (N = 6),+LMNA normalized to GAPDH were expressed as a percentage of 0 0 LMNA siRNA plus IL1B-stimulatedGene/GAPDH cells and plotted as means ± S.E.Significance using ANOVA with a Bonferroni's multiple comparison test is indicated. Significance between: LMNA control siRNA plus IL1B and each of the DUSP1 targeting siRNAs plus IL1B, and the LMNA control plus IL1B plus Dex is shown. *, p < 0.05; **, p< 0.01; ***, p< 0.001.
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5.3.13 Effect of DUSP1 silencing and MAPK inhibitors on IRF1-dependent gene expression
The effect of DUSP1 knock-down on IL1B-induced late-phase mRNAs was examined. IL1B- induced CXCL10 and IFIT3 isoform 2, which showed the greatest enhancements following
DUSP1 over-expression (Fig. 5.1A), both showed trends towards reduced IL1B-induced expression in the presence of the DUSP1 targeting siRNAs (Fig. 5.9A). In the case of IL1B plus dexamethasone, the loss of DUSP1, in the presence of both siRNAs, produced a modest, but significant, reduction in CXCL10 and IFIT3 isoform 2 mRNA expression (Fig. 5.9A). A similar, modest repressive effect, for at least one DUSP1 targeting siRNA, was observed for IFIT3 isoform
1 in the presence of IL1B plus dexamethasone (Fig. 5.9A). A number of the remaining late-phase mRNAs also showed trends towards reduced expression, but this did not reach significance.
Reasons for the variable effect of DUSP1 siRNAs on late-phase mRNA expression are multiple, but may include the fact that MAPKs have multiple, potentially opposing, roles in the regulation of late-phase gene expression. This is illustrated below.
The expression of IFIT3 isoform 1 and UBD were largely unaffected in the presence of MAPK inhibitors (Fig. 5.9B), whereas following the inhibition of all three MAPK pathways, IL1B- induced mRNA expression of CFB, CXCL10, HELZ2, IFIT1 and IFIT3 isoform 2 was significantly enhanced (Fig. 5.9B). Conversely, APOL6, CMPK2 and MX1 were attenuated by the combined MAPK inhibitors (Fig. 5.9B). IL1B-induced release of CXCL10 into the supernatant was significantly increased following MAPK inhibition (Fig. 5.9C).
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A time (h) 6 6 DUSP1 siRNA 2 + + + + DUSP1 siRNA 1 + + + + LMNA siRNA + + + + Dex + + + + + + IL1B - + + + + + + - + + + + + + B C time (h) 18 200 200 time (h) 6 6 CFB APOL6 SB+UO+J8 + + SB+UO+J8 + IL1B - + + - + + IL1B - + + 100 100 150 APOL6 CFB * * 200 * 300 0 0 75 100 150
200 200 (pg/ml) Release Release CMPK2 CXCL10 CXCL10 0 0 0 * 100 100 * CMPK2 225 CXCL10 * 100 150 ** 0 0 75 200 200 HELZ2 IFIT1 0 0 200 HELZ2 IFIT1
100 100 ** 150 *** mRNA 100
75 Gene/GAPDH
0 0 mRNA (%IL1B)
200 200 Gene/GAPDH (%IL1B+LMNAsiRNA) 0 0 IFIT3iso1 IFIT3iso2 150 IFIT3 600 IFIT3 iso2 100 *** 100 * iso1 400 * * 75 200 0 0 0 0 200 200 MX1 UBD 150 MX1 150 UBD * 100 100 75 75
0 0 0 0
Figure 5.9 Effect of DUSP1-targeting siRNA and MAPK inhibitors on IRF1-dependent gene expression. A, A549 cells were incubated with LMNA (control) or DUSP1-specific siRNAs. After 24 h, cells were treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Cells were then harvested at 6 h for real-time PCR analysis of indicated genes and GAPDH. Data (N = 6), normalized to GAPDH were expressed as a percentage of LMNA siRNA plus IL1B-stimulated cells and plotted as means ± S.E. Significance using ANOVA with a Bonferroni's multiple comparison test is indicated.*, p < 0.05; ***, p< 0.001. B, A549 cells were either not stimulated, treated with IL1B (1 ng/ml) or pre-treated with a combination of UO126, SB203580 plus JNK inhibitor 8 each at 10 µM (SB+UO+J8) for 30 min prior to IL1B stimulation. Cells were harvested after 6 h for real-time PCR analysis of the indicated genes and GAPDH. C, Cells were treated as in B and the supernatants harvested after 18 h for CXCL10 release measurement. Data (N = 4) expressed in pg/ml are plotted as means ± S.E. For B & C, significance, relative to IL1B-treated samples, was tested using a paired t-test. *, p < 0.05; **, p< 0.01; ***, p< 0.001.
5.3.14 Role of IRF1 in late-phase gene expression in the presence of IL1B and IL1B plus dexamethasone
A549 cells were treated with IL1B or IL1B plus dexamethasone in the presence of two independent
IRF1-targeting siRNAs. IRF1 expression was profoundly induced by IL1B at 2 h and this was
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partially repressed by dexamethasone (Fig. 5.10A). In both cases, IRF1-targeting siRNAs substantially attenuated IRF1 expression (Fig. 5.10A). Likewise, the expression of the IL1B- induced late-phase mRNAs identified in Fig. 5.1F was also significantly reduced by IRF1 knock- down (Fig. 5.10B). In the context of dexamethasone, as shown in Fig. 5.7A, expression of these
10 late-phase mRNAs was variably affected by dexamethasone co-treatment. IL1B-induced
CXCL10 was unaffected and MX1, IFIT1, UBD or CMPK2 mRNAs were highly repressed by dexamethasone (Fig. 5.10B). However, regardless of the effect of dexamethasone on IL1B- induced late-phase mRNA expression, IRF1-targeting siRNAs produced a further attenuation of late-phase mRNA expression (Fig. 5.10B). This was significant in the case of APOL6, CFB,
CXCL10, HELZ2, IFIT3 isoform 1, MX1 and UBD. However, even though CMPK2, IFIT1 and
IFIT3 isoform 2 showed trends towards further reduction with IRF1-targeting siRNAs, they did not reach significance. Thus, IL1B-induced IRF1 appears necessary for late-phase mRNA expression in the presence of both IL1B and IL1B plus dexamethasone.
Since CXCL10 release was maximum at 18 h following IL1B treatment (Fig. 5.7B), 18 h time point was selected to analyze the effects of IRF1 knock-down. CXCL10 release was significantly induced by IL1B and this was not affected by dexamethasone (Fig. 5.10C, left panel). In both cases, the IRF1 targeting siRNAs produced a significant inhibition of CXCL10 release (Fig. 5.10C, left panel). Thus, IRF1 is required for CXCL10 production, even in the presence of dexamethasone. LMNA siRNA had no effect on IL1B or IL1B plus dexamethasone-induced
CXCL10 release (Fig. 5.10C, right panel).
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A time (h) 2 IRF1 siRNA 2 + + IRF1 siRNA 1 + + LMNA siRNA + + Dex + + + IL1B - + + + + + + IRF-1 GAPDH B time (h) 6 6 6 6 6 IRF1 siRNA 2 + + + + + + + + + + IRF1 siRNA 1 + + + + + + + + + + LMNA siRNA + + + + + + + + + + Dex + + + + + + + + + + + + + + + IL1B - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + APOL6 CFB CMPK2 CXCL10 150 ** 150 ** 150 *** 150 ** 150 *** HELZ2 ** ** *** ** ** *** 100 ** 100 ** 100 100 ** 100 ** ** * 50 50 50 50 50 * 0 0 0 0 0 IFIT1 IFIT3 IFIT3 UBD 150 * 150 *** 150 ** 150 * MX1 150 **
mRNA * *** iso1 ** iso2 * *** 100 100 ** 100 100 100 ** ** Gene/GAPDH 50 50 50 50 ** 50 *** **
(%IL1B+LMNAsiRNA) 0 0 0 0 0 C time (h) 18 time (h) 18 IRF1 siRNA 2 + + LMNA siRNA + + IRF1 siRNA 1 + + Dex + + LMNA siRNA + + IL1B - + + + + Dex + + + IL1B - + + + + + + 15 20 * ** * 10 * ** *
10 (pg/ml) 5
Release Release
CXCL10
(pg/ml) Release Release CXCL10 0 0 Figure 5.10 Effect of IRF1-targeting siRNA on IL1B-induced inflammatory gene expression. A, A549 cells were incubated with LMNA (control) or IRF1-specific siRNAs. After 24 h, cells were treated with IL1B (1 ng/ml) or IL1B plus dexamethasone (1 μM) (Dex) as indicated. Cells were harvested at 2 h and total proteins were prepared for western blot analysis of IRF1 and GAPDH. Blots representative of at least 4 such experiments are shown. B, Cells were treated as in A and harvested at 6 h for real-time PCR analysis of indicated genes and GAPDH. Data (N = 4), normalized to GAPDH were expressed as a percentage of LMNA siRNA plus IL1B-stimulated cells and plotted as means ± S.E. C, Cells were treated as in A and the supernatants harvested after 18 h for CXCL10 release measurement. Data (N = 4) expressed in pg/ml are plotted as means ± S.E. Effect of LMNA siRNA on IL1B-induced CXCL10 release is shown in right panel. For B & C, significance was tested using ANOVA with a Bonferroni post-test. Significance between: LMNA control siRNA plus IL1B and each of the IRF1 targeting siRNAs plus IL1B, and the LMNA control plus IL1B plus Dex is shown. Other comparisons are specifically indicated.*, p < 0.05; **, p< 0.01; ***, p< 0.001.
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5.3.15 Occupancy of IRF1 at the promoters of IRF1-dependent genes in the presence of IL1B and IL1B plus dexamethasone
To explore the mechanistic basis for the regulation of IL1B-induced late-phase gens, ChIP was used to analyse IRF1 occupancy at selected late-phase loci. For this analysis, IRF1 binding peaks, visualized in the UCSC genome browser and a bioinformatics software, MatInspector, were used to identify putative IRF1 binding regions within the CXCL10, IFIT1, IFIT3 and CMPK2 loci.
Occupancy for IRF1 was determined relative to non-occupied control regions (hMYOD1, hOLIG3, hMYOG) following IL1B and IL1B plus dexamethasone treatment for 4 h. IL1B treatment significantly increased IRF1 binding above baseline at tested regions associated with CXCL10 (-
254 to -172), IFIT1 (+180 to +320), IFIT3 (-16 to +79) and CMPK2 (-82 to +20). While co- treatment with dexamethasone produced no effect on IL1B-induced IRF1 occupancy at CXCL10 and IFIT3 loci, a modest, but not significant, reduction in IRF1 occupancy was observed in the case of IFIT1 and CMPK2 loci (Fig. 5.11). These data suggest that following the treatment of A549 cells with IL1B for 4 h, IRF1 was recruited to the late-phase gene promoters and this was not materially affected by dexamethasone co-treatment.
time (h) 4 4 Dex + + Figure 5.11 Characterization of IRF1 occupancy on IL1B - + + - + + inflammatory loci in the presence of IL1B and 15 15 * * dexamethasone. A549 cells were either not treated or 10 10 5 5 stimulated with IL1B (1 ng/ml) or IL1B plus 0 0 dexamethasone (1 μM) (Dex). Cells were harvested at 4 h CXCL10 IFIT1 for real-time PCR analysis of indicated and control genes. (-254 to -172) ( +180 to +320) (Fold) IRF1 occupancy was calculated as a difference between CT 30 * 30 *
IRF1occupancy 20 20 values for each target locus as compared with the geometric 10 10 mean of CT values of three control regions that are not 0 0 occupied by IRF1. Data (N = 4), normalized to control IFIT3 CMPK2 genes were expressed as a fold of non-treated cells and ( -16 to +79) ( -82 to +20) plotted as means ± S.E. Significance, relative to non- treated samples was tested using a Friedman test with Dunn’s multiple comparison test. *, p < 0.05.
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5.4 Discussion
Repression of inflammatory gene expression is a major feature of glucocorticoid action and often involves inhibition of MAPK signalling processes via enhanced expression of DUSP1 (296).
However, in addition to exerting anti-inflammatory effects, glucocorticoids spare or enhance local innate immune and host defense responses of the epithelium (67, 410, 411). In chapter three,
DUSP1 over-expression was shown to repress IL1B-induced MAPK activation and a number of inflammatory mRNAs, including IL8, CSF2 and PTGS2. However, 19 out of 46 IL1B-induced inflammatory mRNAs, including the inflammatory transcription factor, IRF1, showed a significant enhancement following DUSP1 over-expression. Since DUSP1 over-expression inhibits MAPKs, the loss of MAPK activity by DUSP1 represents a valid explanation for the observed enhancement of IRF1 by DUSP1 (382). Thus, the role of MAPKs in the regulation of IRF1 and other inflammatory genes that were enhanced following DUSP1 over-expression was investigated using selective MAPK inhibitors. However, because inhibition of any single MAPK pathway may enhance the activity of other MAPK pathways via cross-feedback control mechanisms (361, 412,
413), and dexamethasone treatment inhibits all three MAPKs (382), MAPK inhibitors were used in combination.
IRF1 was induced by IL1B in A549 cells, and enhanced DUSP1 expression, by switching off
MAPKs, maintained IRF1 expression. Loss of MAPK activity resulted in enhanced IRF1 mRNA and protein expression, and was predominantly attributed to enhanced IRF1 transcription. In this context, MAPKs by their interaction with HATs and HDACs can affect the transcription (414).
Thus, ERK by binding to HDAC4 enhances its nuclear localization and promotes transcriptional
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repression (415). In respect of IRF1 mRNA stability, even though SB203580 had no effect on mRNA stabilisation following 30 min of IL1B treatment, at longer IL1B treatment times,
SB203580 produced a partial stabilisation of IRF1 transcript. Thus, reducing MAPK activity promotes IRF1 mRNA expression. Additionally, IRF1 protein was a very unstable protein (t½ of
~30 mins) and subject to proteasomal degradation. This finding is in line with previous reports indicating that a number of short-lived transcription factors are degraded by the proteasome pathway (416-421). Furthermore, evidence also suggests that MAPK can also target the downstream protein for ubiquitination and degradation (422). Thus, as with the proteasomal inhibitor, MG132, IRF1 stability was also enhanced following the inhibition of MAPKs. However, in both cases, the effect was somewhat modest.
The loss of IL1B-induced DUSP1, at 1 h, in chapter three, was associated with enhanced phosphorylation of p38, ERK and JNK MAPKs. Since IRF1 mRNA and protein expression was reduced by MAPKs just after the peak of their expression (i.e. at 2), increased phosphorylated
MAPK expression in DUSP1 inhibited cells may result in reduced IRF1 expression at later time points. Indeed, in this chapter, silencing of IL1B-induced DUSP1 enhanced the phosphorylation of MAPKs at 1 h and consequently reduced IRF1 protein expression at 4 h post-IL1B treatment.
This confirms a regulatory network, whereby DUSP1 switches off MAPKs to maintain IL1B- induced IRF1 expression, and such data are consistent with the negative regulation of IRF1 by
MAPKs in DUSP1-/- mouse macrophages, primary HBE and BEAS-2B cells (128, 129, 405).
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Kinetic analysis of IL1B-induced, DUSP1-enhanced, mRNAs revealed that except for ICAM1 and
STAT5A, the remaining 11 mRNAs (APOL6, CFB, CMPK2, CXCL10, HELZ2, IFIT1, IFIT3 isoform 1&2, MX1, TLR2 and UBD) were all maximally induced at 6-18 h. Indeed, the IL1B- induced late-phase mRNAs, with the exception of TLR2, were IRF1-dependent. This was consistent with the recruitment of IRF1 to the promoters of CMPK2, CXCL10, IFIT1 and IFIT3 following IL1B treatment. Furthermore, a role for IRF1 in the up-regulation of CFB and CXCL10 is also suggested and ChIP-seq data also show the binding of IRF1 at the APOL6, IFIT1 and IFIT3 genes (188, 423, 424). Thus, a positive role for IRF1 in late-phase gene expression was confirmed.
Furthermore, these data also explain the ability of DUSP1 to enhance the expression of these mRNAs via the maintenance of IRF1 expression. Because IL1B-induced TLR2 was not IRF1- dependent, enhancement of TLR2 by DUSP1 must occur via alternative mechanisms. Conversely,
CCL5 and CXCL5 mRNAs were enhanced following IRF1 knock-down. Thus, an inhibitory role for IRF1 in the regulation of CCL5 and CXCL5 is predicted. These data, with respect to the negative regulation of CCL5 by IRF1, are consistent with previous findings in astrocytoma cells
(425). Since CCL5 plays an important role in the pathogenesis of asthma, maintenance of IRF1 by
DUSP1 could be beneficial in such condition (426).
Even though IL1B-induced IRF1 expression was maintained by both combined MAPK inhibitors and DUSP1 over-expression, effects on downstream IRF1-dependent mRNAs were more variable.
Thus, expression of the 10 IL1B-induced mRNAs shown in Fig. 5.1F was enhanced following
DUSP1 over-expression. However, only 5 of the late-phase mRNAs, including CFB, CXCL10,
HELZ2, IFIT1 and IFIT3 isoform 2, showed enhanced expression following combined MAPK
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inhibitor treatment, and the others were simply not affected, or reduced modestly. Explanations for this variable effects are multiple, but are likely to involve: i) the fact that the expression of late- phase genes may also be regulated by additional non MAPK pathways, and/or factors; and ii) the effect of simultaneous inhibition of MAPKs by combined use of small molecule inhibitors is not entirely identical with DUSP1 over-expression. In addition, the expression of late-phase genes by
IL1B was NF-κB dependent (Fig. 5.12), and MAPKs can show opposing effects on NF-κB- dependent gene expression (427, 428). For example, ERK pathway negatively regulates NF-B- dependent gene expression (428), whereas p38 MAPK may have positive effects (427). Equally, while the selectivity of these small molecule kinase inhibitors is generally considered good, a number of off-target effects are also well established (357, 429, 430). In addition, when DUSP1 is over-expressed to inactivate its preferred MAPK substrate, the activity of other MAPK family members may also get repressed/affected and have additional uncharacterised effects on late-phase gene expression (100, 361). Regardless of the mechanisms enhancing CXCL10 and IFIT3 isoform
2 following MAPK inhibition, DUSP1 switches off MAPK and enhances these mRNAs. Thus, following DUSP1 knock-down, CXCL10 and IFIT3 isoform 2 mRNAs showed trends towards reduced expression at 6 h. However, other late-phase genes were without any obvious effect and this suggests that, unlike IRF1, the regulation of late-phase gene expression by MAPKs may involve multiple, potentially opposing effects.
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time (h) 6 6 6 6 6 PS1145 + + + + + IL1B - + + - + + - + + - + + - + + 150 APOL6 150 CFB 150 CMPK2 150 CXCL10 150 HELZ2 *** *** 100 *** 100 100 ** 100 ** 75 50 50 50 50 0 0 0 0 0
mRNA 150 IFIT1 200 IFIT3 200 IFIT3 150 MX1 150 UBD (%IL1B) * iso1 iso2 ** ** Gene/GAPDH 100 *** 100 *** 100 75 75 50 0 0 0 0 0
Figure 5.12 Effect of PS1145 on IL1B-induced IRF1-dependent inflammatory gene mRNA expression. A549 cells were either not stimulated, treated with IL1B (1 ng/ml) or pre-treated with PS1145 (10 µM) for 30 min prior to IL1B stimulation. Cells were harvested after 6 h for real-time PCR analysis of the indicated genes and GAPDH. Data (N = 4), normalized to GAPDH were expressed as a percentage of IL1B-stimulated cells and plotted as means ± S.E. Significance, relative to IL1B-treated samples was tested using a paired t-test. *, p < 0.05; **, p< 0.01; ***, p< 0.001.
Dexamethasone, in both A549 and primary HBE cells, in conjunction with IL1B produces greatest levels of DUSP1 expression (chapter three & four). In addition, as described in chapter three, enhanced DUSP1 expression, in A549 cells, was important for early onset repression of MAPKs by dexamethasone. Thus, even though co-treatment with dexamethasone produced 32.5 ± 5.6% repression of IRF1 protein at early time (i.e. 2 h), this repressive effect of dexamethasone disappeared at longer treatment times (i.e. 4 and 6 h), and could be attributed to enhanced MAPK repression following IL1B plus dexamethasone co-treatment at 4 or 6 h (283, 382). This was further confirmed by the fact that enhanced phosphorylation of MAPK, following the loss of IL1B plus dexamethasone-induced DUSP1, at 1 h, produced a significant loss of IRF1 protein at 4 h.
Furthermore, dexamethasone also produced a ~50% loss of IL1B-induced IRF1 transcription at 1 h. This effect was largely reflected in IRF1 mRNA levels, which showed ~25-30% repression by
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dexamethasone at all times. Additionally, IL1B-induced IRF1 expression was NF-κB-dependent
(Fig. 5.6) and the repression of IRF1 mRNA by dexamethasone occurs in the presence of cycloheximide (51). Thus, direct NR3C1-mediated repression, i.e. transrepression, may account for this early effect on IRF1 transcription rate by dexamethasone. This is also supported by the data from BEAS-2B cells where TNF-induced IRF1 expression was reduced by dexamethasone
(Shah S. unpublished data) in a manner that correlated with reduced binding of p65 (RELA) to the
IRF1 promoter region (Dr. Anthony Gerber – personal communication). Therefore, the observed repression of IRF1 transcription by dexamethasone could be possibly due to classical NR3C1 transrepression. Regardless of the mechanisms repressing IRF1 transcription and mRNA, dexamethasone induces DUSP1 expression and this inhibits MAPK activation (24). Since MAPKs negatively regulate IRF1 expression (i.e. at 4 or 6 h), inhibition of MAPKs by dexamethasone may contribute towards restoring or maintaining IRF1 expression at longer times following IL1B plus dexamethasone treatment. Thus, the net repressive effect of dexamethasone on IRF1 mRNA was very modest. Furthermore, IRF1 mRNA stability was also not affected by dexamethasone. In addition, as with MAPK inhibition, dexamethasone also modestly enhanced the stability of IRF1 protein, further suggesting that enhanced protein stability may account for the maintenance of
IRF1 by dexamethasone.
In the case of late-phase genes, while IL1B-induced CXCL10 expression was insensitive to repression by dexamethasone, other late-phase genes showed a varying degree of repression by dexamethasone. This supports the previously described variable repressive effect of glucocorticoids on some of these genes (411, 431, 432). Furthermore, silencing of IL1B plus
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dexamethasone-induced DUSP1 significantly attenuated the expression of CXCL10 and IFIT3 isoform 2. Since, both CXCL10 and IFIT3 isoform 2 were IRF1-dependent and the recruitment of
IRF1 at their promoters was not altered (at 4 h) by dexamethasone, these data confirm that glucocorticoids enhance DUSP1 expression to reduce MAPK signalling and thereby maintain
IRF1 and downstream gene expression.
These results in A549 cells suggest that in addition to producing anti-inflammatory actions, glucocorticoids may spare or enhance innate immune and host defense responses through DUSP1- mediated maintenance of IRF1 and CXCL10. In this respect, roles for IRF1 and CXCL10 in innate immunity, anti-viral defenses and in the development of TH1 immunity are suggested and may be promoted by DUSP1 (170, 304, 433). Conversely, enhanced IRF1 expression is associated with reduced glucocorticoid sensitivity (210, 407). Equally, a role for CXCL10 in airway inflammation, airway hyper-responsiveness and virus-induced asthma exacerbation is also indicated (130, 434).
Thus, maintenance of IRF1 and/or CXCL10 by DUSP1 may not be therapeutically desirable.
Furthermore, results presented here add to, indeed modify, the emerging concept of glucocorticoid- insensitivity and suggest that pathways involving maintenance of IRF1 could contribute to poor response to glucocorticoids. Indeed, contrary to initial expectations (296, 392), it may be that novel
NR3C1 ligands that have reduced ability to induce DUSP1 and/or inhibit MAPK pathways could show an improved efficacy in the context of IRF1-dependent inflammatory responses.
Finally, confirmation of some of the findings, with respect to the effects of IL1B and dexamethasone on DUSP1 (in chapter four) and IRF1 expression in primary HBE cells suggest
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that the data presented herein are likely to be physiologically and therapeutically relevant. In addition, data from DUSP1 knockout mouse models also support these findings (128, 405). In summary, loss of glucocorticoid-induced DUSP1 may enhance glucocorticoid sensitivity in the context of certain host defense responses where IRF1 is a key driver. Thus, only by considering such complex interactions the existing anti-inflammatory glucocorticoid therapies can be improved.
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Chapter 6 : Discussion
6.1 Feedback and feed-forward control of inflammatory gene expression
Appropriate regulation of inflammatory gene expression is central to inflammation and its resolution. Equally, understanding these processes will aid the identification of therapeutic agents that target inflammation. However, while the regulation of signal transduction and gene expression involves numerous signalling molecules leading to transcriptional, post-transcriptional, translational and often post-translational control of gene expression, it is routine to depict these assemblies as simple linear pathways. In this regard, the three major MAPK, p38, ERK and JNK, play an important role in the initiation and execution of inflammatory responses in response to inflammatory stimulus, and thus, have been considered as promising targets for the treatment of inflammatory diseases (362, 435). As such, phosphorylation and dephosphorylation of MAPKs and their downstream targets serve as molecular switches for modulating inflammatory processes and in fact the level and magnitude of each of these cellular targets is a highly regulated process
(436). In this context, MAPK pathways control the expression of inflammatory mediators via the phosphorylation of RNA-binding proteins, including ZFP36, and the activation of transcription factors, such as c-Jun, ATF-2, p53, Elk-1, NFAT, NF-B and/or AP-1 (94, 95, 97, 101, 102). Thus, several kinase inhibitors that block the activity of MAPKs either directly or indirectly via the inhibition of upstream/downstream modulators are being evaluated currently to be used as therapeutic agents for the treatment of a number of chronic inflammatory diseases, including asthma (71, 437). Conversely, MAPK-mediated regulation of phosphatases, mainly DUSPs, by limiting the activity of MAPKs, via dephosphorylation, limit inflammatory responses (438). In
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addition, mathematical modelling of MAPK pathways also suggests that negative feedback control of MAPKs by DUSPs is crucial to control the kinetics of MAPK activity (439-441). Furthermore,
MK2-mediated phosphorylation of ZFP36 prevents the mRNA destabilising activity and function of ZFP36 and allows dynamic control of the expression of ARE-containing inflammatory transcripts, including cytokines and chemokines (309, 311, 442, 443). However, dephosphorylation of ZFP36, by phosphatases, such as PP2A, by enhancing the activity of ZFP36, promotes mRNA decay of pro-inflammatory transcripts (312). Thus, even though MAPK- dependent regulation of gene expression is often depicted to follow a linear cascade, in reality, it behaves in a non-linear fashion. This nonlinearity in MAPK signalling and effector responses can be attributed to MAPK-dependent regulation of feedback and feed-forward regulatory proteins, such as DUSP1 and ZFP36 respectively (See Figure 4.1). As a result, to understand the regulation of inflammatory gene expression by glucocorticoids, it is fundamental to consider the feedback and feed-forward regulatory processes that are induced by inflammatory stimuli. These are addressed, in detail, in this thesis.
In A549 cells, the expression of both DUSP1 and ZFP36 was induced by IL1B in a MAPK- dependent manner. In addition, in the presence of IL1B, DUSP1 provided a transient feedback control of MAPKs and multiple IL1B-induced ARE-containing inflammatory transcripts, including CCL2, CCL20, CSF2, CXCL1, CXCL2, CXCL3, IL6, IL8 and PTGS2. In contrast,
IL1B-induced ZFP36 expression was essential to limit the expression of IL1B-induced ARE- containing transcripts, such as TNF. Additionally, siRNA-mediated loss of DUSP1 not only enhanced MAPK phosphorylation (at 1 h) (chapter three), but also increased ZFP36 protein
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expression (at 2 h) post-IL1B treatment (chapter four). Furthermore, even though the mRNA expression of a number of IL1B-induced ARE-containing transcripts (see above) was enhanced at
1 h by the prior loss of DUSP1, by 6 h post-IL1B treatment, loss of DUSP1 attenuated the mRNA expression of ARE-containing transcripts, including CCL2, CXCL3 and PTGS2 (chapter three).
This could be mediated via the increased expression of ZFP36 following the loss of DUSP1, and was confirmed using TNF as a model ARE-containing ZFP36 target gene (chapter four). Thus, in
A549 cells, ZFP36 was induced by IL1B to reduce TNF mRNA stability to exert feed-forward inhibition of TNF mRNA and protein expression. In addition, the attenuation of TNF mRNA at 6 h in the presence of DUSP1 siRNA was prevented by the additional loss of ZFP36. This effect was associated with reduced TNF mRNA stability, which was also inhibited by ZFP36 knock-down.
Thus, even though the current data is restricted to the feed-forward inhibition of TNF by ZFP36, the overall concept may also hold true for other ARE-containing inflammatory transcripts, such as
PTGS2, CSF2 and/or IL8, that are also potential targets of ZFP36 (215, 222, 313).
In conclusion, these data directly confirm that the loss of DUSP1 enhances ZFP36 expression to increase negative feed-forward control of ARE-containing inflammatory transcripts, such as TNF.
However, despite these data, loss of ZFP36 did not completely ablate all the effects of DUSP1 knock-down on TNF mRNA expression. This strongly suggests that there are other non-ZFP36- dependent feed-forward repressive effects that are also dependent on MAPKs and which may also impact inflammatory gene expression. Additionally, confirmation of the regulation of ZFP36 by
DUSP1 in primary human airway smooth muscle cells and mouse models suggest that the regulatory mechanisms described herein using the A549 cells are physiologically and
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therapeutically relevant (320, 404, 405). In particular, Smallie et al., using DUSP1-/- mice, demonstrated that DUSP1 regulates the expression of a number of inflammatory mediators, including TNF, CXCL1, CXCL2 and IL1B, by controlling the activity of ZFP36 (405). Similarly, in airway smooth muscle cells, the mRNA destabilising activity of ZFP36 is controlled by p38
MAPK-mediated phosphorylation (404). Thus, DUSP1, by dephosphorylating p38 MAPK, increases the activity of ZFP36 to repress inflammatory gene expression (404). Taken together, the interaction between DUSP1- and ZFP36-mediated feedback and feed-forward mechanisms respectively reveals why modulation, or targeting, of a single component in a signalling cascade can often produce opposing effects on inflammatory gene expression. Therefore, these data emphasize the need to accurately model network behaviour in order to predict correct biological and/or appropriate therapeutic outcomes.
6.2 DUSP1-mediated regulation of inflammatory gene expression
MAPKs, mainly p38 and JNK, play an important role in the regulation of innate immune responses
(101, 353). In particular, the p38 MAPK pathway plays a crucial role in the development of TH1 immunity and in the production of IFN(444). Inadequate up-regulation of innate immune responses, through the enhanced production of cytokines and chemokines, may lead to the development of chronic inflammatory diseases, including asthma (294, 445). However, termination of MAPK signalling via negative feedback processes could limit the innate immune responses (294, 353, 446). In this regard, as mentioned above, enhanced DUSP1 expression plays an important role in the feedback inhibition of MAPKs. Thus, over-expression of DUSP1 resulted in loss of IL1B-induced MAPK activity and expression of 11 inflammatory cytokines and
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chemokines (chapter three). These data are consistent with findings in DUSP1-/- mouse macrophages, where a number of cytokines and chemokines showed enhanced expression following LPS challenge (352, 353) .
However, contrary to the current dogma, 14 out of 46 IL1B-induced inflammatory mRNAs, including the interferon regulatory factor, IRF1, and the chemokine, CXCL10, were significantly enhanced following DUSP1 over-expression. In addition, 11 of the 14 DUSP1-enhanced mRNAs, including CXCL10, were induced by IL1B in an IRF1-dependent manner with a peak of their mRNA expression at either 6 or 18 h. Like DUSP1 over-expression, inhibition of MAPKs also prolonged IRF1 expression, an effect involving elevated transcription and increased mRNA and protein stability (chapter five). Conversely, DUSP1 silencing increased IL1B-induced MAPK phosphorylation (see above), while significantly reducing IRF1 protein expression (Fig. 6.1 A&B).
Consistent with this, a study from Korhonen et al. also showed that IRF1 expression was reduced in DUSP1-/- macrophages, and pharmacological inhibition of p38 MAPK augmented LPS-induced
IRF1 expression (128). Furthermore, inhibition of MEK1, in human airway epithelial cells, enhances the expression and the activity of HRV-induced IRF1 (129, 188). Thus, these data collectively confirm a regulatory network whereby inhibition of MAPKs maintains IRF1 expression.
Although IL1B-induced CCL5 and CXCL5 mRNAs were reduced significantly following DUSP1 over-expression, siRNA-mediated loss of IRF1 substantially enhanced the mRNA expression of
IL1B-induced CCL5 and CXCL5. This suggests that these mRNAs are negatively regulated by
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IRF1 and is consistent with previous findings in astrocytoma cells, where a negative role for IRF1 on CCL5 expression has been reported (425). Since CCL5 may play an important role in the pathogenesis of asthma, by increasing the concentration of eosinophils in the airways, the maintenance of IRF1 expression by DUSP1 could be beneficial in asthma (10).
Taken together, these results suggest that besides the inhibition of MAPKs and inflammatory gene expression, DUSP1 may also spare or even enhance innate immune and host defense responses through the maintenance of IRF1 and CXCL10. In this respect, the role of DUSP1, IRF1 and
CXCL10 in innate immunity, anti-viral host defense and development of TH1 immunity is clearly indicated (128, 170, 195, 304, 433, 447). Furthermore, in mouse macrophages, DUSP1 deficiency resulted in reduced production of IRF1 and IL12, an important cytokine in the development of TH1 responses (128). Since IL12 antagonizes TH2 responses and inhibits allergic inflammation (25, 26,
448), maintenance of IRF1 by DUSP1 could be beneficial in such conditions.
Another potentially important finding of this thesis is that the mRNA expression of IL1B-induced
TLR2 was also enhanced by DUSP1 over-expression. Even though the time course analyses revealed that TLR2 was a late-phase gene (i.e. expression at 6 or 18 h), unlike other late-phase genes in this study (chapter five), TLR2 expression was not IRF1 dependent. Thus, IL1B-induced
TLR2 expression may involve mechanisms that are independent of IRF1. In this regard, IL1B- induced TLR2 expression, in HBE cells, is NF-B-dependent, and negatively regulated by p38 and JNK MAPKs (449). Thus, loss of MAPK activity, mediated by DUSP1 over-expression, may enhance IL1B-induced TLR2 expression.
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In terms of glucocorticoids, by acting on airway epithelium, they suppress the production of epithelial-derived inflammatory mediators and inhibit the recruitment of inflammatory cells into the airways, resulting in reduced inflammatory responses (9). However, glucocorticoids also enhance the expression of scavenger receptors, TLRs, collectins, complement family members and other antimicrobial and antiviral proteins that are involved in host defense (450, 451). Moreover, glucocorticoids also spare or increase the expression of epithelial genes, involved in innate immunity, and thereby contribute towards the maintenance of epithelial innate immune responses
(67). In this regard, glucocorticoids enhance epithelial expression of TLR2 and a number of other
TLR3-mediated genes associated with host defense mechanisms (411, 452-455). Thus, glucocorticoids can also induce many innate immune responses and presumably this may lead to an enhancement of innate immunity. The data presented in this thesis clearly demonstrate that
DUSP1 expression was induced following dexamethasone treatment and was further enhanced by
IL1B co-treatment. Indeed, in chapter five, siRNA-mediated loss of IL1B plus dexamethasone- induced DUSP1 resulted in enhanced loss of IRF1 and CXCL10 (Fig. 6.1C). Thus, in addition to promoting anti-inflammatory actions of glucocorticoids, DUSP1 may play an important role in the maintenance of innate immune and host defense responses via the maintenance of IRF1 and
CXCL10 in the presence of glucocorticoids.
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A B C IL1B treatment IL1B treatment + DUSP1 siRNA IL1B + glucocorticoid IL1B IL1B IL1B
DUSP1 MAPK IRF1 MAPK IRF1 GC DUSP1 MAPK IRF1
Late-phase Late-phase Other GC inflammatory inflammatory effectors gene gene expression expression
Figure 6.1 Regulation of IRF1 and IRF1-dependent late-phase gene expression by IL1B following DUSP1 silencing and glucocorticoid treatment. Schematics representing possible regulatory networks are shown along with summarized data from chapter five. A, IL1B treatment results in the activation of MAPKs. This, along with the activation of other signalling pathway and inflammatory transcription factors, e.g. NF-κB and AP-1 (not shown), enhances expression of the negative feed-back regulator, DUSP1. Following MAPK activation, the increased expression of DUSP1 is one mechanism by which MAPK activity is restored to basal. Expression of late-phase inflammatory genes by IL1B depends on IRF1 activation. IL1B-mediated activation of MAPKs negatively regulate the expression of IL1B-induced IRF1 and selected IRF1-dependent late-phase genes (e.g. CXCL10). B, With reduced DUSP1 expression there is reduced negative feedback control of MAPKs leading to enhanced MAPK activity (at 1 h) (bold) at the times where DUSP1 expression would have been elevated. This reduces IRF1 (grey) expression. C, In the presence of glucocorticoid co-treatment, DUSP1 expression is enhanced (bold) and promotes inactivation of MAPKs (grey). Loss of MAPK activity maintains the expression of IRF1 and selected IRF1- dependent late-phase genes (e.g. CXCL10) in the presence of glucocorticoids. However, since the expression of some of the IRF1-dependent late-phase genes (e.g. APOL6, CMPK2, CFB, HELZ2, IFIT1, IFIT3 iso1&2, MX1 and UBD) is variably, but significantly repressed by glucocorticoids, additional glucocorticoid effectors must exist to maintain repression of selected IRF1-dependent late-phase genes. Positive signalling/expression (blue) is represented by arrows. Negative effects are indicated (red) by lines ending in a T-bar.
6.3 Glucocorticoid-inducible genes and redundancy
Glucocorticoids repress inflammatory gene expression via mechanisms that may include direct repression of transcription (transrepression) and/or the induction of glucocorticoid-induced effector genes (transactivation) that then reduce inflammatory gene expression (456, 457).
Importantly, inflammatory gene expression may often involve MAPK activation and there is a
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considerable interest in DUSP1 as a major effector of glucocorticoid repression (458, 459). Thus, the role of DUSP1 in glucocorticoid-induced inflammatory gene repression was explored using 11
IL1B-induced inflammatory genes whose mRNA expression was repressed by dexamethasone in a cycloheximide-sensitive manner (460). This implies that glucocorticoid-induced gene expression, i.e. transactivation, is crucial for repression. In this context, DUSP1 is strongly induced by dexamethasone in A549 cells and over-expression of DUSP1 produced a complete loss of
IL1B-induced MAPK activation and inflammatory gene expression (chapter three). Despite this, efficient knock-down of IL1B plus dexamethasone-induced DUSP1 did not completely reverse the repression of MAPKs and inflammatory genes by dexamethasone. In fact, only a transient, non- redundant role, for dexamethasone-induced DUSP1 in the early onset of repression of MAPKs and only certain IL1B-induced inflammatory genes (such as CXCL1, CXCL2 and PTGS2) tested here was documented (chapter three).
Explanations for this partial effect of DUSP1 loss could be many, and may involve DUSP1- independent effector mechanisms that may also be enhanced/induced by dexamethasone. For example, glucocorticoid-induced TSC22D3 and CDKN1C, which may inhibit Ras-Raf activation of MAPKs and JNK signaling respectively, are both induced by dexamethasone in A549 cells, and may therefore play an important role in dexamethasone-induced repression of MAPKs (282, 350,
363, 370-373). Moreover, expression of the IL1B-induced inflammatory genes studied in chapter three was mainly NF-B dependent (273). Thus, inhibition of NF-B-dependent transcription by both DUSP1 and TSC22D3, in addition to NFKBIA, which is modestly induced by dexamethasone in A549, primary HBE and airway smooth muscle cells (Leigh et al. unpublished data), may also
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play a repressive role (114, 461-463). Additionally, NF-B-dependent transcription can also be inhibited by the transcriptional repressor, zinc finger and BTB domain containing 16 (ZBTB16)
(464). Recent data from our laboratory suggest that ZBTB16 mRNA is strongly induced by dexamethasone and budesonide in A549, primary HBE and airway smooth muscle cells (Leigh et al. unpublished data). Additionally, in A549 cells, ZBTB16 mRNA and protein was synergistically induced by IL1B plus dexamethasone co-treatment (Shah S. unpublished data).
Moreover, recent work by Sadler et al., using ZBTB16 knockout mice, demonstrated that ZBTB16 plays an important role in the attenuation of a number of NF-B-induced inflammatory gene expression (465). Thus, enhanced expression of ZBTB16 by glucocorticoids could also inhibit NF-
B and NF-B-dependent gene expression independent of DUSP1. Likewise, ZFP36 is also suggested to inhibit NF-B-dependent transcription by interacting with p65 subunit of NF-B and
HDAC1, -3, and -7 (466). In addition, TNFAIP3 (A20), a key inhibitor of NF-κB, is also induced by dexamethasone, and a feedback inhibitory role for dexamethasone-induced TNFAIP3 in NF-
κB inhibition is also established (467). Equally, IRAK-M (also known as IRAK3), a negative feedback regulator of the TLR/IL1R family signalling, is also induced by dexamethasone in A549 and BEAS-2B cells (468). Thus, even if the expression of DUSP1 was prevented, enhanced expression of TSC22D3, NFKBIA, ZBTB16, TNFAPI3, IRAK-M, ZFP36 and/or other anti- inflammatory effector genes (see section 1.3.4 for more details) by dexamethasone may still inhibit
NF-B and other inflammatory signaling processes.
The majority of inflammatory mRNAs studied in chapter three contain one or more AREs in their
3’-UTR region, and thus, the stability of such mRNAs could be inhibited by RNA binding proteins,
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such as ZFP36. In this context, in chapter four, ZFP36 expression was shown to be modestly enhanced following dexamethasone treatment at 6 h in A549 cells, and is consistent with previous observation within our laboratory (308). Additionally, the repression of IL8 and CSF2 in A549 cells by dexamethasone was partly mediated by ZFP36 (308). Thus, even if DUSP1- and
TSC22D3-mediated repressive effects were prevented, ZFP36 may still exert repressive effects on
ARE-containing inflammatory gene expression at 6 h in the presence of dexamethasone. However, as confirmed in chapter four, siRNA-mediated loss of ZFP36 was not sufficient to reverse the repressive effect of dexamethasone, at least, on TNF mRNA. Therefore, combined/simultaneous inhibition of dexamethasone-induced multiple anti-inflammatory genes may produce a higher repressive effect. This was subsequently tested in chapter four where both DUSP1 and ZFP36 were knocked-down in combination. However, because combined knock-down of both ZFP36 and
DUSP1 did not significantly alter the percentage repression of TNF by dexamethasone, a role for additional non-DUSP1-/non-ZFP36-dependent repressive mechanisms for the repression of TNF by dexamethasone is predicated. Thus, these data show that there are likely to be many more glucocorticoid-induced effector genes, which can exert repressive effects in addition to those described here. Consequently, the repression of inflammatory genes by glucocorticoids may not be affected by a simple knock-down of one, or only a few, glucocorticoid-inducible genes.
Therefore, the data presented in chapters three and four point to the possibility that glucocorticoid- induced genes redundantly repress inflammatory gene expression, and could be a reason as to why
DUSP1- and ZFP36-specific siRNAs did not significantly reverse the dexamethasone-induced repression of TNF.
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One of the major limitations of this work is that these findings are currently restricted to only a few specific inflammatory genes in A549 cells. Therefore, further testing with additional inflammatory genes, in different cell types, using a different inducing agent is required. However, confirmation of some of the findings in chapter four and five, with respect to the effects of IL1B and dexamethasone on DUSP1, ZFP36, TNF and IRF1 expression in primary HBE cells provides considerable support that the regulatory mechanisms described herein for the A549 cells are physiologically and therapeutically relevant. In addition, confirmation of inducibility of a number of genes, including DUSP1, RGS2, TSC22D3, FKBP51 and CDKN1C, with potential anti- inflammatory/bronchoprotective effects, by dexamethasone and budesonide in a variety of cell types, including primary HBE, primary airway smooth muscle, primary human bronchial fibroblast and primary human umbilical vein endothelial cells also supports the concept that multiple effector genes are induced upon treatment with glucocorticoids (Leigh et al. unpublished data) (280, 282, 372, 469). In addition, our laboratory has also demonstrated that the expression of TSC22D3 and FKBP51 was enhanced in asthmatics taking ICS (372). Further support for this work comes from the recent in vivo data showing that the expression of multiple anti- inflammatory/bronchoprotective genes, including DUSP1 and ZFP36, was increased in the human airways following ICS administration (Leigh et al. unpublished data). However, further characterization and functional analyses of such anti-inflammatory genes is warranted to understand the anti-inflammatory action of glucocorticoids in more detail.
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6.4 A possible role for transrepression in the dexamethasone-induced repression of inflammatory genes
The work presented in this thesis has mainly focused on evaluating the role for glucocorticoid- inducible gene expression or transactivation on dexamethasone-induced inflammatory gene repression. However, the previous data from our laboratory indicated that inflammatory genes were subject to repression via both NR3C1 transrepression and transactivation (273). In addition, since the repression of IRF1 and TNF by dexamethasone was insensitive to the translational blocker, cycloheximide, the role for transrepression cannot be ignored (273). In this context, the expression of IL1B-induced TNF and IRF1 is NF-B dependent (chapter 5 and (273)). Thus, the obvious explanation for the observed repressive effect of dexamethasone would be a classical transrepression mechanism involving binding of NR3C1 to inflammatory transcription factors, such as NF-B. This hypothesis of transrepression in respect of TNF is further supported by the data in HeLa cells showing a significant binding of NR3C1 and NF-B to TNF promoter upon glucocorticoid and TNF co-treatment (470). Furthermore, the recent data in BEAS-2B cells suggest that TNF-induced binding of p65 (RELA) to IRF1 and TNF was modestly attenuated by dexamethasone (Dr. Anthony Gerber – personal communication), and further point to the possibility that the observed repression of IRF1 and TNF by dexamethasone may indeed involve classical NR3C1 transrepression. Whilst this may hold true, roles for miRNAs or other RNA- mediated events in the presence of dexamethasone, while completely speculative, cannot be excluded due to the fact that treatment with cycloheximide would not prevent the induction of such
RNAs by dexamethasone (273).
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Furthermore, IL1B-induced transcription of both, TNF (chapter four) and IRF1 (chapter five), was significantly attenuated following dexamethasone treatment. This could be attributed to the effect of dexamethasone on the recruitment of RNA Pol II to target promoters or due to effects on the initiation or elongation of transcription, rate-limiting steps for gene activation (136). In this regard, the transcriptional activity of RNA Pol II is influenced by the three transcription elongation factors, namely, DSIF, NELF and P-TEFb (141). Association of DSIF and NELF with RNA Pol II triggers transcriptional pausing, whereas P-TEFb allows RNA Pol II to enter into the productive elongation phase (142, 471, 472). Furthermore, P-TEFb-mediated phosphorylation of the CTD of RNA Pol
II at S2 facilitates the release of DSIF and NELF from RNA Pol II, thereby reversing the elongation block, leading to elongation of transcription (472, 473). In the presence of glucocorticoids, this P-
TEFb-mediated phosphorylation of the Pol II CTD at S2 is inhibited, resulting in stalling of RNA
Pol II on the DNA template, which in turn may inhibit transcription (474). Thus, in the presence of glucocorticoids, even though RNA Pol II is present at the promoter, RNA pol II may lack the ability to undergo elongation of transcription and consequently resulting in inhibition of transcription by glucocorticoids. In this context, in the case of IL8, NR3C1 competed with P-TEFb for binding to NF-B and inhibited the phosphorylation of S2, thereby producing transcriptional repression (475). Thus, dexamethasone-induced transcriptional repression of IRF1 and TNF could involve mechanisms related to Pol II recruitment or inhibition of initiation or elongation of transcription. However, this needs to be further tested. Furthermore, this hypothesis about the effect of dexamethasone on RNA pol II recruitment to TNF and IRF1 promoter is further supported by the fact that in BEAS-2B cells dexamethasone modestly reduced TNF-induced RNA pol II
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recruitment to IRF1 and TNF promoter (Dr. Anthony Gerber – personal communication). Hence, in addition to the effect on NF-B, inhibition of basic transcriptional machinery by dexamethasone could also play an important role in the repression of TNF and IRF1.
6.5 Implication for glucocorticoid therapy and new drug design
Even though ICS remains the cornerstone treatment for chronic inflammatory diseases, such as asthma (1), poor patient responses to ICS therapies, by asthmatics who smoke, or have viral infection, necessitates high, often oral, doses of glucocorticoids over prolonged periods (476). In such cases, therapeutic usefulness is compromised by systemic contraindications (477). Thus, there remains an urgent unmet clinical need to facilitate the rational design of improved anti- inflammatory NR3C1 ligands and combinatorial add-on therapies with reduced side-effect profile
(392).
Since transactivation was believed to be associated predominantly with the side-effects of glucocorticoids, pharmaceutical companies and a number of researchers sought to separate transactivation from transrepression and designed so-called dissociated steroids. These glucocorticoid ligands have the ability to transrepress, but would lack the ability to transactivate
(478). However, the data presented in this thesis clearly argue against this concept. Truly separating transactivation from transrepression may in fact reduce the anti-inflammatory potential of NR3C1 ligands, at least at early time points, by inhibiting the up-regulation of anti- inflammatory genes, such as DUSP1, by glucocorticoids. Potential dissociated glucocorticoid ligands were screened for transrepression using NF-B- and AP-1-dependent reporter systems or
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by analysing the repression of only few selected inflammatory genes (72, 478, 479). Thus, a dissociated steroidal compound, RU24858, even though being a poor transactivator, shows anti- inflammatory properties by efficiently repressing NF-B- and AP-1-dependent transcriptional responses (478, 479). However, despite retaining their anti-inflammatory properties in vivo and in vitro, dissociated glucocorticoids were not devoid of some of the side-effects associated with conventional glucocorticoids (480). For example, RU24858 was not completely dissociated in vivo and still showed bone metabolism similar to the conventional glucocorticoid, budesonide (480). In addition, the transactivation action of most of the dissociated glucocorticoids was analysed in cell- based assays using simple GRE-based reporters (73). This will only allow the screening of ligands that cannot up-regulate the genes induced by simple GREs. In reality, NR3C1-dependent transactivation can also be induced by multiple mechanisms other than the activation of classical
GRE-dependent transcription (254). Thus, NR3C1 can interact, cross-talk or synergise with
STAT3, C/EBP and other nuclear hormone receptors to induce transcriptional activation (254, 481,
482). Additionally, the data also suggest that interaction of NR3C1 with NF-B and AP-1 also results in transcriptional activation (483, 484). In this regard, computational analysis of the promoter regions of nearly 500 genes revealed that these regions contain multiple transcription factor binding sites, including those for AP-1 and C/EBP, and further suggests that majority of
GRE sites are in fact composite elements that are bound by other transcription factors in addition to NR3C1 (485). Furthermore, mutations in either of the two NR3C1 activation domains, AF1 and
AF2, or dimerization deficient mutant (dim) of NR3C1 (GRdim) did not produce complete loss of dexamethasone-inducible genes (486). This suggests that induction of different dexamethasone-
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inducible genes is regulated by different mechanisms of transactivation, presumably mechanisms other than at a classical GRE (254). Thus, despite the fact that GRdim mutant mice were defective in dexamethasone-induced GRE-dependent transcription (487), a number of glucocorticoid- inducible genes were still induced in a GRdim mutant mice (486). These data clearly suggest that the failure to induce simple GRE-dependent transcription may not reflect an inability to induce the expression of ‘real genes’ by NR3C1 ligands. This argument is also equally supported by the data from our laboratory showing that even though RU24858 is a dissociated glucocorticoid, RU24858 still induces the expression of anti-inflammatory DUSP1 and TSC22D3, and further questions the effect of dissociated NR3C1 ligands (271). In addition, like dexamethasone, RU24858-mediated repression of IL8 and PTGS2 steady-state mRNA and protein is also sensitive to transcriptional and translational blockade (271). These data therefore collectively suggest that glucocorticoid- inducible gene expression or transactivation plays an important role in mediating the anti- inflammatory effects of glucocorticoids. In this context, increased expression of DUSP1 by glucocorticoids inhibits MAPK activation and inflammatory gene expression, thereby is often regarded as a key anti-inflammatory effector mechanism (296, 392). Going with this notion, work presented in chapter three also shows that dexamethasone-induced DUSP1 plays a transient, but partial, role in the repression of MAPKs and inflammatory genes by dexamethasone.
One of the major contraindications associated with long-term usage of high dose glucocorticoids is severe side-effects. Thus, the holy grail of asthma therapy is to reduce the dose of glucocorticoids, so that the side-effects associated with long term glucocorticoid use can be prevented or minimised (GINA, 2009). In this regard, in the presence of LABA, a similar or higher
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clinical efficacy of glucocorticoids can be achieved with relatively lower dosage, thus LABAs are said to be steroid sparing (82). Due to this reason, it was expected that addition of LABA may allow glucocorticoids to be used at relatively lower doses, and will have added benefits to reduce the risk of side-effects (75). Therefore, current asthma therapy recommends using the combination of LABA and ICS (GINA, 2009). In this respect, a number of glucocorticoid-induced genes, including DUSP1, CDKN1C, and RGS2, with potential anti-inflammatory/bronchoprotective effects, show enhanced expression in the presence of LABA (282). This further suggests the importance of glucocorticoid-induced gene expression. These data therefore collectively support the concept that addition of LABA to ICS therapy would likely improve the anti-inflammatory action of glucocorticoids (see chapter one for more detail). In addition, like LABAs, PDE4 inhibitors also act by increasing the intracellular levels of cAMP (488). Therefore, PDE4 inhibitors are also likely to increase DUSP1 expression (489). For example, a PDE4-selective inhibitor, rolipram, was found to enhance DUSP1 expression, and enhanced DUSP1 expression, at least partly, contributed towards the anti-inflammatory effects of rolipram (490). This observation suggests that PDE4 inhibitors may also enhance the anti-inflammatory effects of glucocorticoids.
However, current use of PDE4 inhibitors is limited due to their low therapeutic index (491).
Therefore, if PDE4 inhibitors produce beneficial effects on glucocorticoid action at a dose lower than the one that produces side-effects, they could be used in combination with ICS. Yet it remains to be seen whether this combination has any beneficial effects in asthma. Thus, identifying such treatment combinations where the therapeutic effectiveness of glucocorticoids can be enhanced
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without using LABA may have added benefits especially in conditions where LABA cannot be utilised (492).
Despite the fact that DUSP1 is important for the inhibition of inflammatory gene expression, glucocorticoid-induced DUSP1 may also play a role in the inhibition of osteoblast proliferation and may thereby further contribute to the development of osteoporosis, a known side-effect associated with high-dose glucocorticoid treatment (477, 493). In addition, data presented in chapter five suggested that enhanced DUSP1 expression maintains IRF1 and IRF1-dependent
CXCL10 expression. Since increased expression of IRF1 has been widely associated with asthma pathogenesis and reduced glucocorticoid sensitivity (210, 407), the current findings may potentially help to explain why glucocorticoids are ineffective in viral-induced asthma exacerbations. Furthermore, such observations also indicate a long-awaited need to focus research attention to develop selective GR/NR3C1 agonists (SEGRAs) that do not enhance or up-regulate
DUSP1 and produce improved repressive effect (at least on IRF1 and IRF1-dependent responses).
Furthermore, SEGRAs may also prove to be beneficial for functional analyses of other glucocorticoid-induced genes that are negatively regulated by DUSP1 expression. In this regard, the data presented in chapter four suggested that the loss of dexamethasone-induced DUSP1 results in increased expression of ZFP36, an ARE-binding protein with potential anti-inflammatory effects. Thus, functional analysis of dexamethasone-induced ZFP36 could possibly be carried out using SEGRAs that do not up-regulate DUSP1. However, at this point, it remains to be seen whether this could ever be achieved.
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6.6 Overall conclusion
Taken together, the data presented in this thesis clearly indicates that IL1B-induced DUSP1 and
ZFP36 play an important role in feedback and feed-forward inhibition of MAPKs and ARE- containing inflammatory transcripts, such as TNF, respectively. In addition, the data also suggest that dexamethasone-induced DUSP1 plays a transient, but partial, role in the repression of MAPKs and certain inflammatory genes by dexamethasone. Thus, the role of other glucocorticoid-induced effector mechanisms must be considered and needs to be investigated in order to guide the development of new NR3C1 ligands. In addition, although DUSP1 is essential for early onset of repression of MAPK by dexamethasone, maintenance of IRF1, through DUSP1-mediated inhibition of MAPKs, may, in part, exert insensitivity to glucocorticoids.
In summary, documenting the roles for specific glucocorticoid-inducible genes in the repression of inflammatory genes will substantially impact the rational development of novel NR3C1 modulators and combination therapies for the treatment of asthma. Moreover, the understanding of glucocorticoid-dependent mechanism(s) that may maintain the aspects of the inflammatory response could help to identify new ways of combating glucocorticoid insensitivity in severe asthma.
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Appendix A: Antibodies, siRNA, PCR and ChIP primers
Table A1. siRNA sequence for knockdown studies
(IRF-1 siRNA was provided by Dr. David Proud, University of Calgary, Calgary, Canada and others were supplied by Qiagen, Canada.)
siRNA Target Sequence Lamin A/C (LMNA) 5’-AACTGGACTTCCAGAAGAACA-3’ siRNA DUSP1 siRNA 1 5’-TAGCGTCAAGACATTTGCTGA-3’ DUSP1 siRNA 2 5’-CTGTACTATCCTGTAAATATA-3’ IRF1 siRNA 1 5’-GGGACAUCAACAAGGAUGCCUGUUU-3’ IRF1 siRNA 2 5’-CGGACAGCACCAGTGATCTGTACAA-3’ ZFP36 siRNA 1 5’-ACCGACGATATAATTATTATA-3’ ZFP36 siRNA 2 5’-ACGACTTTATTTATTCTAATA-3’
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Table A2. Antibodies used for western blot analysis
List of antibodies including the working dilution of primary antibody. Antibodies were supplied by: AbD Serotec, Raleigh, NC, USA; Santa Cruz Biotechnology Inc., Santa Cruz, CA, USA; Cell
Signalling, Danvers, MA, USA; Abcam Inc., Cambridge, MA, USA.
Primary Secondary Dilution Company Cat. number Antibody
cJUN Rabbit 1:1000 Cell Signalling #9165 DUSP1 Rabbit 1:250 Santa Cruz sc-1102 GAPDH Mouse 1:40000 AbD Serotac 4699-9555 IRF1 Rabbit 1:15000 Santa Cruz sc-497 Phosho-cJun Rabbit 1:1000 Cell Signalling #9164 phospho-JNK Rabbit 1:1000 Cell Signalling #9251 (Thr183/Tyr185) phospho-p38 Rabbit 1:1000 Cell Signalling #9211 (Thr180/Tyr182) phospho-p44/42 Rabbit 1:1000 Cell Signalling #9101 (Thr202/Tyr204) PTGS2 Goat 1: 1000 Santa Cruz sc-1746 TNF Rabbit 1:1000 Abcam ab66579 ZFP36 Rabbit 1:500 Santa Cruz sc-14030
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Table A3. Primers used for PCR analysis
Forward (F) and reverse (R) primer sequences (5’-3’) are shown in addition to the accession number for each gene. For genes with more than one splice variant, primers were designed to pick up all variants, with the exception of IFIT3 for which two sets of primers were designed. All primers were designed using Primer Express software (Applied Biosystems) and were synthesised by the DNA synthesis lab at the University of Calgary.
Official Human Genome Organization (HUGO) gene nomenclature committee gene symbols have been used for all genes and gene products.
Gene Symbol Accession Primer Sequences Number APOL6 NM_030641.3 F: CCCTGCCAGACCAGGGGACC R: GGAGCGTCATCCTCATCCCTTTGC BCL2A1 NM_001114735.1; F: CCCCGGATGTGGATACCTA NM_004049.3 R: CTAGAAAAGTCATCCAGCCAGA BIRC3 NM_001165.3; F: CCGTCAAGTTCAAGCCAGTTACCC NM_182962.1 R: AGCCCATTTCCACGGCAGCA CCL2 NM_002982.3 F: GCTCGCTCAGCCAGATGCAA R: TCCTGAACCCACTTCTGCTTG CCL4 NM_002984.3 F: CTTCCTCGCAACTTTGTGGT R: GCTTGCTTCTTTTGGTTTGG CCL5 NM_002985.2 F: TGCCTACATTGCCCGCCCAC R: GGGTTGGCACACACTTGGCG CCL20 NM_001130046.1; F: TGACATCAATGCTATCATCTTTCACA NM_004591.2 R: TTTGCGCACACAGACAACTTTT CFB NM_001710.5 F: ATGCCACATACCCCAAAATTTGGGT R: GTTAGTCCCTGACTTCAACTTGTGGT CMPK2 NM_207315.2 F: GCCGGGGCATGGAGAAGACC R: TGGAGGGGCTGGCATCAACCA CSF 2 NM_000758.2 F: CCATGATGGCCAGCCACTACAAGC R: ACTGGCTCCCAGCAGTCAAAGG CSF3 NM_000759.2; F: AAGCTGTGCCACCCCGAGGA NM_172219.1; R: GTGGGACCCAACTCGGGGGA NM_172220.1 CXCL1 NM_001511.2 F: TCAATCCTGCATCCCCCA
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R: CATAGAATCTTCAAAACTAATGAATAAAT CXCL2 NM_002089.3 F: CGCATCGCCCATGGTTA R: TAGAATCTTCTAAAACAAACAAATAAATA CXCL3 NM_002090.2 F: TCATCGAAAAGATACTGAACAAGGG R: GAAGTGTCAATGATACGCTGATAAGC CXCL5 NM_002994.4 F: CCCAAAATGATCAGTAATCTGCAA R: CAAATTTCCTTCCCGTTCTTCA CXCL10 NM_139089.1 F: TTCCTGCAAGCCAATTTTGTC R: TCTTCTCACCCTTCTTTTTCATTGT EFNA1 NM_004428.2; F: TCACAGTCCTCAGGCCCATGACA NM_182685.1 R: GTGGGGCAGCACTGTGACCG FAM129A NM_052966.2 F: GCTGGACGAGGGCAAGTGCG R: AGGCGCCAATGGTGGCTTGG GAPDH NM_002046 F: TTCACCACCATGGAGAAGGC R: AGGAGGCATTGCTGATGATCT GOS2 NM_015714.3 F: GCGCCGTGCCACTAAGGTCA R: CACGCTGCCCAGCACGTACA ICAM-1 NM_000201.2 F: TGCCCTGATGGGCAGTCAACA R: GCAGCGTAGGGTAAGGTTCTTG IFIT1 NM_001548.3 F: AACCCTGCAGAACGGCTG R: TGTAAAGTGACATCTCAATTGCTCC IFIT3iso1 NM_001549.4 F: GCGTGCCCTACTCTCCCACC R: AGCTGTGGAAGGATTTTCTCCAGGG IFIT3iso2 NM_001031683.2 F: TCAGAACTGCAGGGAAACAGCCA R: AGCTGTGGAAGGATTTTCTCCAGGG IFNGR1 NM_000416.2 F: GAATTTGCTGTATGCCGAGATG R: TGATTTGCTTCTCCTCCTTTCTG IKB /NFKBIA NC_000014.9 F: TGGTGTCCTTGGGTGCTGAT R: GGCAGTCCGGCCATTACA IL1B NM_000576.2 F: TGGCAGAAGTACCTGAGCTCGC R: GCCGCCATCCAGAGGGCAGA IL6 NM_000600.3 F: CCTGAGAAAGGAGACATGTAACAAGA R: GGAAGGTTCAGGTTGTTTTCTGC IL8 NM_000584.2 F: GCAGCTCTGTGTGAAGGTGC R: AAAGGTTTGGAGTATGTCTTTATGCA IL12p35 AF180562.1 F: GGGCCGTCAGCAACATG R: CTTCAGAAGTGCAAGGGTAAAATTC IL32 NM_001012631.1; NM_004221.4;
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NM_001012718.1; NM_001012636.1; F: GCAGCACCCAGAGCTCACTCC NM_001012635.1; R: AGGCTCCTCGGTTGCGGGAT NM_001012634.1; NM_001012632.1; NM_001012633.1 IRF1 NM_002198.2 Cytoplasmic IRF1: F: CTCACTGCAGCCCCTGCGTC R: TGGGCATGTTGGCTCTGCTGC Unspliced nuclear IRF1: F: GCGACCGCCGAATCG R: TTCGACCCCCCACTTCCT ISG20 NM_002201.4 F: TCCCTGCGGGTGCTGAGTGA R: GCTCCATCGTTGCCCTCGCA LAMB3 NM_000228.2; F: CAGCCAGGCTCCCCAACGTG NM_001127641.1; R: GGCTCGGCTCCTGGCTTCCT NM_001017402.1 MAP3K8 NM_005204.3 F: CGAAGAAAAGAATGGCGTGTAA NM_001244134.1 R: AGCCTGGATTTCCACATCAGA MX1 NM_001144925.1; F: GGCAGCGGGATCGTGACCAG NM_002462.3 R: CCTTCCCCGGCGATGGCATT NFKB2 NM_001077493.1; F: AACCCAAGGAGCCAGCCCCA NM_002502.3; R: CAGCCATATCGAAATCGGAAG NM_001077494.1 NFKBIZ NM_001005474.1; F: GGCTTCTGGCCAAGCTGTGGAT NM_031419.2 R: TCCCCGGGCGTTGGTGTTTG OLR1 NM_002543.3 F: TGGTGCTGGGCATGCAATTATCCC R: GCCGGGCTGAGATCTGTCCCT PI3 NM_002638.3 F: AGCCTGGCTCCTGCCCCATT R: GCAAGGACCGGCTCCCTCTCA PRIC285 NM_001037335.2; F: ACGGTCATTCAGGGCCCACCA NM_033405.3 R: GGCCTCAGCCTGCTCACTGT PTGS2 NM_000963.2 F: GCTGGGCCATGGGGTGGACT R: CCTGCCCCACAGCAAACCGT SOD2 NM_000636.2; F: CGTGGCTGTGGTGGCTTCGG NM_001024465.1; R: CCTGCTGGTGCCGCACACT NM_001024466.1 TFF1 NM_003225.2 F: TCTGCGCCCTGGTCCTGGTG R: GCACACTGGGAGGGCGTGAC TLR2 NM_003264.3 F: GCTGCTCGGCGTTCTCTC R: AAGCAGTGAAAGAGCAATGGG
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TNF NM_000594.2 Cytoplasmic TNF: F: GTGATCGGCCCCCAGAGGGAA R: TGGAGCTGCCCCTCAGCTTGA Unspliced nuclear TNF: F: TCTCGAACCCCGAGTGACA R: CATCAGCCGGGCTTCAAT TNFAIP3 NM_006290.2 F: AGGCGCTGTTCAGCACGCTC R: CGGGCCATGGGTGTGTCTGT UBD NM_006398.3 F: GGTTTCTGGCCCCTTGTCTGCAG R: ACGCTGTCATATGGGTTGGCATCA U6 NR_004394.1 F: AATTGGAACGATACAGAGAAGATTAGC R: GGAACGCTTCACGAATTTGC ZFP36 NM_003407 F: GCGGGAGTTTTTGCACCA R: GACCGGGCAGTCACTTTGTC
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Table A4. Primers used for ChIP PCR analysis
Forward (F) and reverse (R) primer sequences (5’-3’) are shown in addition to the accession number for each gene. Primer location for each gene was picked based on the presence of IRF1 binding site as shown in the custom tracks of UCSC genome browser database. All primers were designed using primer designing software (Integrated DNA technologies) and were synthesised by the DNA synthesis lab at the University of Calgary.
Official Human Genome Organization (HUGO) gene nomenclature committee gene symbols have been used for all genes and gene products.
Gene Accession Primer Sequences Symbol Number CMPK2 NM_207315.2 F: GCCAGCGGGAAACGAAAG R: AGGAGGAGAGGCCGGAA CXCL10 NC_000004.12 F: ATTTCCCTCTGCTCCTCTTT R: GAATGGATTGCAACCTTTGTTT IFIT1 NM_001548.3 F: CACCATTGGCTGCTGTTTAG R: CTCCTCTGAGATCTGGCTATTC IFIT3 NC_000010.11 F: GTGGAAACCTCTTCAGCATTTG R: CAGAGAAGCAGGGACTATTTACC hMYOD1(A) NC_000011.10 F: TGCAGGAGATGAAATACTAAGCAAGTA R:AGATTGGAAACTGAGGACTTTAGTTAGAG hOLIG3 NC_000006.12 F: GGCAAGGACAGAGACAATCATA R: CTCTGTGTTCTCGCTTTGGA hMYOG NC_000001.11 F: CCAATGAGACTGAGTGGGTTTTC R: TCACCAGAGAAGACTGCTTTGC
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Appendix B: Copyright Permission
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