Metabolomics of Inflammatory Arthritis

319 Pages·2014·7.03 MB·English

by  Sabrina Reenu Kapoor

Metabolomics of Inflammatory Arthritis

Sabrina Reenu Kapoor

Supervisors:
Dr Stephen Young
Professor Karim Raza
Professor Ulrich Gunther

School of Immunity and Infection
College of Medical and Dental Sciences
The University of Birmingham
February 2014
University of Birmingham Research Archive

e-theses repository

This unpublished thesis/dissertation is copyright of the author and/or third
parties. The intellectual property rights of the author or third parties in respect
of this work are as defined by The Copyright Designs and Patents Act 1988 or
as modified by any successor legislation.

Any use made of information contained in this thesis/dissertation must be in
accordance with that legislation and must be properly acknowledged. Further
distribution or reproduction in any format is prohibited without the permission
of the copyright holder.
Abstract
Background Rheumatoid arthritis (RA) is associated with both local and systemic
inflammation which influences the function of the whole body as well as local tissues in
the joints. Significant consequences of this are changes in metabolism. Hence, we
hypothesised that chronic inflammation alters metabolism and that the metabolic profile of
an individual patient with early inflammatory arthritis predicts the subsequent course of
disease. Furthermore, we suggested that these metabolic changes would identify
biomarkers of response to treatment in inflammatory arthritis and provide novel insights
into disease mechanisms.
Methods Using NMR spectroscopy of serum, urine and synovial fibroblasts we derived
metabolic profiles and subjected these to multi-parameter analyses to identify metabolic
differences associated with inflammation.
Results We were able to predict outcome in patients with early arthritis using material
derived from cultured synovial fibroblasts but were unable to do so using serum. There
was a significant association between CRP levels in the patients’ serum and the metabolic
profile of their synovial fibroblasts and their serum. There was also a significant
association between the metabolomic fingerprint of synovial fibroblasts and the
fibroblasts’ IL6 production. We found differences in metabolites between urine samples of
RA and psoriatic arthritis (PsA) patients and were able to predict responses to anti-TNF
therapy in patients with RA.
Discussion Our results demonstrate that underlying inflammatory processes drive
significant changes in metabolism that can be measured in the peripheral blood, synovial
fibroblasts and urine samples in patients with inflammatory arthritis.
Declaration
I have carried out all the work in this thesis myself including writing all the initial
manuscripts that have been published from this thesis apart from:
1. Synovial biopsies which were done by Dr Andrew Filer and initial culture was
done by a technician.
2. Measurement of glutamine levels in the urine which were done at Birmingham
Childrens Hospital
3. Some of the clinical samples were collected by other clinicians including myself
4. The serum samples from group 1 of the early arthritis cohort were processed by Dr
Stephen Young but analysed by me.
Acknowledgements
I owe thanks to many people for their help, support and guidance throughout the project. I
would particularly like to thank my supervisors Steve Young and Karim Raza for their
enthusiasm for science and continued support. I would also like to thank my third
supervisor Ulrich Gunther for his help with NMR. I am also grateful to Dr Andrew Filer
for providing clinical samples and to Professor Chris Buckley for his helpful comments
and advice. I would also like to thank Dr Graham Wallace for reading through my thesis.
Many thanks also to Anne Garfield, Lorraine Yeo, Kath Howlett, John Byrne, Martin
Fitzpatrick, Holly Adams, Rachel Bayley and Mark Pearson for their much valued
technical help. I would like to thank all the members of the Rheumatology Research
Group for all their constant support. Finally, my thanks go to my family in particular Ash,
Priyanka and Sachin for their support during this PHD. I am grateful to Arthritis Research
UK who have provided the funding for this project, along with the Wellcome Trust who
funded the NMR spectrometers and the Henry Wellcome building for NMR.
Publications

• Young SP*, Kapoor SR*, Viant MR, Byrne JJ, Filer A, Buckley CD, Kitas GD and
Raza K. The impact of inflammation on metabolomic profiles in patients with
arthritis. Arthritis Rheum. Aug 2013; 65(8):2015-23 (*Joint first author)

• Kapoor SR, Filer A, Fitzpatrick MA, Fisher BA, Taylor PC, Buckley CD, McInnes
IB, Raza K and Young SP. Metabolic profiling predicts response to anti-tumor
necrosis factor α therapy in patients with rheumatoid arthritis. Arthritis Rheum.
June 2013; 65(6):1448-56.

• Sabrina Kapoor, Martin Fitzpatrick, Elizabeth Clay, Rachel Bayley, Graham R.
Wallace and Stephen P. Young (2012). Metabolomics in the Analysis of
Inflammatory Diseases, Metabolomics, Dr Ute Roessner (Ed.), ISBN: 978-953-51-
0046-1, InTech, DOI: 10.5772/31814. Available from:
http://www.intechopen.com/books/metabolomics/metabolomics-in-the-analysis-of-
inflammatory-diseases.
Abbreviations
ACPA Anti-citrullinated protein antibody
ACR American College of Rheumatology
ANOVA Analysis of variance
Anti-CarP Anti-carbamylated antibodies
AS Ankylosing spondylitis
ATIC 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/inosine
monophosphate cyclohydrolase
BCAA Branched chain amino acids
CD Crohn’s disease
CHD Coronary heart disease
CSF Cerebrospinal fluid
CTLA-4 Cytotoxic T-lymphocyte associated 4
CU Chronic non-infectious uveitis
DAS Disease activity score
DATA Differentiating the mechanism of action of anti-TNF-α agents
DMARDs Disease-modifying anti-rheumatic drugs
D O Deuterium oxide
2
EBV Epstein-Barr virus
ELISAs Enzyme-linked immune-sorbent assays
EPA Eicosapentaenoic acid
EULAR European League Against Rheumatism
GALGO Genetic ALGOrithm
GSTM1 Glutathione S-transferase M1
HIF Hypoxia-inducible factor
IBD Inflammatory bowel disease
IL Interleukin
IP-10 Interferon gamma-induced protein 10
LV Latent variable
LIU Lens-induced uveitis
MCP Metacarpopharyngeal
MCP-1 Monocyte chemotactic protein-1
MIP-1α Macrophage inflammatory protein-1α
MMPs Matrix metalloproteinases
MRI Magnetic resonance imaging
MS Mass spectrometry
NMR Nuclear magnetic resonance
NOAR Norfolk Arthritis Register
OA Osteoarthritis
PADI4 Peptidyl arginine deiminase type IV
PBS Phosphate buffered saline
PCA Principal component analysis
PLSDA Partial least squared discriminate analysis
PLSR Partial least squares regression analysis
PPM Parts per million
PsA Psoriatic arthritis
PTPN22 Protein tyrosine phosphate, non-receptor type 22
RA Rheumatoid arthritis
RANKL RANK ligand
RANTES Regulated on activation, normal T cell expressed and secreted
RASFs Rheumatoid arthritis synovial fibroblasts
ReA Reactive arthritis
RF Rheumatoid factor
SE Shared epitope
SF Synovial fluid
SNP Single nucleotide polymorphism
SIL6R Soluble IL6 receptor
STAT4 signal transducer and activator of transcription 4
TCR T cell receptor
TCZ Tocilizumab
TMA Trimethylamine
TMSP Trimethylsilyl 2,2,3,3-tetradeuteropropionic acid
TNF-α Tumour necrosis factor-alpha
TNFAI3 TNFα-induced protein 3
UC Ulcerative colitis
VEGF Vascular endothelial growth factor
VIP Variable importance of the projection
Contents
1.0 Introduction ................................................................................................................... 1
1.1 Rheumatoid Arthritis ................................................................................................... 1
1.1.1 Classification ......................................................................................................... 1
1.1.2 Aetiology ............................................................................................................... 4
1.1.3 Pathology ............................................................................................................. 12
1.1.4 Autoantibodies .................................................................................................... 16
1.2 Early Rheumatoid Arthritis ........................................................................................ 18
1.2.1 Phases of rheumatoid arthritis ............................................................................. 18
1.2.2 Importance of treating rheumatoid arthritis early ............................................... 20
1.2.3 Predicting outcome in early rheumatoid arthritis ................................................ 20
1.2.4 Predicting response to treatment in rheumatoid arthritis .................................... 21
1.3 Metabolism in inflammatory disease ......................................................................... 25
1.3.1 The inflammatory process ................................................................................... 25
1.3.2 Metabolic consequences of inflammation ........................................................... 25
1.4 Metabolomics ............................................................................................................. 30
1.4.1 The metabolome .................................................................................................. 30
1.4.2 Approaches to metabolomics .............................................................................. 31
1.4.3 Metabolomics of inflammatory disease .............................................................. 33
1.5 Summary .................................................................................................................... 46
1.6 Aims and hypothesis .................................................................................................. 46
2.0 Materials and methods ................................................................................................ 47
2.1 Samples ...................................................................................................................... 47
2.1.1 Serum .................................................................................................................. 47
2.1.2 Urine .................................................................................................................... 47
2.1.3 Fibroblasts ........................................................................................................... 47
2.2 Fibroblast cell culture ................................................................................................ 48
2.2.1 General culture guidelines ................................................................................... 48
2.2.2 Trypsinisation of cells (splitting) ........................................................................ 50
2.2.3 Cell quenching and extraction protocol: ............................................................. 51
2.3 Cell extraction procedure to produce samples for NMR ........................................... 52
2.4 Sample preparation for NMR .................................................................................... 53
2.4.1 Materials .............................................................................................................. 53
2.4.2 Preparation of sodium phosphate buffer ............................................................. 53
2.4.3 Serum samples .................................................................................................... 53
2.4.4 Urine samples ...................................................................................................... 54
2.4.5 Fibroblast medium samples ................................................................................. 54
2.4.6 Fibroblast cells .................................................................................................... 54
2.5 Metabolomic analysis ................................................................................................ 55
2.6 Statistical analyses ..................................................................................................... 56
2.6.1 Principal components analysis (PCA) ................................................................. 56
2.6.2 Partial least square discriminant analysis (PLS-DA) .......................................... 57
2.6.3 GALGO ............................................................................................................... 57
2.6.4 Partial least squares regression analysis (PLS-R) ............................................... 58
2.6.5 Identification of metabolites ............................................................................... 58
2.7 Enzyme-linked immune-sorbent assays (ELISAs) .................................................... 59
2.8 Multiplex analysis ...................................................................................................... 60
3.0 Quality Control of the biofluids used ......................................................................... 62
3.1 Blood .......................................................................................................................... 62
3.1.1 Introduction ......................................................................................................... 62
3.1.2 Materials and methods ........................................................................................ 64
3.1.3 Results ................................................................................................................. 66
3.1.4 Discussion ........................................................................................................... 74
3.2 Urine .......................................................................................................................... 76
3.2.1 First void or random urine ................................................................................... 76
3.2.2 Discussion: urine ................................................................................................. 78
3.3 Number of fibroblasts required to produce a NMR spectrum ................................... 79
3.3.1 Introduction ......................................................................................................... 79
3.3.2 Materials and methods ........................................................................................ 80
3.3.3 Results ................................................................................................................. 81
3.4 Fibroblast medium ..................................................................................................... 86
3.4.1 Introduction ......................................................................................................... 86
3.4.2 Materials and methods ........................................................................................ 87
3.4.3 Results ................................................................................................................. 88
3.5 Discussion: Fibroblasts and fibroblast medium ......................................................... 92
3.6 Conclusions ................................................................................................................ 93
4.0 DATA (Differentiating the mechanism of action of anti-TNF-α agents) study ..... 95
4.1 Introduction ................................................................................................................ 95
4.2 Patients ....................................................................................................................... 97
4.3 Results ........................................................................................................................ 99
4.3.1 RA and PsA at baseline ....................................................................................... 99
4.3.2 RA and predictors of response to anti-TNFα therapy ....................................... 107
4.3.3 Comparison of metabolites predicting response to therapy in RA ................... 113
4.3.4 Effect of TNFα antagonists on metabolite profiles ........................................... 114