Redox Regulation Mechanisms in
Inflammatory Macrophages
Marina Diotallevi
October 2017
A thesis submitted in partial fulfilment of the requirements of the University of
Brighton and the University of Sussex for a programme of study undertaken at
the Brighton and Sussex Medical School for the degree of Doctor of Philosophy
2
Abstract
Long-lasting activation of inflammation leads to chronic conditions and is particularly
common in autoimmune diseases. The causes of this self-activation in those
conditions are still unknown, but these diseases are often also associated with an
increase in oxidative stress. In fact, reactive oxygen species (ROS) are released
during the inflammatory response and can cause oxidative damage, which in turn can
lead to maintenance of inflammation. However, ROS are not only toxic species but
can also act as signalling molecules to regulate immune responses, for instance via
thiol modification. Thiols present in the cysteine residues of protein are among the
most sensitive targets of ROS. They can undergo many redox changes, including
glutathionylation or disulphide-linked dimerisation, all of which alter the protein and
thus its function, localisation and secretion. This “redox regulation” regulates many
cellular processes such as apoptosis, cell development and differentiation,
homoeostasis and the immune response.
In this project, we hypothesise that changes in thiol oxidation affect the inflammatory
response and two different approaches have been set up to track redox changes in
inflammatory conditions.
Firstly, the role of endogenous glutathione (GSH), the main thiol antioxidant, was
investigated. For this purpose, we used RAW cells, a mouse macrophage cell line.
Cells were depleted of endogenous GSH and then stimulated with a standard
inflammatory stimulus, bacterial lipopolysaccharide (LPS). A microarray analysis was
then performed to identify changes in the gene expression profile. Results indicated
that endogenous GSH does not decrease the inflammatory response but, on the
contrary, favours the host antiviral response as its depletion results in an impaired
LPS-induced increase in gene expression of genes in the interferon pathway,
including oas2, mx2 and irf9. The biological significance of these results was later
confirmed in cells infected with influenza A, showing that the antiviral response elicited
by LPS was inhibited by GSH depletion.
The second approach of this work was the use of a pegylated maleimide (MalPEG –
10kDa) to determine the redox state of three “redoxkines”, protein thiol/disulphide
oxidoreductases with inflammatory properties: Trx, Prx1 and Prx2. MalPEG covalently
binds to free thiols causing a mobility shift that can be detected by Western blot,
leading to differences in the migration of oxidised and reduced proteins. After LPS
stimulation, clear changes in the redox state were detected both intracellularly and in
secreted proteins. To identify potential membrane targets of redoxkines, we set up a
3
technique to identify proteins with redox-sensitive exofacial thiols on the cell surface.
The results of this work show that activation of inflammatory pathway in macrophages
brings about a number of redox changes in protein thiols, some of which may be
related to GSH signalling, which are important in the regulation of both inflammation
and host defence.
4
Contents
Abstract .................................................................................................................. 2
Contents ................................................................................................................. 4
List of Figures ........................................................................................................ 7
List of Tables ........................................................................................................11
Abbreviations ........................................................................................................12
Acknowledgments ................................................................................................16
Declaration ............................................................................................................17
Chapter 1 - Introduction .......................................................................................18
1.1 Mechanism of inflammation ......................................................................19
1.1.1 Immune system .................................................................................19
1.1.2 Inflammatory response ......................................................................23
1.1.3 Chronic inflammatory diseases ..........................................................34
1.2 Oxidative stress and inflammation ............................................................36
1.2.1 Source of Reactive Oxygen Species ..................................................36
1.2.2 Oxidative damage ..............................................................................40
1.2.3 Antioxidants .......................................................................................41
1.2.4 ROS in the inflammatory context .......................................................44
1.3 Redox regulation in inflammation ..............................................................47
1.3.1 Redox signalling by ROS ...................................................................47
1.3.2 Thiol oxidation ...................................................................................50
1.3.3 Evidence of thiol oxidation modification in inflammation .....................54
1.3.4 Thiol oxidoreductases ........................................................................55
1.3.5 Oxidoreductases in Inflammation: Redoxkines ..................................61
Aim of this project .................................................................................................63
Chapter 2 - Materials and Methods ......................................................................64
2.1 Instruments ...............................................................................................65
2.2 Chemicals and kits ...................................................................................66
2.3 Reagents and buffers ...............................................................................68
2.4 Antibodies .................................................................................................70
2.5 Software and analytic tools .......................................................................71
2.6 Cell line and cell culture ............................................................................74
2.7 Rat Blood Samples collection ...................................................................76
2.8 Human Blood Samples collection .............................................................77
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2.9 Spin trapping coupled to EPR ...................................................................78
2.10 Gene expression microarrays ...................................................................81
2.11 Quantitative PCR method .........................................................................84
2.12 Cell viability: MTT assay ...........................................................................88
2.13 Protein alkylation with Maleimide-PEG .....................................................89
2.14 SDS-PAGE and Western blot to detect the redox states of proteins from
cell lysates and blood ...............................................................................92
2.15 DTNB assay to measure total free thiols ...................................................96
2.16 Determination of protein concentration. ....................................................97
2.17 Identification of free thiols at the surface of RAW cell ...............................98
Chapter 3 - Regulatory role of glutathione in LPS-stimulated macrophages . 102
3.1 Introduction ............................................................................................. 103
3.2 Viability and GSH/GSSG determination .................................................. 105
3.3 ROS measured by Electron Paramagnetic Resonance ........................... 107
3.4 Microarray analysis: effect of LPS and BSO alone on gene expression in
RAW cells ............................................................................................... 109
3.4.1 Effect of LPS on gene expression profiles at 2h and 6h ................... 111
3.4.2 Functional analysis of the genes whose expression is changed by LPS
........................................................................................................ 113
3.4.3 Functional analysis of the genes affected by BSO ........................... 116
3.5 Microarray analysis: effect of GSH depletion during LPS stimulation on the
gene expression profile of RAW cells ..................................................... 118
3.5.1 Selection of differentially expressed transcripts ............................... 118
3.5.2 Biological functions associated with the LPS-regulated transcripts
affected by BSO .............................................................................. 128
3.6 Validation of the microarray analysis by qPCR ....................................... 130
3.7 Identification of pathways affected by BSO during LPS stimulation ......... 134
3.8 Antiviral response: BSO increases Influenza A replication in LPS-
stimulated RAW cells .............................................................................. 137
3.9 Effect of NAC and Menadione ................................................................ 141
3.10 Discussion .............................................................................................. 143
Chapter 4 - Redox state of Peroxiredoxins and Thioredoxin in LPS-stimulated
macrophages .................................................................................................... 148
4.1 Introduction ............................................................................................. 149
4.2 The Maleimide-PEG technique ............................................................... 152
4.3 Cell viability upon LPS stimulation .......................................................... 155
4.4 Optimisation of MalPEG concentration in RAW cells .............................. 157
4.5 Determination of the redox states of Prx1, Prx2 and Trx ......................... 160
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4.5.1 Trx is secreted and undergoes redox changes in response to LPS
stimulation ....................................................................................... 160
4.5.2 LPS stimulation has distinct effects on the redox state of Prx1 and
Prx2 ................................................................................................. 164
4.6 Applicability of the MalPEG methods to other proteins ............................ 171
4.7 Redox state of Prx2 in HEK cells with TNF and Menadione .................... 173
4.9 Discussion .............................................................................................. 177
Chapter 5 - Changes in the redox state of membrane proteins associated with
the inflammatory response ................................................................................ 181
5.1 Introduction ............................................................................................. 182
5.2 Protocol for extracting membrane proteins from cultured cells ................ 186
5.3 Labelling of cell surface thiols with BIAM ................................................ 188
5.4 Optimization of BIAM concentration ........................................................ 190
5.5 Effect of LPS on the level of surface thiols detected ............................... 192
5.6 Analysis of the proteins identified by MS................................................. 197
5.7 Proteins of interest .................................................................................. 202
5.8 Investigation of the expression of membrane Trx .................................... 204
5.9 Discussion .............................................................................................. 207
Chapter 6 - Redox state of Peroxiredoxin 2 and Thioredoxin as biomarkers of
oxidative stress................................................................................................... 209
6.1 Introduction ............................................................................................. 210
6.2 Assessment of the MalPEG technique in Rat blood ................................ 214
6.3 Measurement of the redox state of Prx2 in human plasma ..................... 216
6.4 Measurement of the redox state of Trx in human red blood cells ............ 218
6.5 Assessment of potential redox changes in Trx and Prx2 after stent insertion
in blood samples from CAD patients ....................................................... 220
6.6 Oxidation of Trx and Prx2 with diamide................................................... 224
6.7 Discussion .............................................................................................. 226
Chapter 7 - Discussion and conclusions .......................................................... 229
7.1 Summary of the study ............................................................................. 230
7.2 Advances in the redox and inflammatory field ......................................... 233
7.3 Conclusion .............................................................................................. 234
Bibliography ........................................................................................................ 236
Appendix ............................................................................................................. 266
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List of Figures
Chapter 1
Figure 1.1: Hematopoietic stem cell lineage and leukocytes origin
Figure 1.2: Chemical structure of LPS
Figure 1.3: TLR4 signalling pathway after LPS induction
Figure 1.4: IFN-signalling pathway
Figure 1.5: Overview of the source of ROS in eukaryotic cells
Figure 1.6: ROS are signalling molecules when produced at moderate levels
Figure 1.7: Thiol oxidation modifications in proteins
Figure 1.8: Catalytic reaction of thiol oxidoreductases
Chapter 2
Figure 2.1: Photo of RAW cells
Figure 2.2: Experimental design of the measurement of ROS in RAW cells by spin
trapping coupled to EPR
Figure 2.3: Experimental design of the gene microarray method and different steps
realised
Figure 2.4: Reverse Transcriptase followed by qPCR
Figure 2.5: Example of amplification plot obtained by qPCR analysis
Figure 2.6: Experimental design of SDS-PAGE followed by Western Blot
Figure 2.7: Reaction of DTNB with free thiols
Figure 2.8: Experimental design of the strategy for the identification of free protein
thiols at the surface of RAW cells before MS analysis
Chapter 3
Figure 3.1: Viability and GSH/GSSG determination
Figure 3.2: ROS are released from RAW cells after 2h LPS stimulation
Figure 3.3: Number of transcripts affected either by BSO or by LPS in RAW cells
after 2h and 6h LPS stimulation
Figure 3.4: Venn diagram of the number of transcripts affected by LPS at 2h (grey)
and 6h (red)
Figure 3.5: Expression View after HCL of transcripts affected at both 2 and 6h after
LPS stimulation
Figure 3.6: 15 most highly represented functional categories of genes affected by
LPS at both 2h and 6h in RAW cells
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Figure 3.7: Ten most highly represented functional categories depending on their
up-regulation with LPS (right panel) or down-regulation (left panel) at
2h (A) and 6h (B) stimulation
Figure 3.8: Venn diagram of the number of transcripts affected by BSO at 2h (grey)
and 6h (red) LPS stimulation
Figure 3.9: Ten most highly represented functional categories depending on their
up-regulation with BSO (right panel) or down-regulation (left panel).
Figure 3.10: Diagram of the filtering strategy applied to the microarray data to
assess the number of transcripts affected by BSO during LPS
stimulation
Figure 3.11: Hierarchical clustering of 142 GSH-dependent LPS-regulated genes at
2h represented as a Heat Map Tree.
Figure 3.12: Expression images of set of genes obtained for cluster 1, 2, 3 and 4 at
2h
Figure 3.13: Hierarchical clustering of 196 GSH-dependent LPS-regulated genes at
6h represented as a Heat Map Tree.
Figure 3.14: Expression images of set of genes obtained for cluster 1, 2, 3 and 4 at
6h
Figure 3.15: Diagram of the filtering strategy applied to the microarray data to
assess the number of transcripts affected by BSO during LPS
stimulation represented as a heat map
Figure 3.16: Functional categories up-regulated or down-regulated by LPS for each
differentially expressed group affected by BSO (Group 1-4).
Figure 3.17: PCR validation of the microarray data at 2 h
Figure 3.18: PCR validation of the microarray data at 6 h
Figure 3.19: Transcription Factor analysis of Group 1 and Group 2 genes
Figure 3.20: Effect of LPS and BSO on influenza A replication in RAW cells
Figure 3.21: Type of Interferon recognised in Group 2 genes
Figure 3.22: Gene expression of seven selected genes from RAW cells after
treatment with NAC, LPS or Menadione
Figure 3.23: GSH acts as a signalling molecule during LPS stimulation in
macrophages
Chapter 4
Figure 4.1: Chemical structure of NEM and MalPEG.
Figure 4.2: Methodology of the MalPEG technique.
Figure 4.3: RAW cells viability after treatment with different LPS concentration.
9
Figure 4.4: Optimisation of MalPEG concentration in RAW cells.
Figure 4.5: Determination of the effect of MalPEG concentration on free thiols
using the DTNB assay
Figure 4.6: Representation of Mouse Trx structure and potential site of MalPEG
fixation
Figure 4.7: Redox state of Trx in RAW cells lysates and in release after LPS
stimulation
Figure 4.8: Densitometry of each form detected in the cell lysate and in the
released from cells (n=12)
Figure 4.9: Representation of Mouse Prx1 and potential site of MalPEG fixation.
Figure 4.10: Redox state of Prx1 in RAW cells lysates and in release after LPS
stimulation
Figure 4.11: Representation of mouse Prx2 and potential site of MalPEG fixation
Figure 4.12: Redox state of Prx2 in RAW cells lysates and in release after LPS
stimulation
Figure 4.13: Densitometry of each form detected in the cell lysate (n=8) and in the
releasate from cells (n=8)
Figure 4.14: Redox state of Prx4 (A); STAT3 (B) and Hsp70 (C); analysed by
Western blotting in non-reducing conditions
Figure 4.15: Redox state of Prx2 in HEK 293 cells treated with TNF and menadione
Figure 4.16: Gene profile expression of Prx1, Prx2 and Trx in RAW cells following
LPS and BSO stimulation
Figure 4.17: Redox forms detected in RAW cells stimulated or not with LPS for 24h
Chapter 5
Figure 5.1: Hypothesis for a mechanism of redox signalling mediated by Prx2 and
Trx released during inflammation
Figure 5.2: Optimisation of the experimental protocol for extraction membrane
proteins from RAW cells
Figure 5.3: Labelling of free thiols with BIAM at the surface of RAW Cells
Figure 5.4: Determination of BIAM concentration to use to label surface free thiols
Figure 5.5: Surface thiols in RAW cells after LPS treatment for 2h or 24h
Figure 5.6: Protein concentration in NT and LPS (24h) treated samples
Figure 5.7: Purification of BIAM-tagged membrane proteins.
Figure 5.8: Ten most overrepresented cell compartments in free thiol-containing
membrane proteins from RAW cells
10
Figure 5.9: Ten most overrepresented functional categories in free thiol-containing
membrane proteins from RAW cells
Figure 5.10: Venn diagram of membrane proteins shared by LPS-stimulated and
untreated (NT) RAW cells identified by MS
Figure 5.11: Detection of Trx in membrane RAW cells
Figure 5.12: Reduction of the 24kDa form of Trx with β-ME and DTT
Chapter 6
Figure 6.1: Measure of the redox state of Trx (A.) and Prx2 (B.) in plasma from two
rats
Figure 6.2: Redox state of Prx2 in plasma from 4 healthy donors (#1 to #4)
Figure 6.3: Redox state of Trx in plasma (A) or in RBCs lysate (B) (1:10 dilution)
from 3 healthy donors
Figure 6.4: Angiographic images of the stenotic coronary artery before and after
stent procedure
Figure 6.5: Stent deployment and time points of collected samples.
Figure 6.6: Redox state of Prx2 in plasma at different time point before or after
stent insertion
Figure 6.7: Redox state of Prx2 in RBCs at different time point before or after stent
insertion
Figure 6.8: Redox state of Trx in RBCs at different time point before or after stent
insertion
Figure 6.9: Effect of diamide on the redox state of Trx and Prx2 in human RBCs
Figure 6.10: Redox state of Trx and Prx2 in human RBCs left 1h at room
temperature
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List of Tables
Chapter 1
Table 1.1: Location and ligands of human TLRs
Table 1.2: Examples of clinical trials testing the effect of NAC/GSH.
Chapter 2
Table 2.1: List of instruments used in this study
Table 2.2: List of reagents, chemicals and kits used in this study
Table 2.3: List of reagents used in this study
Table 2.4: List of antibodies used in this study
Chapter 3
Table 3.1: LPS-induced transcripts most affected by BSO in groups 1 and 2
Table 3.2: LPS-downregulated transcripts most affected by BSO in groups 3 and
4
Table 3.3: Number of genes targeted by NF-κB or Nrf2 belonging to groups 2 and
1
Table 3.4: Summary of studies on the effect of thiols or thiols inhibitors on
cytokines production
Table 3.5: Summary of studies on the effect of BSO and GSH on replication of
different viruses
Chapter 4
Table 4.1: Thiol oxidation modifications reported in Trx, Prx1 and Prx2 and
functional roles associated
Chapter 5
Table 5.1: List of the 30 proteins from which most peptides have been identified
by MS in untreated cells
Table 5.2: List of the 30 proteins from which most peptides have been identified
by MS in LPS-treated cells
Chapter 7
Table 7.1: Findings and further work required for each aim of this study
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Abbreviations
AGC target Automatic gain control target
AGE Advanced glycation end product
AP-1 Activator protein 1
APC Antigen-presenting cell
APS Ammonium persulfate
ATP Adenosine triphosphate
BBP Bromophenol Blue Sodium salt
BCA Bicinchoninic acid
BIAM N-(biotinoyl)-n'-(iodoacetyl) ethylenediamine
BMPO 5-tert‐butoxycarbonyl-5‐methyl-1‐pyrroline N‐oxide
BSA Bovine serum albumin
BSO Buthionine sulfoximine
CAD Coronary artery disease
CGD Granulomatous disease
CREB Camp Response Element Binding Protein
CRP C-reactive protein
Ct Threshold cycle
CVD Cardiovascular disease
Cys Cysteine
CysP Peroxidatic cysteine
CysR Resolving cysteine
DAMP Danger-associated molecular pattern
DAVID Database for Annotation, Visualization and Integrated Discovery
DMEM Dulbecco’s modified eagle’s medium
DMSO Dimethyl sulfoxide
dNTP Deoxynucleoside triphosphate
DTNB 5,5’-dithiobis(2-nitrobenzoic acid)
DTPA Diethylene triamine pentaacetic acid
DTT Dithiothreitol
EASE Expression analysis systematic explorer
ECL Enhanced chemiluminescence
ECM Extracellular matrix
EDTA Ethylenediaminetetraacetic acid disodium
EPO Erythropoietin
EPR Electron paramagnetic resonance
ER Endoplasmic reticulum
ERK Extracellular signal-regulated protein kinase
13
ESR Electron spin resonance
FDR False discovery rate
FGF Fibroblast growth factor
Gclm GSH synthetic enzyme glutamate cysteine ligase modifier
GCS Glutamylcysteine synthetase
G-CSF Granulocyte colony-stimulating factors
GO Glucose oxidase
GO-BP Gene ontology - biological process
GO-CC Gene ontology - cellular component
GO-MF Gene ontology - molecular function
GPx Glutathione peroxidase
Grx Glutaredoxin
GSH Glutathione
GTP Guanosine triphosphate
HCL Hierarchical clustering linkage
HCl Hydrochloric acid
HEK Human embryonic kidney
HIV Human immunodeficiency virus
HMGB1 High mobility group box 1
HNE 4-hydroxy-2-nonenal
HSC Hematopoietic stem cell
Hsp Heat shock protein
IAA Iodoacetamide
IFN Interferon
IFNAR Interferon-α/β receptor
IFNGR Interferon-gamma receptor
IFNLR Interferon-lambda receptor
IgG Immunoglobin G
IKK Iκb kinases
IL-1β Interleukin 1 beta
IL-4i1 Interleukin 4 induced 1
IRAK IL-1R associate kinase
IRF Interferon regulatory factor
IRGs Interferon regulated genes
JAK Janus kinases
JNK C-Jun N-terminal kinase
KCl Potassium chloride
Keap1 Kelch-like ECH-associated protein 1
KEGG Kyoto Encyclopedia of Genes and Genome
KH2PO4 Potassium dihydrogen orthophosphate
14
LPS Lipopolysaccharide
MalPEG; MP Methoxypolyethylene glycol Maleimide (Maleimide-PEG)
MAPK Mitogen-activated protein kinase
MDA Malondialdehyde
MS Mass spectrometry
MTT Thiazolyl blue tetrazalium bromide
Mx Myxovirus
MyD88 Myeloid differentiation primary response 88
Na2HPO4 Disodium hydrogen orthophosphate
NAC N-acetylcysteine
NADPH Nicotinamide adenine dinucleotide phosphate
NCBI National Center for Biotechnology Information
NEM N-ethylmaleimide
NF-κB Nuclear factor kappa B
NGF Nerve growth factor
NH4HCO3 Ammonium bicarbonate
Nos2 Nitric oxide synthase 2
NOX NADPH oxidase
Nrf2 Nuclear factor (erythroid 2-derived)-like 2
Oas Oligoadenylate synthase
ox LDL Oxidised Low-Density Lipoprotein
P/S Penicillin-streptomicin
PAMP Pathogen-associated molecular pattern
PBS Phosphate buffered saline
PDGF Platelet derived growth factor
PDI Protein disulphide isomerase
PolyI:C Polyinosinic:polycytidylic acid
PRR Pattern recognition receptor
Prx Peroxiredoxin
PSM Peptide-spectrum match
Ptgs2 Prostaglandin G/H synthase 2
PX 12 2-[(1-methylpropyl)dithio]-1H-imidazole
qPCR Quantitative Polymerase Chain Reaction
RBC Red blood cell
Rel REL-associated protein
RNS Reactive nitrogen species
ROS Reactive oxygen species
RPMI Roswell park memorial institute
RT Reverse transcriptase
SA beads Streptavidin beads
15
SDS-PAGE Sodium dodecyl sulfate - polyacrylamide gel electrophoresis
Slc7a11 Cysteine/glutamate transporter xct
SOCS Suppressor of cytokine signalling
SOD Superoxide dismutase
SOX Sulfhydryl oxidase
Srxn Sulfiredoxin
STAT Signal transducer and activator of transcription
SVD Singular value decomposition
TAB TAK bonding protein
TAK Transforming growth factor-β activated kinase
TCEP Tris 2-carboxyethylphosphine
TEMED Tetramethylethylenediamine
TF Transcription factor
TGF-β Transforming growth factor
TIR Toll/interleukin-1 (IL1) receptor
TIRAP Toll-interleukin 1 receptor (TIR) domain-containing adaptor protein
TLR Toll-like receptor
TNF Tumor necrosis factor
TRAF6 Tumour necrosis factor receptor-associated factor 6
TRIF TIR-domain-containing adapter-inducing interferon-β
Trx Thioredoxin
TrxR Trx reductase
TYK Tyrosine kinase
XO Xanthine oxidase
β-ME β-Mercaptoethanol
16
Acknowledgments
I would like to express my deep gratitude to my lead supervisor Prof Pietro Ghezzi
for trusting me to complete this PhD, giving me advices and despite being very
busy, always being there when I needed him.
Also many thanks to my supervisor Dr. Lisa Mullen for her dedication to read again
and again my thesis and also together with Dr. Manuela Mengozzi for their help with
my research questions, their patience, their kind support, their wise advices and
comments and their support all the way through my PhD.
Thank you to Philippe Chan, Prof Fabienne Peyrot, Dr. Kahina Abbas and Dr. Eva
Maria Hanschmann for their collaboration and kindness.
Thanks to all the team of BSMS research staff and in particular: Lamia, Steph,
Giselle, Juliet, Sandy, Natacha and Phil.
During these years, moments of doubts and questioning are presents and without
the friendship and comfort of my amazing PhD crowd, the path would have been
less enlightened. Thank you for taking the time to reread and correct my Frenchie
mistakes. Thank you for being here: George G, Sonia L, Ben A.
Also many thanks to the Trafford center and MRB for the laughter, camaraderie and
complicity: Georgie G, Matt S, Kaj K, Little Matt, Ryan T, Laura H, Daire C, Amy P,
Ben T and Sophie B.
Thank you to my friends who have always been there for me: Mathias, Laura W,
Amandine, Victoire, Cecile, Susana, Heloise, Matt, Will.
Finally, because I would not be here without the support and love they gave me,
thank you to my amazing parents, my brother and my family for believing in me and
my dreams.
“Dans la vie, rien n’est à craindre, tout est à comprendre”
(In life, nothing is to be afraid of, everything is to be understood)
Marie Sklodowska-Curie (1867-1934)
17
Declaration
I declare that the research contained in this thesis, unless otherwise formally
indicated within the text, is the original work of the author. These thesis has not
been previously submitted to this or any other university for a degree, and does not
incorporate any material already submitted for a degree.
Signed
Marina Diotallevi
Dated
14 / 10 / 2017
18
Chapter 1
Introduction
19
1.1 Mechanism of inflammation
1.1.1 Immune system
Perfected over millions of years of evolutionary selection, the immune system protects
us from an invasion of microorganisms. It is particularly complex and can be classified
into the innate system and the adaptive system (Iwasaki and Medzhitov 2015). The
innate system first appeared in unicellular and early multicellular organisms with the
recognition of the non-self to itself (Buchmann 2014). Five hundred million years later,
adaptive immunity was acquired by the first jawed vertebrates, the Gnathostomata
(Flajnik and Kasahara 2010). In mammals, both systems work together; a danger is
recognised by the innate system leading to an organised response to eliminate it
which also leads to the activation of the adaptive system (Iwasaki and Medzhitov
2015). These are two distinct systems with their own receptors and immune cell types
(also referred to as white blood cells or leukocytes).
1.1.1.1 Innate immune system
The innate system reinforces the physical and anatomical barrier already present in
the body. These include the skin with epithelial cells at the surface of organisms and
the junction between them to avoid the entrance of pathogens, the mucus with a low
pH to avoid bacteria contamination in the respiratory, stomach, and genitalia tract and
the cilia lining their surface to refresh it (Turvey and Broide 2010, Romo, Perez-
Martinez et al. 2016). The innate system triggers a direct response to the invasion of
pathogens and rupture of these barriers via specific cells and receptors. Innate
immune cells are derived from the myeloid lineage which itself is derived from
hematopoietic stem cells (HSCs) where all leukocytes come from, representing 0.1 to
0.2% of human blood cells. Innate cells represents 53% to 86% of the leukocytes and
include dendritic cells, macrophages, neutrophils, basophils, eosinophils, and
platelets (Figure 1.1) (Chaplin 2010, Geissmann, Manz et al. 2010, Koenderman,
Buurman et al. 2014). These cells bring together their own properties to fight a danger.
Neutrophils are the first to be activated and release a large amount of reactive species
(reactive oxygen species (ROS), reactive nitrogen species (RNS) and chlorinated
species) to kill invading pathogens. Neutrophils, macrophages and monocytes are
able of phagocytose meaning they can recognise, phagocyte and kill pathogens. They
are also capable of releasing enzymes and reactive species. Eosinophils are
specialists in fighting parasites and helminths. Basophils are still under investigation
20
due to their implication in allergy reactions. Finally, dendritic cells present the antigen
to adaptive immune cells and are referred as antigen-presenting cells (APC).
These innate cells circulate within the body and can recognise invaders or harmful
events thanks to the presence of a limited set of receptors, which are germline-
encoded. If a danger is detected, these receptors trigger different signalling pathways
which lead to the release of different effector molecules, in addition to reactive
species. Among these molecules are the cytokines and chemokines, which are
effectors of the inflammatory response triggering elimination of the pathogen,
reparation of the host and preventing infection (see part 1.1.2.4). It comprises also
the lysozyme which is a hydrolytic enzyme degrading the bacterial cell wall composed
of peptidoglycan. It is found especially in body secretions such as tears and saliva
(Callewaert and Michiels 2010). And finally, the complement, of which there are more
than 30 proteins that can attach to a pathogen leading to its elimination, but also
interfere with the adaptive immune response (Dunkelberger and Song 2010). For
instance, macrophages recognise which particles and pathogens to phagocytose due
to the complement proteins attached to them.
1.1.1.2 Adaptive immune system
This study is focused on innate immunity; however, the adaptive system is also
required to complete an appropriate response from the body to a danger. This
immunity is also named acquired or specific immunity and differs from innate immunity
as cell receptors are not germline encoded but undergo rearrangement and
recombination of the DNA. This ensures specific targeting of new pathogens and
being in constant evolution against danger. Due to the rearrangement of genes, the
adaptive response is slow and takes few days before it is able to mount an immune
response (Kimbrell and Beutler 2001) (Chaplin 2010).
The adaptive response is started by the recognition of an antigen that is presented by
innate cells such as dendritic cells and macrophages. This antigen is then recognised
by naïve T lymphocytes, of which there are among million of them, each carrying a
different receptor. In the human body, there is 0.54 to 1.79 x 106 of T lymphocyte cells
per ml of blood. These cells differentiated from the lymphoid lineage representing 14
to 47% of the leukocyte in the blood (Figure 1.1). Once a T lymphocyte is activated,
it divides forming a clonal selection specific to one antigen of the pathogen. Two type
of T cells are activated; the helper T cells, the CD4+, and the cytotoxic T cells, the
CD8+. Helper cells activate B lymphocytes which then produce specific antibodies
toward the antigen. Cytotoxic cells help to eliminate the pathogen by releasing
21
cytotoxins which perforate and degrade pathogens. Some of the expansion clones
survive and become memory cells, which will help to trigger a faster response to the
invader if the host becomes re-infected.
22
Figure 1.1: Hematopoietic stem cell lineage and leukocytes origin; scheme
adapted and simplified from Hayden and Gosh (Hayden and Ghosh 2011)
23
1.1.2 Inflammatory response
Inflammation is part of the innate immune response. It is defined as the response of
the body to dangers such as pathogens or noxious chemicals but also to tissue
damage by proceeding to elimination of the danger, protection of the host and
initiating repair (Weiss 2008). It was initially described 2000 years ago by Aulus
Cornelius Celsus as the combination of four physiological symptoms: pain, swelling,
redness and heat. This definition has since evolved with the addition of two
parameters: tissue damage and the migration of plasma and leukocytes from the
blood to the area of local injury (Medzhitov 2008).
Essentially, tissue resident macrophages can detect the presence of a pathogen or
tissue injury thanks to pattern recognition receptors (PRRs). Once activated, they
trigger cells to release a variety of signalling molecules: cytokines, chemokines, and
growth factors. Physiologically, vessels are dilated allowing the recruitment of other
leukocytes from the blood to the site of infection maximising the number of immune
cells available to eliminate any invading pathogens (Murray and Wynn 2011)
(Medzhitov 2008). Once the danger is eliminated, anti-inflammatory cytokines are
released and any damaged tissue is repaired (Lawrence and Gilroy 2007). This
resolved inflammation (also referred as acute inflammation) is fast and is responsible
for restoring tissue homoeostasis. On the contrary, the permanent activation of the
inflammatory response is associated with certain chronic conditions, such as
rheumatoid arthritis, or atherosclerosis.
1.1.2.1 PAMPs and DAMPs
Two main families of molecular motifs are recognised by the PRRs; the Pathogen-
Associated Molecular Patterns (PAMPs) which are exogenous dangers, and the
Danger-Associated Molecular Patterns (DAMPs) also called alarmins or danger
signals which are endogenous dangers. The former group include essential
molecules of bacteria, mycobacteria, viruses, fungi and parasites. For instance,
surface components such as lipoproteins or peptidoglycan but also DNA such as
unmethylated DNA found in bacteria or single stranded DNA from viruses. DAMPs
include molecules released in response to trauma, physical injury, necrotic cells (not
from programmed cell death (apoptotic cells)), or autophagy. Once released from
cells, these molecules act as danger signals whereas within the cell they have a
different biological role. DAMPs fulfil two criteria: their administration to a disease-free
host initiates inflammation which is in return inhibited by blocking its receptor or
24
signalling pathway, and they can also participate in the healing process and tissue
regeneration (Venereau, Ceriotti et al. 2015) (Shen, Kreisel et al. 2013). DAMPs can
be proteins, lipids, sugars, metabolites and nucleic acids (Kvietys and Granger 2012).
Protein DAMPs include high mobility group box 1 (HMGB1), a nuclear protein binding
the nucleosome and promoting the bending of DNA, which was one of the first DAMPs
to be identified, heat shock proteins (HSPs), chaperone of good protein folding and
release by necrotic cells, and S100 proteins, calcium-binding proteins. The non-
protein DAMPs include uric acid, which can form monosodium urate crystals,
adenosine triphosphate (ATP), RNA and DNA (Tang, Kang et al. 2012).
1.1.2.2 Toll-Like Receptors
The activation of the inflammatory response is based on the recognition of danger.
This is possible thanks to specific PRRs presents in the inflammatory cells such as
macrophages, dendritic cells and monocytes. Those receptors are encoded in the
germline DNA and are set to recognise a large but limited variety of dangers signals
including toxic particles and molecules expressed specifically by pathogens. There
are five main families of PRR grouped into two classes: the membrane-bounded
receptors and the intracellular cytosolic receptors (Brubaker, Bonham et al. 2015).
The membrane receptors are the Toll-like receptors (TLRs) and the C-type lectin
receptors characterized by the presence of a carbohydrate-binding domain. They are
both transmembrane proteins located at the cell surface or in endosomes. The three
other families of PRR are located intracellularly and encompass the NOD-like
receptors, RIG-I-like receptors, primarily involved in antiviral responses, and the
Absent in Melanoma 2 like receptors, which are characterized by the presence of a
pyrin domain and a DNA-binding HIN domain involved in the detection of intracellular
microbial DNA (Mogensen 2009). It has to be noted that environmental irritants such
as asbestos and noxious chemical are recognised by other receptors than PRRs such
as the NACHT-leucine-rich-repeat- and pyrin-domain-containing protein (NALP3) part
of the inflammasome pathway, a multimeric protein complex (Hornung, Bauernfeind
et al. 2008) (Guo, Callaway et al. 2015).
TLR have been particularly well studied and characterised. They represent the major
PRR in the cell (Newton and Dixit 2012). This family was discovered in Drosophila
melanogaster during development studies in the 1980’s. Since then, 10 TLRs have
been identified in humans and 12 in mice with 10 conserved between them both
(Kawasaki and Kawai 2014). This family share the following characteristics: they are
25
type 1 transmembrane glycoproteins with a ligand-binding domain containing leucine-
rich repeat motifs and a Toll/interleukin-1 (IL1) receptor homology (TIR) domain.
TLR1, TLR2, TLR4, TLR5, TLR6, and TLR10 are found at the cell surface while TLR3,
TLR7, TLR8, TLR9 are in endosomes (Mogensen 2009). Despite their similarity, TLRs
can all recognise different molecules and pathogens. The ones at the cell surfaces
are designed to identify bacteria compounds while endosome TLRs specifically
recognise nucleic acids (see Table 1.1). The first ligand identified was
lipopolysaccharide (LPS) a structural element part of the outer membrane of gram-
negative bacteria which binds TLR4 (Yamamoto and Takeda 2010). It is composed
of three part: lipid A, an oligosaccharide domain and O-antigen of oligosaccharides
units as shown Figure 1.2 (Qiao, Luo et al. 2014). LPS is commonly used to induce
an inflammatory response in-vivo and in-vitro and has been used for this purpose
throughout this study. It is specifically recognised by TLR4 but other bacterial
lipoproteins can also be recognised by TLR1, 2 and 6 (Poltorak, He et al. 1998). TLR6
can also recognise mycoplasma molecules. TLR3 can detect double-stranded RNA
found in virus such as the Reoviridae viruses while single-stranded DNA, such as that
of Influenza viruses, is recognised by TLR7. TLR5 recognises Flagellin. TLR9
recognises unmethylated CpG DNA. Human TLR10 has no known ligand, but a recent
review has shown anti-inflammatory properties of this TLR when heterodimerised with
TLR2 (Oosting, Cheng et al. 2014).
Most of these innate immune receptors recognising PAMPs can also identify DAMPs
(Jounai, Kobiyama et al. 2012). HMGB1, for instance, is recognised by TLR4 (Yu,
Wang et al. 2006). Recently, Peroxiredoxin 1 (Prx1), an antioxidant enzyme was
identified as a possible DAMP that directly interacts with TLR4 (Table 1.1) (Riddell,
Wang et al. 2010).
26
Receptor Location Ligands
TLR1/TLR2 Extracellular Bacteria: peptidoglycan, lipoproteins, Fungi: zymosan Synthetic: Pam3Cys
TLR2/TLR6 Extracellular Bacteria: lipoproteins
TLR3 Intracellular Viruses: double-stranded DNA Endogenous: mRNA Synthetic: PolyI:C
TLR4 Extracellular Bacteria: LPS Fungi: mannan Protozoa: Glycoinositolphospholipids Endogenous: saturated fatty acids, oxLDL, Prx1, HMGB1 Synthetic: Lipid A derivatives
TLR5 Extracellular Bacteria: flagellin
TLR7/TLR8 Intracellular Viruses: single-stranded RNA Endogenous: self RNA
TLR9 Intracellular Bacteria, viruses, protozoa: CpG DNA Endogenous: self DNA
TLR10/TLR2? N.D N.D
TLR11 Extracellular Uropathogenic bacteria
Table 1.1: Location and ligands of human TLRs; adapted from Mills (Mills 2011).
Figure 1.2: Chemical
structure of LPS; adapted
from Qiao et al. (Qiao, Luo et
al. 2014).
27
1.1.2.3 Signalling pathways
Once a PRR is activated, the transduction of signalling pathways will depend on few
steps to trigger a series of phosphorylation, ubiquitinylation and other modifications in
many proteins to finally activate transcription factors such as nuclear factor kappa B
(NF-κB), Interferon pathways such as Irf3, and MAPK pathways (Newton and Dixit
2012). An important factor is the homodimerisation or heterodimerisation of the TLR
domains. In the human system, both TLR1 and TLR6 need dimerization with TLR2 to
be activated while the other proceed to homodimerisation. Another important factor is
the presence of adaptor molecules which help to transduce a signal and determine
the appropriate response (Newton and Dixit 2012, Kawasaki and Kawai 2014). These
adaptors include MyD88, TRIF, TIRAP/MAL, and TRAM. All TLR uses Myd88
activating NF-κB and MAPKs pathway. TRIF is an alternative and it is used by TLR3
and TLR4 in the endosome activating IRF3, NF-κB and MAPK.
TLR4 can therefore use both Myd88 and TRIF pathways to produce an appropriate
response with the good level of cytokines and chemokines. TLR4 signalling is
particularly of interest in this study and is shown in Figure 1.3.
In TLR4-MyD88-dependent signalling pathways, MyD88 recruit IL-1R associate
kinase (IRAK) 1 and 4 and tumour necrosis factor receptor-associated factor 6
(TRAF6). The complex IRAK1-TRAF6 form dissociate from this complex and activate
transforming growth factor-β activated kinase (TAK1) and TAK bonding protein 1 and
2 (TAB1 and TAB2). These lead to activation of the IκB kinases (IKK) and MAPK
cascade signalling pathways.
IKKs are required for the activation of NF-κB which represents a family of 5
transcription factors (TF) members: RelA (p65), Rel (B), C-Rel, p50 (p105 precursor),
p52 (p100 precursor), and Relish which all homo- or hetero- dimerise to activate the
transcription of target genes. In fact, NF-κB is sequestered in the cytosol in a complex
form with IκB molecules. IKK allows the detachment of IκB from the complex by
phosphorylating the molecule. IκB molecules are then ubiquitinylated and therefore
degraded. Consequently, NF-κB is free to join the nucleus and target the transcription
of many inflammatory genes: cytokines such as interleukin -1, TNF, interleukin 6 but
also chemokines, growth factors, adhesion molecules leading to a multitude of
physiological functions such as apoptosis, proliferation and development
(Napetschnig and Wu 2013, Cildir, Low et al. 2016).
The MAPK pathway is activated by a series of downstream phosphorylation activation
of many different MAPK molecules; basically, MAPK kinase kinase (MAP3K) activates
MAPK kinase (MAP2K) which then activate MAPK (Arthur and Ley 2013). In the TLR4
28
pathway, TAK1, which is a MAP3K, drive the activation of ERK1/2, JNK and p38
pathway. All leading to activation by phosphorylation of different TF such as AP-1 and
cAMP Response Element Binding Protein (CREB) involved in many cell process and
with many roles in immunity such as macrophage signalling and induction of pro-
inflammatory molecules (Arthur and Ley 2013).
In TLR4-Myd88 independent pathway, TRIF is linked to TLR4 with the adaptor TRAM
to bridge with the TIR domain. TRIF interact with TRAF6 which activate TAK1
activating MAPK pathway and NF-κB. In addition TRIF interacts with TRAF3 which
activates other molecules such as TBK1 and IKKi leading to IRF3 phosphorylation
and thus induction of type 1 interferon.
All transcription factors lead to the induction of cytokines, crucial for the coordination
and communication of cells to control and regulate the inflammatory response. NF-κB
and MAPK target mainly the induction of interleukins and chemokines while IRFs
allow IFN production (Mogensen 2009).
29
Figure 1.3: TLR4 signalling pathway after LPS induction. Two pathways are
activated; the Myd88-dependent signalling pathway and the MyD88-independent
pathway which involved many signalling molecules. Both lead to TF activation to
promote pro-inflammatory molecules.
30
1.1.2.4 Cytokines
Cytokines are proteins that mediate the inflammatory response leading to a variety of
biological effects such as regulation of inflammation and immunity (Dinarello 2007).
Their action depends on the cell type, their location, and the receptor targeted.
The roles of cytokines are mediated through their receptors which transduce a
cascade of signal mainly through the Janus kinases (JAK) signal transducer and
signal transducer and activator of transcription (STAT) pathway involved in diverse
cellular process such as proliferation, differentiation, migration, or apoptosis. When
the inflammatory process is finished, the pro-inflammatory cytokines are usually
submitted to negative regulation using protein downstream signalling pathway such
as suppressor of cytokine signalling (SOCS) acting on the JAK/STAT signalling
pathway in order to down regulate the inflammatory response (Lawrence and Gilroy
2007).
Cytokines are often given different names, and include interleukins, tumour necrosis
factor (TNF), chemokines, growth factors, and interferons.
Interleukins
Interleukins are defined as secreted proteins binding specific receptors and are
grouped in families according to their structure and receptor similarity (Akdis, Burgler
et al. 2011, Akdis, Aab et al. 2016). The main interleukins studied in the context of
inflammation are IL-1, IL-6 and IL-10. IL-1, was the first cytokine described, it acts on
the nervous system and triggers fever. It includes nowadays 11 family members
including agonist ligands and receptors for instance IL-1α, IL-1β, IL-1 receptor
antagonist and IL-18 (Garlanda, Dinarello et al. 2013). Most of the focus is on pro-
inflammatory cytokines, and antibodies to IL-6 or IL-17 are routinely used in the
therapy of inflammatory disease. In fact, some interleukins, particularly IL-10 have an
anti-inflammatory activity. The name “interleukin” has no particular meaning anymore
and the rule is that if a new human cytokine is identified, it will be given an interleukin
number, irrespective of what its biological activities are.
TNF
Tumor Necrosis Factor (also known as TNF-α) is a glycoprotein which was originally
described as an inducer of necrosis in tumours but was then found to be a strong pro-
inflammatory molecule responsible for the symptoms of sepsis (Kalliolias and Ivashkiv
2016). While there is currently very little interest in developing TNF as an antitumor
agent, due to its toxicity, the characterization of its inflammatory activity led to the
31
development of anti-TNF antibodies to become the best efficient molecule used in the
therapy of rheumatoid arthritis.
Growth factors
This class include G-colony stimulating factors such as G-CSF, GM-CSF, and M-CSF
allowing granulocytes and monocytes production; and the transforming growth factor
TGF-β, a pleiotropic cytokine, helping to repair tissue, differentiate leukocytes but
which can also stop their proliferation (Sanjabi, Zenewicz et al. 2009, Turner, Nedjai
et al. 2014). All of them retained their original names because they were identified in
the 1970s, before the terms interleukin or cytokine were coined. Other growth factors
(EPO, NGF, FGF, etc.,) do not have relevance to this study.
Chemokines
Chemokines are defined as chemotactic cytokines directing and locating leukocytes
to the site required (Griffith, Sokol et al. 2014). They are small proteins of 8 to 12kDa
with four conserved cysteine residues which can be divided in many family according
to the position of the first two cysteines; main families are the C-X-C and C-C
chemokines. There are 44 chemokines known using one of the 23 chemokines
receptors identify (Turner, Nedjai et al. 2014). They have chemotactic function using
a gradient of signal perceived by a chemokine receptor and attract the cells. They
also have other functions such as leukocyte differentiation, vascularisation and
notably angiogenesis a problem in cancer. Their main role in the context of
inflammation is to recruit inflammatory cells (mainly neutrophils but also
macrophages) to the site of infection or injury.
Interferons
Interferons (IFNs) are pro-inflammatory molecules intervening in antiviral response.
The IFN family is subdivided in three types which are classify according to their
recognition receptors: type 1, type 2 and type 3 (Hoffmann, Schneider et al. 2015).
Type 1 IFN group 17 genes including 13 IFN-α and one IFN-β which have antiviral
activity. This type signal through IFNAR1 and IFNAR2 receptors found in all cells.
Type 2 only contain one gene IFN-γ, produce only in leukocyte and act as
macrophage-activating cytokine, which is recognise by IFNGR1 and IGNGR2
receptors. Finally, type 3 comprises 4 genes IFN-λs recognised by IL-10 receptors
and IFNLR1.
Once IFNs bonds their receptor, the JAK/STAT pathway are activated (Figure 1.4).
A following cascade of phosphorylation and ubiquinitation mediated four pathway: the
32
Myxovirus (Mx) GTPase pathway, the 2’5’ oligodenylate-synthetase-directed
ribonuclease L pathway, the protein kinase R pathway and the ISG15 ubiquitin
pathway all leading to the degradation of the virus, inhibition of its proliferation and
activity (Sadler and Williams 2008).
33
Figure 1.4: IFN-signalling pathway by Sadler and Williams (Sadler and Williams
2008). Type 1, 2 and 3 are recognised by specific receptors leading to a cascade of
signalling molecules to activate an antiviral response via the JAK/STAT pathway also
used by other cytokines.
34
1.1.3 Chronic inflammatory diseases
The inflammatory response includes macrophage activation, neutrophil infiltration and
involves many signalling pathways and molecules as discussed above. It is an
essential part of innate immunity in which a derangement in its regulation can be
harmful and is implicated in a number of diseases. Among them, chronic inflammation
is observed in many pathologies. This inflammation is defined as a constant activation
of the inflammatory response generating damage and tissue destruction (Nathan and
Ding 2010). Chronic inflammation has been associated with, among other,
neurodegenerative diseases (Amor, Puentes et al. 2010) including multiple sclerosis
(MS) (Compston and Coles 2008), cardiovascular diseases (Tousoulis, Oikonomou
et al. 2016), gout (Martinon, Petrilli et al. 2006), diabetes type 2 (Donath and Shoelson
2011), autoimmune diseases such as rheumatoid arthritis (McInnes and Schett 2007),
cancer (Grivennikov, Greten et al. 2010), bowel diseases (Neurath 2014), lung
diseases (Hogg, Chu et al. 2004) and fibrosis (Wynn 2008).
Effectors of the inflammatory response could therefore be important pharmacological
targets and the therapy of some of the diseases mentioned above is based on the
use of cytokine inhibitors (antibodies to TNF, IL-6 or IL-17) or inhibitors of cytokine
receptors, including glucocorticoids (Diaz-Borjon, Weyand et al. 2006) (Shah and
Mayer 2010). However, inhibition of inflammation, which is a component of innate
immunity, can increase the risk of infection and cancer (Diaz-Borjon, Weyand et al.
2006). This may be important in elderly people, as they are more vulnerable due to
an immune system already compromised and in which these diseases are more
common (Diaz-Borjon, Weyand et al. 2006).
In fact, this dysregulation of inflammation is more and more observed in the population
as a result of different factors, including genetic factors, aging, the environment and
life style (Libby 2007, Nasef, Mehta et al. 2017). According to a study performed in
2015, the incidence of autoimmune diseases increases by 19% each year (Lerner,
Jeremias et al. 2015). Therefore, despite discovery of important pathways, including
cytokines and Toll-like receptors (whose discovery led to the Nobel Prize in 2011),
the causes of this chronic inflammation are not yet fully understood. In addition to
inflammation, these autoimmune diseases are often associated with oxidative stress.
Inflammation triggers the production of ROS to kill pathogens which can, in turn,
activate inflammation in a vicious circle. This oxidative damage has been reported in
neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease and
amyotrophic lateral sclerosis) (Niedzielska, Smaga et al. 2016), diabetes (Giacco and
Brownlee 2010), gout (Harijith, Ebenezer et al. 2014), multiple sclerosis (Gilgun-
35
Sherki, Melamed et al. 2004), cancer (Reuter, Gupta et al. 2010), cardiovascular
disease (Ho, Karimi Galougahi et al. 2013) (Dhalla, Temsah et al. 2000), and lung
diseases (Rahman and MacNee 2000). This link between oxidative stress mediated
by ROS and inflammation is investigated in this study.
36
1.2 Oxidative stress and inflammation
1.2.1 Source of Reactive Oxygen Species
ROS derive from molecular oxygen (O2) which was initially a by-product of
photosynthesis by cyanobacteria more than two billion years ago. Following its
significant accumulation in the atmosphere, life has evolved to draw its metabolic
energy from it with the evolution of aerobic life (Dismukes, Klimov et al. 2001,
Falkowski and Godfrey 2008). Chemically, due to spin restriction laws, O2 can
undergo reduction with one electron at a time leading to highly reactive intermediates,
ROS (Buonocore, Perrone et al. 2010). These species are unstable and have a short
life. ROS include free radicals such as the superoxide anion (O2.-), hydroxyl radical
(HO.) and singlet oxygen (1O2) (Naqui, Chance et al. 1986, Buonocore, Perrone et al.
2010). It also includes the non-radical, but highly reactive, hydrogen peroxide (H2O2)
as well as peroxinitrite (ONOO-) which is derived from the rapid reaction between O2.-
and NO. a nitrogen radical (Brieger, Schiavone et al. 2012). In fact, nitrogen similarly
to oxygen can lead to reactive nitrogen species (RNS) which is the theme of a different
field. Essentially NO. is synthesised by nitric oxide synthase (NOS) which converts
arginine into citrulline. NO. has many physiological roles including vasodilation and
neural activity and its toxicity is mainly due to formation of ONOO- (Pacher, Beckman
et al. 2007). Excess of RNS production can lead to nitrosylation of protein modifying
their properties (Valko, Leibfritz et al. 2007).
A variety of ways lead to ROS generation such as those due to ultraviolet light,
radiation, chemicals, pollution and diet (excess of fat, tobacco, alcohol) (Phaniendra,
Jestadi et al. 2015). In living organisms, the reduction of O2 is also very common due
to the presence of enzymes, biomolecules and metallic atoms, thus resulting in the
production of ROS. The human body has many different sources of ROS; the
mitochondrial respiration, NADPH oxidases (NOX), peroxisomes and various
enzymes such as xanthine oxidase (XO), glucose oxidase (GO), and cytochrome
P450 enzymes which are shown in Figure 1.5 (Holmstrom and Finkel 2014). While
O2.- and H2O2 are produced by these enzymes, HO. , the most reactive species, is
generated by their interaction due to the Haber-Weiss reaction (Equation 3). This is
the net reaction of two chemical reactions (Equation 1 and 2) using Fenton chemistry
and iron as a catalyst (Kehrer 2000):
Equation 1: Fe3++ O2.- → Fe2++ O2
Equation 2: Fe2+ + H2O2 → Fe3+ + OH- + HO.
Equation 3: O2.- + H2O2 → O2 + OH- + HO.
37
Mitochondrial respiration
During respiration, 95% of the oxygen goes to the mitochondria resulting in the
production of ATP, H2O and CO2 (Cadenas and Davies 2000, Kowaltowski, de Souza-
Pinto et al. 2009). O2.- are mainly generated from complex I and complex III of the
electron transport chain due to leakage while O2 is reduced into H2O via oxidative
phosphorylation (Murphy 2009).
Peroxisomes
Peroxisomes are involved in many metabolic pathways, such as the degradation of
fatty acids by beta oxidation leading to the formation of ROS (Wanders and Waterham
2006) (Sandalio, Rodriguez-Serrano et al. 2013). In fact, peroxisomes are rich in
enzymes such as Cytochrome P450 and XO which promote xenobiotics catabolism.
Xanthine oxidase
XO is involved in the purine catabolism (Harrison 2004). Essentially, it is a complex
molybdoflavoprotein homodimer reducing different substrates such as hypoxanthine,
xanthine, aldehydes and N-heterocycles which lead to the production of the by-
products; O2.- and H2O2.
Xanthine +2O2 +H2O Uric acid +2O2.-+2H+
Glucose oxidase
Glucose oxidase is a flavoprotein which promote the oxidation of β-D-glucose to
gluconic acid using O2 as an electron donor and producing H2O2 (Bankar, Bule et al.
2009).
NADPH oxidase
A number of enzymatic systems have evolved with the purpose of producing ROS,
either for regulatory purposes (see below 1.3.1 Redox signalling by ROS) or for host
defence, such as killing of infectious agents, infected cells or tumour cells. The most
important example is probably that of the oxidative burst. LPS binds TLR4 thus
triggering the interaction between the TIR region of TLR4 and the C-terminus of NOX,
ultimately leading to the production of O2- (Park, Chun et al. 2006). In 1933, Baldridge
and Gerard were the first to demonstrate an increase of oxygen uptake in leukocytes
during phagocytose outside normal mitochondrial respiration. This phenomenon was
referred as an “extra respiration” (Baldridge and Gerard 1933). In 1964, Filippo Rossi,
helped by Karnovsky’s previous research on the subject, identified NOX, an electron
38
transport system, that functions to generate the respiratory burst by pumping
excessively O2 (Sbarra and Karnovsky 1959, Rossi and Zatti 1964). In the following
twenty years, the biochemical mechanism underlying this burst has become
progressively more understood: O2 is converted into O2.- by NOX:
2O2 + NADPH 2O2.- + NADP + H+
O2.- is then rapidly converted into H2O2 by superoxide dismutase (SOD). H2O2, in turn,
interacts with one electron and can undergo two transformations, either as
hypochlorous acid (HOCl) via myeloperoxidase or as a HO• via the Fenton reaction
(Iyer, Islam et al. 1961, Klebanoff 1967, Babior, Curnutte et al. 1976, Weiss, Klein et
al. 1982). All these toxic molecules are diffused near to the infectious agent into the
phagolysosome (Weiss and Schaible 2015).
Similarly, NOX structure was demonstrated. NOX (also referred as NOX2 the first
characterised) is composed of six subunits: two integral membrane (gp91phox and
p22phox), a heterodimeric flavocytochrome cytb558 and three cytosolic proteins (p40phox,
p47phox and p67phox). A small GTPase (rac1 or rac2 depending on the cell type)
participate as the activation of NOX by binding guanosine triphosphate (GTP)
translocating to the membrane p40phox, p47phox and p67phox upon phosphorylation
(Panday, Sahoo et al. 2015).
The physiological role of the production of these reactive species diversity was also
investigated. Babior and Klebanoff’s research established that due to their high
toxicity, reactive species are part of the host defence mechanism to kill pathogens
(Babior, Kipnes et al. 1973, Klebanoff 1975). Evidence of their biological importance
is illustrated by children affected with chronic granulomatous disease (CGD), a rare
genetic disease where reactive species are not produced due to a NOX mutation. Due
to the lack of ROS, young patients suffer repetitive bacterial and fungal infection of
the skin or the lung, and may not reach adolescence (Curnutte, Whitten et al. 1974,
Holland 2010).
39
Figure 1.5: Overview of the source of ROS in eukaryotic cells. ROS are in red
while enzymes are in grey.
40
1.2.2 Oxidative damage
ROS are very toxic to organisms as they need to pair with other molecules to gain
stability. Consequently they attack many biomolecules such as lipids, nucleic acids,
proteins and carbohydrates resulting in various biological damages (Valko, Leibfritz
et al. 2007). These damages caused by ROS are referred as oxidative stress and are
due to excessive levels of ROS.
DNA and RNA undergo oxidation which can cause strand breaks and mutations both
of which may be responsible for ageing and carcinogenesis. This oxidation occurs in
components of these nucleic acids such as bases; for instance, guanine base is
oxidised in 8-hydroxydeoxyguanisine, a biomarker of DNA damage. Mitochondrial
DNA is more targeted by ROS than nuclear DNA due to its proximity to the electron
chain transport. Similarly, RNA is more sensitive than DNA as it is composed only of
one strand and does not have DNA repair mechanisms. Lipids are also modified by
oxidation. Essentially, a hydrogen atom is abstract from the double bonds of a
polyunsaturated fatty acid which thus react with free radicals. This new lipid peroxyl
radical formed remove the hydrogen atom of another fatty acid starting a chain
reaction and forming a lipid hydroperoxide. This change of lipids cause cell membrane
dysfunction and loss of signal transduction due to inactivation of receptor and
enzymes. In addition, lipid hydroperoxide can produce malondialdehyde (MDA) or 4-
hydroxy-2-nonenal (HNE), both carcinogenic and mutagenic (Esterbauer, Schaur et
al. 1991). As regards of proteins, many oxidative modifications can occur leading to
inactivation, degradation, structure modification and thus disorders in signalling
pathways. Modifications are usually based on the sulphur group of cysteine (Cys) and
methionine amino acids through thiol oxidation, in the carbonyl group leading to
carbonylation and tyrosine amino acids via nitration (3-nitrotyrosine) (Valko, Leibfritz
et al. 2007). Finally, carbohydrate oxidative modifications lead to production of
advanced glycation end products (AGE) which are associated with diabetes, cancer
and ageing diseases.
41
1.2.3 Antioxidants
To protect from the harmful effect of ROS, aerobic organisms have developed
antioxidant defences which either eliminate these species or prevent their formation.
The amount of reactive species inside the cells is maintained through redox
homeostasis, avoiding oxidative damage. A decrease in antioxidant defence or an
excess production of reactive species will lead to an imbalance and may potentially
lead to oxidative damage.
According to a recent update from Halliwell, a pioneer in the antioxidant field, an
antioxidant is “any substance which significantly delays, prevents oxidation or
removes oxidative damage to a target molecule” (Halliwell 2007). There are a wide
range of antioxidants, each with different targets, mechanisms and efficiencies, some
of which cooperate together (Halliwell 2013).
Antioxidants can be enzymatic:
Superoxide dismutase
SOD reduces O2.- to H2O2 and exists in three forms: the cytoplasmic (SOD1);
the mitochondrial (SOD2) and the extracellular one (SOD3). They all require
either Cu/Zn (SOD1 and 3) or Mn (SOD2) to catalyse their reaction (Fukai and
Ushio-Fukai 2011). SODs work in association with catalase and peroxidases
which in turn can reduce H2O2 (see Figure 1.5).
Catalase
Catalase promotes the dismutation of H2O2 into two molecule of H2O and O2
using a heme iron as an electron donor (Kirkman and Gaetani 2007).
2H2O2 2H2O + O2
Peroxidases
Similar to catalases, peroxidases are heme proteins which use iron as an
electron donor and H2O2 or organic peroxide as substrates (Khan, Rahmani et
al. 2014). Among them, glutathione peroxidase (GPx) transforms H2O2 into
water using GSH but also needs the presence of the cofactor Selenium
(Rotruck, Pope et al. 1973). Selenium is an essential trace mineral, in which
dietary deficiency can lead to pathological conditions such as atherosclerosis
(Rayman 2000).
2GSH + H2O2 GSSG +2H2O
42
Peroxiredoxins (Prxs) are also part of this and will be more discussed in further
detail in part 1.3.4.
Thioredoxin
Thioredoxin (Trx) is a ubiquitous oxidoreductase of 12kDa which is the major
thiol antioxidant system. This enzyme reduces disulphide bonds or oxidised
dithiol in proteins using NADPH as an electron donor, and Trx reductase
(TrxR) as an enzyme to recycle Trx to its reduced form (Lu and Holmgren
2014). See reaction in 1.3.4.
Glutaredoxin
Glutaredoxins (Grx) are small protein similar to Trx which reduce disulphide
bonds and will be discussed in further detail in part 1.3.4.
Antioxidants can also be non-enzymatic:
Glutathione
The tripeptide glutathione (GSH, γ-glutamyl-cysteinylglycine) is the main
intracellular non enzymatic thiol antioxidant. This molecule is mainly produced
in the liver, the detoxifying organ, through two cytosolic enzymes: γ-
glutamylcysteine synthetase (GCS) which catalyse the formation of y-
glutamylcysteine from glutamate and cysteine and GSH synthetase which
catalyse the formation of GSH from y-glutamylcysteine and glycine (Meister
and Anderson 1983). Its production is limited by the availability of free cysteine
in the environment (Lu 2013). In mammals, 90% glutathione is in the cytosol
(1 to 10mM) while less than 1% is in the oxidised form, GSSG (Dalle-Donne,
Rossi et al. 2009). The ratio of GSH/GSSG indicates the redox state of the cell
and intracellular it is generally at 100:1 (Cooper, Pinto et al. 2011).
Vitamins and cofactors
Finally, antioxidants can be provided by the diet (Carocho and Ferreira 2013).
For instance, flavonoids, carotenoids, vitamin E and C are present mainly in
photosynthetic food (fruit, vegetables, grains). These molecules act as
scavengers by catching electrons and transferring the radical property to
themselves. The new species that are formed are stable and allow elimination
of the reactive species (Han, Zhang et al. 2012, Jiang 2014). Minerals and
cofactors such as selenium, zinc and copper are also important elements
43
found in the diet and help the different antioxidant enzymes to function
properly.
44
1.2.4 ROS in the inflammatory context
The relationship between ROS and inflammation is ambiguous and particularly of
interest in chronic inflammatory diseases. In fact the toxic effect of ROS is not the only
phenomenon involved in the inflammatory process but other functions played by ROS
have been described.
Studying the effect of H2O2 and thiols in the replication of human immunodeficiency
virus (HIV) in human cell line, two research teams came to the same conclusion: H2O2
can activate NF-κB but also HIV-Long terminal repeat sequence (LTR) (Schreck,
Rieber et al. 1991, Schmidt, Amstad et al. 1995) and in opposition intracellular thiols
and antioxidants (GSH) block this NF-κB and HIV-LTR activation potentially by
quenching the ROS (Staal, Roederer et al. 1990). Furthermore, Droge et al.
demonstrated that GSH could modulate an anti-inflammatory effect or a pro-
inflammatory effect through NF-κB depending on the ratio of reduced GSH and
oxidised GSSG (GSH/GSSG) which determines the redox state of the cell (Mihm,
Galter et al. 1995).
This regulation of NF-κB by ROS was also reported in a variety of cell lines and
studies; in cardiomyocytes cultures (Eisner, Criollo et al. 2006), in human alveolar
macrophages following bone marrow transplantation (Blackwell, Christman et al.
2000), in mesangial cells (Satriano and Schlondorff 1994), in carcinoma cells (Wang,
Huang et al. 2007) and most recently in liver cells infected with a virus (Narayanan,
Amaya et al. 2014). It has to be noted that though most studies demonstrated that
ROS act as pro-inflammatory molecules some studies showed contrary effect or even
not effect at all meaning that the response of NF-κB to reactive species was
dependent on others factors (Gloire, Legrand-Poels et al. 2006, Oliveira-Marques,
Marinho et al. 2009). The role of endogenous GSH will be thus further investigated in
this study (Chapter 3).
This relationship between ROS and inflammation was also reinforced by a study
demonstrating that a number of antioxidants and oxidants genes are under regulation
by NF-κB. Those include SOD, Trx, NOX, and XO (Morgan and Liu 2011).
More recently, the role of ROS as a direct activator of NLRP3 activation has been
investigated (Abais, Xia et al. 2015) (Harijith, Ebenezer et al. 2014). NLRP3 is part of
the inflammasome an important complex allowing the production of pro-inflammatory
cytokines such as Il-1β and Il-18. This complex has also been associated with many
pathologies (e.g. Gout, multiple sclerosis and neurodegenerative diseases) cited
previously to be linked with oxidative stress and inflammation (Guo, Callaway et al.
2015).
45
All these different studies demonstrate that ROS are not only responsible for damage,
but are also important in mediating inflammatory responses. In-vivo studies were
performed and demonstrated that antioxidants could lower the inflammation
response. For instance, Blackwell et al. injected rats with an endotoxin to stimulate
NF-κB in the lung and could reduce this activation when rats were beforehand treated
with N-acetylcysteine (NAC) a precursor of GSH (Blackwell, Blackwell et al. 1996).
As expected, a multitude of clinical trials using antioxidants as therapeutic molecules
took place and still are ongoing. A number of different antioxidants were tested and
in particular NAC. NAC was preferentially used in trials as GSH is less stable due to
intestinal enzyme degradation (Witschi, Reddy et al. 1992, Schmitt, Vicenzi et al.
2015). A sample of clinical trials has been summarised in table 1.2. Despite what has
been observed in cells and in vivo studies, most of the clinical trials into antioxidant
therapies have been inconclusive. Only, a few disorders were improved with NAC but
mainly when it was applied locally (Meyer, Buhl et al. 1994, Blackwell, Blackwell et al.
1996). In fact, some studies have shown a deleterious effect on the health of
volunteers. One clinical trial in 2011 looked at the effects of selenium and Vitamin E
in preventing prostate cancer. These compounds were given to healthy volunteers
and were found to actually increase the risk of prostate cancer (Klein, Thompson et
al. 2011).
The failure of antioxidant trials have been blamed on three main reasons (Dodd, Dean
et al. 2008, Steinhubl 2008, Cocheme and Murphy 2010). First, the therapies used
ROS scavengers leading to non-specific responses; the development of direct
inhibitors of the enzymes which produce ROS, or of oxidised molecules may be more
effective. Secondly, antioxidant treatment in established diseases may be too late to
avoid, and certainly reverse, symptoms. In fact, Blackwell et al. reported a positive
effect in rats when NAC was injected before the endotoxin (Blackwell, Blackwell et al.
1996). Thirdly, oxidation level may differ between diseases and even between
individual patients so tools to detect the oxidation level should be developed to
establish this before treatment with antioxidants.
To conclude, it is clear that ROS and inflammatory pathways are entangled but the
redox mechanisms underlying an inflammatory process remain unclear and may be
a barrier to improve chronic inflammatory disease therapy. Similarly, there is a need
for in vivo biomarkers of oxidative stress to allow identification of those patients which
are more likely to benefit from therapies designed to reduce oxidative stress. This
aspect will be investigated further in this study in Chapter 6.
46
Table 1.2: Examples of clinical trials testing the effect of NAC/GSH.
PATHOLOGY
TARGETTED YEAR
ANTIOXIDANT
USED PATIENTS
LENGTH OF
TRIAL RESULTS
Chronic obstructive
pulmonary disease and
chronic bronchitis
(Johnson, McEvoy et
al. 2016)
2016 Oral NAC 130 8 weeks No benefit
Autism spectrum
disorder (Wink, Adams
et al. 2016)
2016 Oral NAC 31 12 weeks No
improvement
Postresection liver
failure (Grendar,
Ouellet et al. 2016)
2016 Infusion of NAC 206 No benefit
Type 2 diabetes
(Szkudlinska, von
Frankenberg et al.
2016)
2016 Oral NAC 130 4 weeks No
improvement
Pulmonary sarcoidosis
(Hamzeh, Li et al. 2016) 2016 Oral NAC 11 8 weeks
No
improvement
Coronary artery
surgery (Erdil, Eroglu
et al. 2016)
2016 Localised NAC 82 3 day
preoperation Improvement
Alveolar inflammation
in people exposed to
asbestos (Alfonso,
Franklin et al. 2015)
2015 Oral NAC 66 4 months No
improvement
Raynaud's
phenomenon (RP)
secondary to systemic
sclerosis (SSC)
(Correa, Mariz et al.
2014)
2014 Oral NAC 42 4 weeks No
improvement
A myocardial infarction
(Talasaz, Khalili et al.
2014)
2014 Oral NAC 98
3 days
following
operation
Positive effect
in biomarkers
Cystic fibrosis(Griese,
Kappler et al. 2013) 2013 Inhaled GSH 153 6 months
No
improvement
Chronic hepatitis b
(Shohrati, Dermanaki et
al. 2010)
2010 Oral NAC 43 45 days No
improvement
Hypertensive patients
with type 2
diabetes(Martina,
Masha et al. 2008)
2008 Oral NAC +
arginine 24 6 months Improvement
Idiopathic pulmonary
fibrosis (Demedts,
Behr et al. 2005)
2005 Oral NAC 182 12 months Low
improvement
ARDS (Bernard,
Wheeler et al. 1997) 1997
Intravenous
infusion NAC 6 10 days
Low
improvement
Hepatic cirrhosis (Cook
and Sherlock 1965) 1965 Oral GSH 20 28 days
No
improvement
47
1.3 Redox regulation in inflammation
1.3.1 Redox signalling by ROS
The relationship between ROS and inflammation is complex and requires further
study to fully understand whether oxidative stress is a cause or an effect of
inflammation. Importantly, ROS function as cell signalling molecules. Cell signalling
refers to the communication between cells to allow an organism to adapt to new
conditions and environment. Classically, receptors at the cell surface capture
information in the form of extracellular signals which are then transferred and
integrated in the nucleus (Alberts 2002). This communication is particularly observed
during cell development, in the nervous system with the expansion of axons and
synapses in neurons but it also advanced in immune response as discussed
previously in 1.1.2.3. After an extracellular molecule binds to its target receptor, the
signal is usually transmitted through second messengers which are substances that
mediate cellular activity by modifying the protein function. Ca2+ and Phosphorus (Pi)
are particularly well known second messengers (Berridge, Bootman et al. 2003,
Michigami 2013). Similarly, and despite their noxious effects, ROS can also act as
signalling molecules. This image of ROS as second messengers was controversial
10 years ago because of their high reactivity and involvement in many pathologies
(D'Autreaux and Toledano 2007). However, the increase of studies in redox biology
has changed this dogma and nowadays it is accepted that too many ROS lead to
oxidative damage but too little lead to an impairment of signalling pathways Figure
1.6 (Brieger, Schiavone et al. 2012). Two mains discoveries led to this conclusion: the
presence of NOX in a variety of non-phagocytic cells and the ability of ROS to trigger
different signalling pathways.
In 1989, the presence of a soluble component of NOX in non-phagocytic cells was
identified (Pick, Kroizman et al. 1989). It has been reported since that NOX is present
in a diversity of cell types such as epithelial cells, endothelial cells, and neurons and
is responsive to various stimuli such as growth factors or exogenous molecules
(Bedard and Krause 2007, Jiang, Zhang et al. 2011). The main difference with
phagocytic NOX is that the amount of O2- produced are lower and intracellular
(Lambeth 2004). According to Valko and colleagues, one third of ROS is produced by
NOX in non-phagocytic cells compared to neutrophils cells (Valko, Leibfritz et al.
2007). This physiological low level of ROS production could explain a biological role
of ROS in cell function in opposition with a high level to kill pathogens.
48
Reactive species have also been established to be responsible for signalling
pathways (Forman, Maiorino et al. 2010). Among all the existing ROS, H2O2 is the
most likely to be thought of as a candidate second messenger (Stone and Yang 2006).
It is often considered as a mild oxidant and therefore less unstable than other ROS.
It can easily diffuse through cell membranes using specific aquaporin channels
(Bienert, Moller et al. 2007). The first evidence of a potential signalling role for H2O2
came to light in the early nineties with the discovery that it could regulate NF-κB as
mentioned previously (Schreck, Rieber et al. 1991). Another major discovery was
shown in a study of platelet derived growth factor (PDGF): H2O2 was required to
stimulate rat vascular smooth muscle cells with PDGF via downstream signalling by
mediating tyrosine phosphorylation, mitogen-activated protein kinases activation
(MAPK) and chemotaxis (Sundaresan, Yu et al. 1995).
Since then, H2O2 has been studied in several cell processes (Valko, Leibfritz et al.
2007, Sies 2014). It can activate or deactivate many signalling molecules, such as
extracellular signal-regulated protein kinase 2 (ERK2), a MAPK (Guyton, Liu et al.
1996) and protein tyrosine phosphatases which themselves control a variety of
signalling pathways (Meng, Fukada et al. 2002). It is also involved directly in the
activation of STAT1 and STAT3, which are important in cell signalling after stimulation
by growth factors and cytokines, and also JAK2 and TYK2 (Simon, Rai et al. 1998).
H2O2 can also regulate a large variety of transcription factors. For example Nuclear
factor (erythroid 2-derived)-like 2 (Nrf2), which is involved in physical and chemical
stress responses (Ning, Mo et al. 2010), Foxa2 , which is involved in cell replication
and development (Zhang, Ji et al. 2013), AP-1 and CREB involved in differentiation,
proliferation and apoptosis response (Jaramillo and Olivier 2002).
H2O2 is the main signalling molecule among the ROS family however others reactive
species can also play physiological roles. For example, 1O2 can activate MAPK
involved in p38 and JNK which are both important in apoptosis, proliferation and
differentiation (Klotz, Pellieux et al. 1999).
Thus, ROS are involved in different cellular functions. They are responsible for co-
operating with the phosphorylation and calcium signalling pathways and are involved
in immune responses, cell-cell adhesion, cell proliferation, metabolism, aging, and cell
death.
49
Figure 1.6: ROS are signalling molecules when produced at moderate levels.
Scheme from Brieger et al. (Brieger, Schiavone et al. 2012).
50
1.3.2 Thiol oxidation
These signalling roles of ROS are dependent on the quantity, length, and localisation
of ROS production but most of all they imply the reversibility of the oxidative
modifications induced by ROS in biological samples (Brieger, Schiavone et al. 2012).
This reversibility is the key for the transition of a message as irreversible modifications
belong to the field of oxidative damage. Several different oxidative modifications of
proteins exist (as mentioned previously in section 1.2.2) such as nitration, targeting
tyrosine amino acid (Radi 2013), carbonylation, targeting many amino acid side
chains (Suzuki, Carini et al. 2010) and thiol oxidation, targeting cysteine (Cys). Both
nitration and carbonylation are normally irreversible, although recent evidences
suggests potential regulatory roles (Cai and Yan 2013). Instead, thiol oxidation can
be reversible and is the main post-translational modification. In fact, the main target
sensitive to ROS is the thiol group, a sulfhydryl group (-SH; also called mercaptan),
which is part of Cys residues, one of the 20 amino acids (Figure 1.7). The sensitivity
of thiol toward ROS is due to the chemical properties of sulphur: its electron
configuration allows it to gain or lose many electrons leading to a switch in oxidation
states from -2 to +6. The thiol in cysteine can be fully reduced (-2), and is therefore
very nucleophilic, enticing a range of oxidation reactions (Jacob, Giles et al. 2003,
Poole 2015). In comparison, the methionine, the only other amino acid with a sulphur,
has a different oxidation state which is less ionisable, and is therefore less subject to
redox changes. In fact, new insights are actually showing that methionine also has an
antioxidant role and potential regulatory functions (Drazic and Winter 2014, Wani,
Nagata et al. 2014).
Interestingly Cys is one of the least abundant amino acids in the human body but is
also the most conserved within species, stating to the specific role of thiol in biological
functions as a cofactor and structural chaperone (Fomenko, Marino et al. 2008,
Marino and Gladyshev 2010). The sensitivity of thiols to redox is complex and it is still
a challenge today to identify redox-regulatory cysteines. This property depends on a
few factors: the ionization of the thiol in the protein which is defined by its pKa, which
itself is defined by the molecular environment (acidity for instance). To give an idea of
the order, thiols in proteins usually have a pKa of 8-9 (SH), while the subset of redox-
sensitive thiols have a pKa below 7 (S-). Furthermore, if a pKa is low it has more
chance of being ionisable and therefore redox sensitive (Winterbourn and Hampton
2008, Rudyk and Eaton 2014). The microenvironment is a strong limitation of thiol
oxidation. It allows the thiol in the cytoplasm to be kept in a free-state (ionisable)
(Paget and Buttner 2003). Different studies have shown that each cell compartment
51
has a different redox state which depends on the ratio between reduced glutathione
(GSH) and oxidized GSH (GSSG), the GSH:GSSG ratio, and the ratio between
reduced and oxidised cysteine. Classically, cytoplasmic proteins contain mostly free
thiol (=reduced thiol) as the environment is strongly reducing due to the high
concentration of glutathione (GSH). On the contrary, very little free thiol is found in
extracellular proteins which are in an oxidising environment; proteins of the plasma
membrane are at the interface between these two environments (Ghezzi, Bonetto et
al. 2005) (Banerjee 2012). Finally, another important factor is the accessibility of ROS
to cysteines which is influenced by the structure of the protein but also the spatial
proximity of the target thiol to the reactive species source.
The two major thiol modifications are the intramolecular disulphide bond formation
(Cys-Cys-Protein) and glutathionylation (Cys-GSH). However, other reversible redox
states have been described including cysteinylation with free cysteine, S-nitrosylation
(Cys-SNO), S-sulfenylation (Cys-SOH) and S-sulfinylation (Cys-SO2H). This is
illustrated in Figure 1.7. S-sulfonylation (Cys-SO3H) is the last stage of oxidation and
is non-reversible. Consequently, sulfonylation belongs to oxidative damage rather
than signalling. This has been shown in different pathologies, such as multiple
sclerosis where S-sulfonylation is associated with disease duration (Pieragostino, Del
Boccio et al. 2013).
The diversity of thiol modifications implies to a strong redox dynamic within the cell.
Disulphide dimerization
Disulphide dimerization is a mixed disulphide exchange that forms between two
cysteines from the same protein. This mechanism is stable and is very important in
folding and maintaining the secondary or tertiary structure of the protein (Nagy 2013).
It often happens in oxidising environments, such as the endoplasmic reticulum
compartment during the folding process (Braakman and Hebert 2013), but is also
present in the extracellular compartment. The formation or removal of disulphide
bonds by changing the conformation of the protein can also change the functional
state of the protein, and is a post-translational modification. This modification is
particularly studied in the domain of cell surface receptors and in the activation of
transcription factors. For instance, this has been well characterised for erythropoietin
receptor (EPO-R) which require receptor dimerization to mediate signal transduction
(Watowich, Hilton et al. 1994).
52
Glutathionylation
Glutathionylation is the formation of a mixed disulphide exchange between a cysteine
in a protein and the cysteine of GSH (PSSG). This reaction is thermodynamically
reversible and can be formed by direct oxidation depending on the ratio GSH/GSSH
(equation 1) or catalysed by thiol-disulphide exchange (equation 2) (Ghezzi 2013).
Equation 1: PSH + GSH PSSG
Equation 2: PSH +GSSG PSSG+ GSH
Glutathione S-transferase (GST) or glutaredoxin enzymes (Grx) can performe
glutahionylation of a protein while deglutathionylation can be catalysed by Grx ,
sulfiredoxin (Srxn) or protein disulphide isomerase (PDI) (Grek, Zhang et al. 2013)
(Kalinina, Chernov et al. 2014).
There are also other possible mechanisms to form glutathionylated proteins with for
instance S-nitrosothiols: PSNO +GSH PSSG +HNO.
Glutahionylation can occur during oxidative stress as a protective mechanism but is
also observed in physiological conditions indicating a regulatory role of this reversible
modification (Dalle-Donne, Rossi et al. 2009). In fact, glutathionylation can directly
activate or inhibit proteins. For instance, GST catalyses the glutathionylation of Kelch-
like ECH-associated protein 1 (Keap1), an inhibitor of Nrf2 (Carvalho, Marques et al.
2016). Therefore, glutathionylation regulates the activation of Nrf2. Similarly STAT3,
a transcription factor involved in many signalling process including the inflammatory
response by inducing the transcription of cytokines, is regulated by glutathonylation
(Xie, Kole et al. 2009). When glutathionylated, STAT3 leads to a decrease in the
cascade signalling following IL-6 activation to activate NF-κB and MAPkinase
pathways.
S-sulfenylation and S-sulfinylation
Finally, S-sulfenylation and S-sulfinylation are thiol modifications which are under
current investigation (Lo Conte and Carroll 2013). Sulfenylation acts mainly as an
intermediate stage before the structural or functional change of a protein (Beedle,
Lynham et al. 2016). These two modifications are very difficult to probe and assess,
therefore current techniques and methods of study focuse in computational models
and prediction of outcomes (Lo Conte, Lin et al. 2015).
53
Figure 1.7: Thiol oxidation modifications in proteins (adapted from Leonard et al.
(Leonard and Carroll 2011)). A. Chemical structure of cysteine. B. Thiols in proteins
can undergo a series of ROS/RNS modifications. This is possible if thiols are
accessible and have a low pKa. Red indicate oxidative damage (irreversible reaction).
54
1.3.3 Evidence of thiol oxidation modification in inflammation
It is becoming evident that reversible oxidation of protein thiol play a role in the
regulation of the inflammatory response in a similar manner to phosphorylation to
trigger an appropriate response to the stimulus.
For instance, HMGB1 is one example illustrating the importance of thiol modification
in inflammatory responses. This protein is secreted by monocytes in response to an
inflammatory stimulus and participate to the immune response, although it should be
noted that this occurs later than secretion of classical cytokines such as IL-1β and
TNF (Gardella, Andrei et al. 2002). Once secreted, HMGB1 attracts inflammatory
cells, for instance neutrophils, encourages maturation of myeloid and plasmacytoid
cells and recruits stem cells permitting their proliferation (Messmer, Yang et al. 2004,
Dumitriu, Baruah et al. 2005). In 2010, the role of the three cysteines (C23; C46 and
C106) that are contained in HMGB1 was highlighted for the first time via the
demonstration that the thiol in C106, in its reduced form, is associated with increased
TLR4 signalling (Yang, Hreggvidsdottir et al. 2010). Finally, in 2012, using endotoxin
stimulated RAW 264.7 cells, the same team demonstrated via Mass Spectrometry
that HMGB1 had to form an intramolecular disulphide bond between C23 and C45 in
addition to the reduced thiol at C106 to stimulate cytokine production by TLR4
activation (Venereau, Casalgrandi et al. 2012, Yang, Lundback et al. 2012).
Recently, Stottmeier and colleagues have demonstrated that MyD88-dependent NF-
κB signalling pathway is controlled by the redox regulation of MyD88 cysteines
through thiol disulphide exchange with a member of Trx family (Stottmeier and Dick
2016).
Evidence of glutathionylation modified proteins have also been reported in immune
cells. In fact, a recent assay developed by Mullen and colleagues in human
monocytes cells has detected 38 secreted proteins and 55 intracellular undergoing
glutathionylation giving an idea of the importance of this regulation in immune
response (Mullen, Seavill et al. 2015).
A signalling network involving H2O2 and the antioxidant Peroxiredoxin 2 (Prx2) have
recently been highlighted to modulate STAT3 activity (Sobotta, Liou et al. 2015).
Essentially, Prx2 transduces the signal received by H2O2 and transforms it by thiol
disulphide exchange to oligomerise STAT3 leading to its inactivation.
Furthermore, the emergence of redox sensitive thiols identified in receptors and
enzymes at the surface of immune cells illustrates a mechanism of regulation of those
proteins by thiol modification during the inflammatory process (Metcalfe, Cresswell et
al. 2011).
55
1.3.4 Thiol oxidoreductases
Oxidoreduction of protein thiol-disulfides is catalysed by a variety of enzymes called
thiol oxidoreductases. These enzymes usually contain one or more catalytic redox
active cysteines and can have important physiological functions (Marino and
Gladyshev 2009) (Varlamova, Gol'tiaev et al. 2013). In this work, they have been
separated into two groups: the thioredoxin (Trx) fold-like proteins and the peroxidase
catalytic proteins.
1.3.4.1 Thioredoxin fold like protein
Trx fold-like proteins share one main characteristic. They all have a Trx fold-like
structure which consists of a simpler version of the Trx structure: a central core of four
antiparallel β-strands surrounded by three α-helices with the catalytic motif at the
surface of the α2-helix (Berndt, Lillig et al. 2008) (Collet and Messens 2010). This
motif is highly conserved across species. It comprises an attacking cysteine and a
resolving cysteine (CysR), stabilising the substrate, typically Cys-X-X-Cys with X
representing any amino acid (Fomenko, Marino et al. 2008). The two cysteines of the
redox active centre can shift between dithiol and disulphide forms, performing a signal
relay between redox components (Pedone, Ren et al. 2004). Typically, this family
uses an electron donor, in general NADPH, to reduce or oxidise a substrate and a
cofactor, which is often the isoalloxazinic ring of the Flavin Adenine Dinucleotide
(FAD) enzyme.
These enzymes are ubiquitous and found in all living organisms, from bacteria to
plants and mammals. They include thioredoxin (Trx), Grx, protein disulphide
isomerase (PDI), and the sulfhydryl oxidases (SOXs) which catalyse dithiol–
disulphide exchange reactions and therefore can regulate thiol modification.
Thioredoxin
Trx was first isolated from E.Coli in 1964 and was described as a hydrogen donor to
reduce deoxycytidine diphosphate from the ribonucleotide cytidine diphosphate
(Laurent, Moore et al. 1964). The mammalian structure of Trx was then determined
by Holmgren in 1975 but have been completed since (Holmgren, Soderberg et al.
1975). It consists of a central core of five β-strands surrounded by four α-helices with
the catalytic motif at a surface of the helice α2: Cys-Gly-Pro-Cys. The proline helps to
promote the reducing power of Trx while Glycine help to maintain the conformation of
56
the active site. All Trx in nature have the same three-dimensional structure (Collet and
Messens 2010).
The Trx catalytic system is composed of Trx, NADPH and Trx reductase (TrxR)
(Holmgren 1985). In mammals it exists as two Trx antioxidants systems: the cytosolic
Trx1 and the mitochondrial Trx2 (Lu and Holmgren 2014). The reaction consists of
bimolecular nucleophilic substitution reaction (SN2), which is the transfer of the
disulphide bond from the substrate protein to Trx. Trx becomes oxidised and is
reduced using TrxR via a transfer of electron from NADPH to TrxR to the redox active
disulphide (Figure 1.8A).
Trx is involved in many signalling pathways and could target many nuclear proteins
(Wu, Jain et al. 2014). For instance, Trx inhibits ASK1, a MAP3K which is involved
the JNK pathway (Saitoh, Nishitoh et al. 1998). An updated study has recently shown
that the reduced thiol of the redox catalytic centre of Trx could interact with ASK1,
leading to its inhibition (Kosek, Kylarova et al. 2014).
Glutaredoxins
Grx are small proteins between 9 and 15kDa which exist in two forms; monothiol (Cys-
X-X-Ser) with no enzymatic activity and dithiol (Cys-X-X-Cys) (Lillig and Berndt 2013).
Grx utilise the reducing power of glutathione to catalyse disulphide reduction and
therefore process directly to glutathionylation/deglutathionylation of proteins (Lillig
and Berndt 2013). It exist 4 Grxs in mammals: Grx1, Grx2 Grx3 and Grx5 which are
all dithiol. They are located intracellularly in the cytosol, mitochondria and nucleus,
but can also be secreted. They also have a catalytic system which consists of Grx,
NADPH and Grx reductase (Figure 1.8B) (Fernandes and Holmgren 2004). GrxR is
homologous to TrxR, however for enzymatic activity, it requires GSH as a substrate
donor. Due to this particularity, Grx are the main actors to rearrange to a protein and
therefore to glutathionylation.
Grx also have signalling roles. It has been shown that Grx1 was required to have an
optimal TLR4 signalling induction NF-κB pathway by deglutathionylated Traf6, an
important intermediate molecule (Chantzoura, Prinarakis et al. 2010).
Grx and Trx work in parallel with independent activity. In addition, they both can act
as back up for each other, with Trx using GrxR as an electron donor and vice versa
(Kalinina, Chernov et al. 2014).
Sulfhydryl oxidase
Sulfhydryl oxidase (SOX) catalyses the formation of disulphide bonds of free thiols
using molecular oxygen as an electron donor, which is then reduced to hydrogen
57
peroxide as shown in Figure 1.8C (Faccio, Nivala et al. 2011). They carry the Cys-X-
X-Cys motif and are flavin-dependent enzymes.
Different families exist within the SOXs, those of which include the ERV/ALV family
and the Quiescin/SOX (QSOX) family (Coppock and Thorpe 2006). They have a
variety of substrates such as GSH and thiols in protein.
These oxidoreductases play a role in structure and stability of proteins but can also
be secreted with potential antimicrobial function (Faccio, Nivala et al. 2011). They are
also involved in the structure of extracellular matrix and tissue regeneration (Thorpe,
Hoober et al. 2002).Recently in Drosophila, the redox signalling activity of SOXs was
perceived: Er01L, a SOXs, was required in the ER to form disulphide bond in the
Notch necessary for its activation and signalling in cell communication (Tien, Rajan et
al. 2008). Amusingly, SOX are particularly used in food and cosmetic industry to
improve the conservation of product by oxidising volatile thiols and protect flavours
(Faccio, Nivala et al. 2011).
Protein Disulphide Isomerase
Protein Disulphide Isomerase (PDI) catalyses disulphide formation, reduction and
isomerisation. They can modulate the structure of protein by rearranging disulphide
bonds which is particularly important in case of structure mistakes and misfolding.
Thus they represent one of the most abundant proteins in the ER where protein folding
occurs but they also can be freed in the nucleus, the cytosol, extracellularly and at the
cell surface (Shabab, Khan et al. 2014). In addition, PDI plays an important role in the
health and diseases (Benham 2012). In fact, the aggregation of misfolded protein lead
to pathologies such as in Alzheimer disease and cystic fibrosis (Wilkinson and Gilbert
2004). PDI are organised in five domains with four which are similar to Trx fold.
However only two contain the classic redox active site: Cys-X-X-Cys which allows PDI
to catalyse the formation of disulphide bonds if PDI is in the reduced form (PDI-(SH)2)
but also isomerises if PID is in the oxidised form (PDI-S2) .
Around twenty PDI exist in humans interacting with different proteins and substrates.
For instance ERp57 interacts preferentially with glycosylated substrates via
calnextrin, while ERp44 interact with adipoctenin, a secreted hormone of adipocyte
(Ellgaard and Ruddock 2005). Due to their role in the control of protein structure, they
can also participate in cell signalling (Laurindo, Pescatore et al. 2012).
58
1.3.4.2 Peroxidase Catalysis family
This group includes thiol enzymes reducing H2O2 or peroxide products. They include
Gpxs, Prxs, myeloperoxidase, or eosinophil peroxidase (Rhee, Kang et al. 2001)
(O'Brien 2000). They are also in constant communication with the Trx and Grx, which
they utilise as electron donors to proceed to downstream reactions (Bindoli, Fukuto et
al. 2008).
Peroxiredoxin
Prx family members protect against oxidative stress by transforming peroxides (H2O2,
organic peroxide, peroxynitre) into H2O, becoming themselves oxidised as a result
(Figure 1.8D). To proceed, they use a conserved redox-active cysteine (the
peroxidatic cysteine, CysP) with a pKa around 5-6.
In mammals, Prxs are highly abundant and can represent up to 1% of the total
proteins in the cell. In total, six Prxs encoded by six different genes are found in
different cell locations; Prx1, 2 and 6 are found in the cytosol, Prx3 in the mitochondria,
Prx4 in the endoplasmic reticulum, and Prx5 in the mitochondria, peroxisome and
cytosol. These mammalian Prxs are classified into three different types depending on
the position of the CysR to the CysP (Rhee 2016).
The typical 2-cys (Prx1, Prx2, Prx3 and Prx4) have a conserved CysR at the C
terminal forming intermolecular disulphides between two adjacent subunits of homo
dimeric proteins. Atypical 2-Cys (Prx5) have the CysR in the C terminus of same unit.
Finally, 1-cys Prx (Prx6) have the CysR on a different molecule which is not a Prx
(Hanschmann, Godoy et al. 2013, Rhee 2016).
All Prxs have the same catalytic reaction divided in different steps: Essentially, the
free thiol of CysP (CysP-SH) attacks the hydroperoxide substrate and generates
water, while itself becomes oxidised (CysP-sulfenic acid (CysP-SOH)). The CysR
(CysR-SH) attacks then this CysP-SOH forming an intra or inter subunit. This
disulphide bond is then recycled in CysP-SH using an electron donor such as
thioredoxin (Figure 1.8D) (Perkins, Nelson et al. 2015).
The catalytic activity depends upon the three dimensional form of Prxs. All Prxs
structure are similar and contain a Trx-fold domain with additional features (Wood,
Schroder et al. 2003). Atypical 2-cys exist as functional monomers, while the typical
2-cys and 1-cys exists as domain-swapped homodimers enzymes.
59
Glutathione peroxidase
GPx belongs to the peroxidase family which include myeloperoxidase, cited
previously in the respiratory burst. In mammals, 8 GPx are identified (Brigelius-Flohe
and Maiorino 2013). GPx1-4 and Gpx6 are all selenium dependent while Gpx 5, 7 and
8 are not. Despite their different structure and less than 20% homology, they share
the same catalytic centre which is composed of Seleniumcysteine (or Cysteine) – Gln-
Trp and Asn (Battistuzzi, Bellei et al. 2010). The enzymatic catalytic reaction of GPx
is a ping pong mechanism involving different steps: the enzyme is itself oxidized to
Compound I, which contains an oxyferryl (Fe(IV) = O) center and an organic cation
radical. The cation radical then undergoes a one-electron reduction, oxidizing the
substrate molecule and forming Compound II, which maintains the oxyferryl center
(Deponte 2013). Finally, Compound II is reduced back to the resting ferric state with
the concomitant one-electron oxidation of a second substrate molecule (Deponte
2013).
60
Figure 1.8: Catalytic reaction of thiol oxidoreductases. A. Thioredoxin reduces
disulphide bonds in protein. B. Glutaredoxin can promote deglutathionylation of
proteins. C. Sulfhydryl oxidase promote disulphide bonds formation. D. Peroxiredoxin
reduce hydrogen peroxide and uses Trx as an electron donor.
61
1.3.5 Oxidoreductases in Inflammation: Redoxkines
Thiol oxidoreductases do not only modulate the inflammatory response by controlling
redox reactions but can also exist in extracllular form and have cytokine- or
chemokine-like activities. In that case, thiol oxidoreductases have been defined as
“redoxkines” (Salzano, Checconi et al. 2014).
These properties were particularly observed in Prxs: in fact Prx1, Prx2 and Prx5 seem
to act as pro-inflammatory molecules while Prx4 and Prx6 seem to act as anti-
inflammatory molecules.
Secreted Prx1 interacts with TLR4/ Myd88 dependent pathway promoting TNF and
IL-6 secretion from macrophages and dendritic cells (Riddell, Wang et al. 2010). This
interaction seems due to the interaction of C83 with TLR4 when Prx1 is in a dimer or
tetramer form. Increase of Prx1 expression also correlate with progression and growth
tumour in prostate carcinogenesis (Riddell, Bshara et al. 2011).
Prx2 is released upon 24h LPS stimulation in a glutathionylated dimeric form that can
induce TNF production in a macrophages cell line (Salzano, Checconi et al. 2014).
Interestingly, Prx2 has been detected in elevated concentration in lymphocytes from
rheumatoid arthritis patient, when compared with healthy volunteers (Szabo-Taylor,
Eggleton et al. 2012). Similarly, Prx2 is expressed in elevated concentration in the
white matter lesions of chronic MS lesions correlating with an elevated level of
macrophage infiltration (Voigt, Scheidt et al. 2017).
Finally, Prx5 gene was up regulated in macrophages following LPS-induced MAPK
activation (Abbas, Breton et al. 2009).
Prx4 and Prx6 have anti-inflammatories properties; Prx4 can inhibit IL-1β in
chondrocyte suggesting a potential protecting role of Prx4 toward osteoarthritis a
degenerative joint chronic disease (Rao, Wang et al. 2017). While Prx6 appears to
have a protective role in MS by suppressing inflammation caused by oxidative stress
(Yun, Park et al. 2015). In fact, Prx6 seems to have anti-inflammatory properties. It
was shown to be able to inhibit MAPK pathway after induction with LPS in acute
kidney injury mice (Lee, Park et al. 2017).
Trx is also of a great interest as an inflammatory molecule. It has been found in
elevated concentration in association with many pathologies; in cancer tissues, in the
serum of arthritis and HIV patients, after angioplasty, or in chronic heart failure
(Nakamura, De Rosa et al. 1996, Lincoln, Ali Emadi et al. 2003) (Wahlgren and
Pekkari 2005). Furthermore, Trx was identified as a cell growth factor mediated by its
C73 (Gasdaska, Berggren et al. 1995) (Gasdaska, Kirkpatrick et al. 1996). In addition,
extracellular Trx can act as a chemoattractant of immune cells such as monocytes,
62
granulocytes and T lymphocytes via its C32 and C35 (Bertini, Howard et al. 1999). All
these properties lead to it being investigated as a potential anti-cancer target. Different
clinical trials in oncology have been performed using PX-12, a synthetic inhibitor
developed by Powis and colleagues although these have been unsuccessful so far
(Kirkpatrick, Kuperus et al. 1998, Baker, Adab et al. 2013).
Investigation into the release of Prx1, Prx2 and Trx from LPS stimulated macrophages
demonstrated that NAC and dexamethasone (DEX), an anti-glucocorticoid receptor
that reduces the inflammatory response, could lower the release of Prx1, Prx2 and
Trx in contrast to TNF which was only inhibited with DEX and not NAC (Checconi,
Salzano et al. 2015). These results supported the idea of using these thiol-containing
proteins as promising markers of oxidative stress during the inflammatory response.
Regarding other thiol oxidoreductases, Grx1 depletion in mice leads to diminution of
LPS induced inflammation in lungs and the activation of macrophages (Aesif, Anathy
et al. 2011). Extracellular PDI can modulate the activity of an integrin, a
transmembrane receptor interacting with the extracellular matrix (ECM), helping
neutrophil migration to the site of inflammation (Hahm, Li et al. 2013).
63
Aim of this project
Inflammation is the response of the body to eliminate pathogens, react to danger and
repair damaged tissues. Unsurprisingly its permanent activation leads to chronic
conditions, such as rheumatoid arthritis or atherosclerosis. The causes of this self-
activation are still unknown but these diseases are also associated with oxidative
stress. In fact, ROS are released during the inflammatory response and can damage
tissues which lead to inflammation and vice-versa causing a vicious cycle. However,
ROS are not only toxic species but can also act as signalling molecules co-operating
with the phosphorylation and calcium signalling pathways to trigger immune
responses mainly via thiol oxidation modification. In this study, we hypothesised that
thiol oxidation changes can affect the inflammatory response and therefore
investigation of those changes was required.
For this purpose, four lines of investigation were pursued:
1. Failure of antioxidant therapy leads to doubt about whether ROS act as pro-
inflammatory molecules while thiol antioxidants are anti-inflammatory. To
improve understanding of the mechanism mediated by thiols during inflammation,
the role of endogenous GSH, the main cellular thiol antioxidant, was investigated
in LPS-stimulated macrophages (Chapter 3).
2. Thiol oxidoreductases, the enzymes responsible for thiol modification, are
released from immune cells during inflammation and seem to have cytokine or
chemokine effects. Prx1, Prx2 and Trx, in particular, have been detected in the
secretome from macrophages following LPS-stimulation. We thus investigated if
these proteins undergo redox changes which could be associated with their
secretion and potential functions (Chapter 4).
3. To understand better how these proteins function in the extracellular environment
during inflammatory response, we attempted to identify potential protein targets
of oxidoreduction on the cell surface subjected to LPS stimulation (Chapter 5).
4. Finally, redox changes detected in Prx1, Prx2 and Trx proteins were tested as
biomarkers of oxidation in patient’s blood undergoing angioplasty following
coronary artery disease (Chapter 6).
64
Chapter 2
Materials and Methods
65
2.1 Instruments
Product Supplier
Centrifuge: Eppendorf centrifuge S810R A-4-62 4x1.1kg
Thermo Fisher Scientific
CKX41 inverted microscope Olympus
Film processor: SRX-101A Developer Konica Minolta
Hypercassette GE Healthcare Life Science
Incubator: Hera cell 150 Thermo Scientific
Microfuge R centrifuge Beckman Coulter
Mini centrifuge: Pico 17 Heraeus 24 x 1.5/2.0ml rotor Thermo Scientific
Mini Trans-Blot Cell Biorad
Mini-PROTEAN Tetra Vertical Electrophoresis Cell Biorad
Nanodrop ND-1000 Thermo Fisher
Neubauer Improved Haemocytometer Marienfield Superior
Optima MAX ultracentrifuge 130,000rpm Beckman Coulter
PCR instrument: TC-3000X Techne
Plate Reader : Synergy HT BioTek
Plateform rocker STR6 Science Lab
PowerPac Basic Biorad
qPCR machine: Stratagene MX3000 Agilent Technologies
Rotor TLA x 100 Beckman Coulter
SevenCompact pH Meter Mettler-Toledo
Ultrapure water supplier Adrona
Ultrawave ultrasonic bath Ultrawave
Vacufuge Eppendorf Concentrator Plus Thermo Fisher Scientific
Water bath: SUB Aqua 18 Plus Grant
Table 2.1: List of instruments used in this study
66
2.2 Chemicals and kits
Product Supplier
2-Iodoacetamide (IAA) Sigma–Aldrich
30% Acrylamide/Bis solution Bio-Rad
5,5’-dithiobis(2-nitrobenzoic acid) (DTNB) Sigma-Aldrich
Agarose Beads Pierce™ High Capacity NeutrAvidin™ Agarose
ThermoFisher Scientific
Ammonium Bicarbonate (NH4HCO3) Sigma–Aldrich
Ammonium Persulfate (APS) Sigma-Aldrich
Bovine Serum Albumin (BSA) Sigma-Aldrich
Bromophenol Blue Sodium salt (BBP) Sigma-Aldrich
Buthionine Sulfoximine (BSO) Sigma-Aldrich
Diethylene Triamine Pentaacetic Acid (DTPA) Sigma-Aldrich
Dimethyl Sulfoxide (DMSO) Sigma-Aldrich
Disodium hydrogen orthophosphate (Na2HPO4) Fisher Scientific
Dried skimmed milk Marvel original
Dulbecco’s Modified Eagle’s Medium (DMEM) D5796
Sigma-Aldrich
Dulbecco’s Phosphate Buffered Saline (PBS) Sigma-Aldrich
ECL Blotting Detection Reagents GE Healthcare Life Sciences
Ethanol Fisher Scientific
Ethylenediaminetetraacetic acid disodium (EDTA) Fisher Scientific
Glycerol Sigma-Aldrich
Glycine Sigma-Aldrich
Heat Inactivated Fœtal Calf Serum Sigma-Aldrich
HEPES Melford
Hydrochloric Acid (HCl) Fisher Scientific
Hyperfilm ECL GE Healthcare Life Sciences
Lipopolysaccharide from Eschericha coli 055:B5 List Biological Labs, Incorporated
Menadione Sodium Bisulfite (Menadione) Sigma-Aldrich
Methanol Fisher Scientific
67
Methoxypolyethylene glycol Maleimide (Maleimide- PEG)
Nanocs
miRNeasy Mini Kit Qiagen
Molecular weight ladder precision Bio-Rad
MTT Assay kit Sigma-Aldrich
N-(Biotinoyl)-N'-(Iodoacetyl) Ethylenediamine (BIAM)
Sigma-Aldrich
N-Acetylcysteine (NAC) Sigma-Aldrich
N-Ethylmaleimide (NEM) Sigma-Aldrich
Nitrocellulose blotting membrane (ProtainTM
Premium 0.45μM) GE Healthcare Life Sciences
NP-40 surfact-Amps Deterget solution Thermo Scientific
Opti-MEM I medium Gibco by Life Technology
PageRulerTM Plus Prestained Protein Ladder Thermo Fisher Scientific
Penicilin-Streptomicin (P/S) Thermo Fisher Scientific
Pierce BCA Protein Assay Kit Thermo Fisher Scientific
Potassium Chloride (KCl) Fisher Scientific
Potassium Dihydrogen Orthophosphate (KH2PO4) Fisher Scientific
PX 12 (2-[(1-methylpropyl)dithio]-1H-imidazole) Sigma-Aldrich
Roswell Park Memorial Institute (RPMI) 1640 medium
Sigma-Aldrich
Sequencing-grade modified trypsin Promega
Sodium Chloride (NaCl) Fisher Scientific
Sodium Dodecyl Sulphate (SDS) Sigma-Aldrich
Sucrose Fisher Scientific
Superoxide Dismutase (SOD) Sigma–Aldrich
Temed Sigma-Aldrich
Thiazolyl Blue Tetrazalium Bromide (MTT) Sigma-Aldrich
Recombinant Human TNF-α Peprotech
Tris 2-carboxyethylphosphine (TCEP), Bond Breaker solution
Thermo Fisher Scientific
Trizma base Sigma-Aldrich
Tween 20 Thermo Fisher Scientific
Table 2.2: List of reagents, chemicals and kits used in this study
68
2.3 Reagents and buffers
Reagents and solutions:
Reagent Working concentration Diluent
LPS 200μg/ml Distilled H2O
BSO 250mM Distilled H2O
NAC 0.5M Opti-MEM, pH7.4
APS 1M Distilled H2O
Menadione 500mM Distilled H2O
TNF 200μg/ml Distilled H2O
MalPEG 15mM Distilled H2O
NEM 1M Ethanol
DTT 1M Distilled H2O
IAA 10M Ammonium Buffer
PX-12 50mM DMSO
TCEP 2mM Ammonium Buffer
Table 2.3: List of reagents used in this study
Buffer:
Lysis buffer
NP-40 lysis buffer: 10mM Tris HCl, 150mM NaCl, 1mM EGTA, 1% NP-40, 1mM EDTA
in distilled water pH7.4
Electron Paramagnetic Resonance (EPR)
BMPO buffer: 1mM DTPA and 50mM of 5-tert‐butoxycarbonyl-5‐methyl-1‐pyrroline
N‐oxide (BMPO) with or without 200 U/ml SOD in PBS
MTT assay
MTT 10X Buffer: 5mg of MTT in 1ml of distilled H2O
69
MTT Stop Solution: 10% SDS in 0.01M HCL in distilled water
SDS-PAGE
Running gel buffer: 1.5M Tris HCl in distilled water; pH8.8
Stacking gel buffer: 500mM Tris HCl pH6.8 in distilled water
SDS-PAGE sample buffer (6X): 375 mM Tris-HCl; 1.2% of SDS; 60% glycerol,
0.006% of BPB, in distilled water; pH 6.8
Running buffer (10X -1L): 250mM Tris, 144.13g glycine, 1% SDS in distilled water;pH
8.3
Transfer buffer (1X -1L): 3.027g Tris base, 14.41g glycine, 200ml methanol in distilled
water
Western Blot
PBS for Western Blot (10X): 80g NaCl, 2g KCL, 14.4g Na2HPO4, 2.4g KH2PO4, in
distilled water, pH7.4
PBS-T: PBS 1X+ 0.05%Tween20
DNTB assay
Dilution buffer TE: 30mM Tris, 3mM EDTA in distilled water pH8.2
Lysis buffer for DTNB assay (TE+NP40): add 10% of NP-40 to dilution buffer TE
Membrane protein extraction
Homogenisation buffer: 10mM HEPES, 250mM Sucrose, 1mM EDTA in distilled water
Mass Spectrometry preparation
Ammonium Bicarbonate solution (50mM - 40ml): 158mg of Ammonium Bicarbonate
in 35ml dH2O – pH 7.9
Solubilising buffer 60%vol/vol methanol in 40%vol/vol ammonium bicarbonate: 6ml
methanol + 4ml ammonium bicarbonate
70
2.4 Antibodies
Target Name Dilution* Incubation
time
Primary antibody
Prx1 Polyclonal Rabbit-anti-Prx1 (from Dr. Eva Maria Hanschmann) (Godoy, Funke et al. 2011)
1:1000 2h at RT or
overnight at 4˚C
Prx2 Polyclonal Rabbit-anti-Prx2 (from Dr. Eva Maria Hanschmann) 1:1000
2h at RT or overnight at 4˚C
Prx4 Polyclonal Rabbit-anti-Prx4 (from Dr. Eva Maria Hanschmann) 1:1000
2h at RT or overnight at 4˚C
mouse Trx Polyclonal Rabbit-anti-mouse Trx (from IMCO corporation Ltd) 1:1000
2h at RT or overnight at 4˚C
human Trx Polyclonal Goat-anti-human Trx (from IMCO corporation Ltd) 1:2500
2h at RT or overnight at 4˚C
Stat3 Monoclonal Mouse anti-STAT3 (124H6 – from Cell Signaling Technology)
1 :1000 Overnight at
4˚C
Hsp70 Rabbit polyclonal anti-Hsp70 (ab79852) from Abcam
1 :1000 Overnight at
4˚C GAPDH
Monoclonal Rabbit anti-GAPDH (from Cell Signaling Technology)
1:1000 Overnight at
4˚C ATPase
Monoclonal Mouse anti-Alpha 1 Sodium Potassium ATPase (from Abcam)
1:5000 Overnight at
4˚C
HRP-conjugated
Rabbit IgG Goat-anti-Rabbit IgG (from Sigma-Aldrich)
1:25000 45min-1h Mouse IgG
Goat-anti-Mouse IgG (from Enzo Life Sciences Ltd)
1:5000 45min-1h Goat IgG
Rabbit-anti-Goat IgG (from Sigma-Aldrich)
1:50000 45min-1h Biotin
Streptavidine-POD conjugate (from Roche)
1:25000 Overnight 4˚C
* All dilutions were performed in 5% BSA/PBS-T
Table 2.4: List of antibodies used in this study
71
2.5 Software and analytic tools
1. Genesis v1.7.7
Genesis version 1.7.7 is a software package developed in 2002 using Java,
programming language, specifically designed for the analyses of microarray data
(Sturn, Quackenbush et al. 2002). It can hierarchically classify a large-scale data
according to expression pattern and group them into clusters. For this, it uses a
selection of filters and algorithms such as the k-mean, the principal component
analysis and hierarchical analysis to create a heat map.
In this study, data from microarrays were obtained by calculating the ratio of the gene
expression (gProcessed Signal) for each sample for each condition against the
average of the control group in an excel file (matrix) with genes in rows and different
conditions in columns. Once converted to a text file (tab-delimited flat files), data was
transferred to Genesis and clustered using the Hierarchical tool developed by Eisen
in 1998 (Eisen, Spellman et al. 1998). Basically, genes were linked as a tree heat map
according to the degree of similarity of their expression level versus control. Green
indicates decrease and red increase in the level of expression.
2. DAVID v6.8
The Database for Annotation, Visualization and Integrated Discovery (DAVID) version
6.8 was used to identify likely biological function, signalling pathways, tissue
expression or diseases linked to a list of genes (Huang da, Sherman et al. 2009). This
is performed by many algorithms utilising knowledge from genomic resources such
as PubMed, NCBI, BIOCARTA or SWISS-PROT. Millions of annotations from
thousands of species are reported making the accuracy of those studies impossible
to be checked and therefore a weakness of this software.
DAVID was used to analyse the clustered genes obtained by gene expression
microarrays (see part 2.10) and the proteins obtained by Mass Spectrometry (see
part 2.16).
Among the different analytic modules proposed, the Functional Annotation Chart was
selected so that the most enriched annotation terms in the list of genes of interest
were obtained. The drawback of this tool was that many similar or redundant terms
were obtained diluting potential key functions. Two annotation terms databases were
used: the Gene Ontology (GO)-term and the Kyoto Encyclopedia of Genes and
Genome (KEGG) pathways. GO-term classifies genes according to three features:
their molecular function (MF), where these genes are activated (cellular component
72
(CC)) and their biological process (BP). In comparison, KEGG pathways show
molecular interaction and reaction networks of genes.
Before analysis, different parameters were selected to narrow the information and are
shown in the table below:
Experiments Gene Microarray Mass Spectrometry
Identifier of genes Official_Gene_Symbol Uniprot_ID
Background Mus Musculus Mus Musculus
Module Functional Annotation Chart Functional Annotation Chart
Annotation GO-term Biological Process GO-term Biological Process
KEGG pathway KEGG pathway
GO-term Cell Component
Cut-off p-value (EASE score)< 0.05 p-value (EASE score)< 0.05
To calculate the p-value (Expression Analysis Systematic Explorer (EASE)-score),
DAVID uses a modified Fisher’s test looking at the significance of a term in the gene
list.
3. Interferome v2.01
Interferome is a web software program used to analyse a group of genes thought to
be part of the interferon (IFN) pathway (Rusinova, Forster et al. 2013). It can be used
to identify and discriminate which genes are regulated by one of the three interferons,
IFN1, IFN2 and IFN3, using checked published literature. The list of genes obtained
by microarrays is transferred to the website in the form of a text.file and analysed by
the Mus Musculus database where a total of 4166 Interferon Regulated Genes (IRGs)
were listed.
4. oPOSSUM v3.0
oPOSSUM identifies by statistics the over-represented transcription factor binding
sites (TFBS) and families present in DNA sequences but also in a co-regulated set of
genes (Kwon, Arenillas et al. 2012). Thus, it gives information about which TF is more
likely to regulate the list of genes obtained by microarray. For the purpose of this
study, a signal site analysis (SSA) was performed on the list of genes of interest. This
analysis was the first one developed by oPOSSUM and consists in measured the
73
distance between conserved regions in the gene sequence to the gene transcription
start site (TSS) and ranked those using different scoring tests and parameters to
predict TFBS.
Once the analysis was completed, TF were ranked according to their Fisher score
which is defined as a one-tailed probability comparing the number of genes with the
TFBS motifs in the list of genes of interest versus the background which is a data set
of gene with known TSSs (to date : 29,347 are in the database). The % of GC
composition was also given as an indication of the similarity of the nucleotides in the
conserved site of the genes compared with the GC composition in the background
genes. Ideally the cursor was around 50%.
5. Image Studio Lite by LI-COR Biosciences
This image analysis software was used to determine the densitometry of bands in
autoradiography films following Western blot. Dot box to frame the band are drawn
manually giving a raw signal value (from pixels) compared to the background.
74
2.6 Cell line and cell culture
RAW 264.7 cells
RAW 264.7 cells are an adherent immortalised murine macrophage cell line used in
research for over 40 years (Figure 2.1) (Raschke, Baird et al. 1978). They were
obtained at the end of the 1970’s by inducing tumours in a male BAB/14 mouse using
a retrovirus Abselon murine Leukemia Virus (A-MuLV). This virus specifically
transforms lymphoid cells which proliferate and rapidly develop in solid tumours. 56
days after the virus administration, the lymphoid tumour tissues were extracted and
cultured. The RAW macrophages were the only ones able to grow indefinitely among
the other lymphoid cells.
This cell line is nowadays widely used in studies on inflammatory mechanisms as it
can be stimulated by different endotoxin and particularly via linkage between the
endotoxin LPS and TLR4 (Hambleton, Weinstein et al. 1996).
Raw cells used in this study were a kind gift provided by Dr. Jon Mabley (University
of Brighton, UK). For the EPR experiments performed in Universite Paris Descartes:
RAW 264.7 cells were a gift from Dr. Jean-Claude Drapier (CNRS UPR 2301, Gif‐sur‐
Yvette, France) originally from American Type Culture Collection (CRL‐9609).
Figure 2.1: Photo of RAW cells. Cells are adherents and have a macrophage-like
appearance with pseudopodia and a round shape. They were photographed under
an inverted microscope (Olympus CKX41) with a Micropix 5 megapixel color CMOS
digital camera by magnification x200, after overnight incubation in RPMI.
X 200
75
HEK 293 cells
Human Embryonic Kidney 293 cells (HEK cells) are an immortalised human cell line
obtained by transfection of a primary culture of human embryonic kidney cells with
sheared fragment of human adenovirus type 5 (Ad5) DNA (Graham, Smiley et al.
1977). This cell line has been used for over 40 years in particular for recombination
and neuropharmacology studies (Thomas and Smart 2005). They are easily cultured
and can also be stimulated by TNF to activate the NF-κB signalling pathway (Salzano,
Checconi et al. 2014).
Cell culture
RAW 264.7 and HEK 293 cell lines were both stored in liquid nitrogen in freezing
medium containing their culture media (RPMI-1640 containing L-glutamine and
DMEM respectively) plus 30% of FCS and 10% of DMSO. Before use, cells were
thawed in a water bath at 37˚C and resuspended immediately at 1:10 in culture media
plus 10% FCS and 1% Penicillin-Streptomicin (P/S). Cells were then washed by
centrifugation at 300 x g for 10 minutes. The cell pellet obtained was mixed at 1:5 in
fresh culture media for RAW cells and 1:10 for HEK cells and placed into 75cm2 flask
and placed in an incubator at 37˚C, 95% air and 5% CO2. Cells were routinely split
when 80% confluence was reached or used directly for experiments.
76
2.7 Rat Blood Samples collection
Collection of Rat plasma
Blood was collected from Wistar male rats (3 months old) purchased from Charles
Rivers (UK) after being humanely killed by terminal bleed under anaesthesia (2%
inhaled isoflurane).This was done under by Dr Lamia Heikal home-office personal and
project license (PIL 70/23855, PPL 70/7893). Blood was collected in 1.5ml Eppendorf
tube containing 10% EDTA.
77
2.8 Human Blood Samples collection
Collection of Human blood from healthy donors
10ml of Human peripheral blood was collected from healthy donors in tubes
containing EDTA ((BD Vacutainers, purple cap). All donors gave written informed
consent under the approval of the local Research Governance and Ethics Committee
(RGEC) R&D Ref No: 09/043/GHE - Pharmacomodulation of cytokines.
Collection of Human blood from patients’ donors undergoing angioplasty
Blood was collected from patients with coronary bifurcation disease requiring elective
revascularization. All patients gave informed written consent under the approval of
the local Research Ethics Committee (REC) and the Health Research Authority NHS
ethics Ref No: 15/LO/2106 – the absorb bifurcation coronary study (ABC-ONE); a
randomised trial of provisional T-stenting using absorb bio-absorbable scaffolds in
coronary bifurcations- Pilot study.
Dr Rajiv Rampat kindly collected blood at two times by patient:
Baseline (pre intervention): Intra-arterial blood sampling via peripheral arterial
sheath. 1x10ml of blood was collected into EDTA tube (BD Vacutainers, purple
cap)
Baseline (post intervention): Intra-coronary blood sampling via cardiac
catheter. 10mls of blood was collected in 1x10ml EDTA tube (BD Vacutainers,
purple cap)
78
2.9 Spin trapping coupled to EPR
The EPR technique is described as the most specific to detect reactive species
(Deschacht, Horemans et al. 2010). It detects the energy emitted by electrons after
being exposed to a magnetic field, and can therefore be used to determine the
chemical configuration of compounds. The EPR theory also referred as Electron Spin
Resonance (ESR) theory was established by Zavoisky in 1944 and the first EPR
spectrometer was developed in 1950 (Bacic, Pavicevic et al. 2016). This machine has
since evolved and it is now used in many fields from physics to archaeology.
Regarding the biomedical sciences, EPR spectrometer was elaborate, in the 1990’s,
for imaging and in vivo studies, and it is nowadays under investigation to be used as
a potential tool to diagnose pathologies.
The ability to responds to magnetic field is carried only by unpaired electrons, which
are for instance in transition metals, radical species, or organic radicals. However,
due to their high reactivity and instability, EPR was coupled with the spin trapping
technique (Williams and Janzen 1970). Essentially, nitrone cycles (which are spin
trap) can trap any radical species even inside the cell. Once trapped, the new spin
trap-radical formed become paramagnetic spin adducts. They keep the radical
property but are stable and thus can be assessable. After application of a magnetic
field and microwaves, the unpaired electron in the adduct reemit the energy received
which is then detected by EPR. Results are obtained in spectral form with
characteristic lines. Different spin adducts lead to different spectra, and are specific
for each type of radical species.
This technique was used in this project to evaluate the ROS species release from
cells after stimulation by LPS, and if this environment was modified by treatment with
BSO. BMPO (5-tert-butoxycarbonyl 5-methyl-1-pyrroline N-oxide) was used as a spin
trap. It specifically reacts with O2.- forming a superoxide adduct, BMPO-OOH, or with
HO. forming the hydroxyl adduct, BMPO-OH which is characterised by a 4 lines
absorbance spectrum (Figure 2.2). Living cells can transform BMPO-OOH to BMPO-
OH (Beziere, Frapart et al. 2010). Therefore, to discriminate between these two
species, samples were treated with SOD. SOD catalyses the dismutation of O2.- into
molecular oxygen (O2) and H2O2 but cannot enter the plasma membrane, hence it can
only inhibit extracellular O2.-. If the signal drops it indicates that the signal was due to
extracellular O2.- and not HO..
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Cell preparation
RAW Cells were plated at 5x106cells in 5ml of RPMI media supplemented with
10%FCS and 1%P/S into 25cm2 flasks. At 70% confluence, 120μM of BSO, an
inhibitor of the synthesis of GSH, was added to the culture for overnight incubation.
The next day, cells were stimulated for 2h with LPS. In total four experimental
conditions were tested: control, LPS, BSO, BSO+LPS.
Spin trapping
In each flask, cells were detached using a cell scraper and collected into 5ml of fresh
PBS in 15ml Falcon tubes. Following centrifugation at 900 x g for 5min at 4˚C the cell
pellet was resuspended in 100μl of PBS containing 1mM DTPA and 50mM of 5-tert‐
butoxycarbonyl-5‐methyl-1‐pyrroline N‐oxide (BMPO) (purity above 98%), previously
synthetized by Prof F. Peyrot team as described by Zhao (Zhao, Joseph et al. 2001),
with or without 200 U/ml SOD. The incubation mixture containing living cells was then
transferred by aspiration into gas permeable PTFE tubing folded twice into a W shape
to be inserted into a 4mm EPR quartz tube for EPR analysis.
EPR spectrometry
Before EPR measurement in the Bruker Elexsys 500 EPR spectrometer (Bruker,
Wissembourg, France) tuned at X band (9.85 GHz), different parameters were
adjusted as follows:
microwave power: 10 mW
modulation frequency: 100 kHz
modulation amplitude: 0.2 mT
receiver gain: 60 dB
time constant: 40.96 ms
conversion time: 41.04 ms
sweep width: 15 mT
sweep time: 42.02 s
EPR spectra were then recorded sequentially as a total of 1024 data points were
collected for 30 min at 21 °C.
Data analysis
Data acquisition and processing were performed with the help of Dr Fabienne Peyrot
using the Bruker Xepr software. Briefly, noise in the EPR spectra was filtered using
the singular value decomposition (SVD) method, the sum of a total of 40 spectra scan
were combined and calculated in arbitrary units. Computer simulations of the EPR
spectra expected for hydroxyl adduct BMPO were performed using the program of
Rockenbauer and Korecz.
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Figure 2.2: Experimental design of the measurement of ROS in RAW cells by spin
trapping coupled to EPR
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2.10 Gene expression microarrays
The gene expression profile changes influenced by GSH during the inflammatory
response in macrophages were analysed using gene microarray technology. This
high throughput technique was developed during the 90’s at Stanford University by
Patrick O. Brown’s group and enables the quantification of the expression of
thousands of genes within one mRNA sample (Schena, Shalon et al. 1995). This was
done in five steps (Figure 2.3) (Tarca, Romero et al. 2006):
1- Extraction of RNA from a sample
2- Reverse transcription of RNA in complementary DNA (target) and labelling with
fluorescence
3- Hybridisation by Watson-Crick rules on a glass slide (also called “chip”) containing
thousands of different DNA sequences (the probes) specifically located and from a
particular transcriptome (mouse, human, specific signalling pathways for instance).
4- Washing of the chip to avoid cross-hybridization
5- Measurement of the fluorescence which is proportional to the amount of DNA
hybridised to the probes
Cell treatment
Cells were plated at 106/well in 6-well plates. BSO was added at a final concentration
of 120 µM to deplete cells of GSH. After 24 h, control or GSH-depleted cells were
treated with 10 ng/ml LPS for 2 or 6h. All conditions are described here: untreated
(control), treated with 120μM BSO (BSO), with 10ng/ml LPS (LPS) or both BSO and
LPS (BSO+LPS) (see Figure 2.3).
RNA extraction
After 2 or 6 hours respectively, the experiment was stopped by removing the
supernatant from wells, washing them with 1ml of PBS and adding 1ml of QIAzol, a
phenol/guanidine thiocyanate compound allowing RNA extraction and preventing its
degradation. A mixture of cell lysates and QIAzol were then recovered in Eppendorf
tubes and vortexed thoroughly for 1min.
Extraction of total RNA was performed using the miRNeasy Mini Kit (Qiagen, UK) and
consist of the following steps: Firstly, chloroform was added to samples and shaken
for 15seconds to mix solutions. Tubes were then centrifuged for 15min to allow the
separation in two phases: an upper aqueous phase containing the RNA and a lower
organic phase with denaturized proteins. The upper aqueous phase was mixed with
100% ethanol to bind the RNA to the silica-based membrane in the provided
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Eppendorf tubes. Before collecting the RNA, multiple wash steps were performed with
provided buffers. At the end, water was added and the tubes were centrifuged to elute
RNA from the membrane for collection.
RNA purity (ratio A260/A280) and concentration were measured by the Nanodrop ND-
1000 (NanoDrop Technologies/ Thermo Fisher).
Microarrays Hybridisation and Scanning
The extracted RNA was processed at the Oxford Gene Technology in Begbroke
(Oxfordshire - UK) where its integrity was examined. RNA integrity number (RIN)
obtained for all samples was 10 (1=lowest; 10=highest), meaning excellent quality.
Three RNA samples (A, B, C) were randomly selected from each quadruplicate for
further gene expression analysis: RNA was retro-transcribed into cDNA, amplified,
labelled and hybridized onto Single Color SurePrint G3 Mouse GE 8x60K Microarrays
(AMADID:046066; Agilent) at Oxford Gene Technology. The probes are spotted on
the array, which represent 27,122 genes and 4,578 non coding RNAs. Three slides,
each containing eight high definition arrays were used (see Figure 2.3). Therefore 8
samples were loaded for each chip; each containing: Control 2h, LPS 2h, BSO 2h,
BSO+LPS 2h, Control 6h, LPS 6h, BSO 6h and BSO+LPS 6h.
After scanning each array, feature extraction software v10.7.3.1 was used to generate
the array data from the images.
Analysis and statistics
The raw data obtained in standard format have been deposited in the Gene
Expression Omnibus (GEO) database of NCBI, http://www.ncbi.nlm.nih.gov/geo,
accession number GSE79397.
For each sample, an excel file with normalised gene expression data was obtained
(gProcessed Signal) using GeneSpring (Agilent). Data were normalized with Gene
Spring. All the statistics and analyses were done using the log-transformed data.
Gene expression between the experimental groups was then compared by Student’s
t test on the log2 of the gProcessed Signal with a cut off of p<0.05. Fold change in
the expression was calculated as the ratio between the averages of the Log2
gProcessed Signal of the various groups with a cut off of 1.5 (in log: >0.58 or <-0.58).
Hierarchical cluster analysis was performed using Genesis version 1.7.7 for Windows,
functional annotation and biological term enrichment was done using DAVID version
6.8, transcription factor analysis by oPOSSUM and interferon pathway with
Interferome. All software are described in 2.5 Software and analytic tools.
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Figure 2.3: Experimental design of the gene microarray method and different steps
realised
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2.11 Quantitative PCR method
Contrary to the microarray technology, quantitative Polymerase Chain Reaction
(qPCR) allows the quantification of the expression of a given gene in an individual
sample.
This technique is based on the PCR method developed in the 80’s by Kary Mullis
which later received a Nobel Prize in Chemistry in 1993 for his invention (Saiki,
Gelfand et al. 1988). It allows a small amount of nucleic acid sequences to be
amplified indefinitely. This is based on altering heating and cooling cycles in an
appropriate buffer containing nucleotide bases and a specific enzyme to amplify the
nucleic acid sequence. Firstly the DNA is denaturated at 95˚C, secondly two primers
recognise a small fragment in the sequence and hybridise at temperature between
50-65˚. The sequence is then amplified at 72˚C using the thermostable Thermus
aquaticus (Taq) polymerase. This can be repeated as much as wanted until the buffer
runs out of nucleotide bases, each time doubling the amount of DNA. Rapidly, one
sequence of DNA can be copied thousands of thousands of times.
Quantitative PCR (qPCR) adds another dimension to this technique which is the
quantification in real time of the amplicon (Bustin 2000). This is possible due to the
fixation of fluorescents probes to the sequence of interest: the more the sample is
concentrated in nucleic sequence, the less cycles of amplification are required to
reach a point with a fluorescence signal which is significantly above the background.
This point is defined as the Ct (see Figure 2.5).
Cell treatment
In this study, qPCR was used for two purposes:
1– Validating the microarray data by testing individual genes in the same and new
individual experiment
2– Completing it by testing further hypothesis using NAC and Menadione
Cells were treated exactly as they were in the previous section (2.2.2) except this time
three more treatments were added. Cells were treated for 2 hours with 5mM NAC
(buffered at pH 7.4) before stimulation with 10ng/ml LPS (two new conditions:
NAC+LPS and NAC). Cells were also stimulated with 10μM of Menadione for 2 or 6h
instead of LPS (one condition: Men).
Reverse transcriptase (RT)
Because qPCR uses cDNA as a template, it needs a reverse transcriptase step to
reverse RNA into cDNA (Figure 2.4).
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RNA was extracted as explained previously (2.6. RNA extraction) and was submitted
to reverse transcription into complementary DNA following a two-step protocol. First,
500ng of RNA was incubated for 10min at 65°C with random primers and
deoxynucleoside triphosphate (dNTPs), to dissociate any secondary structures of
RNA and facilitate the RT. Rapidly after this step, reverse transcriptase (RT) buffer,
DTT, RNAse OUT, a ribonuclease inhibitor, and MMLV-RT, a reverse-transcriptase,
were added and samples were incubated at 25°C for 10min, 37°C for 60min and 70°C
for 15min.
qPCR
Two µl of the new cDNA formed was then mixed with 18μl of PCR mix and the mixture
obtained was assayed in duplicate in 96 well plate. The PCR mix contained the
primers of gene-of-interest, the Taqman probe, plus the Brilliant III qPCR master mix
containing all reagents required for PCR amplification: Taq DNA polymerase, dNTPs,
and buffer. Basically, primers recognise the DNA to amplify, the Taqman probe fixed
it and every time the DNA is polymerised the quencher attached to the probe is
released and the fluorescence can be detected (Figure 2.4).
PCR reactions were run on the Stratagene MX3000 PCR machine following this heat-
cool cycle:
1 cycle: 3 min at 95C where Taq polymerase was activated
40 cycles: 15 sec at 95C to denature the DNA + 20 sec at 60C to extend it
For each sample, the housekeeping gene (HPRT1) was measured as a reference
allowing the normalisation of the expression of all genes measured.
Data analysis
The results were then analysed by the Livak method (also referred as the delta delta
CT method) (Livak and Schmittgen 2001). To proceed, the Ct from the housekeeping
gene was subtracted from the Ct of the gene of interest for each sample (= ΔCt). The
ratio of each ΔCt was then expressed versus one of the control samples at 2 or 6 h
chosen as the calibrator. This new value ΔΔCt was log-normalised as 2-ΔΔCt obtaining
the fold change (FC) in gene expression versus the calibrator.
Statistical significance between these samples was determined using the unpaired
two-tailed Student’s t-test.
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Figure 2.4: Reverse Transcriptase followed by qPCR. After reverse transcriptase
of the mRNA, the cDNA formed is mixed with qPCR mix. The fluorophore present in
Taqman probe is cleaved due to displacement due to polymerisation of the cDNA.
Once cleaved, the fluorophore emits fluorescence which is detected by the
Stratagene MX3000 PCR machine.
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Figure 2.5: Example of amplification plot obtained by qPCR analysis. The same
cDNA require different cycles of PCR to reach the background level (Ct) depending
on the amount of mRNA of the gene studied and conditions analysed (experimental
group).
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2.12 Cell viability: MTT assay
Assessment of the cell viability and proliferation of cells under different conditions was
performed by an improved rapid colorimetric assay, the Methyl-Thiazolyl-Tetrazolium
(MTT) assay, developed in 1983 by Mosmann (Mosmann 1983) (Stockert, Blazquez-
Castro et al. 2012). This assay is based on the reduction of yellow tetrazolium salts
(3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide) into purple formazan
crystals by the metabolism of living cells using the dehydrogenase enzymes and
NADH as a cofactor. Formazan production is therefore proportional to the number of
living cells and more exactly, to their metabolism. This insoluble product is then
solubilised in a stop solution made usually of SDS and read by the plate reader at 590
wavelength.
In our studies, RAW Cells were plated in 6-well plates at 1x106 cell per ml of RPMI
media. After overnight incubation, cells were treated with different concentrations of
LPS in quadruplicates: untreated, 50ng, 100ng and 200ng per ml of Opti-MEM. 24h
later, supernatant was removed and cells were washed carefully with PBS. Once PBS
was removed, 1ml of MTT buffer diluted at 1:10 in Opti-MEM was added to wells.
Plates were then left in the incubator overnight allowing formation of Formazan
crystals by living cells. The next day, 1ml of MTT stop solution was added to the wells.
Plates were left in incubator for at least 4h to let the formazan solubilise and the
absorbance of the violet solution formed was read at 590nm.
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2.13 Protein alkylation with Maleimide-PEG
MalPEG alkylation was performed to identify the redox state of proteins among which
Prx1, Prx2 and Trx from different sources including RAW cells, HEK cells and blood.
This part will introduce how samples were alkylated with MalPEG before further
analysis by SDS-PAGE and Western blot however the MalPEG methodology is
explained in Chapter 3. In these experiments, different concentrations of MalPEG
were tested, ranging from 0.1 to 0.8mM. NEM (50mM) was used as a control as it is
also an alkylating agent and can prevent further oxidation of protein. However,
because NEM has a negligible molecular weight compared to the protein, it would not
affect the electrophoretic mobility significantly.
RAW cells
Protein redox states were assessed intracellularly and in the release from RAW cells.
Cell lysis
Cells were plated at 1x106 in 2.5ml of RPMI (supplemented with 10%FCS and
1%P/S). After overnight incubation, media was removed and replaced with 1ml of
Opti-MEM containing different concentrations of LPS. 24h later, cells were lysed using
one if these two buffers: the NP40 lysis buffer or the standard SDS-PAGE sample
buffer.
NP40 Lysis Buffer
After treatment, cells were detached using a cell scraper and collected in 1ml of PBS.
The suspension was centrifuged at 7,000 x g for 5min. After removing the
supernatant, the pellet of cells was mixed with 300μl of NP-40 lysis buffer containing
MalPEG or NEM. NP-40 is a strong detergent breaking cell membranes. Samples
were then left for 15min in the bench at RT to allow the alkylation of free thiols before
centrifugation for 10min x at 4˚C at 17,000 x g to remove cell debris. Supernatant
containing the MalPEG- tagged proteins were collected and processed for Western
Blot analysis.
SDS-PAGE Buffer
In some experiments, cells were lysed directly in the well by adding 300μl SDS-PAGE
sample buffer 1.5X containing MalPEG or NEM. Samples were heated before
processing for Western blot.
Collection of proteins in the supernatant from RAW cells: precipitation with cold
acetone
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The supernatant of RAW cells (approximately 1ml) was collected 24h after treatment
and spun at 7,000 x g for 5min to remove any cells. Then, the supernatant was
incubated with 0.2-0.5mM MalPEG or 50mM NEM for 15min at RT. 500μl of
supernatants were then transferred to 1000μl of cold acetone (ratio 2:3) and vortexed
thoroughly for 30 seconds before incubation at -20˚C overnight. The next day,
samples were centrifuged at 17,000 x g for 10min at 4˚C. Supernatant was removed
leaving a protein pellet in the tubes. To make sure acetone was removed, tubes were
left open for 30min to let any remaining acetone evaporate. The protein pellet was
then mixed with 1.5X SDS-PAGE sample buffer and directly submitted to
electrophoresis.
HEK cells
HEK cells were plated at 1x106 cells in 2.5ml of DMEM (supplemented with 10%FCS
and 1%P/S) and incubated overnight. The following day, 50ng TNF or 25μM
Menadione were added to 1ml fresh medium in the wells. After 24h, the cells were
lysed in NP-40 lysis buffer containing MalPEG or NEM as described above for RAW
cells.
Rat blood
Around 2ml of rat blood was collected in Eppendorf tubes containing 10% of fresh
EDTA. Blood was centrifuged at 300 x g for 10min, the supernatants (serum) obtained
was transferred to new tubes and washed again to remove any red blood cells left by
a quick spin at 7000 x g for 5min. 25μl of the serum was then diluted at 1:10 in distilled
water and tagged either with NEM or MalPEG before analysis by Western blot.
Plasma and red blood cells extraction from human blood
Immediately after collection, blood was centrifuged at 1,000 x g for 10min (brake 4).
The plasma fraction was separated from the red blood cell (RBC) pellet.
Plasma: Supernatants were transferred to 1.5ml Eppendorf tubes and
centrifuge again to clear any RBC by a centrifugation of 17,000 x g for 10min
at 4˚C. The plasma was then transferred to 0.5ml Eppendorf tubes and tagged
with MalPEG or NEM for 15min at RT before Western blot analysis.
RBC: The pellet containing RBC was again subjected to centrifugation to
remove any trace of plasma. Half ml were recovered and lysed with 9.5ml
(dilution at 1:10) of cold distilled water incubated on ice for 5min. After gently
91
mixing, supernatant was centrifuged at 300 x g for 10min at 4˚C and mixed
with NEM and MalPEG before further analysis by Western blot.
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2.14 SDS-PAGE and Western blot to detect the redox states of proteins from
cell lysates and blood
The different redox state of proteins were discriminated according to their molecular
weight by Sodium Dodecyl Sulphate - Polyacrylamide Gel Electrophoresis (SDS-
PAGE) and then identified by Western-blotting (Towbin, Staehelin et al. 1979,
Brunelle and Green 2014). Thanks to the anionic detergent property of SDS, the
protein within the sample loses its secondary and tertiary structures becoming linear
but also gaining a uniform negative charge. Once loaded, a vertical electric current is
applied attracting negative charge to the anode part at the bottom of the cassette
(Figure 2.6) therefore allowing protein migration within the polyacrylamide gel. After
vertical separation, proteins were identified by Western blot. Essentially, the migrated
proteins in the gel were transferred, by applying a horizontal current, to a nitrocellulose
membrane with small pores retaining proteins but not RNA and small molecules. The
proteins were identified using an antibody specific of epitopes of the protein which
was itself identified by a second antibody. This second antibody can be visualised by
autoradiography.
All steps are explained below and illustrated in Figure 2.6.
1. Samples preparation
Different samples were analysed: RAW cell lysates, RAW supernatants, HEK cells
and blood samples (including RBC lysate, plasma or serum from mice or humans).
50μl of this samples were then mixed with 10μl of SDS-PAGE sample buffer 6X.
Depending on the protein studied, when indicated this mixture was supplemented with
reductant such as 10% β-mercaptoethanol or 50mM of DTT.
Eight to 15μl of cell lysate, 10 to 25μl of the mixed supernatant, or 10μl of blood
samples were then subjected to separation on polyacrylamide gels. Samples were
heated 5 min below 65˚C.
2. Preparation of acrylamide gels
Polyacrylamide gels were made in two parts: a stacking gel and a running part gel.
The stacking part of the gel (upper) has a low percentage of acrylamide to let the
sample stack, forming a band, before to enter the running part. It was made with
1.25ml of stacking gel buffer, 50μl of 10%SDS, 75μl of 10% APS, 5μl TEMED, 2.86
ml of distilled water and 835μl of acrylamide/Bis solution.
The running part of the gel (lower) was made with 2.5ml of running gel buffer, 100μl
of 10% SDS, 100μl of 10% APS, and 10μl of TEMED. The volume of acrylamide/Bis
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solution added was dependent of the molecular weight of the protein of interest. In my
work I used either 10% (for Prx1, Prx2) or 15% acrylamide (Trx). To do this, 3.3ml or
5ml of the solution were used. Distilled water was added to complete to 10ml.
The running gel was let polymerised before adding the stacking gel (Figure 2.6).
3. Electrophoresis
Once it is polymerised, the gel was loaded with the samples. The first lane was loaded
with 10μl of molecular weight markers. The gel was then immersed in running buffer
and subjected to electrophoresis at 150V for approximatively 1 to 1.5 hour.
4. Transfer to nitrocellulose membrane
After electrophoresis, the proteins contained in the gel were transferred to a
nitrocellulose membrane. The membrane and the gel formed a sandwich surrounded
by filter paper and sponges in a cassette surrounded by transfer buffer 1X. 400mA
were then applied for 90min.
5. Blocking of non-specific sites and antibody incubation
All following experiments were performed on a rocking shaker. The membrane
obtained was blocked for one hour with 5% BSA in 0.05% tween in PBS-0.05% tween
(PBS-T0.05%) (or 5% Milk in PBS-T0.05%) depending on the antibody requirement
at RT.
A primary antibody (AB1) was then directly added to the membrane blocking buffer
(unless change of buffer was required). This antibody is specific to the protein studied
and ideally polyclonal to recognise different epitopes of the protein. The list of
antibodies used is presented in Table 2.4. The antibody was left overnight at 4˚C but
similar results were obtained by leaving the membrane incubating in primary antibody
for 2h at RT. After three 10min washes with 10ml of PBS-T0.05%, the second
antibody (AB2) carrying a Horseradish Peroxidase (HRP) molecule was added to
10ml in 5%BSA in PBS-T0.05%. This second incubation last 1-2h at RT.
6. Autoradiography
After washing the membrane 3 times with PBS-T0.05% as described above,
Enhanced ChemiLuminescence (ECL) reagent made of 1:1 vol/vol luminol and
hydrogen peroxide (H2O2) was added to the membrane and incubated for 5min.
Luminol is excited by HRP using H2O2 as a substrate and produce light.
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In the dark room, the membrane is placed in an X-ray film cassette and covered with
an autoradiography film for as long as necessary. This film catch the light emit by the
HRP and therefore indirectly the presence of the protein targeted.
7. Densitometry
Densitometric analysis to quantitate the intensity of the bands in the autoradiography
was performed using Image Studio Lite (see 2.5 Software and analytic tools).
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Figure 2.6: Experimental design of SDS-PAGE followed by Western Blot.
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2.15 DTNB assay to measure total free thiols
The total number of free thiols in a sample (released in cell culture medium or cell
lysate) was measured using the DTNB assay.
DTNB (5, 5’-dithiobis-2-nitrobenzoic acid, Ellman’s reagent) is an aromatic disulphide
which reacts rapidly with thiols by an exchange reaction to form a mixed disulphide of
the protein and one mole of TNB (5-thio-2-nitrobenzoicacid) by mole of thiol (Ellman
1959). In diluted buffer, TNB ionizes as a TNB2- ion which has a yellow colour and
therefore can be quantified by measuring the absorbance of visible light at 412 nm in
a spectrophotometer (Figure 2.7).
For each experiment, a standard N-acetyl cysteine (NAC) concentration curve was
prepared in triplicate. For this purpose, a 2mM solution was prepared in dilution buffer
TE. The method is based on a published protocol (Sedlak and Lindsay 1968).
In total, each well of a 96-well plate contained 100μl of sample or NAC standard. 25μl
of DTNB was then added and read in a spectrophotometric plate reader.
Figure 2.7: Reaction of DTNB with free thiols. Modified from Riddles et al. (1978)
(PETER W. RIDDLES 1978)
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2.16 Determination of protein concentration.
The protein concentration was determined using the kit BCA (bicinchoninic acid)
protein assay from Thermo Fisher. Briefly, 25μl of samples were added to a 96 well
microplate. 200μl of prepared BCA working reagent, an alkaline solution, were then
added and the plate was incubated at 37˚C for 30min. Proteins reduce the copper in
the reagent giving a purple colour, thanks to the bicinchoninic acid, which is read at
562nm. The concentration is then determined using to a standard curve made of BSA
diluted in a range of 0 to 2mg/ml.
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2.17 Identification of free thiols at the surface of RAW cell
Cell treatment
RAW cells were plated in 6 well plate at 1x106 in 2.5ml of RPMI. After overnight
incubation, cells were treated with 100ng of LPS for 24h in 1ml of Opti-MEM.
BIAM labelling
Before incubation with BIAM (N-(Biotinoyl)-N'-(Iodoacetyl) Ethylenediamine), cells
were detached using a cell scraper, collected and washed with 1ml of PBS by
centrifugation of 7000 x g for 5min. The cell pellet was then resuspended in 1ml of
PBS containing different concentrations of BIAM and incubated at 37˚C for 15min.
BIAM was then removed by centrifugation and cells were re-washed with PBS by
centrifugation of 7000 x g for 5min (Figure 2.8).
Extraction of membrane proteins
To extract membrane proteins, the cell pellet was recovered in 1ml homogenisation
buffer containing sucrose and Hepes and broken by sonication (2 x 15sec) in ice. For
mass spectrometry (MS) experiments, cells from two tubes treated identically were
pooled together to obtain a final volume of 2ml.
Samples were then washed to remove cellular debris by centrifugation of 7,000 x g
for 10min at 4˚C. The supernatant containing membrane proteins was transferred to
glass-microfuge tube and submitted to ultracentrifugation of 108,382 x g at 4˚C for
30min. The hydrophobic pellet was then washed one or two times as indicated in the
results section with 1m of PBS for 20min at 108,382 x g at 4˚C.
The membrane protein pellet obtained was analysed by SDS-PAGE/Western blot or
processed for MS to identify labelled proteins.
Western blot
The pellet containing membrane proteins was resuspended in 100μl SDS-PAGE
sample buffer 2X, heated at 95˚C for 5 min. 10μl were then loaded to a 12% or 15%
SDS-PAGE and proteins of interest detected by Western-Blot. Primary antibodies are
listed in table 2.4.
Mass spectrometric identification of BIAM-tagged proteins (Figure 2.8)
In these experiments, MS was used to identify BIAM-labelled membrane proteins.
This technique is based on ionising the proteins to convert them into gas-phase ion
allowing their separation due to their masse-to-charge ratio (m/z) by a mass analyser
and recorded by a detector to be analysed (Han, Aslanian et al. 2008).
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Two difficulties were found. First, membrane proteins are hydrophobic and tend to
aggregate thus requiring a specific buffer prior to MS analysis, different from that used
for soluble protein, more often used. Secondly, proteins tagged with BIAM were in a
sample which contained several other proteins, and their separation was challenging.
For these reason, a protocol developed by Blonder et al., for membrane analysis by
MS was used and adjusted (Blonder, Chan et al. 2006); it consists of 3 steps before
MS analysis: reduction/alkylation, solubilisation, and affinity purification.
1. Reduction and Alkylation
Membrane pellet was dispersed by sonication in 50mM ammonium bicarbonate at
pH7.9 (approx. 500μl /tube) directly in the microfuge tube obtained from
ultracentrifugation. Proteins were reduced with 2mM of Tris 2-carboxyethylphosphine
(TCEP) with sonication 1min bath -2min pause in a 5 times cycle. Alkylation was then
performed by adding 10mM of 2-Iodoacetamide (IAA), sonicated in bath and kept in
the dark for 15min at 37C. Samples were then ultracentrifuged at 108,382 x g for 1h
at 4˚C, after saving a small volume for the determination of protein concentration.
2. Solubilisation and denaturation
The reduced and alkylated pellet was then solubilised in a 60% vol/vol of methanol in
40%vol/vol of 50mM ammonium bicarbonate to have a protein concentration around
0.5-1μg/μl. Solubilisation was performed by bath sonication at room temperature for
1min (2min pause in ice for 5 times cycle).
Proteins were then denatured by incubating the tubes in a water bath at 90˚C for 1min
and transferred directly to ice-cold water.
3. Affinity purification and in-column digestion
After denaturation, 250μl of Pierce™ High Capacity NeutrAvidin™ Agarose beads,
previously washed with PBS, were added to the tubes and incubated overnight at 4˚C
in rotary wheel for constant agitation.
The beads where then washed three times by centrifugation. At this point, only BIAM-
tagged proteins should bind the beads.
Solubilising buffer was added along with 0.1μg/μl trypsin for tryptic digestion for 5h at
37˚C with frequent agitation (every 15min).
The supernatants was then recovered and lyophilised.
100
4. qExactivePlus (MS performed by Philippe Chan – Universite de Rouen)
Dried samples were solubilized in 100 μl of 0.1% formic acid (v/v) in 3% acetonitrile.
Samples (1 μl) were injected into the nanoUPLC-system (UtiMate 3000 RSLCnano,
Thermo Fisher Scientific) and analyzed by MS using a binary buffer system of A (0.1%
(v/v) formic acid in H20) and B (0.1% (v/v) formic acid in 80% acetonitrile). Peptides
were first concentrated on a 2 cm x75 μm C18 Pepmap100 pre-column (Thermo
Fisher Scientific) at a flow rate of 5 μl/min using mobile phase A. Separation of the
peptides was achieved by eluting them from the precolumn to a 15 cm × 75 μm Easy
spray Pepmap100 analytical column (Thermo Fisher Scientific) applying a flow rate
of 300 nl/min and using a gradient of 2% to 45% mobile phase B over 45 min. The
mass spectrometric analysis was performed on a QExactive orbitrap mass
spectrometer, equipped with an easy spray nano ion source (Thermo Fisher
Scientific), coupled to the nanoUPLC-system.
MS spectra were acquired using 1e5 as AGC target at a resolution of 70,000 (200
m/z) in a mass range of 400−1800 m/z. A maximum injection time of 100 ms was used
for ion accumulation. MS/MS events were measured in a data-dependent mode for
the 10 most abundant peaks (Top10 method) in the high mass accuracy Orbitrap after
HCD (Higher energy C-Trap Dissociation) fragmentation at 28. The resolution was set
to 17 500 at 200 m/z.
5. MS Data analysis
Proteins were first selected based on a false discovery rate (FDR) < 1 (=-
10logP>19.81). FDR corresponds to the ratio of the false peptide-spectrum match
(PSM) among the true number of PSM; above the score threshold.
101
Figure 2.8: Experimental design of the strategy for the identification of free protein
thiols at the surface of RAW cells before MS analysis.
102
Chapter 3
Regulatory role of glutathione in LPS-stimulated
macrophages
103
3.1 Introduction
The primary role of GSH is to decrease the amount of hydrogen peroxide and other
peroxide molecules. This antioxidant properties of GSH has been particularly
highlighted in the context of inflammation. Indeed, Herzenberg et al. have
demonstrated that N-acetylcysteine (NAC), a GSH precursor, could inhibit NF-κB
(Staal, Roederer et al. 1990), a ubiquitous transcription factor which up-regulates the
expression of pro-inflammatory cytokines such as IL-6, chemokines and immune
receptors (Liu 2005, Hayden and Ghosh 2008). This study was supported by the work
of Baeuerle et al. demonstrating that H2O2 could, by itself, induce the inflammatory
response through NF-κB, suggesting that thiol antioxidants can down-regulate the
inflammatory response by removing excess H2O2 (Schreck, Rieber et al. 1991). All
together these investigations led to the view of ROS as pro-inflammatory signalling
molecules and GSH as an anti-inflammatory mediator. Since then, a succession of
studies have reinforced this hypothesis leading to perform clinical trials by giving
antioxidants to patients with pathologies linked with oxidative stress (Meyer, Buhl et
al. 1994, Blackwell, Blackwell et al. 1996). However, most of the therapies were
unsuccessful as described in Table 1.2: Examples of clinical trials testing the
effect of NAC/GSH.
One possible reason for this is that exogenously administered NAC or GSH are given
at doses higher than the endogenous GSH concentration. This would be effective only
if ROS were produced in excess in those diseases and only caused oxidative damage
or were in any form just harmful. However, as discussed above, ROS also have a role
in the normal signalling mechanism of the cell and their elimination with an antioxidant
would disable a signalling mechanism by which the organism “senses” and responds
to a disease condition. In the context of redox regulation, also the GSH/GSSG couple,
and the change in its ration, could be important in signalling, by relaying signalling
though oxidoreduction of various protein thiols.
Importantly, GSH can regulate protein function via glutathionylation, a post-
translational modification involving thiol-disulphide exchange reactions (Ghezzi
2013). A previous study investigated the regulatory role of GSH (beyond its
antioxidant role) by exposing GSH-depleted monocytes with H2O2 (Fratelli, Goodwin
et al. 2005). As expected, that study observed that genes related with the stress
response were even more up-regulated by GSH-depletion when stimulated by H2O2
supporting the idea that GSH is an antioxidant helping to diminish the stress response
induced by H2O2. However that study also highlighted even more numerous group of
genes, some involved in the immune response, up- or down-regulated directly
104
affected by the absence of GSH but not by H2O2 alone suggesting that GSH was an
inhibitor or activator of their expression. It was then hypothesised that GSH acts as a
signalling molecule that regulates gene expression. In fact, some of the cell responses
are mediated by the GSH/GSSG couple, for instance, through glutathionylation of
regulatory proteins.
In this chapter, the role of endogenous GSH was investigated during the inflammatory
response to identify potential inflammatory genes regulated by GSH. We wanted to
test the hypothesis that some of the changes in gene expression profile induced by
inflammation require glutathione as a signalling molecule. We thought that one way
to do it was to deplete cells from GSH and observe their response to LPS. If induction
of inflammatory and stress genes by LPS was due to LPS induced oxidative stress,
then GSH depletion would amplify their induction and exacerbate the inflammatory
response. However, if some genes were induced by LPS by a signalling cascade
requiring the participation of GSH/GSSG (either by glutathionylation, thiol-disulfide
exchange or other reactions), then we would observe that some of the genes would
not be induced by LPS in the absence of GSH. On the other hand, in those genes that
would be induced by LPS though mechanisms not requiring GSH or redox regulation,
their induction would be unaffected by GSH depletion.
For this purpose, RAW cells were stimulated with the endotoxin LPS with and without
pre-treatment with the GSH synthesis inhibitor BSO (Griffith and Meister 1979). After
RNA extraction, DNA microarrays were used to analyse changes in the gene
expression profile and identify patterns of LPS-induced genes affected by BSO and
thus reliant on GSH or inhibited by it.
Aim: Study of the role of endogenous GSH in the regulation of the changes in
gene expression profile associated with the inflammatory response in
macrophages.
105
3.2 Viability and GSH/GSSG determination
RAW cells were exposed to 120μM BSO for 24h before stimulation with 10ng/ml of
LPS for 2h or 6h. Dose-response experiments carried out in the laboratory by
Dr.Coppo demonstrated that 120μM BSO was sufficient to inhibit γ-GCS and thus
GSH.
Toxicity of BSO and LPS treatment was assessed by Dr. Mengozzi (Brighton &
Sussex Medical School) and is shown in Figure 3.1A. BSO at 120 μM, alone or with
LPS, was not toxic to the cells as detected by Cell-Titer-Blue (viability was: control,
100±1%; BSO, 98±2%; BSO+LPS, 95±1%).
Depletion of GSH and GSSG was measured thanks to a collaboration with Dr. Coppo
(Karolinska Institutet). As shown in Figure 3.1B, after 24h, 120μM BSO pre-
treatment, GSH and GSSG levels were decreased by 99%, whether or not LPS was
present. LPS itself did not significantly affect GSH+GSSG levels.
106
Figure 3.1: Viability and GSH/GSSG determination. A. RAW cell viability after
exposure to BSO and LPS measured by Cell-Titer-Blue by Dr. Mengozzi. Data are
expressed as the % percentage of viability of control cells. B. Total glutathione level
(GSH+GSSG) determination (hashed bars) and ratio GSH/GSSG (black bars)
measured by Dr. Coppo.
0
20
40
60
80
100
no LPS LPS 10ng/ml
% v
iab
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s c
on
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BSO 120 µM
0
20
40
60
80
100
120
140
0
1
2
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4
5
GS
H/G
SS
G (
rati
o)
To
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+G
SS
G (
nm
ol/
mg
pro
tein
)
GSH+GSSG GSH/GSSG
A
B
*** ***
*
*
107
3.3 ROS measured by Electron Paramagnetic Resonance
ROS are produced by macrophage to kill pathogens (Forman and Torres 2001).
However an excess of ROS production can create an imbalance in cellular redox
status, leading to intracellular damage increasing the inflammatory response (Valko,
Leibfritz et al. 2007). In our study, GSH, the main intracellular antioxidant, was
depleted from RAW cells thus leading to a potentially oxidative environment. To
measure if LPS stimulation leads to an increase in ROS and if BSO has an impact on
the oxidative burst, superoxide (O2.-) and hydroxyl radical (HO.), the two main ROS
produced, were quantified (Forman and Torres 2001). Due to their high reactivity and
instability, their quantification is difficult and is usually performed indirectly (Tarpey
and Fridovich 2001, Chen and Junger 2012). In our study, quantification of ROS in
live cells was performed using the state of the art technology based on spin trapping
coupled with Electron Paramagnetic Resonance (EPR) (Abbas, Hardy et al. 2014,
Abbas, Babic et al. 2016). This method is described Chapter 2 – 2.8. RAW cells were
pre-treated or not with BSO for 24h and then stimulated with LPS for 2h before being
directly mixed, alive, with BMPO(5-tert-butoxycarbonyl 5-methyl-1-pyrroline N-oxide)
for EPR analysis. BMPO reacts with O2.- forming a superoxide adduct, BMPO-OOH,
or with HO. forming the hydroxyl adduct, BMPO-OH. Both spin adducts are
characterised by distinct spectra comprised of 4 lines. All spectra obtained are shown
Figure 3.2A. The area under the spectra was also measured and is shown in Figure
3.2B. The spectra obtained for LPS and BSO+LPS were characteristic of the hydroxyl
radical adduct BMPO-OH. However, conversion of superoxide to hydroxyl radical
adduct in cells due to thiol molecules is well known (Beziere, Frapart et al. 2010).
Therefore to discriminate between these two species in our study, samples were
treated with Superoxide Dismutase (SOD). SOD catalyses the dismutation of O2.- into
molecular oxygen (O2) and H2O
2 but cannot enter the plasma membrane, hence it
can only inhibit extracellular O2.-. Because the signal obtained with either LPS or
BSO+LPS dropped with SOD it can be concluded that most of the signal observed
was mainly due to extracellular O2.- released by RAW cells. The signal obtained was
similar for the control and BSO treated cells meaning that BSO did not increase a
superoxide environment. Similarly, treatment with BSO+LPS did not result in a
change of signal above that obtained for LPS alone suggesting that BSO did not affect
the oxidative release with LPS stimulation. These results demonstrate the release of
radicals following LPS stimulation confirming an oxidative burst. However depleting
cells of their main antioxidant (GSH) did not affect the amount of ROS
(superoxide/hydroxyl radical) nor the oxidative burst response.
108
Figure 3.2: ROS are released from RAW cells after 2h LPS stimulation. A. EPR
spectra obtained as the sum of 40 scans after LPS stimulation with or without BSO treatment.
Each experimental condition was repeated at least 4 times and individual experimental are
shown in shade of grey. Black lines corresponds to the average of individual replicate of each
condition. Red line correspond to the expected spectra for hydroxyl adduct BMPO (n=40)
obtained by computer simulation. B. Quantification of the area under spectra of EPR signal. *
P value < 0.05 versus control by paired two-tailed Student’s t-test (n=4).
109
3.4 Microarray analysis: effect of LPS and BSO alone on gene expression in
RAW cells
A microarray analysis was performed using total RNA isolated from RAW cells that
had been pre-treated with BSO for 24h, then stimulated for 2h or 6h with LPS, as
described above. In fact, pro-inflammatory genes are expressed at early time points
(few hours) following endotoxin stimulation according to Medzhitov and Horng
(Medzhitov and Horng 2009).The individual impact of LPS and BSO on the gene
expression of RAW cells was examined by comparing experimental groups to the
untreated group (control) using an unpaired Student’s t test and a Fold Change cut
off at 1.5.
Transcripts affected by LPS at 2h and 6h represent less than 15% of the total data
set whereas the total of transcripts affected by BSO alone represent less than 2% as
shown in Figure 3.3. Interestingly, BSO and LPS modified a similar number of
transcripts at 2h and 6h.
110
Figure 3.3: Number of transcripts affected either by BSO or by LPS in RAW cells
after 2h and 6h LPS stimulation. Experimental groups were compared using an
unpaired two-tailed Student’s t-test with a cut off threshold of P value < 0.05 and a
Fold Change>1.5 or <-1.5.
111
3.4.1 Effect of LPS on gene expression profiles at 2h and 6h
Due to the similar number of transcripts regulated by LPS at 2h and 6h (4823 versus
5019) we considered whether those transcripts were identical. The list of transcripts
modified by LPS at 2h was compared to the list obtained at 6h and the results are
illustrated by Venn diagrams. As shown in Figure 3.4, almost half of the transcripts
affected at 2 and 6h were identical.
We then asked if these transcripts had a different expression pattern at 2 and 6h. The
FC of each triplicate modified by LPS versus the average of the gProcessed signal of
the controls was obtained and submitted to Genesis v1.7.7. Results are expressed as
a GeneTree view as shown Figure 3.5. Only 148 of the 2250 transcripts identical
between 2 and 6h were expressed differentially over the time course (lines in red and
green). Remaining transcripts were expressed similarly at 2 and 6h. Therefore most
of the identical transcripts affected by LPS at 2 and 6h were either up-or down-
regulated independently of the time point.
112
Figure 3.4: Venn diagram of the number of transcripts affected by LPS at 2h
(grey) and 6h (red).
Figure 3.5: Expression view after HCL of transcripts affected at both 2 and 6h
after LPS stimulation obtained by Genesis v1.7.7. A, B, C represents the
triplicates. Red lines represent transcripts up-regulated at 6h and green lines
represent transcripts down-regulated at 6h. Grey lines represent transcripts with
similar expression at 2 and 6h either up-regulated or down regulated.
113
3.4.2 Functional analysis of the genes whose expression is changed by LPS
The biological role of the list of the 2250 transcripts affected by LPS at both 2h and
6h was then analysed via the functional annotation and term enrichment software
‘DAVID’ v6.8 (Huang da, Sherman et al. 2009). The 15 most overrepresented
categories obtained are shown in Figure 3.6. As observed all functional categories
obtained were related to the host defence system in response to LPS. For instance,
“TNF signalling pathways”, “NF-κB signalling pathways” and “immune system
process” were among the most overrepresented functional categories shared by the
genes affected at 2h and 6h of LPS stimulation.
Each group affected by LPS was also analysed separately at 2h and 6h by DAVID as
shown in Figure 3.7. These groups include:
- Up-regulated at 2h: 2327 transcripts
- Down-regulated at 2h: 2496 transcripts
- Up-regulated at 6h: 2499 transcripts
- Down-regulated at 6h: 2520 transcripts
Only the first ten biological categories are shown and were ordered by their p-value
(EASE score). Genes down-regulated by LPS are shown in the left panel while those
up-regulated by LPS are in the right panel. The list of the first 100 genes more up- or
down-regulated by LPS at 2h and 6h are shown Appendix 1-4.
Unsurprisingly, the genes affected by LPS after 2h and 6h of stimulation belonged to
similar biological categories.
The down-regulated categories at 2h were associated mainly with the transcription
process, and at 6h with cell division, mitosis, and DNA replication. Therefore, LPS
seems to decrease the growth rate of RAW cells. No genes linked with apoptosis or
necrosis were observed.
As expected, at both 2h and 6h, LPS activated immune response and defence system
genes. Two signalling pathways in particular were up-regulated by LPS stimulation,
TNF and NF-κB signalling pathways.
114
Figure 3.6: 15 most highly represented functional categories of genes affected
by LPS at both 2h and 6h in RAW cells. Analyses were performed via the Functional
Chart Annotation analytic module using the GOTERM-biological process (grey bars)
and KEGG pathway (black bars) classification in DAVID. Only the 15 most
significantly enriched biological categories are shown here. The number of genes
obtained per category is shown in brackets.
Immune system process (90)
Response to virus (37)
Innate immune response (85)
Defense response to virus (46)
Inflammatory response (70)
Apoptotic process (97)
Negative regulation of viral genome replication (18)
Lipopolysaccharide-mediated signaling pathway (17)
Response to lipopolysaccharide (45)
NF-kB signaling pathway (39)
TNF signaling pathway (38)
Influenza a (48)
Herpes simplex infection (50)
Measles (38)
0 5 10 15 20-log10(P value)
KEGG pathway GO TERM_Biological Process
115
Figure 3.7: Ten most highly represented functional categories obtained by
Functional Chart analysis in DAVID depending on their up-regulation with LPS
(right panel) or down-regulation (left panel) respectively after 2h (A) and 6h (B)
stimulation. Analyses were performed via the Functional Chart Annotation analytic
module using the GOTERM-biological process (grey bars) and KEGG pathway (black
bars) classification in DAVID. Only the ten more significantly enriched biological
categories are shown here. The number of genes obtained per category is shown in
brackets.
116
3.4.3 Functional analysis of the genes affected by BSO
The process outlined in 3.3.3.2 was repeated for the genes whose expression was
affected by BSO; however only 77 transcripts were affected by BSO at both 2 and 6h
as shown in the Venn diagram in Figure 3.8.
Due to the small number of transcripts affected by BSO (less than 2%), the data
collected at the two time points 2h and 6h were combined according to their differential
expression by BSO (up- or down-regulated by BSO) before being submitted to DAVID.
The data are shown in Figure 3.9. Unsurprisingly, categories up-regulated by BSO
are involved in drug metabolism and GSH metabolism suggesting that the cell detects
the lack of GSH and stimulates the expression of genes involved in GSH synthesis.
Biological categories down-regulated with BSO are diverse and include genes related
to chronic inflammation (inflammatory bowel disease, lens fibre cell differentiation,
intestinal immune network), calcium import, or transcription for instance.
117
Figure 3.8: Venn diagram of the number of transcripts affected by BSO at 2h
(grey) and 6h (red) LPS stimulation.
Figure 3.9: Ten most highly represented functional categories obtained by
Functional Chart analysis in DAVID depending on their up-regulation with BSO
(right panel) or down-regulation (left panel). Analyses were done via the Functional
Chart Annotation analytic module using the GOTERM-biological process (grey bars)
and KEGG pathway (black bars) classification in DAVID. Only the ten more
significantly enriched biological categories are shown here. The number of genes
obtained per category is shown in brackets.
118
3.5 Microarray analysis: effect of GSH depletion during LPS stimulation on
the gene expression profile of RAW cells
3.5.1 Selection of differentially expressed transcripts
The effect of GSH depletion on the expression of genes affected by LPS was analysed
in two steps. First, the LPS experimental group was compared to the control group to
obtain the list of transcripts affected by LPS; then, the transcripts also affected by
BSO were filtered via comparison of the BSO+LPS experimental group versus the
LPS group. As shown in the flow chart in Figure 3.10, 142 transcripts out of 4823
affected by LPS at 2h and 196 out of 5019 affected by LPS at 6h were affected, 0.42%
and 0.58%, of the total transcriptome, respectively. Therefore the effect of BSO on
genes affected by LPS had a minor impact on the expression of the genes.
A hierarchical cluster analysis was done with Genesis to cluster the genes whose LPS
regulation was significantly affected by BSO at 2h (Figure 3.11) and 6h after LPS
stimulation (Figure 3.13). As observed in both heat maps, four clusters were
identified:
Cluster 1: up-regulated by LPS and increased further by BSO
Cluster 2: up-regulated by LPS and decreased by BSO
Cluster 3: down-regulated by LPS and increased by BSO
Cluster 4: down-regulated by LPS and decreased further by BSO
Once a cluster was selected, its attributes were further examined using the GeneTree
expression view mode in Genesis as shown in Figure 3.12 for 2h and Figure 3.14 for
6h to confirm that the cluster was appropriate. The expression view of each clustering
can be found in the Appendix 5-6. As some genes were left out of the clusters, for
instance interleukin 1 beta (IL-1β) was not clustered even though its expression was
induced by LPS but inhibited by BSO and would have been missed by the hierarchical
cluster analysis, we decided, as an additional strategy, to obtain the clusters by
filtering similarly to the first step. Filtering was performed as in Figure 3.10 but in
addition the experimental group BSO+LPS was compared to the LPS group. The flow
chart illustrating this filtering strategy is shown in Figure 3.15. The different clusters
obtained by Genesis after manual addition of some genes and the groups obtained
statistically by cut off were identical. We choose to name them:
Group 1 (G1): up-regulated by LPS and increased further by BSO
Group 2 (G2): up-regulated by LPS and decreased by BSO
Group 3 (G3): down-regulated by LPS and increased by BSO
Group 4 (G4): down-regulated by LPS and decreased further by BSO
119
For each group, the 15 transcripts whose regulation by LPS was the most affected by
BSO according to fold change ranking ordering are shown in Table 3.1 (up-regulated
by LPS) and Table 3.2 (down-regulated by LPS). The full list of genes for each group
can be found in the Appendix 7-10 for 2h and Appendix 11-14 for 6h.
As observed, most of the genes in Group 1 were related with response to oxidative
stress such as Peroxiredoxin1 (Prx1), Sulfiredoxin 1 (Srxn1), the GSH synthetic
enzyme glutamate cysteine ligase modifier (Gclm), and the cysteine/glutamate
transporter xCT (Slc7a11). Therefore the effect of BSO alone on these genes was
also investigated in order to see if these genes were also up-regulated without GSH.
Most of the genes affected by BSO alone were part of Group 1 (indicated with * an
asterisk in the tables). In Group 2, most of the genes were linked with the inflammatory
process, including IL-1β. Group 3 and 4 were more heterogeneous.
120
Figure 3.10: Diagram of the filtering strategy applied to the microarray data to
assess the number of transcripts affected by BSO during LPS stimulation. At
each step, the dataset was filtered to select transcripts that were affected by LPS (first
step), then by BSO (second step). Criteria for selection were a minimum of 1.5 Fold
Change and a P value < 0.05 obtained by an unpaired two-tailed Student’s t-test
comparing three individual samples.
121
Figure 3.11: Hierarchical clustering of 142 GSH-dependent LPS-regulated genes
at 2h represented as a Heat Map Tree. Data represent the expression change as
log2 ratio of each triplicate (A-B-C) compared with the mean of 3 control samples.
Red indicates an increase in the level of expression while green shows a decrease.
Black indicates no change in the level of expression. Clusters are indicated by vertical
bars (1 and 2 in black and 3 and 4 in grey).
122
Figure 3.12: Expression images of set of genes obtained for cluster 1, 2, 3 and
4 respectively at 2h. The median of the expression for a cluster is in pink. The level
of expression is indicated by the ratio of each condition versus the average of the
control (+4;-4).
123
Figure 3.13: Hierarchical clustering of 196 GSH-dependent LPS-regulated genes
at 6h represented as a Heat Map Tree. Data represent the expression change as
log2 ratio of each triplicate (A-B-C) compared with the mean of 3 control samples.
Red indicates an increase in the level of expression while green shows a decrease.
Black indicates no change in the level of expression. Clusters are indicated by vertical
bars (1 and 2 in black and 3 and 4 in grey).
124
Figure 3.14: Expression images of set of genes obtained for cluster 1, 2, 3 and
4 at respectively 6h. The median of the expression for a cluster is in pink. The level
of expression is indicated by the ratio of each condition versus the average of the
control (+4;-4).
125
Figure 3.15: Diagram of the filtering strategy applied to the microarray data to
assess the number of transcripts affected by BSO during LPS stimulation
represented as a heat map. At each step, the dataset was filtered to select
transcripts that were affected by LPS (first step), then by BSO (second step). Criteria
for selection were a minimum of 1.5 Fold Change and a P value < 0.05 obtained by
an unpaired Student’s t-test on three individual experiments. Number of transcripts
selected are indicated either in red for up-regulated transcripts or in green for down-
regulated transcripts. G1, G2, G3 and G4 stand respectively for Group 1, Group 2,
Group 3 and Group 4.
126
Table 3.1: LPS-induced transcripts most affected by BSO in groups 1 and 2.
Transcripts were selected for their differential expression between the two groups
BSO+LPS vs LPS alone (cut-off was: fold change, 1.5; P<0.05). Only the top 15
transcripts most affected by BSO (sorted by fold change) are shown. *Transcripts
affected by BSO alone (BSO vs control, with a cut off of FC 1.5, P<0.05). The full list
can be seen in Supplementary file 1. Transcripts in bold were selected for PCR
validation.
GeneSymbol Genbank
Accession
P value log2(FC) GeneSymbol Genbank
Accession
P value log2(FC)
Ptpn14 * NM_008976 1.28E-05 1.85 Hunk NM_015755 1.20E-03 1.97
Serpinb1b * NM_173052 5.20E-05 1.60 Serpinb1b * NM_173052 1.38E-04 1.85
Ccdc54 NM_027046 1.92E-02 1.31 Gclm * NM_008129 1.28E-03 1.80
Srxn1 * NM_029688 7.32E-05 1.28 Hunk NM_015755 2.37E-05 1.73
Tex19.1 * NM_028602 4.60E-03 1.22 Ptpn14 * NM_008976 1.46E-05 1.52
Hunk * NM_015755 8.32E-05 1.14 Serpina3g NM_009251 4.28E-04 1.42
Esd * NM_016903 9.18E-04 1.14 Slc39a4 * NM_028064 1.15E-04 1.40
Procr NM_011171 3.30E-04 1.13 Mep1a * NM_008585 5.56E-03 1.21
Tfec * NM_031198 6.30E-03 1.03 Tfec* NM_031198 1.31E-02 1.14
Slc7a11 * NM_011990 2.54E-05 1.00 Prdx1 NM_011034 3.35E-05 1.12
Fosl1 NM_010235 5.50E-04 0.99 Fosl1 NM_010235 2.23E-02 1.11
3110068a07rik * AK039947 8.95E-03 0.97 Gem NM_010276 4.86E-02 1.09
Prdx1 * NM_011034 5.28E-05 0.97 Slc7a11 * NM_011990 2.20E-04 1.07
Hunk * NM_015755 2.13E-03 0.94 Acta2 * NM_007392 1.82E-03 1.01
Zdhhc18 NM_001017968 3.07E-03 0.70 Vwce NM_027913 6.72E-03 1.00
Sox14 NM_011440 3.97E-02 -1.52 Nup62cl NM_001081668 6.59E-03 -4.34
Olfr1412 NM_146277 1.35E-02 -1.51 Psg26 * NM_001029893 3.65E-04 -4.21
Il1b NM_008361 3.39E-03 -1.49 Kif12 * NM_010616 3.89E-02 -2.56
Fabp2 NM_007980 2.27E-02 -1.48 LOC102634429 XM_006521612 2.55E-02 -2.29
Mx2 NM_013606 2.46E-04 -1.39 C230030N03Rik AK082264 7.91E-03 -2.22
Epb4.1l5 NM_001113416 3.63E-02 -1.29 Pdia2 NM_001081070 2.37E-02 -1.79
Otor NM_020595 8.70E-03 -1.29 Adrbk2 AK048763 1.25E-02 -1.65
Aldh16a1 AK164374 1.06E-02 -1.24 Glipr1l1 NM_027018 2.02E-02 -1.56
Zfp811 NM_183177 1.50E-04 -1.13 Urah NM_029821 4.09E-04 -1.36
Urah NM_029821 2.60E-03 -1.09 Urah NM_029821 1.80E-04 -1.25
Edn1 NM_010104 2.39E-03 -1.04 Cabp4 NM_144532 2.40E-02 -1.20
Il4i1 NM_010215 5.24E-03 -1.04 Tcp10c NM_001167578 7.71E-03 -1.18
Oas2 NM_145227 1.89E-03 -1.03 ND6 AK140300 2.90E-03 -1.13
5830411N06Rik AK030813 1.49E-02 -1.00 Adora2a NM_009630 2.11E-02 -1.07
Upp2 NM_029692 4.25E-02 -0.99 Faah * NM_010173 1.31E-02 -0.94
6h2h
Up-regulated by BSO (Group 1)
Down-regulated by BSO (Group 2)
127
Table 3.2: LPS-downregulated transcripts most affected by BSO in groups 3
and 4. Transcripts were selected for their differential expression between the two
groups BSO+LPS vs LPS alone (cut-off was: fold change, 1.5; P<0.05). Only the top
15 transcripts most affected by BSO (sorted by fold change) are shown. *Transcripts
affected by BSO alone (BSO vs control, with a cut off of FC 1.5, P<0.05). The full list
can be seen in Supplementary file 1. Transcripts in bold were selected for PCR
validation.
GeneSymbol Genbank
Accession
P value log2(FC) GeneSymbol Genbank
Accession
P value log2(FC)
Enho NM_027147 1.44E-04 1.81 4930415C11Rik AI606402 1.69E-02 1.71
Kcnma1 NM_001253364 2.82E-03 1.53 AU019990 NR_033469 8.49E-03 1.70
Sirpb1b NM_001173460 3.89E-03 1.48 AI661453 NM_145489 3.69E-02 1.64
Tmem116 * AK039773 3.62E-02 1.43 Clec4a4 NM_001005860 6.60E-03 1.43
Msx3 U62523 1.19E-02 1.38 Clec4n * NM_020001 2.86E-04 1.34
Tmem51 NM_145402 4.10E-03 1.37 Abhd15 NM_026185 3.57E-02 1.33
Rnf150 NM_177378 3.45E-02 1.36 Zfpm2 NM_011766 2.25E-02 1.32
AI844869 * BE862015 5.22E-06 1.29 D930027P08Rik AK086428 4.17E-02 1.30
Zfp318 NM_021346 2.79E-02 1.21 Ltb4r2 NM_020490 9.21E-03 1.28
Osgin1 * NM_027950 5.05E-04 1.18 Osgin1 NM_027950 5.17E-06 1.26
Bcl2l14 NM_025778 3.59E-02 1.13 5430425K12Rik NR_103550 9.14E-03 1.24
Ypel2 NM_001005341 1.79E-02 1.10 Prss46 NM_183103 1.87E-05 1.23
4930556M19Rik AK047938 3.24E-02 1.09 Arhgap8 NM_028455 4.17E-06 1.20
Rcvrn * NM_009038 7.55E-03 1.03 Zfyve28 NM_001015039 3.48E-04 1.13
Dact1 NM_001190466 2.48E-02 0.99 Rhox2h NM_001100465 4.37E-02 1.09
Gm10778 NM_001142963 4.89E-02 -1.04 Maf NM_001025577 1.56E-02 -1.33
1600014C10Rik NM_028166 7.88E-03 -1.01 Sulf2 NM_028072 6.27E-03 -1.29
E230012P03 AK054025 3.43E-02 -0.88 C1qa * NM_007572 1.79E-02 -1.24
Gm9779 BC006743 2.74E-02 -0.85 C1qc * NM_007574 8.32E-04 -1.09
Slc25a40 XR_376693 9.89E-03 -0.80 Slc9a3r2 NM_023055 2.49E-02 -0.92
C1qa * NM_007572 1.85E-02 -0.75 Pi16 NM_023734 1.12E-02 -0.92
1700027A07Rik AK006404 2.94E-02 -0.72 Cx3cr1 * NM_009987 3.80E-03 -0.82
A430106G13Rik AK053260 2.12E-02 -0.64 Cx3cr1 * NM_009987 3.54E-03 -0.81
Themis2 NM_001033308 2.93E-02 -0.61 Odf3l1 NM_198673 3.47E-03 -0.75
Cx3cr1 * NM_009987 3.33E-02 -0.69
Sepp1 NM_001042614 3.14E-02 -0.69
Fgf11 XM_006532188 3.07E-03 -0.67
Cenpf NM_001081363 5.89E-03 -0.67
Frat2 NM_177603 1.08E-04 -0.64
4831440E17Rik NR_030700 3.53E-03 -0.62
Up-regulated by BSO (Group 3)
Down-regulated by BSO (Group 4)
2h 6h
128
3.5.2 Biological functions associated with the LPS-regulated transcripts
affected by BSO
The molecular basis and biological function of these groups of genes were then
investigated by DAVID using the Functional Annotation Chart Tool. Groups of similar
expressed genes obtained at 2h and 6h were combined and only GOTERM_BP and
Kegg pathway categories with at least 3 genes were considered. As shown in Figure
3.16, Group 1 included functional categories associated with the response to oxidative
stress confirming the hypothesis made previously in 3.3.3. Group 2 was the group
with the most functional categories represented (51 in total; full list shown in
Appendix 15). These categories were mainly associated with immune responses,
inflammation and antiviral host defence including interferon and TLR signalling.
Genes included IL-1β, interferon regulatory factor 9 (Irf9) and signal transducer and
activator of transcription 1 (Stat1). Amongst the genes whose expression was
inhibited by LPS, i.e. Group 3 and Group 4, only few mapped to some functional
categories despite the fact that genes at 2h and 6h were combined obtaining a higher
number of genes in Groups 3 and 4 than in Groups 1 and 2. Group 3 included genes
associated with xenobiotics metabolism such as GSH transferases and cytochrome
P450. The only genes that were part of a functional category in Group 4 were C1q
components and Cx3cr1.
129
Figure 3.16: Functional categories up-regulated or down-regulated by LPS for
each differentially expressed group affected by BSO (Group 1-4). Functional
categories were obtained by DAVID using the Functional Annotation Chart tool with
GO TERM BP and KEGG pathway for each group independently of the time point and
were ordered by EASE score a modified Fisher’s Test (P value <0.5). Genes
belonging to each categories are annotated near to the respective bar in italic. P value
is expressed as a log 10. Only the 15st out the 51 overrepresented categories for G2
are shown here (the rest are shown in Appendix 15).
130
3.6 Validation of the microarray analysis by qPCR
These results were validated by RT-qPCR for 11 genes from Group 1 (IL-1β, Irf9,
myxovirus resistance 2 (Mx2), interleukin 4 induced 1 (IL-4i1), suppressor of cytokine
signalling 1 (Socs1), 2'-5' oligoadenylate synthetase 2 (Oas2), nitric oxide synthase 2
(Nos2) and prostaglandin-endoperoxide synthase 2 (Ptgs2)) and from Group 2
(Srxn1, Prdx1, and Slc7a11) at 2h (Figure 3.17) and 6h (Figure 3.18).
Validation was performed using two sets of samples, one with the same RNA used
for the microarray experiment (qPCR1) and one with the RNA from an entirely
independent experiment (qPCR2).
For all 11 genes tested, the qPCR1 confirmed the differential expression detected by
microarray analysis both at 2h and 6h. The reproducibility of the data obtained was
then tested with the qPCR2 which confirmed the results for 5 out of 7 genes at 2h,
including IL-1β, Irf9, Mx2, IL-4i1, and Srxn1. At 6h, 3 genes out 4 were validated
including Prdx1, Nos2 and Slc7a11.
131
Figure 3.17 (part 1/2): PCR validation of the microarray data at 2 h. Results for 7
genes (Il1b, Irf9, Mx2, Il4i1, Srxn1, Oas2, Socs1) from Group 1 and Group 2
comparing expression data from microarrays (N=3 biological replicates; left graphs)
with results from PCR analysis using all the 4 replicates of the RNA from the same
experiment (N=4 biological replicates; middle graphs) and RNA from an independent
experiment (N=4 biological replicates; right graphs). Data are expressed as fold
change vs one of the respective control samples. For each experimental group, the
mean is also shown. *P<0.05; **P<0.01; ***P<0.001 by unpaired two-tailed Student’s
t-test.
132
Figure 3.17 (part 2/2): PCR validation of the microarray data at 2 h. Results for 7
genes (Il1b, Irf9, Mx2, Il4i1, Srxn1, Oas2, Socs1) from Group 1 and Group 2
comparing expression data from microarrays (N=3 biological replicates; left graphs)
with results from PCR analysis using all the 4 replicates of the RNA from the same
experiment (N=4 biological replicates; middle graphs) and RNA from an independent
experiment (N=4 biological replicates; right graphs). Data are expressed as fold
change vs one of the respective control samples. For each experimental group, the
mean is also shown. *P<0.05; **P<0.01; ***P<0.001 by unpaired two-tailed Student’s
t-test.
133
Figure 3.18:PCR validation of the microarray data at 6 h. Results for 4 genes
(Prdx1, Nos2, Ptgs2, Slc7a11) from Group 1 and Group 2 comparing expression data
from microarrays (N=3 biological replicates; left graphs) with results from PCR
analysis using all the 4 replicates of the RNA from the same experiment (N=4
biological replicates; middle graphs) and RNA from an independent experiment (N=4
biological replicates; right graphs). Data are expressed as fold change vs one of the
respective control samples. For each experimental group, the mean is also shown.
*P<0.05; **P<0.01; ***P<0.001 by unpaired two-tailed Student’s t-test.
134
3.7 Identification of pathways affected by BSO during LPS stimulation
The effect of BSO on the transcription of those LPS-induced genes that are related to
oxidative stress (Group 1) and inflammatory and immune response (Group 2) was
further investigated due to the high number of genes belonging to these categories.
To identify possible common pathways, an unbiased analysis for the over-
represented transcription factor (TF) binding sites was performed using the
oPOSSUM software (Kwon, Arenillas et al. 2012). The results of the analysis are
shown in Figure 3.19. It can be seen that in Group 1 (Figure 3.19A) the TF resulting
in the highest Fisher score and a high number of target genes is NFE2L2 (Nrf2),
whose main function is the response to oxidative stress, thus confirming the results
obtained with DAVID. The TF analysis for Group 2 transcripts is shown in Figure
3.19B. From these data, the TF that had the highest score was NF-κB with its various
subunits. To confirm these results, the dataset for the expression of all known Nrf2
and NF-κB target genes obtained from the literature was compared with the genes for
Group 1 and Group 2 respectively (Table 3.3). From this research, 30% (11 out 38)
transcripts in Group 1 were part of the Nrf2 target genes. However only 3% (13 out
364) of the genes of Group2 were part of the NF-κB target genes. Thus, as only a
very small percentage of NF-κB target genes induced by LPS are in Group 2 (down-
regulated by BSO), the fact that BSO could act simply by down-regulating NF-κB was
ruled out.
135
Figure 3.19: Transcription Factor analysis of Group 1 (A) and Group 2 (B) genes
using the database oPOSSUM. Only the first five TF are shown according to their
Fisher score ranking. The number of genes targeted is in brackets near its TF. The
threshold (dotted line) is set to the mean+1xSD.
136
TF Number target genes
Reference
Match with
microarrays
Match
with
Group
% of TF
target
Group1 Nrf2
25 (Soriano, Baxter et al.
2009, Gorrini, Baniasadi
et al. 2013)
38 11/65 30%
Group2 NF-κB 434 (Boston 2017) 364 12/118 3.2%
Table 3.3: Number of genes targeted by NF-κB or Nrf2 belonging to Group 2 and
Group 1 respectively.
137
3.8 Antiviral response: BSO increases Influenza A replication in LPS-
stimulated RAW cells
Interestingly, the genes identified from Group 2 were associated with the response to
viral infection (herpes, influenza A, and measles for instance) suggesting that BSO
inhibits the antiviral response during LPS stimulation.
To test this hypothesis, RAW cells were infected for 1h with Influenza Virus strain
Puerto Rico/8/34 H1N1 (PR8) after depletion of GSH with BSO for 18h and
subsequent LPS stimulation for 2h. All experiments were carried out by Dr Checconi
from “Università di Roma” and the results are shown in Figure 3.20. Briefly, the levels
of different virus particles (Nucleoprotein (NP), neuraminidase (NA), hemagglutinin
(HA1), and matrix protein (M1)) and the cytosolic protein β-actin as a control from cell
lysates were measured by western blot (Figure 3.20A-B). The ratio NP to β-actin was
then measured by densitometry and shown in Figure 3.20C. In cells infected with
influenza and treated with BSO, the virus particles and particularly NP were higher
than in the control cells. On the contrary, in cells infected but stimulated only with LPS,
virus particles were slightly decreased in comparison with the control cells suggesting
a protection effect triggered by LPS stimulation. Finally, when cells were stimulated
with LPS and BSO, the numbers of virus particles found in the cells was even greater.
These results demonstrate that BSO prevents the antiviral response activated by LPS
signalling, thus suggesting that GSH has an antiviral effect.
These data also raise the question of why antiviral pathways are activated while
stimulating cells with LPS, a TLR4 activator. In fact it has been reported that activation
of TLR4 could lead to induction of antiviral proteins including IFNs and IFN-related
genes (Takeuchi and Akira 2010, Newton and Dixit 2012) such as MxA and OAS
(Barjesteh, Behboudi et al. 2014, Baharom, Thomas et al. 2015). Our data, although
obtained in a model were infectivity was low, suggest that GSH is important for the
activation of an antiviral response. This happens without affecting inflammatory
genes, except for IL-1b whose induction was also facilitated by the presence of GSH.
There is evidence for a fine tuning of TLR signalling (O'Neill 2008), and these data
indicate that GSH may be important in directing it towards specific small patterns of
genes implicated in host defense rather than towards those responsible for the
inflammatory response. Furthermore, recent studies have highlighted the link
between LPS stimulation and activation of the interferon pathway via Stat1 and Traf6
signalling molecules (Sikorski, Chmielewski et al. 2011, Luu, Greenhill et al. 2014).
The list of genes from Group 2 was therefore transferred to the website Interferome
which uses programming language to identify potential interferon downstream
138
signalling molecules (Rusinova, Forster et al. 2013). As shown in the Venn diagram
Figure 3.21, 33% of the genes were IFN targets particularly of type 1 but also type 2
IFN.
These data suggests that GSH induces the antiviral response activated by LPS
signalling via the interferon pathway of type 1 and 2.
139
Figure 3.20: Effect of LPS and BSO on influenza A replication in RAW cells. A
and B. Western blot for influenza virus proteins in RAW cells stimulated or not 2h with
LPS and pre-treated or not with BSO for 18h. β-Actin was used as loading control.
C. Ratio of NP viral protein versus β-Actin in RAW cells using densitometric analysis
expressed as the mean ± SE of the ratio NP/β-Actin (n=8), *P<0.05, ***P<0.0005,
****P<0.0001 versus infected cells (=I) by a paired two-tailed Student’s t-test.
140
Figure 3.21: Type of interferons recognised in Group 2 genes (up-regulated by
LPS but inhibited by BSO). A total of 118 genes (Group 2) were submitted to the
database Interferome v2.01 which classifies genes according to the interferon they
are part of the downstream signalling. It include three types; type 1, type 2 and type
3.
141
3.9 Effect of NAC and Menadione
To understand if the regulation of Group 1 and Group 2 was due to regulatory
properties of GSH or rather to its ROS-scavenging action, further experiments were
performed. We reasoned that if GSH was acting as a ROS scavenger then another
thiol antioxidant would behave in the same way, while a ROS-generating oxidant
would have an opposite effect. For this purpose, we used NAC that is a precursor of
GSH by providing free cysteines and an antioxidant reacting with superoxide (Samuni,
Goldstein et al. 2013). To generate ROS we used menadione that is a quinone
producing ROS constantly (Hassan and Fridovich 1979).
NAC was added to RAW cells one hour before the 2h LPS stimulation and menadione
was added to the cells for 2h without LPS. The doses of NAC and menadione and
time of incubation were selected as reported in published studies (Checconi, Salzano
et al. 2015, Mullen, Hanschmann et al. 2015). The expression level of selected genes
from Group 1 and Group 2 was then analysed by qPCR. As shown in Figure 3.22A,
NAC did not alter the induction of Group 1, genes up-regulated by LPS and further
up-regulated by BSO (Srx1, Prdx1, and Slc7a11). On the other hand, all these genes
where induced by menadione alone suggesting that they were sensitive to ROS.
However, although they are affected by ROS, if these genes were up-regulated by
the ROS produced during LPS stimulation, then they would have been inhibited by
NAC.
Regarding the Group 2, gene up-regulated by LPS and down-regulated by BSO
(Figure 3.22B), NAC did not increase further, or affect in any way, the LPS induction
of these genes (Il-1b, Mx2 or Irf9); on the contrary, NAC inhibited the induction of Il-
1b by about 45% similarly to what was observed with BSO. Similarly, menadione by
itself did not induce the expression of any of the Group 2 genes studied. TNF, which
was also analysed and used as a control for the inflammatory response, was not
affected either by NAC or by menadione (Figure 3.22C). We could then conclude that
ROS are not involve in the antiviral response of RAW cells as neither menadione nor
NAC affected their expression. GSH mediate the antiviral response via another
mechanism that simply a ROS-scavenger.
142
Figure 3.22: Gene expression of seven selected genes from RAW cells after
treatment with NAC, LPS or Menadione. A. qPCR analysis of genes from Group 1.
B. qPCR analysis of genes from Group 2. C. qPCR analysis of TNF used as a marker
of the inflammatory response. Cells were treated with 5mM NAC for 1 h and then
stimulated with 10 ng/ml LPS for further 2 h. Men was added at 10μM for 2 h. Gene
expression was measured by qPCR. Data are expressed as fold change vs one of
the control samples, and are the mean ± SD of six biological replicates from two
independent experiments (For TNF – mean ± SD of three biological replicates).
**P<0.01, ***P<0.001 vs control; ∆ P<0.01 vs LPS by unpaired two-tailed Student’s t-
test.
143
3.10 Discussion
The main aim of this work was to identify genes regulated by GSH during the
inflammatory response. Using microarray analysis, we could identify three groups of
genes affected differently by GSH following LPS stimulation in RAW cells. Firstly,
genes which are regulated by LPS but not by GSH. In fact GSH has a minimal impact
on the gene expression as only 0.42% at 2h and 0.58% at 6h genes affected by LPS
are regulated further by GSH depletion. Thus, 99% of LPS-induced genes are not
regulated by GSH.
Secondly, LPS-induced genes that are inhibited by endogenous GSH. Most of these
genes were targets of Nrf2, a transcription factor involved in the oxidative stress
response (Brigelius-Flohe and Maiorino 2013).One could hypothesize that these
genes are induced by an increased production of ROS triggered by LPS. In fact, using
the EPR spectrometer, we could demonstrate an increase in O2.- produced from RAW
cells following 2h of LPS stimulation. This ROS is known to be rapidly reduced to H2O2
both spontaneously and by the catalytic action of SOD (Fukai and Ushio-Fukai 2011).
Then, H2O2 is reduced by peroxidases to water (Brigelius-Flohe and Maiorino 2013)
and thus depletion of GSH will increase its accumulation explaining why Nrf2 is
activated. This result also provides evidence for the link between LPS and the release
of O2.-, through TLR4 signalling, as demonstrated by previous studies using indirect
assays to measure O2.- in neutrophils and in monocytes following LPS stimulation
(Figure 3.23 - ①) (Landmann, Scherer et al. 1995, Remer, Brcic et al. 2003, Gill,
Tsung et al. 2010).
Finally, we could identify a third group of genes requiring GSH for their induction by
LPS. Among those, genes involved in the antiviral response were identified indicating
that GSH is important for an optimal antiviral response.
Surprisingly, GSH-depletion did not exacerbate the induction of inflammatory
cytokines or genes associated with the inflammatory response, with the only
exception of CXCL10, an interferon gamma induced protein, was up-regulated during
GSH-depletion and LPS stimulation (Appendix 11). Our results differ from what have
been reported in other published studies reporting that thiol antioxidants inhibit NF-κB
expression and decrease several inflammatory genes (Schreck, Rieber et al. 1991)
(Staal, Roederer et al. 1990, Gosset, Wallaert et al. 1999). In fact, that evidence is
based on in vitro or in vivo experiments using exogenously administered thiol
antioxidants or pro-oxidants as shown in Table 3.4. Importantly, our results are not in
contrast with those studies. We believe that all the studies where inhibition of the LPS
144
induction of inflammatory cytokines was achieved by exogenous NAC or GSH are
reproducible. Only, we don’t think that one can draw conclusions on the role of
endogenous GSH, such as hypothesize its anti-inflammatory role, from the results of
those studies (Gosset, Wallaert et al. 1999). Interestingly, we observed a decrease of
IL-1β with NAC and this was observed in another published study by Cu and
colleagues (Cu, Ye et al. 2009). Even though the induction of this inflammatory
cytokine requires GSH, further supporting the idea that one thing it to study the role
of endogenous GSH and a different thing is adding an exogenous ROS scavenger.
Thus, in our study, the results indicate that the large majority of LPS-induced
inflammatory genes do not require GSH or ROS for their induction.
Most importantly, we find that genes involved in the antiviral response that were up-
regulated by LPS were inhibited by GSH depletion. This suggests that GSH is
required to induce an optimal antiviral response. In addition, we observed that LPS
reduced influenza virus infection in our model but this required the presence of GSH.
Most studies investigating the effect of GSH or GSH depletion on viral replication have
shown similar results which have been summarised in Table 3.5. Other studies have
equally shown that viral infection were often linked with reduced GSH level. This GSH
depletion was observed in erythrocytes of patient infected with hepatitis (Swietek and
Juszczyk 1997), in the T CD4+ of patients with HIV (Herzenberg, De Rosa et al. 1997),
in HeLa cells infected with Rhinovirus type 16, the common cold virus (Papi, Contoli
et al. 2008) and in mice infected with a retrovirus inducing immunodeficiency (Brundu,
Palma et al. 2016). Therefore our studies suggest that depletion of GSH in infected
cells could be a virus strategy to increase its replication as GSH stimulated the
antiviral response.
We then highlighted that the interferons type 1 and 2 response were potentially the
pathways affected by GSH depletion. Indeed most of the genes (Mx2, Oas2, Irf9,
Stat1) belong to the Interferon type 1 and type 2 response (Sadler and Williams 2008).
Recent studies have demonstrated a cross-talk between TLR4 signalling and
interferon downstream pathways via Stat1 and Traf6 (Sikorski, Chmielewski et al.
2011, Luu, Greenhill et al. 2014). Our data suggest then that LPS activates TLR4
signalling which then also stimulates the interferon response pathways. According to
this hypothesis, and our results, GSH is essential in this signalling pathway (Figure
3.23 - ②; ③; ④).
Finally, BSO amplifies the expression of genes up-regulated by LPS which are
associated with oxidative stress (Srxn1, Prx1 and Slc7a11). Using menadione, a
ROS-generating agent, we observed a similar up-regulation of their expression. One
could have hypothesize that, as LPS promote ROS generation and ROS induces the
145
expression of these genes by LPS, NAC, by reducing the ROS, will down-regulated
their expression. Therefore, it seems possible that LPS induces Nrf2 target genes
independently of ROS, for instance via the small GTPase RAC1, as described by
Cuadrado and colleagues (Cuadrado, Martin-Moldes et al. 2014). This could suggest
that endogenous GSH might be important for reasons other than scavenging ROS.
For instance, Nrf2 activation depends on oxidation of Keap1, its redox sensor, whose
thiol groups can also be oxidized by GSSG through a thiol/disulfide exchange reaction
(Turpaev 2013). The change in GSH/GSSG ratio caused by BSO could then causes
Nrf2 activation by oxidation of Keap1 leading to the RAC1-dependent activation by
LPS. This direct effect of GSH to Keap1 in the form of glutahionylation could be a
mechanism by which GSH can regulate Nrf2.
In conclusion, in our study, endogenous GSH does not seem to down-regulate the
inflammatory response contrary to what most studies have reported by measuring the
classic cytokines such as IL-6, IL-10 and TNF. However, most studies came to the
conclusion that GSH is an inhibitor of inflammation using either exogenously
administered NAC or GSH, or ROS. Therefore, we hypothesised that the antioxidant
therapy could be efficient only in conditions where increase of ROS or lack of
antioxidants is verified but will be useless if not damageable to other conditions. Also,
it will be important to ensure that therapy does not increase the level of thiol
antioxidants above the concentration of endogenous GSH, in order to not interfere
with physiological redox signalling.
To strengthen our work quantification of proteins from those genes identified in this
study is required. Indeed, a limitation of microarray analysis is to make sure that
proteins will behave similarly to the genes they are translated from in terms of
abundance and mechanism (Waters, Pounds et al. 2006).
146
Figure 3.23: GSH acts as a signalling molecule during LPS stimulation in
macrophages. GSH is required for optimal induction of Nrf2 genes by LPS and
antiviral response by LPS bus does not regulate most inflammatory genes targeted
by NF-κB. ① Activation of TLR4 signalling through LPS. ② Activation of NADPH
oxidase and downstream TLR4 signalling. ③ Activation of transcription factors. ④
Antiviral response.
147
Experiments Thiol /
inhibitor thiol used
Observation Ref
Injection intraperitoneal of
GSH in rats 30min before
LPS injection
Exogenous GSH
GSH decreased mortality in LPS-injected rats by suppressing the inflammatory response
(Sun, Zhang et al. 2006)
In vitro culture of alveolar macrophages stimulated with LPS
NAC Down-regulation of TNF production and its soluble receptors and IL-1β
(Cu, Ye et al. 2009)
HeLa cells transfected with component of NF-κB and stimulated with TNF
NAC Suppression of NF-κB activation
(Oka, Kamata et al. 2000)
Oral administration of NAC to mice treated with LPS
NAC; BSO Inhibition of TNF in serum ; BSO induce TNF production; no effect of NAC in IL-6 or IL-1α in serum
(Peristeris, Clark et al. 1992)
Human alveolar macrophages stimulated with LPS
GSH; NAC; BSO
BSO increase TNF and IL-8 secretion; NAC and GSH decrease TNF, IL-6 and IL-8 secretion.
(Gosset, Wallaert et al. 1999)
Oral administration of NAC to diabetic induced rats treated with LPS
NAC Decrease of TNF induced by LPS in serum
(Sagara, Satoh et al. 1994)
Table 3.4: Studies on the effect of thiols antioxidants (NAC, GSH) or thiols inhibitors
(BSO) on production of pro-inflammatory cytokines.
Table 3.5: In-vitro studies on the effect of BSO and GSH on replication of different
viruses.
Virus Ref Year Effect of BSO on virus proliferation
Herpes simplex virus type-1 replication
(Palamara, Perno et al. 1995)
1995 BSO increases
Human Immunodeficiency virus
(Garaci, Palamara et al. 1997)
1997 BSO increases
Rat cytomegalovirus (Vossen, Persoons et al. 1997)
1997 BSO increases
Sendai virus (Macchia, Palamara et al. 1999)
1998 BSO increases
Influenza A (Cai, Chen et al. 2003) 2003 BSO increases
Human echovirus 9 (Mikami, Satoh et al. 2004)
2004 BSO decreases
Picomavirus virions (Smith and Dawson 2006)
2006 BSO decreases
Dengue virus production (Tian, Jiang et al. 2010) 2010 BSO increases
Porcine circovirus type 2 (Chen, Ren et al. 2012) 2012 BSO increases
148
Chapter 4
Redox state of Peroxiredoxins and Thioredoxin
in LPS-stimulated macrophages
149
4.1 Introduction
Thiol oxidation is a reversible form of post-translational protein modification (PTM)
which can result in structural and functional changes in thiol-containing proteins.
HMGB1 illustrate the importance of this PTM in an inflammatory context. This classic
DAMP secreted by monocytes after an inflammatory stimulus can attract inflammatory
cells and helps in their maturation when its C23 and C45 form a disulphide bonds and
its C106 is in the reduced form allowing it to bind TLR4 and stimulate cytokine
production (Messmer, Yang et al. 2004, Dumitriu, Baruah et al. 2005, Venereau,
Casalgrandi et al. 2012, Yang, Lundback et al. 2012). However, a change in its
cysteines redox state inhibits its inflammatory functions.
Similarly to HMGB1, Prx1, Prx2 and Trx are released from RAW cells after LPS
stimulation later than classical cytokines (Salzano, Checconi et al. 2014, Checconi,
Salzano et al. 2015). Furthermore, many studies have demonstrated immune
properties of these redox proteins. Trx acts as a chemoattractant for leukocytes
potentially due to its enzymatic activity on cell surface protein through its cysteines;
C32 and C35 (Bertini, Howard et al. 1999). Extracellular dimeric Prx1 binds to TLR4
leading to secretion of cytokines such as TNF and IL-6, helped by its C83 which
stabilise its dimeric form (Riddell, Wang et al. 2010). Finally, it has been demonstrated
that exogenous Prx2 can trigger TNF production from RAW cells indicating potential
pro-inflammatory properties (Salzano, Checconi et al. 2014).
The role of cysteines in the mechanism of secretion of these proteins was also
investigated. As it is the case for HMGB1 and Il-1β, these proteins lack a secretory
signal peptide and therefore use a non-classical secretory pathway that is non-Golgi
dependent (Rubartelli, Bajetto et al. 1992, Nickel 2003). Tanudji and colleagues
investigated the release of Trx by mutating the cysteines contained in its redox active
site C32 and C35 but also C73 which is involved in Trx dimerization (Andersen,
Sanders et al. 1997, Tanudji, Hevi et al. 2003). The study demonstrated that mutation
of these cysteines did not alter Trx release from cells. One limitation of this study is
that it focused on the three main functional cysteines excluding possible thiol
modifications to the two other cysteines present in human Trx. Similarly to this study,
Mullen and co-workers examined the role of cysteine oxidation in the secretion of Prx1
and Prx2 (Mullen, Hanschmann et al. 2015). A series of mutations were performed on
the cysteines in Prx1 and Prx2. Mutation of resolving or peroxidatic cysteines was
sufficient to prevent the release of these Prxs from cells demonstrating that these
proteins were subjected to dimerization through C52 and C173 for Prx1 and C51 and
C172 for Prx2 before secretion via exosomal vesicles. Although the importance of the
150
dimerization of Prx1 and Prx2 has been shown, both techniques were performed on
mutant proteins excluding the redox sensitivity of endogenous proteins.
All post translational modification in those proteins cysteines and associated
properties are shown in Table 4.1 in addition to other post translational modifications
previously reported. To understand better the role of endogenous Prx2, Prx1 and Trx
in inflammation, analysis of the overall redox state of these proteins is required.
However, the lack of sensitive techniques limit previous study and thus development
of techniques to identify redox sensitive cysteine is challenging (Riederer 2009).
Currently, new tools have been developed allowing the direct study of thiol oxidation.
As described by Eaton, a variety of alkyl agents react directly with reduced thiols such
as iodoacetamide, iodoacetate and maleimide. These agents can be attached to
chemical compounds to enable detection, for instance biotin or horseradish
peroxidase which are detectable using downstream processes such as
immunoprecipitation, proteomics such as mass spectrometry or Western blot (Eaton
2006). Cox and colleagues analysed the redox state of Prxs using thiol alkylation with
N-Ethylmaleimide (NEM) by immunoblotting (Cox, Winterbourn et al. 2010). NEM is
an alkene which reacts covalently with the sulfhydryl group of cysteines in proteins to
form an irreversible carbon-sulphur bond. In this study, two redox states of Prx2 were
detected by Western blot: the reduced monomer and the oxidised dimer (catalytic
form). However, although this agent blocks further oxidation it does not indicate the
number of free thiols and thus exclude potential discrimination between hyperoxidised
monomer and completely reduced monomer (Cox, Winterbourn et al. 2010).
In this chapter, we used a 10kDa PEGylated maleimide to binds covalently free thiols
contained in Trx, Prx1 and Prx2 from intracellular and extracellular samples of
untreated and LPS-stimulated RAW cells. This MalPEG fixation of proteins will
increase the molecular weight of protein with free thiols and then be determined by
Western blot analysis in order to detect potential redox changes linked with the
inflammatory response.
Aim: Prxs and Trx are released from macrophages following endotoxin
stimulation. Cytokines- and chemokines-like functions have also been
identified in these proteins. In this chapter, we investigated whether Prxs and
Trx are in a different redox state when secreted than within the cells in
association with potential functions. For this purpose, we set up a methodology
based on labelling protein thiols with PEGylated maleimide (maleimide-PEG;
MalPEG), an alkylating agent.
151
Protein Cysteine Thiol oxidation
modification reported
Role Ref
mouse and human Trx
C62
S-nitrosylation; Disulphide formation C62-C69
n.d (Gasdaska, Kirkpatrick et al. 1996, Haendeler 2006, Wu, Parrott et al. 2011)
C69
S-nitrosylation; Disulphide formation C62-C69
n.d
C73
Glutathionylation, Dimerisation, S-nitrosylation of C73
Growth factor
C32
Redox activty, disulphide formation C32-C35
Catalytic activity / Chemotaxis / Secretion
(Bertini, Howard et al. 1999) C35
Redox activty, disulphide formation C32-C35
Catalytic activity / Chemotaxis / Secretion
mouse C46 n.d n.d
mouse and human Prx1
C52-C173 Interchain disulphide bonds (dimerisation)
Catalytic activity /Secretion
(Mullen, Hanschmann et al. 2015)
C83 n.d Binding TLR4 (when Prx1 is in a dimer)
(Riddell, Wang et al. 2010)
C71 S-nitrosylation n.d
(Engelman, Weisman-Shomer et al. 2013)
mouse and human Prx2
C51-C172 Interchain disulphide bonds (dimerisation)
Catalytic activity / Secretion
(Mullen, Hanschmann et al. 2015)
C51-C172 Glutahionylation Transmission of redox signal with H2O2
(Peskin, Pace et al. 2016)
C70 n.d n.d
Table 4.1: Thiol oxidation modifications reported in Trx, Prx1 and Prx2 and functional
roles associated.
152
4.2 The Maleimide-PEG technique
The MalPEG technique was previously described by Momand and co-workers (Wu,
Thomas et al. 2000). Of note, MalPEG, like NEM, a maleimide group i.e. it is an alkene
which covalently reacts with sulphur groups as shown Figure 4.1. However due to its
10kDa polyethylene glycol (PEG), a long linear hydrosoluble and highly stable
polymer, any protein bound with MalPEG will have a greater molecular weight
(increase by 10kDa) causing a visible shift on a Western blot due to retarded mobility.
As shown Figure 4.2, if the protein does not contains free thiols or if thiols are already
engaged in disulphide bonds (oxidised thiol), the maleimide cannot bind the molecule
and therefore does not change the molecular weight. In the present study, cell lysate
and supernatant (containing potential secreted proteins) were mixed with either NEM
as a control or with MalPEG.
153
Figure 4.1: Chemical structure of NEM and MalPEG. The alkene function of the
maleimide reacts specifically with protein thiols. Skeletal structures were obtained
from Sigma and NANOCS for NEM and MalPEG respectively.
154
Figure 4.2: Methodology of the MalPEG technique. Protein with a free thiol
(=reduced protein) binds with MalPEG increasing its molecular weight by 10kDa. The
change of molecular weight induced by MalPEG is detected by Western blot due to
electrophoresis mobility shift assay. On the contrary if the protein does not contain
free thiol (=oxidised protein), the MalPEG will not bind the protein and therefore its
molecular weight will not change.
155
4.3 Cell viability upon LPS stimulation
A MTT assay was performed to assess the viability of RAW cells with different
concentrations of LPS; 0, 50, 100 and 200ng/ml. As observed by the MTT assay in
Figure 4.3, the viability was affected similarly by concentration of LPS ranging from
50 to 200ng/ml with a 20% decrease in cell viability. Therefore, a concentration of
100ng/ml of LPS was used in most experiments to ensure a robust stimulation that
was sufficient to result in protein secretion (Salzano, Checconi et al. 2014).
156
Figure 4.3: RAW cells viability after treatment with different LPS concentration.
RAW cells were plated at 1x106 cells overnight before treatment in 1ml of Opti-MEM
with or without 50, 100 or 200ng/ml of LPS for 24h. Following the stimulation, cells
were analysed by MTT assay.
0
20
40
60
80
100
120
Control LPS 50 LPS 100 LPS 200
Ab
so
rba
nc
e a
t 5
90
nm
(%
)
157
4.4 Optimisation of MalPEG concentration in RAW cells
Ideally, the MalPEG added to cell lysates or to the supernatant should be sufficient to
block all free thiols present in the sample. RAW cells were stimulated 24h with
100ng/ml of LPS to ensure release of Prxs and Trx. MalPEG was then added to the
lysis buffer at different concentrations from 0 to 1mM. The MalPEG concentration was
then selected using two techniques: a Western blot to detect Trx and a colorimetric
assay, DTNB assay, measuring free thiols in lysates. Of note, in all these experiments,
samples not treated with MalPEG were treated with NEM to block any free thiol and
prevent artefactual formation of oxidized forms during the sample processing. The
Western blot in Figure 4.4 with NEM treatment shows a band at 12kDa corresponding
to the Trx monomer. Using MalPEG, Trx was separated into two forms one around
32kDa and one between 50 and 75kDa. The 32kDa form started to disappear with
concentration greater than 0.4mM MalPEG and the band observed between 50 and
75kDa was also less clear. This was explained by the viscosity of the MalPEG
preventing samples from running correctly through the polyacrylamide gel. A
difference between LPS and non-treated cells was observed at MalPEG
concentrations of 0.2 and 0.3mM. However to make sure that MalPEG reacted with
the majority of free thiols in the sample, the DTNB assay was performed. As shown
Figure 4.5, MalPEG at a concentration of 0.2 and 0.3Mm bound two thirds of the total
free thiols. Therefore, MalPEG was used in this concentration range in further
experiments.
158
Figure 4.4: Optimisation of MalPEG concentration in RAW cells. Two
independent experiments were performed in the same conditions (treated or not with
100ng/ml of LPS) and cell lysates were either mixed with NEM as a control or with
increasing concentrations of MalPEG from 0 to 0.7mM. Tagged Trx was then
analysed by Western blot in non-reducing conditions using polyclonal antibody anti-
mouse Trx.
159
Figure 4.5: Determination of the effect of MalPEG concentration on free thiols
using the DTNB assay. After treatment with 100ng/ml of LPS), cell lysates were
either mixed with NEM as a control or with increasing concentration of MalPEG from
0 to 1mM. Standard curve of NAC concentration (μM) was used for calculation of free
thiols concentration in samples. Assay were performed in triplicate for each condition
and read at 412nm. *<0.005 versus control by T.test.
160
4.5 Determination of the redox states of Prx1, Prx2 and Trx
The redox states of Prx1, Prx2 and Trx were assessed in samples consisting of RAW
cell lysates or their culture supernatants.
4.5.1 Trx is secreted and undergoes redox changes in response to LPS
stimulation
Mouse Trx (12kDa) has six cysteines, and therefore six potential free thiols: C32, C35,
C46, C62, C64 and C73. This is illustrated with the model structure of mouse Trx
monomer obtained from SWISS-MODEL showing two sides of the same molecule in
Figure 4.6A. Therefore MalPEG could theoretically increase the molecular weight of
Trx from 12kDa to 72kDa depending on the number of free thiol as summarise in the
table in Figure 4.6 B.
RAW cells were treated with MalPEG or NEM (which does not modify the molecular
weight) after stimulation with 100ng/ml LPS and the following cell lysates and
supernatant were analysed by Western blot.
As observed in Figure 4.7, samples treated with NEM indicates that Trx is found
intracellularly as a 12kDa band, consistent to its molecular weight. However,
regarding the protein secreted from cells, Trx is only released when the cells are
stimulated with LPS. MalPEG could then discriminate Trx by fixing free thiols. As
observed out all the possibility stated previously, in the table Figure 4.6B, only two
forms were detected in the cell lysate: one of 32kDa (=2 MP / 2 free -SH) and one of
62kDa (= 5 MP / 5 free -SH) and two forms in the release of 32kDa (= 2 MP/2 free -
SH) and 52kDa (= 4 MP/4 free -SH). Molecular weights of the bands were estimated
manually using the migration distance of each band versus the dye front with the
ladder as the standard curve.
Further analysis were then carried on by measuring the densitometry of each band.
These are summarised in a graph in Figure 4.8. It can be noted that the 32kDa form
was considered more oxidised compared to the 52/62kDa forms as they contain more
than 4 free thiols. As observed, the intracellular oxidised form (red bar) disappears
from the cell lysate samples when treated with LPS. The same form seems to appear
in the supernatant where the reduced forms (blue bar) were quasi inexistent
independently of the treatment. This suggest that only oxidised Trx is released.
The fact that at least 4 free thiols could be tagged (MW: 52kDa) indicate that MalPEG
was able to tagged most of the free thiol and therefore these thiol were exposed to
MalPEG. Because intracellular Trx with LPS was mainly reduce, it could also indicate
a conformational change preventing MalPEG fixation in the extracellular media.
161
Figure 4.6: Representation of Mouse Trx structure and potential site of MalPEG
fixation. A. Two sides of the model structure of mouse 105 aa Trx obtained from
SWISS-MODEL by X-RAY diffraction (last reviewed 15th January 2017). Cysteines
are indicated in yellow. B. Table illustrating all the potential possibilities for MalPEG
to bind free thiols in Trx.
162
Figure 4.7: Redox state of Trx in RAW cells lysates and in release after LPS
stimulation. Representation of two Western blot analysis of RAW cell lysate (upper
panels) and RAW cell release after acetone precipitation (lower panels) after
stimulation with LPS. Analysis were performed in reducing conditions using polyclonal
anti-mouse Trx antibody. The black arrows indicate the position of reduced or oxidised
forms determined by the number of MalPEG fixed to the protein.
163
Figure 4.8: Densitometry of each form detected in the cell lysate (n=8) and in
the released from cells (n=12) of Trx. Reduced form (4/5MalPEG = 4 free -SH) are
in blue while oxidised form (2MalPEG = 2 free -SH) are in red. Data are expressed at
the ratio of the mean +/- standard deviation. * P<0.05; ** P<0.005; *** P< 0.001 versus
LPS are a paired two-tailed T.test for eight independent experiments for the cell lysate
and twelve for the release.
164
4.5.2 LPS stimulation has distinct effects on the redox state of Prx1 and Prx2
4.5.2.1 Prx1 is released but does not undergo intracellular redox changes
following LPS stimulation
Mouse Prx1 (22kDa) contains 4 cysteines: C52, C71, C83 and C173 as illustrated in
Figure 4.9A. C52 and C173 can engage in disulphide bonds resulting in a dimer of
44kDa (red dotted line). The table in Figure 4.9B shows all the possibilities for the
bands that could be observed after treatment with MalPEG.
As observed in the Western blots in Figure 4.10 the monomeric form of Prx1 is never
observed in the cell lysate tagged with NEM independently of LPS stimulation. Only
the dimer is observed at a band of 40kDa. Similarly to Trx, this dimer is detected in
the released from cells mainly with LPS stimulation.
The redox state, assessed by the presence of MalPEG, in the cell lysate did not
change whether the cells were treated with LPS or not. But overall, intracellular Prx1
was detectable as a mixture of reduced forms as the dimer shift to a range of
molecular weight from 54 to 94kDa with MalPEG (= 1 to 5 MP/1 to 5 free -SH). In the
supernatant, only LPS-stimulated cells released Prx1 and only two forms were
detected with MalPEG: one at 64 kDa (= 2 MP/2 free -SH) indicating a dimer with two
free thiols and one that was fully oxidised (no MP/no free -SH). This fully oxidised
form was not observed in the cell lysates.
165
Figure 4.9: Representation of Mouse Prx1 and potential site of MalPEG fixation.
A. Scheme of Mouse Prx1 with the four cysteines present in each monomer (22kDa)
and the potential intermolecular disulphide bonds in red dotted line. B. Table
illustrating all the potential possibilities for MalPEG to bind free thiols.
166
Figure 4.10: Redox state of Prx1 in RAW cells lysates and in release after LPS
stimulation. Representation of two Western blot analysis of RAW cell lysate (upper
panels) and RAW cell release after acetone precipitation (lower panels) after
stimulation with LPS. Analysis were performed in non-reducing conditions using
polyclonal anti-mouse Prx1 antibody. The black arrows indicate the position of
reduced or oxidised forms determined by the number of MalPEG (MP) fixed to the
protein.
167
4.5.2.2 LPS stimulation causes a change in the redox state of Prx2 and its
release from RAW cells
Mouse Prx2 (22kDa) contains three cysteines: C51, C70 and C172 as illustrated in
Figure 4.11A, with two cysteines potentially involved in an intersubunit disulphide link
to form a 44kDa dimer. Similarly to Prx1, but simpler due to the presence of three
rather than four cysteines, there are a number of possibilities for MalPEG to bind to
Prx2 as shown in table Figure 4.11B. Analysis of the Western blot of the cell lysate
tagged with NEM only shows the dimer form of Prx2 (Figure 4.12). The monomer was
not detected. Following LPS stimulation this dimer was present in lower amounts
intracellularly but in greater quantities outside the cells. With MalPEG, the dimer was
discriminate in two forms: one fully oxidised at 44kDa (= 0 MP/ no -SH) and one with
two free thiols at 64 kDa (= 2 MP). Interestingly, in the cell lysate, both forms were
observed but only the more reduced one (64kDa) was observed when treated with
LPS. The opposite was observed extracellularly, both form were observed with LPS
but only the fully oxidised form (44kDa) in non-treated cells. Overall, Prx2 is more
reduced inside cells than in the supernatant.
These results were analysed further by densitometry of the bands on the Western
blots as shown Figure 4.13. The oxidised form of Prx2 in the cell lysate was not
present after LPS stimulation. On the contrary, a reduced form was detected in the
supernatant following LPS stimulation. These data illustrate a redox dynamic as the
oxidised disappear with LPS stimulation and a reduced form appeared extracellularly.
168
Figure 4.11: Representation of mouse Prx2 and potential site of MalPEG
fixation. A. Scheme of Mouse Prx2 with the three cysteines present in each monomer
(22kDa) and the potential intermolecular disulphide bonds in red dotted line. B. Table
illustrating all the potential possibilities for MalPEG to bind free thiols.
169
Figure 4.12: Redox state of Prx2 in RAW cells lysates and in release after LPS
stimulation. Representation of two Western blot analysis of RAW cell lysate (upper
panels) and RAW cell release after acetone precipitation (lower panels) after
stimulation with LPS. Analysis were performed in non-reducing conditions using
polyclonal anti-mouse Prx2 antibody. The black arrows indicate the position of
reduced or oxidised forms determined by the number of MalPEG fixed to the protein.
170
Figure 4.13: Densitometry of each form detected in the cell lysate (n=8) and in
the releasate from cells (n=8) of Prx2. Reduced form (2 MalPEG = 2 free -SH) are
in blue while oxidised form (0 MalPEG = 0 free-SH) are in red. Data are expressed at
the ratio of the mean +/- standard deviation. * P<0.05; ** P<0.005; *** P< 0.001 versus
LPS are a paired two tailed T.test for eight independent experiments for the cell lysate
and for the release.
171
4.6 Applicability of the MalPEG methods to other proteins
Once the redox state of Prx1, Prx2 and Trx was monitored, the MalPEG technique
was also tested to other potential proteins that may be redox sensitive such as Prx4,
Hsp70, and STAT3. Prx4 and Hsp70 were studied in RAW cell lysates with and
without LPS stimulation and STAT3 was studied in the rat oligodendrocyte cell line,
CG4 cell lysate without stimulation. Prx4 belongs to the same family as Prx1 and Prx2:
the typical 2-cys peroxidase. Prx4 has four cysteines. This protein has been recently
identified as a potential biomarker in cardiovascular disease (Abbasi, Corpeleijn et al.
2012). Hsp70 is a heat shock protein which is released in stress conditions and
contains five cysteines and has been identified as glutathionylated by redox
proteomics (Fratelli, Demol et al. 2002). Finally, STAT3 which is involved in the
intracellular signalling pathways downstream of TLR activation that result in the
production of cytokines and other inflammatory molecules was investigated in CG4
cells in our laboratory. Unfortunately, most of these studies were unsuccessful. The
anti-Prx4 antibody was not able to detect mouse Prx4. Two antibodies were tested
(Figure 4.14A). Anti-Stat3 could not detect the protein labelled with MalPEG which
could be explained by the fact that MalPEG change the conformation of the protein
and peptide recognise by the antibody (Figure 4.14B). Only Hsp70 was detectable in
the cell lysate but also in the released from cells after LPS stimulation (Figure 4.14C).
However, its redox state was not affected by LPS as no change was observed. These
results show that this technique is wholly dependent upon the availability of specific
antibodies that are able to detect the protein of interest, even when MalPEG is present
on their cysteine residues.
172
Figure 4.14: Redox state of Prx4 (A); STAT3 (B) and Hsp70 (C); analysed by
Western blotting in non-reducing conditions. Redox state of proteins was
measured by adding MalPEG to cell lysate and supernatant. NEM was used as a
control. Proteins were then detected by Western blot following non-reducing SDS-
PAGE using anti-Prx4, anti-STAT3, or anti-Hsp70 antibodies. Molecular weight of
each protein and their number of cysteines are shown in brackets.
173
4.7 Redox state of Prx2 in HEK cells with TNF and Menadione
In order to see if the redox change observed in response to LPS was cell specific, the
redox state of Prx2 was investigated in HEK cells, a human embryonic cell line,
stimulated with TNF and Menadione. TNF was previously used to increase the
secretion of Prx2 in HEK 293 cells while Menadione increase the ROS production
thus implying an oxidant environment (Salzano, Checconi et al. 2014). As shown in
Figure 4.15, without MalPEG, Prx2 was detectable as a monomer (22kDa) and a
double dimer (around 44kDa) as two bands were observable corresponding to the
glutathionylated form and unglutathionylated form (Salzano, Checconi et al. 2014).
When tagged with MalPEG, the monomer form disappear. The double dimer around
44kDa was still present but a new form at 64kDa was also detected. The monomer
form may have disappear by mixing with the dimer form suggesting that the monomer
was not completely oxidised but contains two free thiols (20kDa (=2 x 10kDa MP) +
22kDa (Prx2 monomer) = 42kDa). The form at 64kDa could be explained if the dimer
form contains in total 2 free thiols which correlate with the idea that two out three
cysteine by monomer are engaged in intramolecular disulphide bonds. Interestingly,
no difference in redox forms was observable between the different treatments
indicating that the redox state of Prx2 was not sensitive to the treatment applied.
174
Figure 4.15: Redox state of Prx2 in HEK 293 cells treated with TNF and
menadione. Cells lysate were mixed with 0.2 mM of MalPEG or 50mM NEM used
as a control. Redox state of Prx2 was then analysed by Western blot in non-
reducing condition using polyclonal anti-Prx2 antibody.
175
4.8 Gene profile expression of Prx1, Prx2 and Trx in RAW cells
We wondered if the release of Trx, Prx1 and Prx2 proteins from RAW cells was due
to induction of their respective transcription following LPS stimulation or if these
proteins were secreted from an existing pool of proteins already present in the cells
without LPS-stimulation.
For this purpose, we analysed the data obtained from the gene expression microarray
experiment previously described in Chapter3-3.5 - Effect of GSH depletion during
LPS stimulation on the gene expression profile of RAW cells.
As previously observed and as shown in Table 3.1, Prx1 is up-regulated by LPS
treatment and further up-regulated if cells were previously depleted with GSH using
its inhibitor, BSO. On the contrary, Trx and Prx2 were not affected neither at 2h nor
6h following LPS stimulation and were not affected by GSH depletion (Figure 4.16).
We could then hypothesise that while Prx1 gene is up-regulated by LPS before its
protein secretion, Prx2 and Trx are secreted from a present pool of proteins.
176
Figure 4.16: Gene profile expression of Prx1, Prx2 and Trx in RAW cells
following LPS and BSO stimulation. Data are provided from Chapter3-3.5 - Effect
of GSH depletion during LPS stimulation on the gene expression profile of RAW cells.
RAW cells were incubated 24h with 120μM BSO before being stimulated with 10ng/ml
of LPS for 2 or 6h. Total RNA was extracted and hybridised in cDNA Chip before
further analysis by microarray technology as described Chapter 3-3.5. Data are
expressed as the fold change compared with one chosen control (mean ±SD of three
biological replicates).** P value < 0.001 versus Control by unpaired two-tailed
Student’s t-test, ∆<0.005 versus LPS by unpaired two-tailed Student’s t-test.
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
Prx1 Prx2 Trx Prx1 Prx2 Trx
2h 6h
Control LPS BSO BSO+LPS
**
**
**
**
∆
∆
177
4.9 Discussion
The MalPEG technique has allowed us to define the different redox states of Prx1,
Prx2 and Trx in RAW cells following LPS stimulation. These redox changes are
summarised in Figure 4.17. While we often consider Prx1/2 existing only as two
forms, a reduced monomer or a disulphide-linked oxidised dimer, we found that there
are further degrees of oxidation. Prx1 exists in various reduced dimer forms
intracellularly which are not affected by LPS stimulation. However, LPS led to the
secretion of Prx1 in two forms, a reduced one, with 2 free SH (suggesting one free
SH by monomer), and a fully oxidised dimeric form which is exclusively detected
extracellularly. Prx2 and Trx are both affected similarly by LPS stimulation: in normal
conditions, they both exist as a mixture of reduced and oxidised forms but after LPS
stimulation the oxidised forms disappear from the cell lysate. Trx is then detected as
an oxidised form in the supernatant while Prx2 is detected as a reduced form in the
supernatant. This suggests that Prx2 is released in the oxidised form but is reduced
extracellularly using an electron donor such as Trx (Perez-Perez, Mata-Cabana et al.
2009). This observation also supports the hypothesis that there is an extracellular
redox dynamic mediated by Prx2 and Trx which could catalyse, by thiol disulphide
exchange, extracellular substrates at the surface of cells or in the secretome
(Salzano, Checconi et al. 2014) (Figure 4.17). According to this hypothesis, Trx could
oxidise protein thiols due to the absence of Trx reductase in the extracellular
environment (Salzano, Checconi et al. 2014). This has already been observed in
bacteria where Trx, in an oxidising environment, can act as an oxidant (Debarbieux
and Beckwith 2000). In fact, our work supports this theory, as Trx was mainly found
oxidised in the secretome and could not therefore act as a reductant.
It also can be noted that 24h after LPS stimulation, the cell lysate contains mainly
reduced forms of the proteins compared to the non-treated cells. In fact, cells could
secrete the oxidised form in order to keep a highly reduced intracellular environment,
or because the oxidised form could be toxic. This hypothesis is reinforced by the
microarray analysis data showing that transcription of Trx and Prx2 is not increased
by LPS. Thus, it seems that Prx2 and Trx are released from an existing pool of protein
rather than by increasing their transcription, in contrast to Prx1. Actually, other
significant differences between Prx1 and Prx2 have been reported (Lee, Choi et al.
2007). Although, both are 2-cys Prxs, 90% identical in amino sequence and only differ
by the presence of one extra cysteine in Prx1, they seem to be regulated differently
and have different biological roles (Lee, Choi et al. 2007).While Prx1 acts as a
178
molecular chaperone by protecting substrate from oxidative stress, Prx2 privileges its
detoxifying peroxidase function becoming itself oxidised (Lee, Choi et al. 2007). This
could explain why we detect Prx1 in many reduced forms but never fully oxidised
independently of LPS stimulation while Prx2 is detected in the oxidised and reduced
form prior to LPS stimulation.
Interestingly, the fully oxidised form of dimeric Prx1 is only observed in the
extracellular environment after LPS stimulation and this could be important in its role
as a DAMP which binds TLR4 (Riddell, Wang et al. 2010). It could also be possible
that Prx1 is released in its reduced form (dimer with 2 free SH) and become fully
oxidised extracellularly (Figure 4.17). Although our previous work ruled out this
possibility at least in term of oxidation to a disulphide-linked homodimer (Mullen,
Hanschmann et al. 2015). Oxidation may take place in the form of sulfenylation which
acts as an intermediate stage before interacting with TLR4 (Beedle, Lynham et al.
2016). In fact, this fully oxidised form also suggest inactivation of its catalytic activity
as it cannot interact with molecules and supports the DAMP role of Prx1. However, at
this stage, we have no evidence to tell whether the DAMP activity of Prx1 is restricted
to a specific redox form or it is an intrinsic property of the protein, independently on
its redox state.
Surprisingly, despite the fact that proteins studied have various cysteines, the same
ones were targeted by MalPEG suggesting a stability in the mechanism of maintaining
these redox forms. This was even more evident for Trx which contains 6 cysteines in
the mouse isoform and is only detected in two redox states; with two and five free
thiols in the lysate and with two and four free thiols in the releasate from RAW cells.
Therefore, MalPEG can target most of the thiols and is not limited by steric hindrance
of the 10kdA PEG. However, it is possible that after LPS stimulation, conformational
change of the protein take place and that is why the oxidised Trx form is mainly
observed extracellularly.
Finally, redox changes of Prx2 were not observed in HEK cells despite the use of the
strong oxidant menadione and TNF which helps to secrete Prx2. This suggests a
specificity of the redox change to LPS stimulation as both LPS and TNF stimulate
different signalling pathways. Indeed, LPS bind the TLR4 receptor while TNF is
recognised by TNF Receptor 1 or 2 leading to the activation of different signalling
mechanisms (Wajant, Pfizenmaier et al. 0000). It is possible that oxidation of Prx2
has a role in this series of activation of signalling proteins specific of the TLR4
pathway.
179
We should acknowledge that the MalPEG method has some limitations as shown by
attempting to measure the redox state of other proteins (Prx4, Stat3). Firstly, steric
hindrance of the 10kdA PEG could prevent MalPEG to alkylate non accessible Cys
located in a small pocket or inside the protein. Secondly, the quality, specificity and
sensitivity of the antibodies used are the main technical limitation. Thirdly, another
inconvenience was that samples, once reacted with MalPEG could not be stored
frozen. Indeed it was observed in this study that running previously frozen samples
lead to unclear bands in Western blot possibly due to instability of the MalPEG.
Finally, a recent study has also shown the loss of the PEG during SDS-PAGE and
recommended to avoid heating MalPEG containing-samples above 60˚C; our study
followed that recommendation (Zhang, Liu et al. 2015).
In conclusion, the MalPEG technique has allowed us to detect redox change of
proteins and highlight a redox dynamic performed by extracellular Prx2 and Trx in
response to LPS stimulation in RAW cells. In fact, we could identify different degree
of oxidation of Prxs and Trx and identification of specific redox-dependent biological
function is required to strengthen our findings. Further work to understand the
signalling pathway leading to redoxkines secretion and redox changes is also needed.
The identification of the targets of Prx2 and Trx redox catalytic cycle will be
investigated in the following chapter (Chapter 5).
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Figure 4.17: Redox forms detected in RAW cells stimulated or not with LPS for
24h. Prx1 (blue), Prx2 (green) and Trx (yellow) are schematised in reduced (red) or
oxidised (ox) forms intracellularly and extracellularly with or without LPS stimulation
according to their relative abundance detected by Western blot. NT, untreated cells.
Following LPS stimulation, Prx1 is released extracellularly and could act as a DAMP
while Prx2 and Trx undergo catalytic activity oxidising free thiol at the cell surface.
181
Chapter 5
Changes in the redox state of membrane
proteins associated with the inflammatory
response
182
5.1 Introduction
Prx1, Prx2 and Trx are released from cells following inflammatory stimuli in specific
redox states probably associated with their pro-inflammatory functions as discussed
in Chapter 4. For instance, extracellular Prx2 can stimulate TNF production in
macrophages, and extracellular Trx can act as a chemokine to attract immune cells
(Salzano, Checconi et al. 2014) (Bertini, Howard et al. 1999). This secretion of
enzymes linked with oxidative stress and redox regulation following inflammatory
stimuli was also reported for protein disulphide isomerase (PDI) in eosinophils (Dias,
Amaral et al. 2014), for myeloperoxidase in neutrophils (Nussbaum, Klinke et al.
2013), and more recently for superoxide dismutase (SOD) in human neutrophils
(Iversen, Gottfredsen et al. 2016).
The secretion role of these redox enzymes in the inflammatory context is currently
under investigation in particular for thiol oxidoreductase enzymes such as Prx and Trx
which could control thiols at the surface of cells to modulate immune functions
(Metcalfe, Cresswell et al. 2011, Salzano, Checconi et al. 2014). According to this
model, and as seen in Figure 5.1, Prxs use Trx as an electron donor which in return
oxidoreduce surface thiols, thus changing target protein conformation for instance by
dimerization. Released PDI could, in addition, control the level of these exofacial thiols
at the surface of immune cells (Jiang, Fitzgerald et al. 1999).
In fact and reinforcing this hypothesis, a number of studies have demonstrated that
an increase in surface free thiols on immune cells is linked with activation of immune
responses (Metcalfe, Cresswell et al. 2011). This was mainly demonstrated in
lymphocytes such as in isolated CD8+ T from mice where an increase in surface free
thiols was observed after stimulation of their T receptor with LCMV-Armstrong a virus
(Pellom, Michalek et al. 2013). Similarly, rats in which surface free thiols of
lymphocytes were artificially increased were more likely to develop arthritis than
control rats (Gelderman, Hultqvist et al. 2006). This was explain by an excessive
immune reactivity following this increase of free thiols demonstrated with an excess
release of IL-2 a pro-inflammatory cytokine previously associated with arthritis.
Recently, this increase of thiols at the cell surface was also detected in monocytes
cell lines following TNF or LPS stimulation and detected using a fluorescent derivative
of maleimide (Szabo-Taylor, Toth et al. 2017). Furthermore, the same research group
observed a correlation between elevated levels of free thiols at the surface of
circulating monocytes in rheumatoid arthritis disease patients in comparison with
healthy donors.
183
In addition, a number of immune receptors in which thiols were sensitive to redox
changes have also been identified. For instance, CD44, a transmembrane protein
receptor on leukocytes that binds hyaluronic acid (HA) a component of extracellular
matrix (ECM). This interaction facilitates migration of leukocytes to sites of
inflammation. When its thiols in Cys77 and Cys97 are in the reduced state and stop
forming a disulphide bonds, this interaction with the ECM is lost (Kellett-Clarke,
Stegmann et al. 2015). This redox reduction were identified by Mass Spectrometry
after reduction using the strong reducing agent TCEP but also the recombinant Trx to
simulate inflammatory conditions. It therefore suggest that these disulphide bonds
was sensitive to redox changes and potentially regulated by redox enzymes.
Likewise, the reduction of a disulphide bond (Cys183-Cys232) in CD132 of IL-2
receptor prevents IL-2 binding, a pro-inflammatory cytokine secreted by lymphocytes,
and therefore its signalling pathway to respond to potential infection (Metcalfe,
Cresswell et al. 2012).
Membrane proteins are rich in proteins linked with cell signalling such as receptors,
channels, transporters and enzymes and are at the forefront of dynamic processes
such as cell migration and cell communication (Winterbourn and Hampton 2008).
Therefore, they represent a particular interest to find key proteins in which immune
function is regulated by thiol exchange inflammation. To detect potential redox
sensitive thiols and therefore identify those targets, the set-up of an appropriate
method is a crucial and challenging step due to the reversibility of thiol exchange but
also the difficulty to study membrane proteins. These are particularly difficult to
analyse due to their hydrophobicity which leads them to aggregate.
In this chapter we tried to set up a technique to identify protein containing free thiols
at the surface of RAW cells in an inflammatory condition by stimulating cells with LPS
for 24h. At that time point, an increase in Prxs and Trx is observed and we
hypothesized than both proteins cooperate to oxidise free thiols by thiol disulphide
exchange. The identification of potential target could help to understand immune role
of Trx and Prx2. For this purpose, RAW cells were stimulate 24h with LPS to make
sure Trx and Prx2 were released and in sufficient amount. Free thiols were then
labelled with a biotin device and membrane proteins were extracted using sonication
and ultracentrifugation. Finally, tagged thiols protein were collected by affinity
purification and analysed by MS-based proteomics techniques to identify potential
targets of redox change during the inflammatory response.
184
Aim: Prx2 and Trx are released in a potential redox dynamic as shown in the
previous chapter (Chapter 4). Furthermore both protein shave cytokine and
chemokine like functions. To understand better how these proteins function in
the extracellular environment, we wanted to identify potential protein targets of
oxidoreduction on the cell surface. Thus, we developed techniques for
measuring and identifying surface protein free thiols that were subject to redox
regulation under LPS stimulation.
185
Figure 5.1: Hypothesis for a mechanism of redox signalling mediated by Prx2
and Trx released during inflammation (Salzano, Checconi et al. 2014).
186
5.2 Protocol for extracting membrane proteins from cultured cells
Before labelling surface thiols, the protocol for extracting membrane proteins
described previously by Laragione et al. was tested and adapted to ensure it was
applicable to our cell system (Laragione, Bonetto et al. 2003). Briefly, cells were
sonicated in homogenization buffer containing Hepes and sucrose and were then
submitted to ultracentrifugation (Chapter 2 – 2.12). As shown in Figure 5.2, without
further washing of the pelleted material (experiment 1), GAPDH, a marker of cytosolic
proteins, was present in similar amount (ratio 50:50) as ATPase, a plasma membrane
marker, indicating contamination with cytosolic proteins.
However the addition of an extra wash with PBS followed by ultracentrifugation
(experiment 2) allowed us to decrease significantly the level of the cytosolic marker
GAPDH detected by Western blotting. With this supplementary wash, the ratio of
GAPDH/ATPase was then close to 1: 100.
187
Figure 5.2: Optimisation of the experimental protocol for extraction membrane
proteins from RAW cells. Final preparations of membrane proteins from untreated
samples (NT) were analysed by Western blot using anti-ATPase antibody as a protein
representative of membranes and anti-GAPDH antibody as a cytosolic marker.
Membrane pellet was either no washed (experiment 1) or undergone an additional
washing step (experiment 2). Densitometry of each band was assessed by Image
Studio Lite.
188
5.3 Labelling of cell surface thiols with BIAM
Surface thiols in RAW cells were labelled using N-(Biotinoyl)-N'-(Iodoacetyl)
Ethylenediamine (BIAM), a biotinoylated iodoacetamide, which irreversibly alkylates
free thiols (Figure 5.3). BIAM is not cell-permeable and therefore can only label
exofacial thiols in proteins located at the surface of the cell. In addition, due to the
covalent linked with BIAM, free thiols from membrane proteins carry a biotin tag.
Therefore, after extraction of membrane protein, this biotin tag could be detected by
Western blot using streptavidin-HRP following SDS-PAGE to separate the membrane
proteins. This biotin tag was also used for affinity purification on streptavidin beads.
189
Figure 5.3: Labelling of free thiols with BIAM at the surface of RAW Cells. RAW
cells are treated with or without LPS before to be labelled with BIAM. Once tagged,
free thiols are extracted from RAW cells and were detected by Western blot using
Streptavidin-HRP.
190
5.4 Optimization of BIAM concentration
The optimal BIAM concentration used to tag surface thiols on RAW cells was
investigated by applying concentrations of BIAM between 0 and 200mM directly to
cells. This optimisation was performed to avoid potential unreacted biotin
contamination and therefore too much background in Western blot. For this purpose,
1x106 of RAW cells were plated in 2.5ml of RPMI, incubated overnight and treated
with 100ng/ml of LPS as described in Chapter 2 – 2.16. Once collected, cells were
incubated 15min at 37˚C with BIAM, cells were then washed with PBS and
membranes proteins extracted by using Homogenisation buffer containing Hepes and
sucrose and sonication. Free thiols contained in isolated membranes proteins from
untreated and LPS stimulated cells were then analysed by Western blot using
streptavidin-HRP. As shown Figure 5.4, a concentration between 10μM and 50μM
seems enough to block all free thiols without excess of biotin leading to a strong
signal. Therefore, in further experiments, a concentration of 20μM of BIAM was used
to label cell surface thiols.
191
Figure 5.4: Determination of BIAM concentration to use to label surface free
thiols. RAW cells were incubated 15 min with different concentration of BIAM. After
isolation and separation by SDS-PAGE, membrane proteins were analysed by
Western blot using HRP-streptavidin. A marker of cytosolic protein (GAPDH) and of
membrane proteins (ATPase) were also analysed by Western blot and their
respective expecting molecular weight are indicated by red arrows.
192
5.5 Effect of LPS on the level of surface thiols detected
Once the protocol for extracting membrane proteins from RAW cells was optimised
and that BIAM working concentration was determined, RAW cells were stimulated for
2h or 24h with 100ng/ml of LPS. After LPS stimulation, cells were labelled with BIAM
and membrane proteins were isolated. It can be seen in Figure 5.5 that membrane
proteins from cells that had been stimulated 2h with LPS had a similar, if not lower,
number of free thiols detected than the one from untreated samples.
At the contrary, following a 24h-LPS stimulation, free thiols detected from the
membrane proteins were more numerous than the one detected from the non-treated
(NT) cells.
Supernatants obtained from the different steps of the membrane protocol extraction
were also analysis. Spin 1 corresponds to the sample containing membrane proteins
just before the first ultracentrifugation. It can be seen that it contained more cytosolic
proteins than membrane proteins as shown by the GAPHD/ATPase bands ratio. Spin
2 which is the first supernatant following ultracentrifugation contained mainly cytosolic
proteins as the membrane proteins are contained in the pellet. Finally, in Wash 1 and
Wash 2 which corresponds to the supernatant of ultracentrifugation with PBS, no free
thiols were detected neither ATPase nor GAPDH marker. All these samples indicate
that membrane proteins were successfully collected.
We then decided to identify the BIAM-labelled proteins from the NT cells and cells
treated for 24h with LPS via Mass Spectrometry (MS). For this purpose we used a
protocol specifically adapted for membrane analysis (Blonder, Chan et al. 2006).
Once membrane proteins were reduced and alkylated with iodoacetamide, protein
concentration in both the NT and LPS samples were assessed using a BCA assay in
Figure 5.6. Respectively, 125μg/ml of membrane protein were collected for the
untreated sample and 149μg/ml for the LPS-stimulated sample. This could explain
why more free thiols were detected in the LPS-treated sample. A similar concentration
of membrane protein from untreated and LPS stimulated samples were then
solubilised in a mixture of methanol and ammonium bicarbonate to solubilise
hydrophobic proteins. This solution was then mixed with streptavidin-agarose beads
to affinity-purify BIAM-tagged proteins. After three washes with PBS, samples were
treated with trypsin in order to collect peptides of protein of interest (Biotin tagged
protein). The peptides obtained were then identified using mass spectrometry (MS).
All samples recovered during each steps during solubilisation and affinity purification
of BIAM-tagged membrane thiols are shown in a Western blot in Figure 5.7. It can be
193
seen in samples 3, 4 and 5, which are the wash supernatant of beads, that no free
thiols were detected suggesting that they were all bind to streptavidin beads.
194
Figure 5.5: Surface thiols in RAW cells after LPS treatment for 2h or 24h.
Membrane proteins were labelled with 20μM BIAM for 15min before extraction and
analysed by Western Blot using streptavidin-HRP. Washes and cytosolic proteins
recovered during the extraction process were also analysed. A cytosolic protein
(GAPDH) and a membrane protein (ATPase) were also analysed. NT, untreated cells.
195
Figure 5.6: Protein concentration in NT and LPS (24h) treated samples. Protein
concentration was analysed with the BCA kit assay. After plate incubation at 37˚C of
the samples with the different reagents for 30min, the absorbance at 562nm was
determined. The concentration was then determined using a standard curve made of
BSA diluted in a range of 0 to 2mg/ml.
196
Figure 5.7: Purification of BIAM-tagged membrane proteins. All samples obtained
during the processes of purification and immunoprecipitation of BIAM-tagged protein
were analysed by Western blot using biotinylated anti-ATPase antibody, a component
of the plasma membrane, followed by streptavidin-HRP. RAW cells were previously
treated with 100ng/ml of LPS (LPS) or untreated (NT).
197
5.6 Analysis of the proteins identified by MS
In the control sample, 1871 proteins were identified while 2427 proteins were
identified in the sample obtained from LPS-treated cells (Appendix 16-17). Proteins
recognised by only one peptide were filtered out leaving 1346 for untreated cells (NT)
and 1757 proteins for LPS-treated cells as shown in Table 5.1 and Table 5.2
respectively. These results correlate with the Western blot using HRP-streptavidin
which shows that membranes from LPS-treated cells contained more free thiols that
NT.
Different types of bioinformatic analyses were then performed. First, both lists were
submitted to DAVID in order to determine in which cellular location the proteins
identified were the most representative among different cellular compartments using
the GOTERM Cell Compartment classification. It can be seen in Figure 5.8 that the
most represented compartment was the membrane which includes any lipid bilayers
within the cells such as exosomes, endoplasmic reticulum and plasma membrane.
However in both control and LPS-treated cells, a similar proportion of proteins
identified were associated with the nucleus, cytoplasm and extracellular exosomes.
Similarly the main functions and molecular pathways within these groups of protein
were analysed using GOTERM Biological Pathways and Kegg pathways
classifications in DAVID (Figure 5.9). The same functional categories were obtained
for both groups. Although “transport” was the category with the largest number of
proteins, “translation” was the second most important functional category confirming
the presence of a large number of nuclear proteins.
198
Description -10logP Coverage (%) #Peptides #Unique Avg. Mass
Myb-binding protein 1A 210.76 27 32 32 152036 MYB binding protein (P160) 1a isoform CRA_b 210.76 27 32 32 152036 Myosin-9 190.03 20 31 26 226370 78 kDa glucose-regulated protein 245.64 46 30 28 72422 Putative uncharacterized protein 245.64 46 30 28 72406 Putative uncharacterized protein 245.64 46 30 28 72423 Calnexin 225.25 41 30 30 67278 Putative uncharacterized protein 225.25 41 30 30 67268 Calnexin isoform CRA_a 225.25 41 30 30 67278 Protein disulfide-isomerase A3 205.55 58 28 27 56678 ATP synthase subunit beta mitochondrial 214.47 56 23 23 56301 Tubulin beta-5 chain 191.48 69 23 4 49671 Dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 184.75 39 23 23 68528 Rpn1 protein (Fragment) 184.75 39 23 23 68397 Putative uncharacterized protein 184.75 39 23 23 68527 GN=Atp1a1 189.62 23 22 17 112982 Endoplasmin 206.34 32 21 20 92476 Heat shock protein 90kDa beta (Grp94) member 1 206.34 32 21 20 92476 Putative uncharacterized protein 206.34 32 21 20 92476 Heat shock protein 90 beta (Grp94) member 1 206.34 32 21 20 92490 Prelamin-A/C 180.42 35 21 21 74238 Catalase 206.21 46 20 20 59731 Catalase 206.21 46 20 20 59765 ATP synthase subunit alpha mitochondrial 192.33 45 20 20 59753 60S ribosomal protein L4 190.08 48 20 20 47154 Ribosomal protein L4 190.08 48 20 20 47154 Tubulin beta-4B chain 184.02 56 20 1 49831 60S ribosomal protein L7a 186.8 56 19 18 29977 Uncharacterized protein 186.8 56 19 18 29965 MCG11348 186.8 56 19 18 29977
Table 5.1: List of the 30 proteins from which most peptides have been identified
by MS in untreated cells. Proteins were previously selected with a FDR<1 (=-
10logP).
199
Description -10logP Coverage (%) #Peptides #Unique Avg. Mass
Myb-binding protein 1A 254.04 42 53 53 152036 MYB binding protein (P160) 1a isoform CRA_b 254.04 42 53 53 152036
Myosin-9 200.78 23 33 27 226370
Heat shock protein HSP 90-beta 200.11 45 29 19 83281
Calnexin 210.57 42 29 29 67278
Actin cytoplasmic 1 217.4 72 29 0 41737
Actin cytoplasmic 2 216.72 72 29 1 41793
Actin beta 217.4 72 29 0 41737
Putative uncharacterized protein 210.57 42 29 29 67268
Putative uncharacterized protein 217.4 72 29 0 41709
Putative uncharacterized protein 217.4 72 29 0 41811
Putative uncharacterized protein 217.4 72 29 0 41751
Putative uncharacterized protein 207.72 69 29 0 41769
Actin gamma cytoplasmic 1 216.72 72 29 1 41793
Calnexin isoform CRA_a 210.57 42 29 29 67278
Heat shock protein 84b 200.11 45 29 19 83281
78 kDa glucose-regulated protein 214.44 44 28 27 72422 Heterogeneous nuclear ribonucleoprotein M 215.35 43 28 28 77649
Putative uncharacterized protein 215.35 45 28 28 73741
Putative uncharacterized protein 214.44 44 28 27 72406
MKIAA4193 protein (Fragment) 215.35 44 28 28 75182
Putative uncharacterized protein 214.44 44 28 27 72423
Vimentin 193.79 59 27 27 53688 Heterogeneous nuclear ribonucleoprotein U 201.91 38 27 27 87918
Putative uncharacterized protein 201.91 38 27 27 87946
Putative uncharacterized protein 201.91 38 27 27 88017
Putative uncharacterized protein 201.91 38 27 27 87846
Vimentin 193.79 59 27 27 53688
Prelamin-A/C 207.4 51 26 26 74238
Tubulin beta-5 chain 210.42 71 26 5 49671
Table 5.2: List of the 30 proteins from which most peptides have been identified
by MS in LPS-treated cells. Proteins were previously selected with a FDR<1 (=-
10logP).
200
Figure 5.8: Ten most overrepresented cell compartments in free thiol-
containing membrane proteins from RAW cells. The lists of proteins from
untreated or LPS-treated cells obtained by MS were analysed using the GOTERM
cell compartment classification in DAVID. In NT samples, 549 proteins out of 1346
and 699 out of 1757 in the LPS one had a functional classification in DAVID and
therefore were recognised. The ten categories containing the most number of proteins
are shown.
201
Figure 5.9: Ten most overrepresented functional categories in free thiol-
containing membrane proteins from RAW cells. The lists of proteins from
untreated or LPS-treated cell obtained by MS were analysed using the GOTERM
Biological Process (grey bars) and Kegg pathways (black bars) classification in
DAVID. In NT samples, 549 proteins out of 1346 and 699 out of 1757 in the LPS one
had a functional classification in DAVID and therefore were recognised. The ten
categories containing the most number of proteins are shown.
202
5.7 Proteins of interest
We then decided to identify proteins of specific interest linked with redox signalling
pathway or inflammation. For this purpose, those proteins that were identified in both
LPS and untreated cells were displayed using a Venn diagram as shown Figure 5.10.
The proteins in the three groups (those identified only in the membranes from LPS-
treated cells, only in untreated (NT) cells or identified in both samples) were then
analysed manually using selective key words such as “redox”, “cytokine”, or “receptor”
for instance.
In proteins detected only in NT cells (= 416 proteins), we could identify thioredoxin-
related transmembrane protein 2 (2 proteins recognised out 416 identified = 2/416),
insulin-like growth factor 2 receptor (2/416 proteins), PDI (9/416 proteins), NADH
dehydrogenase (16/416 proteins), integrin alpha (12/416 proteins), integrin beta
(2/416 proteins), CD14 antigen fragment (6/416 proteins), CD166 antigen (3/416
proteins).
Among the 826 proteins bearing an exofacial thiol identified exclusively in membranes
from LPS-treated cells, we could recognise proteins linked with the 2'-5'-
oligoadenylate synthase family (Oas1 and Oas3) (5/826); signal transducer and
activator of transcription (STAT) (6/826 proteins) and prostaglandin G/H synthase
precursor (Ptgs2) (1/826 proteins). Interestingly these genes were previously
identified in the microarray experiment (Chapter 3) as up-regulated by LPS and
further down-regulated by BSO, an inhibitor of GSH, therefore suggesting that GSH
was important to mediate their transcription during the inflammatory response.
Other proteins linked with stress and inflammation response were also identified such
as Interferon-induced transmembrane protein (1/826) and eight heat shock proteins
(Hsp) (8/826 proteins), among them Hsp70, Hsp105, and Hsp75. Surprisingly,
although more than 10 integrins were found in the NT group, these proteins were not
detected in the LPS group.
Among the proteins identified in both LPS-treated and control cells were Trx-related
transmembrane protein 1 (1/930 proteins), peroxiredoxin-1 (1/930 proteins), profilin
(4/930 proteins), heme oxygenase (4/930 proteins), CD44 antigen (9/930 proteins),
cytochrome C oxidase (16/930 proteins), PDI (3/930 proteins).
203
Figure 5.10: Venn diagram of membrane proteins shared by LPS-stimulated and
untreated (NT) RAW cells identified by MS.
204
5.8 Investigation of the expression of membrane Trx
Although Trx was not present in the lists of protein identified by MS, some papers
have pointed out its existence at the surface of human cell lines (Sahaf, Soderberg et
al. 1997, Wollman, Kahan et al. 1997). The absence of Trx in the protein identified by
MS could be explained by the fact that membrane Trx is in the oxidized state. In fact,
if a protein was present in the absence of free thiols, the protein would not be labelled
with BIAM and would not attach to the streptavidin beads and consequently the
protein would not be identified by MS.
Therefore, we attempted to detect the protein in the membrane of RAW cells from the
same samples we used for MS identification. As shown in Figure 5.11A, three bands
were detected with the antibody anti-Trx; one at the expected molecular weight (MW)
of 12kDa but also one at a higher MW band (around15kDa) and one at 24kD (all forms
were indicated by black arrows). It has to be noted that no difference was detected
between untreated and LPS-treated samples.
The presence of Trx was confirmed in two other experiments (experiment 2 and
experiment 3) as shown in Figure 5.11. While the form at 24kDa was observed, the
12kDa and 15kDa bands were barely detected (Figure 5.11B and C). Usual controls
of the membrane purification were also made using GAPDH and ATPase marker. This
controls confirmed the low amount of cytosolic protein as illustrated Figure 5.11D, it
also suggests that 12kDa and 15kDa forms of Trx could be due to contamination from
the cytosolic compartment. The 15kDa form could be a Trx in association with a small
chaperone molecule of 3kDa and could belong to the nucleus or other organelles as
cells were lysed differently than in our previous work (Chapter 4).
The form identified at 24kDa could correspond to the molecular weight of the Trx
dimer. To confirm that, β-mercaptoethanol (β-ME) was mixed with the sample in order
to reduce intramolecular disulphide bonds. However, the protein was not reduced
completely as seen Figure 5.12A. DTT, another reducing agent was also tested but
the same result obtained with β-ME was observed (Figure 5.12B). In fact, this dimer
resistance to reductant was recently demonstrated in shrimp Trx as a result of the
disulphide bond Cys73-Cys73 (Campos-Acevedo, Sotelo-Mundo et al. 2017). This
disulphide bonds resist to elevated concentration of DTT higher than 50mM. We
therefore hypothesise that Trx was present at the membrane of RAW cells as a stable
dimer.
205
Figure 5.11: Detection of Trx in membrane RAW cells. A; B; C. Three individual
experiments: Cells were treated or not with 100ng/ml of LPS for 24h, then membranes
were extracted and submitted to electrophoresis. Trx was analysed by Western blot
using a polyclonal antibody anti-mouse Trx antibody. D; Western blot of a marker of
cytosolic protein (GAPDH) and of membrane proteins (ATPase) used as controls for
the membrane extraction in Experiment 2.
206
Figure 5.12: Reduction of the 24kDa form of Trx with β-ME (A) and DTT (B).
Membrane samples were separated by electrophoresis after being mixed with
different concentrations of β-ME or DTT. Trx was then analysed by Western blot using
a polyclonal antibody specific for mouse Trx
207
5.9 Discussion
In the present Chapter, we have successfully set up a protocol for the identification of
proteins with free thiols present at the surface of RAW cells stimulated or not with LPS
for 24h. Despite the limitation that a number of proteins belonging to the nucleus and
cytosolic compartments were identified, suggesting some degree of contamination of
our membrane preparations, a number of observations were made and are discussed
below.
The overall amount of free protein thiols at the surface of 24h LPS-treated cells is
higher than the level of free thiols present in untreated cells. This result is similar to
what was observed in other monocytes cells lines using 100ng/ml of LPS for 24h
(Szabo-Taylor, Toth et al. 2017). This increase in free thiols is also observed in
association with activation of the immune response (Metcalfe, Cresswell et al. 2011).
On the contrary, the levels of surface thiols detected from cells stimulated only 2h with
LPS were similar to the levels observed in untreated cells if not slightly lower. This
may relate to the fact that we detected an increase generation of superoxide anion
(O2.-) at this early time point, as described in Chapter 3 - 3.3. Superoxide, a ROS, is
a highly reactive molecule able to oxidise rapidly proteins and especially free thiols,
and one could have expected an even stronger decrease in the level of free thiols.
Additionally, a number of receptors and signalling proteins were identified by MS
among the membrane proteins bearing a free surface thiol. Integrins were present in
untreated cells but not LPS-treated cells. In fact, integrin α-4, was previously identified
as a redox target in human peripheral blood mononuclear cells and could be oxidised
by oxidoreductases released during LPS stimulation (Laragione, Bonetto et al. 2003).
PDIs were also identified in untreated cells but were less present in LPS-treated cells.
It could be that they actively catalyse thiol disulphide exchange with other molecules
at the surface of the macrophages during LPS stimulation as it was demonstrated in
neutrophils (Hahm, Li et al. 2013). On the other hand, heat shock proteins were only
found in LPS-treated cells and thus correlate with an inflammatory and stress
response.
The fact that some proteins identified in the LPS-treated cells (Oas family; STAT and
Ptgs2) were identified previously as being transcriptionally induced by GSH during
LPS stimulation (as shown in Chapter 3-3.5) strengthens the hypothesis that several
redox changes occur in RAW cells following LPS stimulation, although our results also
suggest some cytosolic contamination.
208
Finally, Trx was detected by Western blot in membrane samples but was not identified
by MS. Although this suggests that membrane Trx is normally in the oxidized state, it
is also possible that its peptides were not identified by MS for technical reasons. In
fact not all peptides ionize to the same extent and some may be difficult to detect by
MS. A better way to confirm one or the other hypothesis would be to use the MalPEG
method; unfortunately Trx detection was not possible potentially due to
conformational changes in the epitopes recognised by the antibody anti-Trx after
attachment of MalPEG molecule(s).
In addition, in our experimental model, Trx was identified as a dimer. This
conformational structure has been suggested in many structural studies but, to our
knowledge, was never detected in the membrane from mammalian cells.
Interestingly, structural studies have suggested that the Trx homodimer plays an
important physiological role due to its high stability, its conserved amino acids
sequence and the fact that it is not a substrate for Trx reductase (Weichsel, Gasdaska
et al. 1996). Possible biological roles of the dimer are still unknown but sensing the
cell redox state has been postulated (Weichsel, Gasdaska et al. 1996). Furthermore,
the oxidized dimer leads to the loss of cytokine-like activity by dimerization of C73
(Gasdaska, Kirkpatrick et al. 1996).
To conclude, the methodology probably needs further refinement including, for
instance, additional solubilisation steps and washes. Nevertheless, we obtained a set
of data pointing at potentially interesting findings. Since the reason why we set up this
technique was to identify potential surface target of extracellular Trx and Prx2, the
next step will consist in inhibiting Trx, to see which protein thiol/disulphides at the cell
surface are among its possible targets.
209
Chapter 6
Redox state of Peroxiredoxin 2 and Thioredoxin
as biomarkers of oxidative stress
210
6.1 Introduction
Coronary artery diseases (CAD) are part of the cardiovascular diseases (CVDs) which
are the main cause of death in the world (World Health Organization n.d.). In 2015,
31% of the deaths worldwide were attributable to CVDs and nearly half of them were
caused by CAD (World Health Organization n.d.). Thus, a decrease of this disease’s
incidence is one of the priorities of the WHO.
Physiologically, CAD occurs when an artery is narrowed, preventing the blood flow to
transport nutrients and oxygen to the heart (also referred as ischemia), and therefore
leading to myocardial infarction, where part of the heart muscle dies. According to the
WHO, risk factors include unhealthy food, alcohol abuse, and tobacco, lack of physical
exercise, diabetes, stress, and ageing. All these factors are linked with oxidation and
inflammation, themselves associated with coronary heart diseases (Hansson 2005,
Libby and Theroux 2005). In fact, CAD is mainly a consequence of years of
atherogenesis, a disorder of the artery walls, which starts as an inflammatory process
through the oxidation of fatty acids activating macrophages and immune response
(Ross and Agius 1992, Libby and Theroux 2005, Hansson and Hermansson 2011).
Essentially, proinflammatory cytokines and chemokines are released in the vessels,
by different processes, attracting monocytes and lymphocytes to the vessels and
leading to their adhesion to endothelium and smooth muscle cells into the intima layer
(just below the endothelium forming the walls of the blood vessels). This forms an
extracellular matrix which binds lipids and lead to the deposit of lipid-laden
macrophages (“foam cells”), differentiated monocytes which ingest lipids. This deposit
undergoes fibrosis and calcification forming an atheroma (Latin for “tumour full of
gruel-like matter”) that stop the blood from correctly supplying the heart (Libby 2002).
This inflammatory process which has become excessive and chronic is defined as
atherosclerosis (Ross and Agius 1992).
Unfortunately, the atheroma cannot be completely removed due to its anchorage in
tissues but can only be controlled by taking drugs such as aspirin in addition of
significantly improving life style with healthy diet and physical activity. However, in
some cases, surgical intervention is the only solution to restore proper blood flow.
This is referred as angioplasty, a common procedure, to flatten the atheroma; a
balloon is inflated directly into the blocked area, pushing the atheroma to the vessels
and placing a stent to prevent it from blocking, again, the blood flow in the vessel.
Ironically, angioplasty can also result in cellular damage due to the reperfusion
ischemia, that is a short period of ischemia followed by reintroduction of molecular
211
oxygen (O2) increasing the formation of ROS and further damage in the vessels
(Kalogeris, Baines et al. 2012 1159).This series of events can initiate inflammatory
responses and aggravate the local injury (Preeshagul, Gharbaran et al. 2013). To
evaluate the potential complication post-injury performed by the insertion of the stent
and to assess patient survival and adapt treatment (Preeshagul, Gharbaran et al.
2013), research has focused on the investigation of damage and inflammation
biomarkers easily accessible in the bloodstream. Ideally a large set of biomarkers is
evaluated for each patient by combining all the data collected leading to different and
specific therapy (Kleber, Goliasch et al. 2014). This required a large pallet of
biomarkers to identify the different facets of the disease; oxidation, inflammation, and
injury.
Currently, the most common biomarkers of CAD are Troponin T, a contractile
component of the heart muscle which is released into the circulation after heart failure,
and C-reactive protein (CRP), an acute protein synthesised in response to
inflammatory cytokines, such as IL-6 and TNF (Saunders, Nambi et al. 2011,
Ikonomidis, Michalakeas et al. 2012, Shrivastava, Singh et al. 2015). More recently,
circulating microRNAs have also been investigated as novel more reliable
biomarkers, easily measured in plasma (Wang, Zhu et al. 2010).
Assessing oxidative damage, an important factor in the progression and complication
of the disease, is however still problematic (Strobel, Fassett et al. 2011). Two main
oxidative biomarkers are currently used: an oxidised lipid, oxidised low-density
lipoprotein (LDL), and myeloperoxidase, a heme peroxidase (Huang, Mai et al. 2008,
Karakas and Koenig 2012). Limitation have been raised in regards to the handling
and standardisation of the samples to keep the same level of oxidative stress
reference (Pastori, Carnevale et al. 2014). Another issue is that the impact of
antioxidant level present in the environment which is not understood, can lead to
different interpretation of these markers (Pastori, Carnevale et al. 2014).
Thiol oxidation of blood proteins could be a response to this difficulty by assessing
potential oxidative damage to the vessels through the redox state of a chosen thiol
protein. In fact, blood, in addition to be easily accessible, is a rich environment in
terms of redox reactions and therefore in potential thiol oxidised molecules. As
explained by Butera et al, at least one fifth of all proteins present in blood contain
disulphide bonds which can be involved in processes such as coagulation, thrombosis
and inflammation (Butera, Cook et al. 2014). Human plasma is rich with free cysteine
or cysteine disulphide which are considered as biomarkers of oxidation due to their
sensitivity to smoke, alcohol abuse and high-fat diet all of which are risk factors for
212
CVD (Go and Jones 2011). Red blood cells are also very sensitive to redox
equilibrium and considered as entities with complex antioxidant systems and carrier
of oxidative biomarkers (Pandey and Rizvi 2011).
Interestingly, studies performed on Trx, Prx1 and Prx2 support the importance of
using thiol oxidation biomarkers in assessing oxidative damage in cardiovascular
disease (Choi, Lee et al. 2005, Martinez-Pinna, Lindholt et al. 2010, Martinez-Pinna,
Ramos-Mozo et al. 2011, Madrigal-Matute, Fernandez-Garcia et al. 2015).
A correlation between increase level of Prx1 and Trx and the evolution of the disease
in abdominal aortic aneurysm (AAA), a type of atherosclerotic disease, suggested that
both of them could be biomarkers of its severity (Martinez-Pinna, Lindholt et al. 2010,
Martinez-Pinna, Ramos-Mozo et al. 2011). Furthermore, recently, the same research
group has shown a dependency of their extracellular release, in plasma patient, with
NOX activation suggesting antioxidant response to atherosclerosis (Madrigal-Matute,
Fernandez-Garcia et al. 2015).
Additionally, overoxidation of Prx2 with sulfenic or sulfonic acid could be used as a
biomarker of the thickness of the intima layer in atherosclerosis and its evolution
(Kang, Lee et al. 2013). This overoxidation of Prx2 was observed in rats intima layer
after injury provoked by balloon insertion in the artery, a model of endothelium
damage, but also in human atherosclerotic lesions. Previous studies lead to
hypothesise that overoxidised Prx2 play a role in the healing process of the injury by
mediating the vascular signalling process (Choi, Lee et al. 2005).
Oxidation is present from the beginning of atherosclerosis in CAD patients until
medical intervention via the reintroduction of molecular oxygen in the blood stream.
This inflammatory disease is therefore an ideal condition to study the potential
occurrence in vivo of the redox changes in Trx and Prx2 previously detected in RAW
cells. To study this, in the experiments described in this chapter, we set up a technique
to measure the redox state of Prx and Trx in the blood of patients with CAD
undergoing percutaneous coronary intervention. During this procedure, the stenotic
portion of the artery is dilated with an intracoronary balloon after which a stent is
deployed to provide structural support to the artery.
Aim: Trx and Prx2 go through redox changes specific to LPS stimulation which
can be detected both intracellularly and in the secreted proteins. We wanted to
investigate whether these changes could be used as biomarkers of oxidation in
patient’s blood. Thus, in this chapter, the redox state of Trx and Prx2 will be
213
studied in human blood from healthy donors before to be determined in blood
from patients undergoing angioplasty.
214
6.2 Assessment of the MalPEG technique in Rat blood
Before applying this methodology to studies with human blood, the MalPEG-
technique was tested in rat plasma. In fact, the viscosity of MalPEG, mentioned
previously, added to the viscosity of blood could be problematic by preventing a
correct migration of the proteins in the polyacrylamide gel electrophoresis. As shown
Figure 6.1, both Trx and Prx2 were detectable in normal rat plasma. Two redox states
were observed with Trx: one fully oxidised at 12kDa (= 0 MP) and one more reduced
at 32kDa having two-SH groups (= 2 MP). For Prx2, two forms were also detected in
the MalPEG-treated proteins: an oxidised dimer without any MalPEG attached (= 0
MP), and a more reduced dimer, with two –SH groups, at 64kDa (= 2 MP).
215
Figure 6.1: Measure of the redox state of Trx (A.) and Prx2 (B.) in plasma from
two rats. Plasma from two independent rats (#1 and #2) were treated with 0.5mM of
MalPEG or with 50mM of NEM as described in the methods section. Trx and Prx2
were then analysed by Western blot in non-reducing or reducing conditions. The
arrows indicate the position of reduced or oxidised forms determined by the number
of MP fixed to the protein.
216
6.3 Measurement of the redox state of Prx2 in human plasma
Having shown that this technique is applicable to the detection of the redox state of
proteins in rat plasma, we assessed the redox state of Prx2 in human plasma from
four healthy human donors.
As shown Figure 6.2, Prx2 treated with NEM (which does not modify the molecular
weight) was mainly found as an oligomer (around 250 kDa) but also as two dimeric
forms, both around 44kDa in the plasma. These two dimers were already described
in human serum (Mullen, Hanschmann et al. 2015, Peskin, Pace et al. 2016). The
micro heterogeneity of the dimer, showing as a doublet, is probably due to small
changes in mobility associated with posttranslational modifications such as
glutathionylation. It has been suggested that they correspond to the glutathionylated
form (upper one) and non-glutathionylated dimer (lower one). The MP technique
allowed to discriminate between oxidized dimers, migrating to a putative MW of 44kDa
(=0MP), therefore no free –SH) and reduced dimers at 64kDa (which implies 2 MP
bound and therefore 2 free –SH).
The four healthy donors have a similar redox profile independent of the total amount
of Prx2 present.
217
Figure 6.2: Redox state of Prx2 in plasma from 4 healthy donors (#1 to #4).
Plasma samples were treated with 0.5mM of MalPEG (MP) or 50mM of NEM. Prx2
was then analysed by Western blot following non-reducing conditions. The arrows
indicate the position of reduced or oxidised forms determined by the number of MP
bound to the protein.
218
6.4 Measurement of the redox state of Trx in human red blood cells
In contrast with Prx2, we could not define the redox state of Trx in human plasma.
Indeed, anti-Trx detected aggregates and several bands at different molecular
weights, making the study of its redox state impossible as shown Figure 6.3A. To
overcome this difficulty, we studied Trx in red blood cells (RBCs) lysate. The
advantage of using biomarkers from RBCs is that they do not have a machinery to
synthetize new proteins due the lack of nucleus but RBCs are also the first cell
exposed to redox changes (Pandey and Rizvi 2011). Therefore oxidised protein are
less diluted than in the plasma and are constantly undergoing redox changes. In
addition, RBCs are full of antioxidant proteins such as Prx2, the third most abundant
protein in RBCs (Low, Hampton et al. 2008). The redox state of Trx was measurable
as three bands were observed: one at 12kDa for the completely oxidised form (= 0
MP), one at 32kDa (= 2 MP) and one around 62kDa (= 5 MP) (Figure 6.3B). The
three donors studied had a similar redox profile for Trx.
219
Figure 6.3: Redox state of Trx in plasma (A) or in RBCs lysate (B) (1:10 dilution)
from 3 healthy donors. Plasma or red blood cells samples were treated 0.5mM of
MalPEG (MP), 50mM of NEM, or left untreated (Ctrl). Trx was then analysed by
Western blot following reducing condition. The arrows indicate the position of reduced
or oxidised forms determined by the number of MP fixed to the protein.
220
6.5 Assessment of potential redox changes in Trx and Prx2 after stent
insertion in blood samples from CAD patients
Blood was taken from patients suffering from coronary artery disease before and after
angioplasty. During the medical procedure, a biodegradable stent was inserted into
the artery via a catheter and directed by radio-imaging to the injury allowing the blood
to flow freely as visualized in Figure 6.4A before the stent and Figure 6.4B after the
stent. Blood samples were collected at 3 time points (Figure 6.5). Firstly, before the
intervention, an intra-arterial blood sample was taken via peripheral arterial sheath (=
time point 1). Secondly, a sample was collected from the affected coronary artery after
insertion of the ballon but before stent insertion (=time point 2) or, thirdly, 30min after
the insertion of the stent (= time point 3).
As shown in Figure 6.6, the investigation of the redox state of Prx2 in plasma gave
inconsistent results. Indeed, either the Prx2 from samples treated with MalPEG was
not detected or the control Prx2 was not detected.
Therefore, we decided to measure both Prx2 and Trx in RBCs (Figure 6.7 and Figure
6.8). Results were consistent between the two patients where Prx2 was studied and
between the three tested for Trx. As shown in Figure 6.7, two bands were observed
in the MP-treated samples, one at 44kDa which corresponds to the dimer and one
staining stronger at 64kDa (2 MP). In Figure 6.8, MalPEG-Trx migrated as three
bands: one band at 12kDa (=0MP/0SH) which could be either the completely oxidised
form, another band at 32kDa (= 2 MP/2-SH) and a band at 62kDa (= 5 MP/5-SH).
There was no difference detected before and after stent insertion in any of the three
patients tested.
In that we could distinguish the different redox states but they did not change before
and after the stent (time point 1 versus time point 2 or 3). Two hypothesis could explain
the absence of redox changes; neither Trx nor Prx2 underwent redox modifications
under these conditions or because any specifically-oxidized protein would be
immediately washed away and diluted in the total body’s blood volume. However,
due to the difficulty to access blood samples, the study was stopped at that stage.
221
Figure 6.4: Angiographic images of the stenotic coronary artery before (A) and
after (B) stent procedure. The black arrow indicates the cardiac injury before and
after the stent. Images were kindly provided by Dr. Rajiv Rampat.
Figure 6.5: Stent deployment and time points of collected samples.
222
Figure 6.6: Redox state of Prx2 in plasma at different time point before or after
stent insertion. Plasma samples were treated with 0.5mM MalPEG. Prx2 was then
analysed by Western blot in non-reducing condition. The arrows indicate the missing
bands.
Figure 6.7: Redox state of Prx2 in RBCs at different time point before or after
stent insertion. RBCs samples were treated with 0.5mM MalPEG. Prx2 was then
analysed by Western blot in non-reducing condition. The arrows indicate the position
of reduced or oxidised forms determined by the number of MP fixed to the protein.
223
Figure 6.8: Redox state of Trx in RBCs at different time point before or after
stent insertion. RBCs samples were treated with 0.5mM MalPEG. Trx was then
analysed by Western blot in non-reducing condition. The arrows indicate the position
of reduced or oxidised forms determined by the number of MP fixed to the protein.
224
6.6 Oxidation of Trx and Prx2 with diamide
In order to see whether our technique could detect redox changes in Trx/Prx in patient
RBCs lysates, RBCs samples were treated with 10mM diamide, a thiol-oxidising
agent, for 10min (Kosower, Kosower et al. 1969). When treated with diamide, the
most reduced form of Trx disappeared as indicated by the red arrow (Figure 6.9). The
same observation was made for Prx2 (Figure 6.9). Therefore, our methodology can
detect oxidation of Trx and Prx2.
We then studied the applicability of our technique to a clinical setting, where the blood
may not be processed immediately after sampling which is an issue in the
standardisation of oxidative marker reproducibility between studies.
For this purpose, samples were either processed immediately (Control) or left on the
bench at room temperature for one hour. These were compared with samples
oxidised with diamide for 10min. A shown Figure 6.10, Prx2 was not affected by the
length of time of the procedure. Indeed, the two characteristic redox states of Prx2
(i.e. 44kDa and 64kDa) were still present in the same proportion in the sample left in
the bench for an hour and the one processed immediately. On the contrary, the redox
state of Trx was sensitive to the new condition: the most reduced form that is the band
migrating at 62kDa (= 5 MP/5-SH) disappeared similarly to samples exposed to
diamide.
225
Figure 6.9: Effect of diamide on the redox state of Trx and Prx2 in human RBCs.
After 10mM diamide treatment, proteins were treated with 0.5mM MP and analysed
by Western blot. 1 and 2 indicate the time point samples blood were collected. The
red arrows indicate the missing band. The black arrows indicate the different redox
states of Trx and Prx2.
Figure 6.10: Redox state of Trx and Prx2 in human RBCs left 1h at room
temperature. RBCs were tagged with 0.5mM MalPEG after 1h left on the bench or
after being treated 10min with 10mM diamide. Control are samples processed
immediately. Trx and Prx2 were then analysed by Western blot. The arrows indicate
the different redox states of Trx and Prx2.
226
6.7 Discussion
The aim of this chapter was to investigate the redox states of Prx2 and Trx in human
blood and thus measure redox changes in patients undergoing angioplasty due to
stent insertion by percutaneous coronary intervention.
The redox states of Prx2 and Trx were successfully measured in blood using the
MalPEG technique. However, no measurable redox changes were detected in the
blood of patients undergoing stent insertion, despite the fact that the blood was taken
from the injured area. There are two possible explanations for these observations.
First, it is possible that there are no biological redox changes occurring during the
stent insertion. However, it is well known that damage occurs to the artery due to
mechanical rupture of the atheroma by the stent, the presence of a foreign device and
also due to the reintroduction of oxygen in previously deprived blood vessels (Otsuka,
Finn et al. 2012) (Boos, Balakrishnan et al. 2007, Pelliccia, Del Prete et al. 2012).
Similarly, evidence of inflammation and oxidative stress evidences have been
reported during percutaneous coronary intervention (Cordis, Maulik et al. 1998,
Iliodromitis, Kyrzopoulos et al. 2006) (Chao, Li et al. 2004, Berg, Jynge et al. 2005).
In fact, one could hypothesise that the damage perpetuated by the stent insertion will
release DAMPS, and thus activate the TLR4 pathway leading to an inflammatory
response (Lee, Hutchinson et al. 2016). This is also supported by a recent study
demonstrating that hydrocortisone could reduce the inflammatory effects of stent
insertion by reducing TLR4 expression (Bagheri, Sohrabi et al. 2014). Thus, we could
hypothesise that as seen in RAW cells with LPS stimulation of TLR4 pathway, in
Chapter 4, redox changes may occur later than the time points chosen. Indeed, these
changes were measured 24h later from RAW cells, in both intracellular and
extracellular compartments. So it could be that the redox state of the proteins studied
is not affected by those damages yet.
The second possibility is that redox changes were undetectable due to the rapid
speed of blood flow through the systemic circulation. Cardiac output at rest is 5L/min
so one could expect local changes to be quickly diluted by this rapid blood flow.
In the experiments using diamide we could demonstrate that the redox state of Prx2
is quite stable and not too sensitive to experimental conditions, particularly when
compared to Trx. Incubation at room temperature for one hour, did not result in any
detectable change in redox forms identified in the case of Prx2, while a similar
incubation of Trx resulted in the loss of the more reduced form. Thus, Trx seems to
be more sensitive to oxidation and could therefore be a better, more sensitive, marker
227
of oxidative conditions. For instance, measuring the redox state of Trx could also help
in the quality control of human blood stored for transfusion as transfusion of oxidised
erythrocytes is often associated with dangerous side effects such as myocardial
infarction which can cause mortality (Bayer, Hampton et al. 2015). Another recent
study also demonstrated the role of Prx2 in erythrocytes as an indicator of cell damage
during storage (Harper, Oh et al. 2015). In fact, stored erythrocytes show increased
oxidative damage due to the lack of oxygenation and Prx2 is known to limit this
damage as long as its function is not altered. In their study, Harper and colleagues
attempted to measure Prx2 dimeric or monomeric forms in RBCs in blood stored in
transfusion bags at 4˚C in the dark from 7 to 35 days in order to control the quality of
erythrocytes as they hypothesised that the Prx2 dimeric form will indicate oxidation of
the blood (Harper, Oh et al. 2015). They then conclude that because Prx2 was present
as a dimer in the RBCs of stored blood that its activity was compromised (Harper, Oh
et al. 2015). The MalPEG technique described here could provide more insight into
whether Prx2 is present as a reduced or a fully oxidised dimer as we believe that there
are different degrees of oxidation of Prx2 (Chapter 4) and thus could provide further
information to stratify the degree of blood oxidation.
It has also to be noted that a previous study has demonstrated that the native redox
state of Prx2 from healthy donors obtained by mixing samples with NEM prior to lysis
is mainly found as a reduced monomer in RBCs whereas the dimer was present due
to overoxidation in lysis buffer and thus this dimeric form is overoxidised (Low,
Hampton et al. 2007). In our study, RBCs were lysed in ice-cold conditions and mixed
directly with MalPEG and Prx2 was found as a dimer. Under these conditions, it is
expected that the oxidation of Prx2 occur at a slow rate. In fact, we could see that our
dimer contained free thiols detected by MalPEG and thus was not overoxidized. This
also demonstrates the importance of defining what it is overoxidised. In our opinion,
overoxidation will mean that the monomers cannot dimerize via disulphide bond
formation and thus cannot catalyse the removal of peroxide substrates, while the
dimer form indicates that the protein is active and can catalyse this removal.
Remarkably, the redox profile of Trx and Prx2 in the RBCs from both patients and
healthy donors were similar to the redox profile obtained in RAW cells lysate: Prx2
with no free -SH or 2-SH and Trx with 0, 2 free -SH or 5 free-SH. This consistency of
the overall redox state detected for Prx2 and Trx suggest that the redox state of these
proteins is maintained both in cultured cells and in human blood and that the redox
state of these proteins is likely to be important for their biological functions.
228
In conclusion, we set up a method for identifying the redox state of Prx2 and Trx in
the blood. Further work will be needed to investigate if redox changes of Prx2 and Trx
occur in disease and if they could have a prognostic or diagnostic value.
229
Chapter 7
Discussion and conclusions
230
7.1 Summary of the study
The main aim of this work was to identify molecular redox mechanisms participating
in the inflammatory response in macrophages. This was achieved by investigating
and measuring redox changes in different ways during the inflammatory response. It
was examined at the transcriptome level by depleting GSH, the main thiol antioxidant
but also a regulator of the redox state of protein thiols and disulphides (Chapter 3),
and in the intracellular and extracellular proteome by measuring the redox state of
redoxkines Prx1, Prx2 and Trx (Chapter 4) as well as at the cell surface of
macrophages by identifying proteins with free thiols and thus potential targets of those
redoxkines (Chapter 5). Redox changes of Prx2 and Trx were also tested as oxidative
biomarkers in human blood from healthy donors and patients undergoing angioplasty
due to coronary artery disease; however, no changes in the redox states of these
proteins were detected (Chapter 6). The main findings of each chapter as well as
further work required are summarised in Table 7.1.
We demonstrated that GSH, the main intracellular antioxidant and ROS scavenger,
also acts as a signalling molecule required for antiviral response but also controls the
amount of Prx1 transcribed. In fact, the microarray data has shown that Prx1
transcription is induced by LPS and its level is regulated by GSH, while both Prx2 and
Trx genes are not affected by either LPS stimulation or GSH depletion. Both of them
are redox-regulated at latter steps, by post-translational modification after LPS
stimulation. In fact, we could determine three redox profiles specific of each of these
redoxkines (Prx1, Prx2 and Trx) depending on their cell location and exposure to
inflammatory stimulation. Prx2 and Trx are both detected as a mixture of reduced and
oxidised forms prior to LPS stimulation. After exposure of the cells to LPS, only the
reduced form is observed intracellularly while the oxidised form seems to be released.
This could be a homeostatic measure to maintain a reduced intracellular environment.
Furthermore, while the oxidised form of Trx is detected extracellularly, Prx2 is found
in the reduced form, suggesting that the oxidised form could have been reduced by
Trx, its main electron donor outside of the cell. Thus, the redox state of both proteins
appears to be in dynamic equilibrium with their catalytic activity dependent on LPS.
On the contrary, Prx1 is only found as a reduced form with 2 free-SH (one for each
monomer) and as a fully oxidised form when released, which could correlate with its
DAMP activity due to inactivation of its catalytic centre.
We then hypothesised that the reduced form of Prx, in cooperation with Trx, could
catalyse thiol disulphide exchanges outside the cell using extracellular substrates
231
such as proteins with free thiols at the cell surface. We thus set up a method to detect
those potential targets by merging alkylation of free thiols at the cell surface with a
biotin tag plus MS identification following streptavidin bead enrichment. Although
preliminary, this technique has allowed the detection of interesting receptors such as
integrin-α previously identified as redox regulated by inhibitors of GSH and by NAC
at the surface of PBMC (Laragione, Bonetto et al. 2003).
Unexpectedly, we could also identify Trx in a dimeric form. It seemed that this form
was only present in the membrane (plasma or exosome) as it was not identified in our
previous work in cell lysates and supernatants. This is in agreement with other studies
suggesting that a Trx dimer could be associated with a redox sensor function
(Weichsel, Gasdaska et al. 1996). At the same time, redox changes detected after
LPS stimulation in macrophages were analysed in the plasma and red blood cells
from healthy donors as well as patients undergoing angioplasty due to coronary artery
disease in order to detect potential redox changes. However, no changes were
observed but the same overall redox state in human red blood cells was detected as
observed in RAW cell lysates demonstrating the stability of Prx2 and Trx redox state
across different cells and under different conditions.
We could then also hypothesise that those redox changes of redoxkines were
dependent on LPS. In fact, this endotoxin only reacts with TLR4 triggering a specific
signalling pathway. Therefore, further work to identify these signalling pathways is
required.
232
Chapter Aim Results Further work
3
Identify genes regulated by GSH during LPS stimulation in RAW cells
GSH acts as a signalling molecule to fine tune the antiviral response
Test this hypothesis at the proteomic level
4
Identify redox state changes of Prx1, Prx2 and Trx in LPS-stimulated RAW cells
Following LPS stimulation in RAW cells, Prx2 and Trx undergo redox changes prior to secretion and seem to have a catalytic activity extracellularly while Prx1 is not sensitive to redox change and seems to act as a DAMP
Identify the signalling pathway of secretion; identify specific biological functions for each redox form detected
5
Identification of surface thiols targets of thiol oxidoreductases released in LPS-stimulated RAW cells
Identification of interesting receptors such as integrin-α; Trx exists as a dimer
Optimisation of the technique of membrane extraction is required as well as inhibiting Trx prior to LPS stimulation
6
Utilisation of the redox state change of Prx2 and Trx as biomarkers of oxidative stress
Redox state of Prx2 and Trx are measurable in human blood; No redox changes in patients undergoing angioplasty
Determination of “use-by date” for stored human blood + use another model or use other tissues than blood
Table 7.1: Findings and further work required for each aim of this study.
233
7.2 Advances in the redox and inflammatory field
Potential for novel antioxidant therapies
It is interesting to note that in our experimental model, endogenous GSH does not
inhibit the inflammatory response and that this is contrary to what has been concluded
by pioneers in this field who used a different experimental approach by adding
exogenously thiols antioxidants (Schreck, Rieber et al. 1991). However, one gene,
CXCL10, was increased when cells were depleted of GSH and then stimulated with
LPS. This chemokine, formerly known as IFNγ-inducible protein 10 (IP-10) has been
recently studied in chronic inflammatory diseases such as diabetes (Antonelli, Ferrari
et al. 2014), autoimmune diseases (Lee, Lee et al. 2009), multiple sclerosis
(Vazirinejad, Ahmadi et al. 2014), and cancer (Lunardi, Jamieson et al. 2014) and
therefore could be linked with the increase of ROS due to GSH depletion. In fact, the
effects of thiol antioxidants and ROS vary in studies depending on the experimental
model used as discussed previously (Chapter 3). The fact that some molecules such
as CXCL10 have been identified in a number of studies strengthens the evidence for
its role in, and as a potential therapeutic target for, chronic inflammatory diseases.
Overall, the microarray analysis also brought new insights in defining how complex
the thiol system antioxidants is, highlighting GSH as a signalling molecule important
for host defence. Our results are consistent with other studies as mentioned
previously in Table 3.5 investigating the effect of GSH depletion on viral replication.
It could also explained why viral infection is often linked with reduced GSH level
(Swietek and Juszczyk 1997) (Herzenberg, De Rosa et al. 1997) (Papi, Contoli et al.
2008) as this could be a viral strategy to increase replication and survival by down-
regulating the host antiviral response.
Prxs and Trx have specific redox states depending on their localisation and
could be used as biomarkers or therapeutic targets
In this study, we identified different redox states of Prx1, Prx2 and Trx specific to their
localisation as well as the condition of the cells. It is possible that these specific redox
states could be used as biomarkers of protein oxidation. The MalPEG technique
developed in this study seems a promising technique to identify different degrees of
oxidation of a protein which could be useful to measure the oxidation and hence
quality of stored blood intended for transfusion (Bayer, Hampton et al. 2015). In
addition, it seems that the redox changes in the proteins studied here were specific to
234
LPS stimulation and thus the TLR4 signalling pathway. This activation of TLR4 has
been described, in many autoimmune diseases (Liu, Yin et al. 2014) such as
inflammatory bowel disease (Oostenbrug, Drenth et al. 2005) or Alzheimer’s disease
(Walter, Letiembre et al. 2007) which could be used as models to determine whether
Prx2 and Trx redox changes could be used for prognostic or diagnostic purposes.
A number of studies have pointed out the potential cytokine- and chemokine- like roles
of Prx2, Trx and Prx1 during the inflammatory response. To summarize, Prx1 acts as
a DAMP (Riddell, Wang et al. 2010), Trx acts as a chemoattractant (Bertini, Howard
et al. 1999) but also as a growth factor (Gasdaska, Berggren et al. 1995) and finally
recent studies have shown that Prx2 can promote release of TNF from cells (Salzano,
Checconi et al. 2014). In addition, and in particular for Trx, these proteins are often
detected in elevated concentrations in autoimmune diseases and cancer compared
to healthy individuals (Nakamura, De Rosa et al. 1996, Lincoln, Ali Emadi et al. 2003)
(Wahlgren and Pekkari 2005) (Riddell, Bshara et al. 2011, Szabo-Taylor, Eggleton et
al. 2012). Thus research to inhibit these proteins has started but has been
inconclusive thus far as illustrated by a recent clinical trial inhibiting Trx from
gastrointestinal cancers patients (Baker, Adab et al. 2013). This failure may be related
to the strategy adopted consisting of completely inhibiting Trx instead of specific
functions of Trx. In fact targeting specific redox states of these proteins could be more
efficient to suppress the pro-inflammatory roles of these proteins. Our study has been
able to identify these specific forms and future work will focus on identifying what
biological roles these forms have and which post-translational modifications are
present. In fact, the development of new therapeutic approaches such as antibodies
targeting specific thiol modifications is being investigated in the field of chronic
diseases (Ryan, Nissim et al. 2014) and one could think that targeting specific redox
states of Prx2 and Trx could help to reduce the inflammatory response in chronic
inflammatory conditions.
7.3 Conclusion
Redox regulation is involved in many cellular processes such as apoptosis, cell
development and differentiation, homoeostasis and the immune response. However
due to the reactivity of ROS, the main effectors, the importance and involvement of
this regulation in signalling functions is difficult to elucidate fully. In this project we
successfully demonstrated the importance of protein and non-protein thiols as targets
235
of redox changes during the inflammatory response directly linked with the TLR4
signalling pathways in macrophages.
236
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Appendix
Appendix 1: Transcripts induced by LPS at 2h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05). Only the top 50 transcripts most affected by LPS (sorted by fold
change) are shown.
Appendix 2: Transcripts down-regulated by LPS at 2h. Transcripts were selected
for their differential expression between the two groups LPS vs Control (cut-off was:
fold change, 1.5; P<0.05). Only the top 50 transcripts most affected by LPS (sorted
by fold change) are shown.
Appendix 3: Transcripts induced by LPS at 6h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05). Only the top 50 transcripts most affected by LPS (sorted by fold
change) are shown.
Appendix 4: Transcripts down-regulated by LPS at 6h. Transcripts were selected
for their differential expression between the two groups LPS vs Control (cut-off was:
fold change, 1.5; P<0.05). Only the top 50 transcripts most affected by LPS (sorted
by fold change) are shown.
Appendix 5: Expression images of cluster 1, 2, 3 and 4 at 2h. The median of the
expression for a cluster is in pink. The level of expression is indicated by the ratio of
each condition versus the average of the control (+4;-4).
Appendix 6: Expression images of cluster 1, 2, 3 and 4 at 6h. The median of the
expression for a cluster is in pink. The level of expression is indicated by the ratio of
each condition versus the average of the control (+4;-4).
Appendix 7: List of genes in Group 1 at 2h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05) and the two groups BSO+LPS vs LPS alone (cut-off was: fold
change, 1.5; P<0.05).
267
Appendix 8: List of genes in Group 2 at 2h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05) and the two groups BSO+LPS vs LPS alone (cut-off was: fold
change, 1.5; P<0.05).
Appendix 9: List of genes in Group 3 at 2h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05) and the two groups BSO+LPS vs LPS alone (cut-off was: fold
change, 1.5; P<0.05).
Appendix 10: List of genes in Group 4 at 2h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05) and the two groups BSO+LPS vs LPS alone (cut-off was: fold
change, 1.5; P<0.05).
Appendix 11: List of genes in Group 1 at 6h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05) and the two groups BSO+LPS vs LPS alone (cut-off was: fold
change, 1.5; P<0.05).
Appendix 12: List of genes in Group 2 at 6h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05) and the two groups BSO+LPS vs LPS alone (cut-off was: fold
change, 1.5; P<0.05).
Appendix 13: List of genes in Group 3 at 6h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05) and the two groups BSO+LPS vs LPS alone (cut-off was: fold
change, 1.5; P<0.05).
Appendix 14: List of genes in Group 4 at 6h. Transcripts were selected for their
differential expression between the two groups LPS vs Control (cut-off was: fold
change, 1.5; P<0.05) and the two groups BSO+LPS vs LPS alone (cut-off was: fold
change, 1.5; P<0.05).
Appendix 15: Functional categories of Group 2. Functional categories were
obtained by DAVID using the Functional Annotation Chart tool with GO TERM BP and
268
KEGG pathway for each group independently of the time point and were ordered by
EASE score a modified Fisher’s Test (P value <0.5).
Appendix 16: List of the 80 first proteins out of 1871 from which most peptides
have been identified by MS in untreated cells. Proteins were previously selected
with a FDR<1 (=-10logP) and were ordered by their number of peptides identified by
MS.
Appendix 17: List of the 80 first proteins out of 2427 from which most peptides
have been identified by MS in LPS-treated cells. Proteins were previously selected
with a FDR<1 (=-10logP) and were ordered by their number of peptides identified by
MS.
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Appendix 13
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Appendix 15
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Appendix 16
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Appendix 16
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Appendix 17
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Appendix 17
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