IDENTIFICATION AND VALIDATION OF

CANDIDATE SOLUBLE BIOMARKERS FOR

PSORIATIC ARTHRITIS USING QUANTITATIVE

PROTEOMICS

by

Daniela Cretu

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Laboratory Medicine and Pathobiology

University of Toronto

© Copyright by Daniela Cretu 2015

ii

Identification and Validation of Candidate Soluble Biomarkers for

Psoriatic Arthritis Using Quantitative Proteomics

Daniela Cretu

Doctor of Philosophy

Laboratory Medicine and Pathobiology

University of Toronto

2015

Abstract

There is a high prevalence of undiagnosed PsA in psoriasis patients; therefore identifying soluble

biomarkers for PsA will help in screening psoriasis patients for appropriate referral to a

rheumatologist. Potential PsA biomarkers likely originate in sites of inflammation, such as

inflamed joints and skin, and subsequently enter systemic circulation. Therefore, we aimed to

discover novel biomarkers, to facilitate PsA recognition in psoriasis patients. To achieve this

objective, quantitative proteomic analyses of synovial fluid (SF) samples and skin biopsies

obtained from PsA patients were performed. SF was obtained from swollen knee joints of 10

PsA patients, and age/sex matched early osteoarthritis (OA) controls. Likewise, skin biopsies

were obtained from involved and uninvolved skin of 10 PsA, and 10 age/sex matched psoriasis

patients without PsA (PsC). Using strong cation exchange chromatography, followed by tandem

mass spectrometry, we characterized the proteomes of pooled SF and pooled skin samples.

Extracted ion current intensities were used to calculate protein abundance ratios, and utilized to

classify upregulated proteins. Selected reaction monitoring assays were developed to quantify

these potential PsA markers in individual patient samples. Verified markers were subsequently

measured in serum samples from 100 PsA, 100 PsC patients, and 100 healthy controls, using

iii

commercially available or in-house developed enzyme-linked immunosorbent assays. We

quantified a total of 443 and 1922 proteins in SF and skin extracts, respectively, but only 17

proteins represented upregulated proteins in PsA SF, while 47 proteins were specifically elevated

in PsA-derived skin. SRM verification confirmed that 12 and 8 proteins were indeed elevated in

an independent set of PsA SF and involved PsA skin, respectively. Based on the fold change

between PsA and controls, the associated P-values, and the cellular localization, we ranked the

proteins, and selected the following putative markers for validation in the serum - M2BP, CD5L,

MMP3, CRP, MPO, and ITGB5. Increased levels of CRP, M2BP, MMP3, and ITGB5 were

independently associated with PsA, when compared to PsC. ROC analysis of this model showed

an AUC of 0.851 [95% CI (0.799, 0.904)]. Thus, serum CRP, M2BP, and ITGB5 are potential

biomarkers of PsA in patients with psoriasis.

iv

Acknowledgments

First and foremost, I would like to thank my supervisor, Dr. Eleftherios Diamandis who,

throughout the course of my studies, has shown nothing but patience, support, and

encouragement. Dr. Diamandis, I took the keys you provided, and I drove the car. Thank you for

your support!

I would also like to thank Dr. Vinod Chandran, my “unofficial co-supervisor”. I have, and will

always appreciate the guidance and support you have given me over the years. It is through you

that I discovered the field of PsA biomarker research, and your guidance is what contributed to

the successful completion of my research project.

I am grateful for Dr. Dafna Gladman and Dr. George Yousef, who have volunteered their time

and provided me with great advice over the years, as well as the remainder of my defense

committee Drs. Oliver FitzGerald, Laurence Rubin, and Ivan Blasutig.

The Diamandis laboratory, past and present members, will forever be a part of my life. Apart

from the help everybody has given me, I have also made life-lasting friendships. Specifically,

thank you Natasha, Felix, Yiannis, Ihor, Uros, and Antoninus; you made the lab life even more

enjoyable and I thank you all for that.

Above all, I would like to thank my family. Over the years, my parents, sister, and the

Krawczyk/Kmin family have given me overwhelming support, which has kept me motivated on

this journey. To my husband, Michal, I thank you for sticking by my side, loving me, and

supporting and motivating me over the past three years. I am indebted to you all.

Last, but not least, I’d like to specifically express my gratitude to my mother, Mihaela, to whom I

dedicate this thesis. Without your guidance, love, and support, I would not be who I am today.

v

Publications arising from this thesis

Articles

Cretu D, Diamandis EP, Chandran V. Delineating the synovial fluid proteome: Recent

advancements and ongoing challenges in biomarker research. Crit Rev Clin Lab Sci,2013;50:51-

63.

Cretu D, Prassas I, Saraon P, Batruch I, Gandhi R, Diamandis EP, Chandran V. Identification of

psoriatic arthritis mediators in synovial fluid by quantitative mass spectrometry. Clin

Proteomics.2014;11:27.

Cretu D, Liang K, Saraon P, Batruch I, Diamandis EP, Chandran V. Quantitative tandem mass-

spectrometry of skin tissue reveals putative psoriatic arthritis biomarkers. Clin

Proteomics.2015;12:1.

Poster Presentations

Cretu D, Liang K, Gladman D, Diamandis EP, Chandran V. Quantitative proteomic analysis of

synovial fluid and skin identifies putative psoriatic arthritis biomarkers. Arthritis Rheumatol.

2014;66:S701.

Chandran V, Cretu D, Batruch I, Ghandhi R, Diamandis EP. Identification of psoriatic arthritis

putative biomarkers in synovial fluid by quantitative mass spectrometry. J Rheumatol.

2014;41:1446-7.

Cretu D, Pellett F, Gandhi R, Diamandis E, Chandran V. Proteomic profiling of synovial fluid

for the identification of psoriatic arthritis soluble biomarkers. Arthritis Rheum. 2013;65:S142.

Chandran V, Cretu D, Saraon P, Batruch I, Ghandhi R, Pelett F, Gladman DD, Diamandis EP.

Quantitative proteomic analysis of synovial fluid identifies putative psoriatic arthritis

biomarkers. J Rheumatol. 2013;40:1018.

Cretu D, Batruch I, Saraon P, Gladman D, Pellett F, Diamandis E, Chandran V. Proteomic

Profiling of Synovial Fluid Reveals Candidate Psoriatic Arthritis Biomarkers. Arthritis Rheum

2012;64:S244.

Cretu D, Gladman DD, Pellett FJ, Diamandis E, Chandran V. Proteomic Profiling of Synovial

Fluid for the Identification of Psoriatic Arthritis Soluble Biomarkers. Arthritis Rheum

2011;63:S533.

vi

Contributions

Dr. Daniela Cretu prepared the thesis, and is responsible for the conceptualization and

experimental content.

Drs. Eleftherios Diamandis and Vinod Chandran provided supervision, guidance, and expertise,

and thus assisted in the successful completion of this work.

Dr. Ioannis Prassas assisted in helpful discussions, and drafting of Chapter 2.

Dr. Punit Saraon assisted in helpful discussions, and drafting of Chapters 2 and 3.

Ihor Batruch provided mass spectrometry expertise, and assisted with data analysis and SRM

development in Chapters 2 and 3.

Dr. Rajiv Gandhi provided early osteoarthritis synovial fluid samples in Chapter 2.

Dr. Kun Liang provided statistical expertise, and assisted with statistical analysis in Chapters 3

and 4.

vii

Table of Contents

Acknowledgments iv

Publications arising from this thesis v

Contributions vi

Table of Contents vii

List of Tables ix

List of Figures xi

List of Abbreviations xii

Chapter 1 1

1 Introduction 2

1.1 Psoriasis 2

1.1.1 Statistics and epidemiology 2

1.1.2 Aetiology 3

1.1.3 Clinical features 3

1.1.4 Diagnosis, measurement, and treatment 5

1.2 Psoriatic Arthritis 6

1.2.1 Statistics and epidemiology 6

1.2.2 Aetiology and pathogenesis 6

1.2.3 Clinical features 9

1.2.4 Relationship between the skin and joint 10

1.2.5 Diagnosis of PsA 10

1.2.6 Treatment 11

1.2.7 Identifying PsA early 12

1.3 Biomarker Discovery in rheumatology 13

1.3.1 Definition of biomarkers 13

1.3.2 Ideal PsA biomarkers 14

1.3.3 Advantages of protein markers 14

1.3.4 Serum biomarkers in PsA 15

1.3.5 Proteomics PsA in biomarkers discovery 18

1.4 Proteomic methods used in discovering putative

biomarkers in rheumatology 20

1.4.1 Basic components of a mass-spectrometer 20

1.4.2 Sample fractionation methods 22

1.4.3 Tandem mass spectrometry 23

1.4.4 Quantitative mass spectrometry methods 24

1.4.5 Biomarker development pipeline 26

1.4.6 Sources to mine for PsA biomarkers 27

1.5 Rationale and Aims of the Present Study 29

1.5.1 Rationale 29

1.5.2 Hypothesis 30

1.5.3 Specific aims 31

Chapter 2 32

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2 Quantitative proteomic analysis of psoriatic arthritis synovial fluid

33

2.1 Introduction 33

2.2 Methods 36

2.2.1 SF Proteomic analysis 36

2.2.2 Verification of identified proteins using SRM assays 40

2.3 Results 45

2.3.1 Delineating the PsA SF proteome 45

2.3.2 SRM Verification of putative markers in individual SF

samples 53

2.4 Discussion 58

Chapter 3 64

3 Quantitative proteomic analysis of psoriatic arthritis skin 65

3.1 Introduction 65

3.2 Methods 68

3.2.1 Skin proteomic analysis 68

3.2.2 Verification of identified proteins using SRM 72

3.2.3 Validation of verified proteins 74

3.3 Results 78

3.3.1 Delineating the PsA skin proteome 78

3.3.2 SRM verification of putative markers in individual skin

samples 81

3.3.3 Small-scale ELISA validation in serum 86

3.3.4 Correlation amongst markers 87

3.4 Discussion 88

Chapter 4 92

4 Validation of four candidate PsA biomarkers in serum 93

4.1 Introduction 93

4.2 Methods 95

4.2.1 Clinical samples 95

4.2.2 Measurement of CD5L, ITGB5, MPO, POSTN, MMP3,

CRP, and M2BP 97

4.2.3 Statistical analysis 99

4.3 Results 101

4.4 Discussion 106

Chapter 5 109

5 Summary and future directions 110

5.1 Summary of findings and implications 110

5.2 Limitations and considerations 111

5.2 Future directions 114

5.3 Conclusion 116

References 117

ix

List of Tables

1.1 The CASPAR criteria for classification of PsA

1.2 List of soluble biomarkers used in the diagnosis and treatment of joint diseases

2.1 Differentially expressed proteins between early OA and PsA groups identified by

LC-MS/MS

2.2 Summary of Ingenuity Pathway Analysis-generated functional pathways and

diseases associated with elevated proteins identified in PsA SF

2.3 Tissue expression of the top 20 elevated proteins identified from PsA SF

2.4 Fold change of 17 elevated and two housekeeping proteins in PsA SF and the

corresponding peptides

2.5 Summary of the fold change of candidate markers in SF set I and II

3.1 Fold change of 47 elevated proteins identified by LC-MS/MS in PsA skin

when compared to PsC skin

3.2 Summary of the fold change of candidate markers in skin set I and II

4.1 Demographics, disease characteristics, and serum biomarker levels in studied

cohorts

4.2 Fold change of serum biomarker levels when comparing PsA to PsC, and PsA to

controls

x

4.3 Polychotomous logistic regression analysis to identify biomarkers associated with

PsA and PsC

4.4 Logistic regression analysis comparing patients with PsA to PsC to identify

biomarkers associated with PsA in patents with PsC

4.5 Correlation between markers

xi

List of Figures

2.1 Summary of the biomarker discovery and verification experimental

workflow in SF

2.2 Cellular localization of the 44 upregulated proteins based on GO

annotation

2.3 Verification of elevated proteins in PsA SF (set I) by selected reaction

monitoring assays, normalized against housekeeping proteins

2.4 Verification of elevated proteins in PsA SF (set II) by selected reaction

monitoring assays, normalized against heavy-labeled peptides

3.1 Summary of the biomarker discovery, verification, and preliminary

validation experimental workflow in skin

3.2 Distribution of significant markers across the set I PsA and PsC skin

samples

3.3 Distribution of significant markers across the set II PsA and PsC skin

samples

3.4 Distribution of markers across PsA (n=33) and PsC (n=15) serum sets

3.5 Correlation between ITGB5 and POSTN across the PsA and PsC serum

sets

4.1 Distribution of markers across control (n=100), PsA (n=100), and PsC

(n=100) serum sets

4.2 ROC curve showing the AUC for the logistic regression model comparing

PsA to PsC patients

xii

List of Abbreviations

ACE: Angiotensin-converting enzyme

ACN: Acetonitrile

ACPA: Anti-citrullinated protein antibody

ACTB: Beta-actin

ACTH: Adrenocorticotropic hormone

ANA: Anti-nuclear antibody

APCS: Amyloid P component

APOC1: Apolipoprotein C1

APR: Acute phase reactants

AUC: Area under the curve

BSA: Body surface area

C1-2C: Collagen 2-3/4short

C16ORF62: Chromosome 16 open reading frame 62

C2C: Collagen 2-3/4Clongmono

C4BP: C4b-binding protein

CASPAR: Classification criteria for Psoriatic Arthritis

CD5L: CD5-like protein

CHI3L1: Chitinase-3-like protein 1

CI: Confidence interval

CPII: Procollagen 2 peptide

CPN2: Carboxypeptidase N: polypeptide 2

CRP: C-reactive protein

DEFA1: Neutrophil alpha defensin 1

DIP: Distal interphalangeal

DKK1: Dickkopf-1

ELISA: Enzyme-linked immunosorbent assay

ESI: Electrospray ionization

ESR: Erythrocyte sedimentation rate

FA: Formic Acid

FC: Fold change

FDR: False discovery rate

FHL1: Four and a half LIM domain1 1

FT-ICR: Fourier-transform ion-cyclotron resonance

GPS1: G-protein pathway suppressor 1

H2AFX: Histone 2A type I A

H4: Histone 4

HLA: Human leukocyte antigen

HPLC: High-performance liquid chromatography

IL: Interleukin

xiii

ITGA5: Integrin alpha 5

ITGB5: Integrin-beta 5

LC-MS/MS: Liquid chromatography and tandem mass spectrometry

LFQ: Label free quantification

LZIC: Leucine Zipper And CTNNBIP1 Domain-Containing Protein

M2BP: Galectin-3-binding protein

MALDI: Matrix-assisted laser desorption ionization

M-CSF: Macrophage colony stimulating factor

MHC: Major histocompatibility complex

MMP3: Matrix metalloproteinase 3 Stromelysin 1

MPO: Myeloperoxidase

MS/MS: Tandem mass spectrometry

MS: Mass spectrometry

MTX: Methotrexate

NSAID: Non-steroidal anti-inflammatory drugs

OA: Osteoarthritis

OPG: Osteoprotegerin

ORM1: Orosomucoid 1

PAFAH1B2: Platelet-activating factor acetylhydrolase 1b: catalytic subunit 2

PASI: Psoriasis Area and Severity index

PBS: Phosphate buffered saline

PFN1: Profilin 1

PIP: Proximal interphalangeal

POSTN: Periostin

PPP2R4: Protein phosphatase 2A activator: regulatory subunit 4

PsA L: Lesional Psoriatic Arthritis

PsA N: Non-lesional Psoriatic Arthritis

PsA: Psoriatic arthritis

PsC L: Lesional Psoriasis

PsC N: Non-lesional Psoriasis

PsC: Psoriasis

RA: Rheumatoid arthritis

RANKL: Receptor activator for nuclear factor κB ligand

RF: Rheumatoid factor

ROC: Receiver operating characteristic

RP: Reverse phase

S100A9: Protein S100A9

SAA1: Serum amyloid A1

SCX: Strong cation exchange

SD: Standard deviation

xiv

SF: Synovial fluid

SNCA: Alpha synuclein

SRM: Selected reaction monitoring

SRP14: Signal recognition particle 14kDa

SRPX: Sushi-repeat containing protein

TFA: Trifluoroacetic acid

TGF: Transforming growth factor

TMB: 3:3′:5:5′-Tetramethylbenzidine

TNF: Tumor necrosis factor

TNFSF14: TNF superfamily member 14

TOF: Time-of-flight

TUBB: Beta-tubulin

XIC: Extracted ion current

1

Chapter 1

Sections of this chapter have been published in Critical Reviews in Clinical Laboratory

Sciences:

Cretu D, Diamandis EP, Chandran V. Delineating the synovial fluid proteome: Recent

advancements and ongoing challenges in biomarker research. Crit Rev Clin Lab Sci,2013;50:51-

63.

2

Introduction

1.1 Psoriasis

1.1.1 Statistics and epidemiology

Psoriasis is an immune-mediated inflammatory skin disease, characterised by scaly, red and

well-demarcated skin plaques, resulting from keratinocyte hyperproliferation and altered

differentiation, the presence of an inflammatory cell infiltrate and neovascularisation (1). The

prevalence of psoriasis varies with race and geographical location, with an estimated prevalence

of 3% in North America (2, 3). The lowest prevalence has been reported in Asia, aboriginal

Australians, Native American Indians, and West Africans (0-0.3%) (2, 3). Based on age of

onset, two types of psoriasis have been described: Type I psoriasis, which has a peak onset

between age 20-30, and Type II psoriasis, which develops after the age of 40 (3, 4). Type I

psoriasis is the most common, as onset before the age of 40 occurs in up to 75% of patients, but

it also takes a more severe course when compared to Type II psoriasis, which tends to be more

mild (4, 5).

Severe psoriasis is associated with an increased risk of mortality, whereby male and female

patients appear to die 3.5, and 4.4 years earlier, respectively, when compared to age matched

controls (6). Psoriasis is also associated with increased mental health disease and suicidal

ideation in patients (5, 7). In a recent study, the adjusted hazard ratio of depression was higher in

patients with severe psoriasis compared to patients with mild psoriasis (8).

3

1.1.2 Aetiology

Psoriasis results from the interplay between genetic and environmental factors. Many population

and family studies have shown higher incidence of psoriasis in relatives. Evidence supporting

genetic predisposition was demonstrated by twin studies, which showed higher concordance

rates in monozygotic twins (65-72%), when compared to dizygotic twins (15-30%) (9). The

absence of 100% concordance in monozygotic twins, and the lack of a clear inheritance pattern

in families, indicates the presence of possible environmental triggers in those who are

genetically susceptible (9). As a result, much research has been conducted to understand the

genetic and environmental basis of psoriasis. Several loci and genes (PSORS1-10, HLA-

C*0602, IL12B, IL12R, TRAF3IP2) have been identified through linkage analysis, and by gene-

association studies (2, 10, 11). Additionally, environmental triggers identified include

streptococcal infection (12), physical trauma (13), medication (14), smoking (15), and alcohol

(16).

1.1.3 Clinical features

Psoriasis is identified by the presence of characteristic plaques (or lesions), which are well-

circumscribed red, raised, scaly skin lesions. The redness results from increased growth and

dilatation of superficial blood vessels (17, 18). The epidermis of a psoriatic lesion is thicker, and

the epidermal rete is elongated due to abnormal proliferation of keratinocytes, which is known

as psoriasiform hyperplasia (17, 18). The characteristic scales seen in psoriatic lesions are

formed by the rapid maturation and hyperproliferation of epidermal keratinocytes. Lesions are

also rich in activated T-lymphocytes which release pro-inflammatory cytokines, leading to the

characteristic inflammation seen in the disease (17, 18).

4

Psoriasis vulgaris occurs in 85-90% of cases, and represents the most common type of psoriasis.

It usually affects young adults with plaques involving the scalp, extensor aspect of the elbows,

knees, and back (5). Guttate psoriasis is characterized by the acute onset of many small psoriatic

lesions, (approximately 1-10mm in diameter). It typically occurs 1-2 weeks following a

streptococcal infection, and mainly affects children and young adults (9, 12). Inverse psoriasis is

distinct, since it does not exhibit the characteristic plaques, and it affects flexures, typically

armpits, groin, and under the breast. These lesions do not present with scales, and appear as red,

shiny, and well demarcated plaques. Erythrodermic psoriasis results in a scaling, itching,

inflammatory process involving most of the body surface. This may occur as a result of chronic

plaques which progress and become confluent, or it may result from unstable psoriasis due to

infection, drugs, and stress (5). Erythroderma occurs in 3% of psoriasis patients, and its

complications are highly life-threatening, since patients may develop severe infections,

pneumonia, and cardiac failure (5, 19). Pustular psoriasis is characterized by white, non-

infectious pustules, surrounded by red skin. There are two types of pustular psoriasis: von

Zumbusch, or generalized pustular psoriasis, and palmo-plantar pustulosis. The von Zumbusch

type is rare, and unstable, where flare-ups occur in repeated waves lasting days or weeks (5, 19).

Finally, psoriatic nail disease is seen in 40-45% of patients with cutaneous psoriasis (5). The

most common finding is pitting of the nails, resulting from psoriatic involvement of the nail bed.

The nail may also detach from the nail bed distally or laterally (known as onycholysis) (5).

Psoriatic nails tend to be deformed and thickened and also exhibit yellow-brown discoloration

(5). Psoriatic nail disease is particularly relevant to psoriatic arthritis, as will be discussed later

(20).

5

1.1.4 Diagnosis, measurement, and treatment

Psoriasis is most often diagnosed by history and physical examination, as no diagnostic

laboratory tests are available (21). The Psoriasis Area and Severity Index (PASI) and the Body

Surface Area (BSA) tools are used in assessing the severity of the disease. PASI combines

scoring the severity of the lesions, and the area affected into a single score, whereby 0 indicates

no disease, and 72 indicates maximal disease (22, 23). A PASI score greater than 10 indicates

severe psoriasis. BSA represents an estimate of the percent body surface that is affected by

psoriasis. A BSA score greater than 10% indicates severe psoriasis (22, 23). Treatment varies

based on the severity of the disease. Topical corticosteroids, tar, retinoids, and Vitamin D

derivatives are generally used for mild psoriasis. Severe disease is treated with phototherapy,

and systemic agents such as, methotrexate, cyclosporine, apremilast, or biologic agents, such as,

anti-TNF-α, anti-IL12/23 or anti-IL-17 agents (24-26).

6

1.2 Psoriatic Arthritis

1.2.1 Statistics and epidemiology

Psoriatic arthritis (PsA) is defined as a rheumatoid factor-negative inflammatory arthritis,

associated with psoriasis (27). Moll and Wright demonstrated a 19-fold increase in psoriasis

prevalence, amongst first-degree relatives with PsA, when compared to the general population

(28, 29). The most recent estimate of the prevalence of PsA in North America is 0.25% (6), and

the incidence of PsA in the general population ranges from 3-23.1 per 100,000 (30). PsA occurs

in around 30% of patients with psoriasis (2, 31-33). Most commonly, psoriasis precedes PsA,

but arthritis may also precede the psoriasis (32). Approximately 70% of the patients develop

psoriasis before arthritis, and psoriasis and PsA develop simultaneously in an additional 15%,

while 15% develop the arthritis before the detection of psoriasis (34). Males and females are

equally affected by both, psoriasis and PsA (33). The onset of PsA occurs between the ages of

30-55 years, and most studies report no relationship between the type or severity of skin disease,

and the joint manifestations as most patients with PsA have mild or moderate skin disease (35),

although patients with more severe psoriasis are at a higher risk of developing (6).

1.2.2 Aetiology and Pathogenesis

There are several factors such as genetic, immunological, and environmental that have been

proposed to be of importance for the aetiology and progression of PsA, and this section will

discuss the similarity and differences between psoriasis and PsA.

As in psoriasis, genetic factors contributing to the susceptibility for PsA have been analyzed by

linkage and association studies (9, 29, 35, 36). A number of familial studies have suggested

7

first-degree relatives to be at risk of developing PsA (9, 29, 34). A parental gender effect has

been demonstrated in both psoriasis and PsA, whereby more patients have an affected father,

rather than an affected mother. Several studies of the major histocompatibility complex region

on chromosome 6p, have found that HLA-C*06 are more prevalent among patients with

psoriasis and PsA, when compared to healthy controls, especially in patients with Type I

psoriasis (36-39). When comparing patients with PsA with those with psoriasis alone, HLA-

B*08, HLA-C*12, HLA-B*27, and HLA-B*38 alleles were found to indicate an increase in the

odds of developing PsA, while HLA-C*06 was found to decrease these odds (40-42).

Additionally, HLA-B*27 has been associated with axial involvement, while HLA-B*38 and

B*39 have been associated with peripheral disease (40-45); it has been demonstrated that HLA-

B*27 represents a strong genetic marker for PsA among psoriasis patients (41, 42). HLA-DQ3

has been suggested as a marker for disease progression (43), and an increased frequency of DR4

has been associated with disease severity, development of polyarticular symmetrical arthritis,

and with joint erosions (43, 46, 47).

Non-HLA genes that map close to the MHC region of chromosome 6p, such as MHC class I

chain-related gene A (MICA), is in linkage disequilibrium with HLA-B alleles and is reported to

have an increased frequency in psoriasis and PsA patients (48-51). Cytokine-related genes, such

as IL-23R, IL-12p40, IL23p19, IL-21, IL-4, IL-5, and IL-13, have all been associated with PsA

(36, 52).

In addition to genetic factors, immunological mediators have also been described in the

pathogenesis of both psoriasis and PsA. In psoriatic skin lesions, increased levels of CD4 T-

lymphocytes are found in the dermis (53), while in PsA CD8 T-lymphocyte population is

8

significantly increased in patients’ synovium (54). Synovial fluid (SF)-derived CD8 T-cells are

mature (expressing CD45RO), activated (expressing HLA-DR), and express low levels of CD25

(which represents the α chain of the IL-2 receptor) (55). Additionally, the cytokine profile in

PsA is characterized by the presence of Th1 cytokines such as interleukin (IL)-1β, IL-2,

interferon-λ, TNF-α, and IL-10 (56). More recently, a number of studies have demonstrated that

IL-17, IL-22 and IL-23 are increased in psoriatic skin lesions and the synovium of PsA patients,

and have a role in the pathophysiology of these diseases by inducing hyperproliferation of

keratinocytes and promotion of synovitis (57-59). These studies were further supported by the

fact that inhibition of these cytokines demonstrated clinical benefits of both PsA and psoriasis

alone (24, 60). Macrophages, and increased levels of metalloproteases have also been

documented in PsA (56, 61, 62). These are just a few factors which are known to play a role in

initiating and maintaining the inflammatory milieu observed in PsA and psoriasis patients; a

comprehensive description of the inflammatory mediators of PsA is given by Wittmann et al.

(26).

Environmental factors playing a role in the aetiology and progression of PsA have been difficult

to separate from the immunological factors (63). Initially, due to increased levels of antibodies

against bacterial cell wall peptidoglycan found in PsA patients, it was believed that bacterial

infections had a role in PsA pathogenesis (12, 54, 55, 64). However, the Th1 cytokine pattern

suggests that synovial inflammation is not only driven by an immune response to a bacterial

antigen (35, 56). A number of groups have shown that physical injury more often triggers PsA

than other arthritis diseases; this is known as the Koebner phenomenon (65-67). The underlying

reason for this is not known, but the idea that the release of neuropeptides which can stimulate

the synovial membrane and lead to hypervascularization, has been proposed (68). Lifting heavy

9

loads and smoking have also been associated with the occurrence of arthritis among psoriasis

patients, but no association has been found between PsA and alcohol consumption, vaccination,

stress, or female hormonal exposures in the most recent study (65).

1.2.3 Clinical Features

PsA has a heterogeneous pattern and patients can present with various symptoms such as mild

mono-oligo arthritis or very severe, erosive, and destructive polyarthritis (32, 69). The

frequently involved joints are, distal interphalangeal (DIP) and proximal interphalangeal (PIP)

joints, the wrists, the metatarsophalangeal joints, the joints of the lower extremities, the

sacroiliac joints, and the spinal column (32, 69).

Enthesitis is a characteristic feature of PsA, with inflammation at tendon or ligament attachment

sites. Interestingly, MRI detection of enthesitis in clinically uninvolved joints led to the

suggestion that enthesitis may be the primary lesion in PsA, which is also supported by the

observation that entheseal inflammation may extend as far as the synovial cavity (70, 71).

Dactylitis, or inflammation involving a complete digit, is also characteristic of PsA, occurring in

48% of the patients. Dactylitis has been associated with worse radiographic appearance (20, 72-

74). These extra-articular symptoms differ from patients with rheumatoid arthritis (RA), but do

occur among patients with other spondyloarthritides. Other manifestations in patients with PsA

include uveitis, distal extremity swelling, and discoloration of the skin over affected joints, and

inflammatory bowel disease (75-78). Radiological changes in PsA include: erosion of terminal

phalangeal tufts, whittling of bone ends, cupping of proximal ends of bones, severe destruction

of isolated small joints, sacroiliitis, or syndesmophytes (69, 75). During recent years it has

10

become apparent that PsA can be a very destructive disease, and approximately 20% of patients

develop severe, deforming arthritis (79).

1.2.4 Relationship between the skin and joint

Since in most patients psoriasis precedes PsA, the skin disease most often serves as a marker for

subjects at risk of developing PsA. As described previously, immune cells and the mediators

they secrete influence disease initiation and progression in psoriasis and PsA. In psoriasis,

trauma may lead to skin lesions, which is reflected by the lesion pattern seen in psoriasis (35);

areas repeatedly exposed to trauma or pressure, such as knees and elbows, often develop plaque

psoriasis (13, 67). Interestingly, in PsA, DIP joints are often involved with adjacent psoriatic

nail disease. Thus, it has been proposed that inflammation of the nail/skin, initiates

inflammation in the closest joint leading to arthritis or enthesitis, in patients that are susceptible.

Alternatively, it has also been suggested that with the extensor tendon enthesitis linking the joint

to the nail bed, the involvement of the nail bed may be due to extending inflammation from the

joint (35). It is still unclear, which of the two proposals are correct.

1.2.5 Diagnosis of PsA

The diagnosis of PsA is considered when inflammatory arthritis, spondylitis or enthesitis occurs

in patients with psoriasis. The criteria defined by Moll and Wright were used as ‘diagnostic

criteria’ over the last three decades (68). There were no universally agreed upon classification

criteria until the ClASsification of Psoriatic Arthritis (CASPAR) criteria were developed (27).

The CASPAR criteria have high sensitivity and specificity for PsA (91.4% and 98.7%,

11

respectively), but require a rheumatologist to determine whether a patient has inflammatory

musculoskeletal disease (80, 81) (Table 1.1).

Table 1.1 The CASPAR criteria for classification of PsA

1.2.6 Treatment

Non-steroidal anti-inflammatory drugs (NSAIDs) are the basic treatment for the pain, and

stiffness experienced in mild arthritis. In NSAIDs non-respondents, traditional ‘disease

modifying anti-rheumatic drugs (DMARDs)’ such as, sulfasalazine, methotrexate, leflunomide,

and cyclosporine, are used and often effective against arthritis, either as single treatments or in

combination (22, 82-84). Newer agents that include the phosphodiesterase inhibitor apremilast

and biological agents, such as anti-TNF-α and IL 12/23 agents, have improved signs and

symptoms of PsA and psoriasis (24, 26, 82, 85, 86).

Inflammatory articular disease (joint, spine, entheseal) with more than 3 points from the

following categories:

1. Current (2 points) or past (1 point) presence of skin psoriasis, or a family history of

psoriasis (1 point)

2. Psoriatic nail lesions (1 point)

3. Dactylitis (1 point)

4. Negative rheumatoid factor (1 point)

5. Radiographic evidence of juxta-articular new bone formation (1 point)

12

1.2.7 Identifying PsA Early

As mentioned previously, the presence of psoriasis indicates a high risk for the existence or

future development of PsA since 85% to 90% of patients with PsA have psoriasis at the onset of

PsA. Early diagnosis and management of PsA leads to better long-term outcomes (87-89). In a

recent survey conducted in Canada they found that although 18% of patients were diagnosed

with PsA the number who reported joint pain or stiffness was 51%, suggesting that a number of

patients may have had early or undiagnosed PsA (90). Additionally, other studies conducted in

dermatology clinics have also shown a high prevalence of undiagnosed PsA in patients with

psoriasis (72). Therefore dermatologists have the potential to play an important role in

preventing joint destruction in psoriasis patients, by screening for signs of PsA, initiating

treatment, and referring patients to a rheumatologist if needed (91). Unfortunately, for

dermatologists, identifying inflammatory musculoskeletal disease early in patients with psoriasis

is a very difficult task. Although there are clinical features that may help predict PsA, such as

nail, scalp and intergluteal/perianal psoriasis, and psoriasis severity, these symptoms are non-

specific (80, 92). Additionally, acute phase reactants may be normal, and there are no validated

biomarkers. Therefore much of the recent research in PsA, has focused on identifying, serum-

based biomarkers. These laboratory tests could be used to screen psoriasis patients, in order to

identify those that may have or develop PsA; patients that screen positive can then be referred to

a rheumatologist for appropriate investigation and treatment (10, 11, 93).

13

1.3 Biomarker discovery in rheumatology

1.3.1 Definition of biomarkers

The National Institutes of Health Biomarker Definition Working Group, defined a biomarker as

“a characteristic that is objectively measured and evaluated as an indicator of normal biologic

processes, pathogenic processes, or a pharmacologic response to a therapeutic intervention”

(94). As such, these biomarkers can be used in the clinic to diagnose, predict disease

progression, monitor activity of the disease, assess therapeutic response, or guide molecular

targeted therapy (94). Biomarkers for joint diseases may be clinical, histological or imaging

features, as well as genomic, proteomic, and transcriptomic markers (10, 11, 76, 93, 95). Table

1.2 outlines examples of commonly used serum-based markers for various joint diseases (96-

105). Apart from classical acute phase reactants, the limitations of which will be discussed later,

such a biomarker does not currently exist for PsA, and the current need lies in identifying a

prognostic serum-based marker (or markers), which can be used to screen psoriasis patients to

determine those that are at risk of having or developing PsA (10, 11, 93, 95).

14

Table 1.2 Examples of soluble biomarkers used in the diagnosis and treatment of joint

diseases

1.3.2 Ideal PsA biomarkers

Ideal PsA biomarkers should be produced by cells within involved tissues, such as synovial

fibroblasts in the inflamed joint, or keratinocytes in the psoriatic epidermis, and enter systemic

circulation where they can be detected. Thus, the ideal PsA biomarker should not already be

present in high concentration in the blood of healthy individuals, and in patients with psoriasis

without arthritis, since the biomarker contributions from PsA will be difficult to measure.

Finally, ideal markers should demonstrate high sensitivity and high specificity.

1.3.3 Advantages of protein biomarkers

The foremost advantage of studying proteins is that the actual functional molecules of a cell are

being investigated, elucidating a reliable picture of what is occurring in the tissue/joint. Proteins

are more diverse than nucleic acids, since alternative splicing and post-translational

modifications may result in multiple proteins from one gene (112, 113). Further, many

Marker Molecular Class Application

Creatinine Metabolite Drug toxicity (98)

CRP Protein Identify acute inflammation (99)

ANA Autoantibody Diagnostic for various joint diseases

(100)

RF Autoantibody Diagnostic for RA (101, 102)

ACPA Autoantibody Diagnostic and prognostic for RA (103,

104)

Anti-dsDNA Autoantibody Diagnostic for SLE (105)

15

physiologic changes are mediated post-transcriptionally, and therefore will not be reflected at

the nucleic acid level (112, 113). Proteins are also more dynamic, and more reflective of cellular

physiology (113). For example, a double stranded DNA break results in a cascade of protein

phosphorylation. Finally, and most importantly in the context of biomarkers, proteins are more

stable and easily measureable, compared to mRNA which can be rapidly degraded (114).

Therefore, protein biomarkers carry several advantages over genomic and transcriptomic

biomarkers.

1.3.4 Serum biomarkers in PsA

Serum protein biomarkers represent the most cost-effective, non-invasive, and objective way to

detect, stage, prognosticate, and monitor disease activity and response to treatment. A number of

putative PsA biomarkers have already been identified, but to date, none have been validated in a

clinical setting. The types of serum protein PsA biomarkers may be divided into acute phase

reactants, markers of cartilage repair and destruction, inflammatory markers, markers of bone

destruction and new bone formation, and markers of extracellular matrix destruction. As it will

become apparent, PsA is a highly heterogeneous disease, and it is unlikely that a single marker

will prove sufficient in serving as an ideal biomarker (10, 11, 93).

1.3.4.1 Acute phase reactants

Acute phase reactants (APR) represent serum proteins that increase or decrease in response to

inflammation. APRs such as C-reactive protein (CRP) and Erythrocyte Sedimentation Rate

(ESR) are only elevated in 50% of PsA, even in the presence of active disease. A highly-

sensitive CRP (hsCRP) assay has been developed, whereby CRP is measureable in levels

16

<5mg/l. This assay has shown a lot of promise, since it was demonstrated, in a pilot study, that

hsCRP levels in PsA are higher than those in psoriasis (10, 11, 93, 115, 116).

1.3.4.2 Markers of cartilage repair and destruction

Cartilage destruction and repair is characteristic of inflammatory arthritis, and the products of

synthesis and destruction are released in the serum where they can be measured (117). Cartilage

oligomeric matrix protein (COMP), a glycoprotein expressed in cartilage, tendons, synovial

membrane, and scarified skin, is also found to be elevated in PsA, and correlated strongly with

inflammatory parameters and number of inflamed joints (115). Unfortunately, COMP was also

elevated in psoriasis patients, and a difference between levels of COMP in PsA and psoriasis

was not observed (115, 118). Additionally, the articular cartilage is composed of Type II

collagen network complexed with aggrecan (115, 119). It is known that cleavage of type II

collagen in articular cartilage by collagenases generates neoepitopes Col2-3/4Clong mono (C2C)

and Col2-3/4Cshort (C1-2C) (115). As Type II collagen is degraded, chondrocytes respond by

upregulating production of procollagen (115). The procollagen peptide (CPII) is cleaved when

procollagen is secreted; therefore the rate of type II collagen synthesis is directly proportional to

the amount of CPII in the cartilage (115). While neither of the peptides are informative on their

own, the ratio of CPII to C2C (CPII:C2C) represents the balance between type II collagen

synthesis and degradation. In a small-scale study, increased levels of CPII:C2C were

independently associated with PsA, when compared to psoriasis alone

[OR(95%CI)=4.76(1.35,16,77)] (115). Additional validation studies are still underway.

17

1.3.4.3 Cytokines and chemokines

Inflammation is the hallmark of PsA, and several inflammatory markers have also been

proposed. Chandran et al. investigated the ability of IL-12p40 to distinguish between PsA and

psoriasis (115). Although the levels of IL-12p40 were higher in PsA, compared to controls, the

elevation was not significant (115). Cumulative evidence strongly supports the involvement of

IL-23/IL-17 axis in the pathogenesis of PsA, and a number of compounds that target

components of these pathways have been recently used in PsA clinical trials (24, 82, 120). IL-23

acts synergistically with IL-6 and TGF-β to promote rapid Th17 cell development and IL-17

release (121, 122), which in turn, plays a central role in sustaining chronic inflammation (121).

Serum IL-6 levels are elevated in PsA patients compared to psoriasis, and correlate well with

joint counts, ESR, CRP, and IL-2Rα (11, 93, 116). Additionally, in a recent study, IL-17

secretion was elevated in both PsA and psoriasis when compared to healthy controls, with no

significant difference between PsA and psoriasis, while IL-22 expression was 2-fold higher in

PsA when compared to psoriasis without arthritis (57). Additional studies are needed to show

reproducibility of IL-22 expression.

1.3.4.4 Markers of bone destruction and new bone formation

Bone resorption, mediated by osteoclasts, and new bone formation, mediated by osteoblasts, has

been increasingly investigated in the context of PsA. Osteoclast activity is modulated by

macrophage colony stimulating factor (M-CSF), the receptor activator for nuclear factor κB

ligand (RANKL) and osteoprotegerin (OPG). In turn, TNF superfamily member 14 (TNFSF14)

promotes RANKL-dependent osteoclastogenesis. In the same study by Chandran et al., OPG,

TNFSF14, and RANKL were measured in patients’ serum, but only elevated OPG was found to

18

have discriminatory ability between PsA and psoriasis [OR(95%CI)=1.01(1.00,1.02)] (115).

OPG is a cytokine that inhibits osteoclast differentiation and promotes new bone formation. It is

thus believed to be a marker of periostitis and new bone formation, the combination of which

represents a characteristic that differentiates PsA from other inflammatory arthritides (27).

Additionally, Dickkopf-1 (DKK-1), a regulatory molecule of the Wnt signalling pathway,

inhibits osteoblast function and is also a key regulator of joint remodelling (123). DKK-1 levels

are increased in PsA compared to psoriasis without arthritis (124). Its elevation has also been

demonstrated in patients with active rheumatoid arthritis (123), casting doubt on the role and

significance of circulating DKK1 in PsA; further studies are thus warranted.

1.3.4.5 Markers of extracellular matrix destruction

Matrix-metalloproteinase 3 (MMP3) is involved in the breakdown of extracellular matrix and

tissue remodelling in normal, as well as pathologic conditions. In PsA, as well as other

rheumatic conditions characterized by synovitis, MMP3 is involved in the destruction of

cartilage and bone. In a pilot study, serum MMP3 levels were increased in patients with PsA,

and could be used to discriminate between PsA and psoriasis without arthritis

[OR(95%CI)=1.28(1.02,1.60)] (115). These results are preliminary, and require validation in a

larger study.

1.3.5 Proteomics in biomarker discovery

Genomic and transcriptomic studies have been robust and led to the identification of a number

of susceptibility genes and expression profiles in PsA (10, 11, 93, 106). Proteomic studies to

identify serum protein biomarkers, represent a more recent approach to PsA biomarker

discovery, and has the potential to complement genomics-based approaches by bridging the gap

19

between what is encoded in the genome and what is occurring at the tissue/joint level (107). It is

well known that genomic and proteomic data sets have different sources of bias and variance, so

combining them may lead to a more precise view of the differential protein abundance (108,

109). The key benefit of the integration of proteomic and transcriptomic data in the field of

biomarker discovery is its potential for improving the selection of candidates to validate. If both

transcriptomic and proteomic platforms agree on a strong differential expression between the

groups of patients being compared, the attractiveness of a candidate is strengthened (110, 111).

20

1.4 Proteomic methods used in discovering putative biomarkers

in rheumatology

Mass-spectrometry (MS) is a technique that measures the mass-to-charge ratio of ions, to

identify and quantify molecules in simple and complex mixtures. MS has become an invaluable

tool across many fields and applications, including proteomics. The initial development of high-

throughput qualitative, and more recently quantitative proteomic methods, have expanded our

knowledge of protein structure, function, modification, and profiles in disease states.

1.4.1 Basic components of a mass-spectrometer

MS is performed on a mass-spectrometer, which consists of an ionization source, a mass

analyzer, and an ion detector. The specifics of these components, the type of data generated

(qualitative, quantitative, post-translational modifications, etc.), and the physical properties of

the samples that can be analyzed, vary across different instruments (125).

Following standard sample preparation procedures which typically includes protein

denaturation, reduction, alkylation, and peptide separation, samples are loaded into the mass-

spectrometer in liquid form, and the next step is to convert the sample into gas-phase ions using

the ionization source. Ionization sources can vary largely, but are typically classified as either

electrospray ionization (ESI), or matrix-assisted laser desorption ionization (MALDI) (125). In

ESI, ions are produced at atmospheric pressure by running the sample through a narrow

capillary tube in an electrostatic field. The resulting electric potential difference generates a fine

mist of charged droplets. The solvent is evaporated, typically using Nitrogen gas, and along with

Coulombic forces, nanometer-sized droplets are produced (125). Alternatively, MALDI ions

21

result from combining sample molecules with small organic molecules which are capable of

absorbing light when irradiated with a laser beam. The matrix absorbs light at the wavelength of

the laser, which leads to desorption and ionization of both, the matrix and sample. MALDI is

well-suited for analyzing samples containing analytes greater than 200kDa, while ESI allows

analysis of smaller analytes (less than 1KDa). Therefore, the choice of ionization depends

largely on the nature of the sample being analyzed (125).

The charge received from ionization allows the mass spectrometer to accelerate the ions through

the remainder of the instrument. The gas-phase ions enter the mass analyzer, where they

encounter electrical or magnetic fields, which deflect the paths of individual ions based on their

mass to charge ratio (m/z). Mass analyzers can also vary, and include quadrupole time-of-flight

(Q-TOF), triple quadrupole, orbitrap, quadrupole ion traps, TOF, and TOF/TOF, and Fourier-

transform ion-cyclotron resonance (FT-ICR) analyzers. Ion-trap mass analyzers utilize magnetic

and radio frequency to hold ions, while quadrupole ion-trap analyzers use an oscillating electric

field for ion storage and mass analysis (125). Using the electrode, the orbitrap mass analyzer

traps ions in an electrostatic field, causing the ions to move in a spiral pattern. The quadrupole

analyzer consists of four parallel metal rods, where each opposing rod pair is electrically

connected. Ions are separated based on their trajectory in the oscillating electric fields that are

applied to the rods (125). In a triple quadrupole (QQQ), the first quadrupole (Q), and the last

quadrupole act as mass analyzers, while the second quadrupole is used as a collision cell where

peptides are fragmented. The performance of a mass analyzer depends on the maximum

allowable mass that can be analyzed (mass range), the smallest amount of analyte that is

detected with high confidence (detection sensitivity), and its ability to separate two mass ions

(resolution) (125).

22

Ions successfully passing through the mass analyzers are then detected. The detectors are, most

often, electron multipliers or microchannel plates, which emit electron cascades when each ion

hits the detector plate. This results in the amplification of each ion, and improves sensitivity

(125). The detector measures the electric current in proportion with the number of ions striking

it, and generates a mass spectrum, which is analyzed using various pattern-matching algorithms.

1.4.2 Sample fractionation methods

The dynamic range in protein concentration in many biological fluids and tissues extends from

mg/mL for abundant structural and carrier proteins such as albumin and keratin, to pg/mL for

signaling molecules such as TNF-α. This is similar to plasma, where the concentration range is

approximately 12 orders of magnitude. The proteomic analytical methods currently available

have a concentration range within 2-5 orders of magnitude, leading to low detection of less

abundant proteins (126-129). A common way to overcome this problem and reduce protein

complexity is by implementing pre-fractionation methods and/or depleting the sample of the

most abundant proteins using depleting columns. Proteins bind directly to the column or

indirectly through secondary binding to immunoglobulins or albumin (or other high-abundant

proteins), Although all commercially available columns have been designed for serum and

plasma, they are also valuable for other biological fluids such as synovial fluid and ascetic fluid,

because most abundant proteins in these fluids correspond to those in serum/plasma (130). It is

important to note that, high-abundance proteins can physically mask less abundant proteins with

similar isoelectric points and molecular weights. Depletion columns may also bind proteins in a

non-specific manner (131, 132). Depletion should only be used when whole sample integrity is

not essential. According to a study by Chen et al., treatment of synovial fluid prior to

23

fractionation will decrease reproducibility and increase protein loss; therefore, it should also be

avoided (133). Instead, low molecular weight components of samples, which may represent

putative biomarkers, can be enriched by size exclusion liquid chromatography (LC), strong

cation/anion exchange (SCX/SAX) LC, or reverse phase (RP) LC. In size exclusion

chromatography, molecules are separated based on their size or molecular weight. This method

is generally good for separation of large molecules from small ones, but it can result in

approximately 50% total protein loss. SCX and SAX lead to the separation of analytes based on

their charge. These represent the most frequently used separation methods, as they have a high

resolving power, capacity, and simplicity. Finally, in RP-LC molecules are separated by

hydrophobicity, whereby proteins are eluted using a gradient of hydrophobic solvent. This

method is commonly used as a last separation step following sample preparation and prior to

MS analysis.

1.4.3 Tandem mass spectrometry

Tandem mass spectrometry (MS/MS) offers additional information about ions being analyzed.

In this instance, ions of interest are selected based on their m/z from the first round of MS

(MS1), and these are then fragmented, usually via collisions with inert gas atoms, as is the case

in collision-induced dissociation. The resulting fragments are then separated based on their

individual m/z in an additional round of MS (MS2) (125, 129). This process ultimately allows

the identification and characterization of proteins.

24

1.4.4 Quantitative Mass-Spectrometry Methods

Quantification of protein levels to achieve accurate differential protein profiling between

biological samples has been a major challenge in proteomics. These methods require chemical

labeling of the samples prior to MS analysis, or can be performed label-free, termed label-free

quantification (LFQ). More recently, targeted quantitative mass-spectrometry methods have

been developed, in the form of selected reaction monitoring (SRM) assays (134-136).

1.4.4.1 Label-free quantification

The label-free protocol is semi-quantitative, and is based on the peak intensity of the peptides in

the MS scan, or on the number of observed spectra per peptide across different samples (125). In

general, the advantage of label-free approaches over chemical labeling lies in the low cost and

the high number of samples that can be easily included in the experiment (136). Potential

disadvantages of label-free techniques include the lower reproducibility of results, which

compromises detection of smaller quantitative changes between samples (136). In terms of data

analysis, spectral counting is the simplest label-free method but is also the least reliable because

quantification accuracy drops when the number of spectra representing a protein becomes very

low, usually less than two (136). As a result, classifying smaller changes among the identified

low abundance proteins, which typically have low numbers of observed spectra, will be difficult

with this method. Alternatively, intensity-based quantification is, in theory, more capable of

obtaining accurate values for lower abundance proteins (137). Computer algorithms such as

MaxQuant are used to analyze the data, and require the identification of only one peptide in at

least one of the samples being analyzed to extract the peak intensity information and to quantify

the peptide in all the analyzed samples (138, 139).

25

1.4.4.2 Chemical labeling

As an alternative to label-free approaches, chemical labeling of peptides/proteins prior to

fractionation and MS analysis has also been used. There are a number of available labeling

strategies: tandem mass tags (TMT) (140), isobaric tags for relative quantification (iTRAQ)

(141), and isotope-coded affinity tags (ICAT) (142) are the most popular commercial

alternatives. In the case of iTRAQ (4-plex and 8-plex) and TMT (6-plex), free amines generated

from trypsin digestion and present in all the peptides are labeled and, therefore, theoretically no

information is lost (136). Reporter ions relating to the various samples being analyzed are

released from the peptides during MS/MS fragmentation and are thus used to represent the

sample from which specific proteins originate; no quantitative information is obtained from the

MS scan (140, 141). These methods are not without challenges, as utilizing these protocols in

complex samples may result in the partial suppression of the quantitative reporter ion signal

(143). Low-abundance proteins are more vulnerable to this effect, and unfortunately these are

also the proteins that represent potential biomarkers. ICAT only labels cysteine residues

therefore proteins that do not have peptides containing cysteine, will not be quantified. In

addition, the quantitative information from each protein with this method is sparse, and ICAT

only appears as duplex labels (143). Generally, chemical labeling protocols are rather lengthy

and involve many steps, resulting in compromised reproducibility.

1.4.4.3 Selected reaction monitoring

Recently, there has been a paradigm shift toward the use of targeted MS-based methods. SRM

assays exploit the capabilities of triple quadrupole or Q-Trap instruments (135). For reliable

quantification of a protein of interest, proteotypic peptides or peptides unique to a particular

26

protein of interest, are first selected. The corresponding predefined precursor masses of these

peptides are selected in the first quadrupole and fragmented in the second quadrupole, with

predefined fragmentation masses selected in the third quadrupole. This unique pair of precursor

and fragmentation mass is termed a transition. The SRM method can be applied simultaneously

to multiple proteins (MRM), a protocol that is further reviewed by Lange et al. (135). Stable

Isotope Dilution-SRM (SID-SRM) is based on the selection of three to five peptides resulting

from tryptic digestion from each protein to be quantified (144). Synthetic peptides containing

heavy lysine and arginine residues (which have incorporated 13C/15N atoms) are then added to

all samples. These peptides serve as internal standards providing relative quantitative ratios for

each proteotypic peptide corresponding to each protein of interest between all samples (144).

The nature of this approach allows for very high-molecular selectivity, and if interference is

present it can also be detected (144).

1.4.5 Biomarker development pipeline

Three different phases exist in the discovery of novel biomarkers: discovery, verification, and

validation (145). In the discovery phase, a small number of well-characterized, high-quality

samples are compared using fractionation and quantitative proteomics methods to generate an

extensive list of protein components. Subsequent phases in the biomarker development pipeline

replace the unbiased experimental design with target-driven quantitative strategies relying

mainly on target-driven analytical methods. During the verification phase, preliminary assays

are employed, such as enzyme-linked immune sorbent assays (ELISA) and selected reaction

monitoring assays, to measure the levels of selected proteins in relevant biological samples. To

advance the development, all biomarker candidates also require validation, which is undertaken

27

on only a subset of verified candidates and is performed in, ideally, thousands of samples (145).

This stage requires the development of robust immunoassays to measure the proteins accurately

in serum samples. Potential biomarkers showing good sensitivity and specificity are considered

for further clinical evaluation (145).

1.4.6 Sources to mine for PsA biomarkers

In the context of PsA, blood obtained by venipuncture is the most accessible, minimally

invasive, and the most practical human specimen that can be monitored over long periods of

time. Blood plasma contains proteins shed from all organs and tissues. However, performing

mass spectrometric analysis of plasma or serum analysis presents with several challenges. These

include high complexity in the number of proteins and protein isoforms, a concentration range

between high-abundance and lows-abundance proteins of 12 orders of magnitude, and changes

in protein concentration, structure, and function resulting from physiological and pathological

processes. Therefore, the discovery of biomarkers from serum by mass-spectrometry analysis

has proven to be a challenging task (126-128).

Fortunately in the case of PsA, the affected joint offers access to synovial fluid. SF is a potential

source of valuable markers. Synovial fluid is obtained from affected patients by arthrocentesis

(joint aspiration). Synovial fluid is secreted by the synovial membrane, and is in direct contact

with both the synovial membrane, and the articular cartilage (146). Consequently, SF contains

specific additions made from proximal joint tissue, including the synovial membrane and

cartilage, therefore changes in the cellular metabolism and structure of these tissues as they

occur in a disease state may be reflected by changes in SF composition (146). This particular

characteristic can be exploited when searching for biomarkers of any joint disease. In a study by

28

Mateos et al., SF was utilized to mine for RA biomarkers using MALDI-TOF, which resulted in

the identification of 136 differentially expressed possible RA biomarkers (147). The synovial

membrane also represents an excellent reservoir of possible PsA biomarkers. Obviously the

synovial membrane has an advantage over synovial fluid since it is the site of inflammation in

PsA, but these samples, as well as appropriate controls are hard to obtain. Another important

source to investigate for potential biomarkers is the skin, as it represents another PsA target

tissue. The premise is that certain proteins originating/elevated from this tissue during disease,

could subsequently enter the bloodstream. The inflammatory process, as it occurs in PsA, is

characterized by vascular changes, such as vasodilation, increased permeability and increased

blood flow (148). These processes may facilitate shedding or secretion of skin proteins into the

blood stream. In a 2011 study, tandem mass-spectrometric analysis of psoriatic skin tissue

showed differential expression of 146 proteins in lesional (affected) psoriatic skin, when

compared to nonlesional (unaffected and healthy skin (149). Although these markers were never

validated, this study provides evidence that proteomic signatures differ between lesional and

nonlesional skin.

29

1.5 Rationale and Aims of the Present Study

1.5.1 Rationale

The presence of cutaneous psoriasis is a high risk for developing PsA, but several studies that

have been conducted in dermatology clinics have shown a high prevalence of undiagnosed PsA

in psoriasis patients. This is due to the fact that identifying inflammatory musculoskeletal

disease early in patients is difficult since the symptoms are non-specific, and acute-phase

reactants are often normal. While there are clinical features that can be utilized to predict PsA,

such as nail, scalp, intergluteal/perianal psoriasis, and psoriasis severity, these are common

among patients with psoriasis. Identifying biomarkers that can recognize PsA in psoriasis

patients may help in early diagnosis and subsequent prevention of disability and improvement in

quality of life.

There is no doubt that genomic and transcriptomic methods are powerful, as they resulted in the

identification of a number of significant susceptibility genes and expression profiles in PsA.

But, proteins are more diverse and carry more information that nucleic acids. Post-translational

modification and alternative splicing may lead to numerous protein variants from the same gene.

Additionally, information provided by nucleic acids is limited, as they are unable to predict

downstream events, such as what protein, and in what quantities will be expressed in a particular

tissue or fluid in a pathologic state. Candidate PsA protein biomarkers have been selected for

validation based on their assumed importance in disease pathogenesis, and the availability of

assays, but to date, the need for discovery of soluble PsA biomarkers in psoriasis patients has

remained unmet. Classical acute phase reactants, such as ESR and CRP are the only laboratory

tests currently used in clinic, but these are non-specific markers of inflammation that have poor

30

sensitivity and specificity. Therefore, analyzing proteins can provide additional information, by

relating specific proteins to a disease such as PsA.

MS-based proteomic approaches are well-suited for the discovery of protein mediators of

disease. While early studies relied on qualitative analysis, more recently, semi-quantitative and

quantitative comparisons of protein abundance are the preferred methods for identifying

differentially expressed proteins in biological samples.

Blood obtained by venipuncture is the most practical human specimen that can be used to

monitor disease status over long periods of time. However, high throughput mass-spectrometry

analysis of serum or plasma has proven to be a difficult task. Due to its close proximity to the

diseased joint, synovial fluid represents an ideal source to mine for potential PsA biomarkers.

Additionally, since skin is the other important target tissue in PsA, proteins elevated in the skin

may also present candidate PsA biomarkers.

1.5.2 Hypothesis

Quantitative mass spectrometry-based proteomic analysis of synovial fluid and skin tissue from

PsA patients and appropriate controls, will generate a comprehensive list of proteins specific to

PsA, facilitating the identification of candidate PsA screening biomarkers.

31

1.5.3 Specific aims

1) To perform high-throughput label-free quantitative proteomics to identify upregulated

PsA-specific proteins in synovial fluid.

i) Develop sample preparation method best suited for label-free quantification analysis

of synovial fluid.

ii) Utilize bioinformatics tools to filter and select the most informative proteins.

iii) Develop selected reaction monitoring assays and verify candidate markers in

individual synovial fluid patient samples.

2) To perform high-throughput label-free quantitative proteomics to identify upregulated

PsA-specific proteins in skin.

i) Develop sample preparation method best suited for label-free quantification analysis

of skin.

ii) Utilize bioinformatics tools to filter and select the most informative proteins.

iii) Develop selected reaction monitoring assays and verify candidate markers in

individual skin patient samples.

3) To validate verified markers in PsA serum samples using enzyme-linked immunosorbent

assays.

32

Chapter 2

The data presented in this chapter has been published in Clinical Proteomics:

Cretu D, Prassas I, Saraon P, Batruch I, Gandhi R, Diamandis EP, Chandran V. Identification

of psoriatic arthritis mediators in synovial fluid by quantitative mass spectrometry. Clin

Proteomics.2014;11:27.

33

Quantitative proteomic analysis of psoriatic arthritis synovial

fluid

2.1 Introduction

Psoriatic arthritis is an inflammatory arthritis distinguished by bone resorption and periarticular

new bone formation, and bears its name from its association with the cutaneous disease,

psoriasis (150). PsA occurs in approximately 30% of psoriasis patients and in about 85% of

cases psoriasis precedes or occurs simultaneously with PsA (2, 6, 32). PsA has a predicted

prevalence of 0.16 to 0.25% in the general population, and is a complex, potentially disabling

musculoskeletal disorder often arising early in age. Patients with PsA are also at increased risk

of co-morbidities, such as obesity, metabolic syndrome, diabetes, and cardiovascular disease

(31, 32). The aetiology of psoriasis and PsA remains unclear, but studies indicate that

interaction between multiple genetic components and environmental factors are important in the

disease pathogenesis (2, 9, 65, 106, 151). It is proposed that environmental factors such as

infections by Streptococci or articular trauma (2, 41, 42, 65, 67, 152, 153) may trigger

immunological alterations in genetically predisposed individuals that play important roles in the

appearance of both skin and articular disease. From the immunological point of view, changes

are observed in both innate and adaptive immunity. Undoubtedly, the identification of key PsA

mediators will not only provide valuable information towards a deeper understanding of the

molecular basis of the disease, but it might also uncover important PsA biomarkers potentially

useful for clinical follow-up and response to treatment.

Mass spectrometry-based proteomic approaches are well-suited for the discovery of protein

mediators of disease. Early studies relied on qualitative (identity-based) analysis, and

34

performance was depended mainly on the sensitivity of the available MS platforms and sample

processing prior to MS-analysis (154, 155). More recently, semi-quantitative and quantitative

comparisons of protein relative abundance are the preferred methods for identifying

differentially expressed proteins (156). In many cases, chemical or metabolic labeling of

samples prior to analysis has been utilized for quantitation, but it has been associated with

technical challenges (136). Label-free quantification (LFQ) methods have also been recently

optimized, in which quantification is based on the differential peak intensity [extracted ion

current (XIC)] of the peptides in each MS scan (125, 157).

LFQ quantitative proteomics presents a robust means for obtaining proteome profiles of

virtually any biological material (125, 157). Human plasma represents a diverse proteome and is

an excellent source for protein mediators of disease, but proteins secreted by adjacent tissues are

diluted in blood, and are often undetectable by current MS methods (97). To circumvent this

issue, attention has been focused on proximal fluids, such as ascites (158), and seminal fluid

(159) to search for tissue-associated markers. For instance, in the case of PsA, as mentioned

previously, synovial fluid (SF) represents an interesting source of PsA-related proteins secreted

by the synovium, ligament, meniscus, articular cartilage, and joint capsule. Moreover, it is well

known, that there is an exchange of proteins between SF and the systemic circulation through

the synovial lymphatics and vasculature (129). In support of this, we have demonstrated that

proteins elevated in the SF of PsA patients, are likewise upregulated at the serum level (160).

We have therefore decided to focus on SF for the discovery of putative PsA biomarkers.

In the present study, we performed label-free MS quantitation of SF proteins from PsA and early

osteoarthritis (OA). Using a highly sensitive and specific MS-based approach, we confirmed the

35

elevation of specific elevated proteins in an independent set of samples from patients with PsA.

These observations may shed new light on the pathogenesis of PsA, offer insights into disease

progression, and might reveal potential PsA biomarkers.

36

2.2 Methods

2.2.1 SF Proteomic analysis

2.2.1.1 Human subjects and clinical samples

The study received institutional review board approval from the University Health Network, and

informed consent was obtained from all patients.

For the discovery phase, SF was obtained from 10 cases with PsA (6 males, 4 females; age

range 30-76 years), and 10 age- and sex-matched controls (early OA) (Set I). PsA patients had

psoriasis and satisfied the CASPAR classification criteria (27). Inclusion criteria included

symptom duration ranging from 1 to 10 years (to capture both relatively early and established

disease), and at least one inflamed and accessible large joint. The inflammatory nature of the SF,

and the absence of other causes of inflammation, such as infection and/or crystal disease, was

confirmed by laboratory investigations.

SF from joints with early OA was obtained during an arthroscopic procedure. Early OA was

defined as only a partial thickness cartilage defect in any compartment of the knee, and further

defined by a grade I or II lesion by the Outerbridge classification (161).

For the verification (quantification) phase, an independent set of SF samples (Set II) was

acquired from 10 PsA patients (7 males, 3 females; age range 21-66 years), and 10 age- and sex-

matched early OA controls. Inclusion and exclusion criteria were the same as described above.

37

2.2.1.2 Pre-analytical sample processing

Synovial fluid samples were stored at -80°C until use. Samples were centrifuged upon thawing

at 1800g for 10 minutes, to remove any debris, and the total protein was measured in each

sample using the Coomassie (Bradford) total protein assay (Pierce Biotechnology, IL). Equal

protein amounts of each SF sample were combined, to obtain two (1mg total) pools (PsA vs.

early OA), which were analyzed in triplicates. Proteins in each pool were denatured using heat

(95°C for 10 minutes), reduced with 5mM dithiothreitol at 60°C for 45 minutes, and alkylated

with 15mM iodoacetamide, in the dark at room temperature for 45 minutes. Sequencing grade

trypsin (Promega, WI) was added in a 1:50 (trypsin: protein) ratio, and allowed to digest for 18

hours at 37°C. The samples were subsequently acidified (pH 2) using 1uL of formic acid, to

inhibit trypsin activity. The resulting peptides were then subjected to high-performance liquid

chromatography (HPLC) using strong cation exchange (SCX) columns to reduce peptide

complexity.

2.2.1.3 HPLC-SCX

Digested samples were diluted 1:2 in mobile phase A SCX buffer (0.26M formic acid (FA),

10% acetonitrile (ACN); pH 2-3) and loaded directly onto a 500µL loop connected to a

PolySULFOETHYL A column (2.1 mm × 200 mm; 5 μ; 200 °A; The Nest Group Inc., MA),

containing a silica-based hydrophilic, anionic polymer (poly-2-sulfotheyl aspartamide). An

Agilent 1100 HPLC (Agilent Technologies, Germany) system was used for fractionation. A 60-

minute gradient was employed with a linear gradient starting at 30 minutes and consisting of

mobile phase A and mobile phase B (0.26M FA, 10% ACN, 1M ammonium formate; pH-4-5)

38

for elution of peptides (flow rate 200uL/min). The fractionation was monitored at a wavelength

of 280 nm, and performed in triplicate. Fractions were collected every two minutes from 20 to

55 minutes, and those with a low peak absorbance were pooled, resulting in a total of 10/15

fractions per sample (10 fractions for early OA replicates, and 15 fractions for PsA replicates).

This amounted to a total of 75 SCX fractions, which were then subjected to liquid

chromatographic and tandem mass spectrometric analysis (LC-MS/MS). SCX column and

system performance was ensured by running a quality control peptide mixture consisting of

1ug/uL Alpha Bag Cell peptide, 1ug/uL Fibrinogen fragment, 5ug/uL Human ACTH, and

5ug/uL ACE Inhibitor (American Protein Company, CA) after every sample.

2.2.1.4 LC-MS/MS

The SCX fractions were purified through C-18 OMIX Pipette Tips (Agilent Technologies,

Germany), to remove impurities and salts, and eluted in 5µL of 65% MS buffer B (90% ACN,

0.1% FA, 10% water, 0.02% Trifluoroacetc Acid (TFA)) and 35% MS buffer A (5% ACN,

0.1% FA, 95% water, 0.02% TFA). The samples were diluted to 85µL in MS buffer A, and

injected into a nano-LC system (Proxeon Biosystems, FL) connected online to an LTQ-Orbitrap

(Thermo Fisher Scientific, USA). A 90 minute linear gradient reversed phase chromatography

using MS buffer A and MS buffer B, was performed at a flow rate of 400nL/min to resolve

peptides on a C-18 column (75uM x 5 cm; Proxeon Biosystems, FL). The MS parameters were:

300 Da minimum mass, 4000 Da maximum mass, automatic precursor charge selection, 10

minimum peaks per MS/MS scan; and 1 minimum scan per group. XCalibur software v.2.0.7

(Thermo Fisher Scientific, USA) was used for data acquisition.

39

2.2.1.5 Protein identification and quantification

Raw files corresponding to early OA, and PsA data sets were uploaded into MaxQuant v. 1.2.2.2

(www.maxquant.org) (139) and searched with Andromeda (built into MaxQuant) (138) against

the nonredundant IPI.Human v.3.71 (86, 309 sequences; released March 2010) database, which

contains both forward and reverse protein sequences. Search parameters included a fixed

carbamidomethylation of cysteines, and variable modifications of methionine oxidation and N-

terminal acetylation. Data was initially searched against a “human first search” database with a

parent tolerance of 20 ppm and a fragment tolerance of 0.5 Da in order to calculate and adjust

the correct parent tolerance to 5 ppm for the search against the IPI.Human fasta file. During the

search, the IPI.Human fasta database was randomized and false detection rate was set to 1% at

the peptide and protein levels. Data was analyzed using “Label-free quantification” checked,

and the “Match between runs” interval was set to 2 min. Proteins were identified with a

minimum of one unique peptide. “LFQ Intensity” columns corresponding to the extracted ion

current (XIC) value of each protein in replicate early OA and PsA groups were averaged and

used to calculate PsA/early OA ratios (fold change; FC).

2.2.1.6 Bioinformatics analysis

To minimize false positives, we excluded from further analysis any protein with an individual

false detection rate >0.05. Proteins displaying an XIC value lower than 100,000 were regarded

as absent (noise). We also excluded proteins present in only one of the three technical replicates.

Averages of the technical replicate XIC values were calculated for each PsA and early OA

group, and ratios of PsA/early OA were used to identify deregulated proteins. Upregulated

40

proteins were denoted with a PsA/early OA ratio (FC) greater than 2, while downregulated

proteins had a PsA/early OA ratio (FC) less than 0.8. Housekeeping proteins were represented

with a PsA/early OA ratio (FC) of approximately 1.0. To determine the possible origin of

differentially expressed proteins at the tissue level, and identify the most probable mediators of

PsA, gene names were checked against gene [BioGPS (http://biogps.org/#goto=welcome) (162),

and protein [Human Protein Atlas (http://www.proteinatlas.org/) (163) databases, to identify

proteins with strong expression in PsA-associated tissues and cell types (skin, bone, immune

cells). The Plasma Proteome Database (http://www.plasmaproteomedatabase.org/) (126) was

employed to identify proteins present in high-abundance in the serum, which could represent

potential contaminants. Selected reaction monitoring (SRM) assays were developed for the top

upregulated proteins in the PsA group and housekeeping proteins, and relative protein

quantification was performed in individual SF samples to confirm their elevation in PsA SF.

2.2.1.7 Network analysis

The list of dysregulated proteins identified by LC-MS/MS was analyzed by pathway analysis

using the network-building tool, Ingenuity Pathways Analysis (IPA; Ingenuity Systems,

www.ingenuity.com) (164).

2.2.2 Verification of identified proteins using SRM assays

SRM methods were developed for verification of protein ratios in SF, following our in-house

protocols (165).

41

2.2.2.1 SRM assay development

PeptideAtlas (http://www.peptideatlas.org/) was utilized to select the most commonly observed

3-4 tryptic peptides for the proteins of interest. Their presence was confirmed using our LC-

MS/MS identification data. The uniqueness of peptides was verified using the Basic Local

Alignment Search Tool (BLAST; https://blast.ncbi.nlm.nih.gov/Blast.cgi). To identify peptide

fragments (transitions) to monitor, in silico peptide fragmentation was performed using Pinpoint

software (Thermo Fisher Scientific, USA) and 5-6 transitions were selected for each peptide.

For method optimization, digested pooled samples of SF used in our LC-MS/MS analysis, were

loaded onto a C-18 column (Proxeon Biosystems, FL) coupled to a triple quadrupole mass

spectrometer (TSQ Vantage; Thermo Fisher Scientific, USA), and approximately 350 transitions

were monitored in 6 subsequent runs. Three transitions of the most intense peptides (per

candidate protein) were used for subsequent quantification assays.

2.2.2.2 SF sample preparation

Fifty µg of total protein of each SF sample, were denatured by heat, reduced, alkylated, and

trypsin-digested as described previously. In the independent SF sample set (Set II), heavy-

labelled versions of the peptides of interest (JPT Peptide Technologies, Germany) ranging from

1 to 1000 fmol/µl of sample were also added, as internal standards. Heavy peptides had identical

sequences to the endogenous peptides, except the C-terminal lysine or arginine was labeled with

13C and 15N. The resulting peptides were purified through C-18 OMIX Pipette Tips (Agilent

Technologies, Germany), and eluted in 3µL of 65% MS buffer B (90% ACN, 0.1% FA, 10%

water, 0.02% TFA) and 35% MS buffer A (5% ACN, 0.1% FA, 95% water, 0.02% TFA).

42

Samples were diluted to 40µL of MS buffer A, randomized, and loaded onto a C-18 column

coupled to a triple quadrupole mass spectrometer.

2.2.2.3 SRM assays

SRM assays were developed on a triple-quadrupole mass spectrometer (TSQ Vantage; Thermo

Fisher Scientific, USA) using a nanoelectrospray ionization source (nano-ESI, Proxeon

Biosystems, FL), as previously described (165). Briefly, a 60-minute, three-step gradient was

used to load peptides onto the column via an EASY-nLC pump (Proxeon Biosystems, FL), and

peptides were analyzed by a multiplexed SRM method using the following parameters:

predicted CE values, 0.002 m/z scan width, 0.01 s scan time, 0.3 Q1, 0.7 Q3, 1.5 mTorr Q2

pressure and tuned tube lens values. Quantification, in SF set I, was executed after

normalization against a set of 4 peptides corresponding to 2 housekeeping proteins (LRG1,

CFI), to offset technical variations. Each sample was analyzed in duplicate, using a 60-minute

method, whereby 117 transitions were monitored. Quantification in SF set II was executed

following normalization against the added heavy-labelled peptides, as described earlier. Each

sample was analyzed in duplicate, using a 60-minute method, whereby 134 transitions were

monitored. Reproducibility of the SRM signal was confirmed by running a quality control

solution of 0.1 fmol/µL bovine serum albumin, every 10 runs.

2.2.2.4 SRM protein quantification

Raw files recorded for each sample were analyzed using Pinpoint software (Thermo Fisher

Scientific, USA) (166), and peptide XICs were extracted. Pinpoint was used for identification

and visualization of transitions, as well as manual verification of co-elution of heavy and

43

endogenous peptides. In the first SF set, in order to control for technical variation between the

samples, XIC corresponding to each endogenous peptide replicate were divided by the XIC

corresponding to the average of the two housekeeping proteins. This value was then averaged

amongst the two replicate runs, to obtain “XIC Average normalized to housekeeping proteins”.

In SF set II, in order to control for technical variation between the samples and obtain a more

robust quantitative value for our proteins of interest, the XIC value corresponding to each

endogenous peptide, was divided by the XIC value corresponding to each spiked-in heavy

peptide, in order to obtain a L:H (light:heavy) ratio. Since we added a known amount of each

heavy peptide to our samples prior to analysis, we used the L:H ratio to calculate the relative

concentration of each endogenous peptide corresponding to our proteins of interest.

2.2.2.5 Statistical analysis

Results were analyzed using nonparametric statistics with the Mann-Whitney U test. P-values

(P) less than 0.05 were considered statistically significant.

Figure 2.1 outlines the overall experimental workflow.

44

Figure 2.1 Summary of the biomarker discovery and verification experimental workflow

in SF

45

2.3 Results

2.3.1 Delineating the PsA SF proteome

Our LC-MS/MS analysis of SF identified and quantified 443 proteins, which were present in at

least two of the three technical replicates representative of each PsA and OA pool. Of these, 137

proteins were differentially regulated (2.0<XIC ratio<0.8) between the PsA and early OA SF, as

shown in Table 2.1. A total of 44 proteins were upregulated with a PsA/OA ratio greater than 2,

while 93 proteins were downregulated, with a ratio less than 0.8. The IPA software was used to

identify dysregulated functional pathways associated with both the upregulated and

downregulated proteins in the PsA SF proteome. The top five molecular and cellular functions

associated with upregulated proteins were, cell-to-cell signalling and interaction, cell movement,

antigen presentation, cell cycle, and cell morphology, some of which have been shown to be

attributes of PsA. The top canonical pathways associated with these proteins were acute-phase

response signalling, granulocyte adhesion and diapedesis, and production of nitric oxide and

reactive oxygen species in macrophages (Table 2.2). While some of the functions were similar,

the downregulated proteins were less associated with inflammatory processes. Since our

ultimate goal is to identify candidate biomarkers for PsA, we decided to focus on proteins that

were overexpressed in PsA SF.

46

Table 2.1 Differentially expressed proteins between early OA and PsA groups identified by

LC-MS/MS

Upregulated Proteins Downregulated Proteins

Protein Name PsA:OA FC Protein Name PsA:OA FC

S100A9 19.4 FN1 0.8

CRP 18.7 FN4 0.8

DEFA1 13.1 IGHK 0.8

SAA1 12.7 PLG 0.8

MMP3 10.6 HPX 0.8

H2AFX 9.7 SNC73 0.8

PFN1 9.1 IGHD 0.8

H4 8.6 Anti-RhD monoclonal T125 gamma1 heavy chain 0.7

BASP1 7.4 BDK 0.7

CDABP0047 6.7 F2 0.7

CTSG 6.7 IGFALS 0.7

APOC1 6.3 IGHK 0.7

MMP1 6.1 IGHM 0.7

C4B 4.4 ITIH2 0.7

SERPINB1 4.3 MIH 0.7

ACTA1 3.9 Putative Uncharacterized Protein cDNA FLJ78387 0.7

MPO 3.9 Putative Uncharacterized Protein DKFZp686C11235 0.7

PLS2 3.9 Putative Uncharacterized Protein DKFZp686G11190 0.7

M2BP 3.8 Putative Uncharacterized Protein DKFZp686K03196 0.7

C1QB 3.5 Putative Uncharacterized Protein DKFZp686K18196 0.7

ACTB 3.4 Putative Uncharacterized Protein DKFZp686O01196 0.7

FGG 3.3 SERPINA1 0.7

APC2 3.2 SERPINA8 0.7

CD5L 3.1 IGHD 0.7

ACTBL2 3.0 VH 0.7

C1R 2.9 Cold agglutinin FS-2 L-chain (Fragment) 0.7

CP 2.9 57 kDa protein 0.6

FGB 2.8 APOA4 0.6

A2M 2.6 BDK 0.6

PZP 2.6 BTD 0.6

IGCJ 2.5 F12 0.6

ORM1 2.5 GC 0.6

APCS 2.4 IGHG1 0.6

FGA 2.3 IGHG4 0.6

ACBP 2.3 Putative Uncharacterized Protein 56 kDa protein 0.6

APOB 2.2 Putative Uncharacterized Protein 59 kDa protein 0.6

APOBR 2.2 V<kappa>3 Chain 0.6

47

C4BP 2.2 VH3 0.6

CLEC3B 2.2 C2 0.6

IGHM 2.1 Putative Uncharacterized Protein DKFZp686P15220 0.6

ITIH3 2.1 ECM1 0.6

ITIH4 2.1 Putative Uncharacterized Protein DKFZp686F0970 0.6

VL4 2.1 IGHV 0.6

F5-20 0.6

IGK 0.6

Putative Uncharacterized Protein 26 kDa protein 0.6

Putative Uncharacterized Protein DKFZp686I04196 0.6

IGK 0.6

AMBP 0.6

Putative Uncharacterized Protein 26 kDa protein 0.6

AZGP1 0.6

CLU 0.6

FETUB 0.5

MMP2 0.5

GSN 0.5

IGKC 0.5

IGKC 0.5

SERPINA4 0.5

HBA1 0.4

Putative Uncharacterized Protein DKFZp686H17246 0.4

SERPINF2 0.4

MSF 0.4

VH4 0.4

SERPINA5 0.4

SEPP1 0.4

F13A 0.4

AFM 0.4

PLG 0.4

APOA1 0.4

IGHFv 0.4

IGHV 0.4

TSP4 0.4

IGH 0.4

SERPINF1 0.4

PCOLCE 0.3

IGL 0.3

EFEMP1 0.3

LDC 0.3

CRTAC1 0.3

48

FGFBP2 0.3

ACAN 0.2

49

According to gene ontology functional annotation, the majority of these proteins were

extracellular, plasma membrane-associated, or proteins with unknown localization (Figure 2.2),

consistent with the fact that SF is a proximal fluid, and most of the proteins are likely shed or

secreted by chondrocytes, synoviocytes, and inflammatory cells which come in direct contact

with this biological fluid. Since inflammation-driven cell death naturally occurs during PsA, this

could lead to the release of cytosolic proteins into the SF, therefore the cytosolic proteins we

identified also hold biological importance. We focused on proteins displaying strong expression

in skin, bone, and immune regulatory cells (Table 2.3), but excluded immunoglobulins from

further analysis. This reduced our list to 20 proteins, from which, high abundance serum

proteins, as identified using the Plasma Proteome Database, were excluded (SAA1, APCS,

APOC1), as we assumed that these were most likely serum contaminants from the joint

aspiration or arthroscopic procedure. This filtering yielded a final set of 17 proteins (Table 2.4),

which we deemed likely to be associated with PsA. The validity of our discovery approach was

further enhanced by the discovery of previously investigated PsA- associated proteins (CRP,

MMP3, S100A9) (115, 167, 168, 169).

50

Table 2.2 Summary of Ingenuity Pathway Analysis (IPA)-generated functional pathways

and diseases associated with elevated proteins identified from PsA SF

IPA

Number of

Components

Identified Proteins P-Value

Diseases and Disorders

Connective Tissue Disorders 8 ACTA1, DEFA1, IGHM, PLS2,

MMP1, MMP3, MPO, S100A9 1.21E-07

Inflammatory Disease 8 ACTA1, DEFA1, IGHM, PLS2,

MMP1, MMP3, MPO, S100A9 1.21E-07

Skeletal and Muscular Disorders 8 ACTA1, DEFA1, IGHM, PLS2,

MMP1, MMP3, MPO, S100A9 1.21E-07

Inflammatory Response 10

CTSG, FGA, PLS2, S100A9,

APCS, MPO, SAA1, DEFA1, ORM1,

MMP1

2.86E-06

Immunological Disease 5 ACTA1, DEFA1, PLS2, MPO,

S100A9 1.04E-04

Molecular and Cellular Functions

Cell-to-cell Signaling and Interaction 11

CTSG, FGA, PLS2, S100A9,

MPO, ORM1, DEFA1, APCS,

APOC1, SAA1, MMP1

2.86E-06

Cellular Movement 8 CTSG, DEFA1, SAA1, PLS2,

MMP1, MMP3, S100A9, PFN1 5.69E-05

Antigen Presentation 1 PLS2 2.11E-03

Cell Cycle 1 PFN1 2.11E-03

Cell Morphology 2 PLS2, PFN1 2.11E-03

Physiological System Development and Function

Hematological System Development and Function 10

CTSG, FGA, PLS2, S100A9,

MPO, ORM1, DEFA1, SAA1, APCS,

IGHM

2.86E-06

Immune Cell Trafficking 10

CTSG, FGA, PLS2, S100A9,

MPO, ORM1, DEFA1, SAA1, MMP1,

APCS

2.86E-06

Tissue Development 8 CTSG, FGA, PLS2, S100A9,

MPO, ORM1, SAA1, ACTA1 2.86E-06

Cell-mediated Immune Response 3 CTSG, DEFA1, PLS2 1.51E-03

Organismal Survival 2 ACTB, PLS2 3.33E-03

51

Top Canonical Pathways

LXR/RXR Activation 5 APOB, APOC1, FGA, ORM1,

SAA1 2.70E-06

Acute Phase Response Signaling 5 APCS, C4BP, FGA, ORM1,

SAA1 1.53E-05

Clathirin-mediated Endocytosis Signaling 5 ACTA1, ACTB, APOB, APOC1,

ORM1 2.61E-05

Granulocyte Adhesion and Diapedesis 4 ACTA1, ACTB, MMP1, MMP3 3.53E-04

Production of Nitric Oxide and Reactive Oxygen

Species in Macrophages 4 APOB, APOC1, MPO, ORM1 3.09E-04

52

Figure 2.2 Cellular localization of the 44 upregulated proteins based on GO annotation.

The numbers depicted represent the

number of proteins with the specified

cellular localization.

Table. 2.3 Tissue expression of the top 20 elevated proteins identified from SF

*BioGPS does not contain expression data for bone tissue

Human Protein Atlas BioGPS*

Protein Name Tissue Expression Tissue Expression

C4BP Immune Bone Skin Immune

APCS Skin Immune Immune

ORM1 Immune Bone Immune

CD5L Immune Skin Immune

M2BP Skin Bone Skin Immune

PLS2 Immune Immune

MPO Immune Immune

SERPINB1 Immune Immune

MMP1 Bone

APOC1 Skin Immune

CTSG Immune Immune

BASP1 Skin Immune Immune

H4 Skin Immune Bone Immune

PFN1 Skin Immune Skin Immune

H2AFX Skin Immune Bone Skin Immune

MMP3 Skin Immune Bone Skin Immune

SAA1 Skin

DEFA1 Immune Bone Immune

CRP Skin Immune Skin Immune

S100A9 Skin Immune

Extracellular, 30

Plasma-membrane, 6

Cytoplasmic or Nuclear, 6

Unknown, 2

53

Table 2.4 Fold change (FC) of seventeen elevated and two housekeeping* proteins in PsA

SF and the corresponding peptides

2.3.2 SRM Verification of putative markers in individual SF

samples

To verify the differences in protein expression between PsA and early OA SF samples, as

identified by LC-MS/MS, we developed multiplexed selected reaction monitoring assays.

Reactions were developed for 17 peptides representative of the 17 proteins with increased

expression in the SF of PsA patients, as well as 4 peptides representing the 2 housekeeping

proteins that had unchanged expression between PsA and OA SF (Table 2.4). Consistent with

Protein Description Gene Name PsA:OA FC Corresponding SRM

peptides

Orosomucoid 1 ORM1 2.5 WFYIASAFR

Cathepsin G CTSG 6.7 NVNPVALPR

Profilin 1 PFN1 9.0 TLVLLMGK

Histone 4 H4 8.6 VFLENVIR

Histone 2A type I A H2AFX 9.7 NDEELNK

Brain acid soluble protein 1 BASP1 7.4 ESEPQAAAEPAEAK

22 kDa interstitial collagenase MMP1 6.1 DIYSSFGFPR

Leukocyte elastase inhibitor SERPINB1 4.3 LGVQDLFNSSK

Myeloperoxidase MPO 3.9 IGLDLPALNMQR

Plastin 2 PLS2 3.9 NWMNSLGVNPR

Galectin-3-binding protein M2BP 3.8 AVDTWSWGER

C4b-binding protein C4BP 2.1 YTCLPGYVR

C-reactive protein CRP 18.7 ESDTSYVSLK

Protein S100A9 S100A9 18.9 LTWASHEK

Stromelysin 1 MMP3 10.6 VWEEVTPLTFSR

Neutrophil alpha defensin 1 DEFA1 13.1 IPACIAGER

CD5-like protein CD5L 3.0 IWLDNVR

Leucine-rich alpha-2-glycoprotein 1*

LRG1

1.0

DLLLPQPDLR

GQTLLAVAK

Complement Factor I*

CFI

1.0

FSVSLK

YTHLSCDK

54

our LC-MS/MS analysis of the pooled samples, overexpression of 13 out of these 17 proteins

was also verified in the individual PsA SF samples (Set I), as compared to the early OA SF

(Figure 2.3). Each sample consisted of two technical replicates. CRP, MMP3, and S100A9,

were amongst these 13 verified proteins. The mean fold change of each protein and the

associated P-values are depicted in Table 2.5.

55

Figure 2.3 Verification of elevated proteins in PsA synovial fluid (Set I) by selected

reaction monitoring assays, normalized against housekeeping protein

OA

PsA

0

5

10

15

20

CTSG

***

OA

PsA

10

12

14

16

18

DEFA1

**

OA

PsA

0

5

10

15

20

25

H2AFX

**

OA

PsA

16

18

20

22

CD5L

***

OA

PsA

0

5

10

15

20

MPO

***

OA

PsA

0

5

10

15

20

25

CRP

***

OA

PsA

0

5

10

15

20

25

MMP3

***

OA

PsA

0

5

10

15

20

M2BP

**

OA

PsA

10

12

14

16

18

20

22

H4

*

OA

PsA

12

14

16

18

20

22

ORM1

*

OA

PsA

0

5

10

15

20

S100A9

***

OA

PsA

0

5

10

15

20

C4BP

**

OA

PsA

0

5

10

15

20

PFN1

***

OA

PsA

1.00

1.05

1.10

1.15

1.20

1.25

1.30

CFI

ns

OA

PsA

0.7

0.8

0.8

0.9

0.9

LRG1

ns

Dots represent SF samples from individual subjects; thin horizontal lines depict the mean,

and vertical lines the SD. **** indicates P<0.0001; ***P<0.001; **P<0.01; *P<0.05; ns:

non-significant.

Norm

aliz

ed X

IC

56

Additionally, we also confirmed the elevation of 12 out of the 17 proteins, in an independent set

of 10 PsA, and 10 early OA SF samples (Set II). In this case, we utilized heavy-labeled peptides

in order to obtain an absolute concentration of these peptides in SF (Figure 2.4). The proteins

included MPO, M2BP, DEFA1, H4, H2AFX, ORM1, CD5L, PFN1, and C4BP, as well as our

positive controls MMP3, S100A9, and CRP. The mean fold change and P-values corresponding

to each protein from SF set II are also depicted in Table 2.5.

Table 2.5 Summary of the fold change (FC) of candidate markers in SF Set I and II*

*Set I and Set II are described in the experimental methods section

Set I Set II

Gene Name PsA:OA FC P-Value PsA:OA FC P-Value

ORM1 2.1 0.0217 1.6 0.0487

CTSG 2.6 0.0001 2.4 0.0887

PFN1 2.6 0.0006 1.9 0.0299

H4 2.2 0.0271 2.5 0.0015

H2AFX 2.2 0.0022 3.3 0.0092

BASP1 1.9 0.8115 2.9 0.1220

MMP1 1.9 0.5580 1.5 0.6182

SERPINB1 2.0 0.4623 1.4 0.3298

MPO 3.5 0.0001 2.8 0.0039

PLS2 2.0 0.3051 1.0 0.9365

M2BP 2.3 0.0041 3.0 0.0048

C4BP 2.3 0.0016 2.1 0.0105

CRP 2.9 0.0001 2.8 0.0010

S100A9 2.8 0.0001 3.9 0.0010

MMP3 2.9 0.0001 3.8 0.0001

DEFA1 2.1 0.0086 2.9 0.0001

CD5L 2.1 0.0005 2.5 0.0002

57

Figure 2.4 Verification and concentration of elevated proteins in PsA synovial fluid (Set II)

by selected reaction monitoring assays, normalized against heavy-labeled peptides.

Dots represent SF samples from individual subjects; thin horizontal lines depict the

mean, and vertical lines the SD. **** indicates P<0.0001; ***P<0.001; **P<0.01;

*P<0.05; ns: non-significant.

ORM1

OA

PsA

0

50

100

150

*

fmol/ul

H2AFX

OA

PsA

-500

0

500

1000

1500

*

fmol/ul

C4BP

OA

PsA

0

5

10

15

*

pmol/ul

MMP3

OA

PsA

0

50

100

150

***

fmol/ul

PFN1

OA

PsA

0

5

10

15

*

fmol/ul

MPO

OA

PsA

0

5

10

15

**

fmol/ul

CRP

OA

PsA

0

5

10

15

20***

pmol/ul

DEFA1

OA

PsA

0

500

1000

1500***

fmol/ul

H4

OA

PsA

0

50

100

150

**

fmol/ul

M2BP

OA

PsA

0

5

10

15*

fmol/ul

S100A9

OA

PsA

0

500

1000

1500

***

fmol/ul

CD5L

OA

PsA

0

50

100

150

***

fmol/ul

58

2.4 Discussion

In the present study, we performed proteomic analysis of SF, and identified 137 proteins that

were differentially expressed between PsA and OA SF. Since one of our future goals is to

investigate these proteins as serum biomarkers, we chose to focus only on the 44 upregulated

proteins. Moreover, these proteins were reduced to 17 promising candidates that are likely

produced by the synovial membrane, cartilage, or surrounding inflammatory cells, and are

secreted into the synovial fluid compartment. The elevation of 12 of these proteins was

confirmed in an independent SF cohort, using SRM assays, in order to provide sensitive and

specific quantification. As an internal validation of our approach, we re-discovered a number of

proteins previously known to be involved in the context of PsA or psoriasis progression. For

example, our top 3 up-regulated proteins (based on fold change) - S100A9 (168, 169), MMP3,

and CRP (115, 167) - have been extensively studied in the context of PsA.

Psoriasis is a T-cell driven disease that causes epidermal hyperplasia, with an influx of

autoimmune effector cells including monocytes, neutrophils, and dendritic cells (1). Erythema is

also an important feature of psoriasis and is caused by increased growth and dilation of

superficial blood vessels in the skin (1). Joint and synovial findings in PsA are T-cell driven, as

in the skin, and the prominent hyperplasia of blood vessels in skin is echoed in the synovial

membrane (148). PsA also causes hyper proliferation of the lining cells of the synovium,

resulting in the formation of invasive, inflammatory tissue (150, 170, 171). These aspects are

reflected in the current study, through the identification of upregulated proteins belonging to I)

acute-phase response signalling, II) granulocyte adhesion and diapedesis, III) production of

59

nitric oxide and reactive oxygen species in macrophage pathways, and IV) cell-to-cell signalling

and interaction, cell movement, and antigen presentation.

One of these proteins, alpha defensin 1 (DEFA1), is secreted by neutrophils in response to

various antigens. Elevated levels have been associated with inflammatory activity in both

rheumatoid arthritis (172) and psoriasis (173). Additionally, antimicrobial defense mechanisms

mediated by DEFA1 have also been described in inflammatory synovium (174). Although a

minor inflammatory process is present in OA synovial tissue, PsA patients demonstrate a more

aggressive state of synovial tissue inflammation compared with OA patients, therefore elevation

in DEFA1 may be a good indicator of PsA pathogenesis.

The enzyme myeloperoxidase (MPO) also seems to play a role in the joint inflammation

associated with PsA, as we have determined that it is elevated in PsA SF. MPO is the

predominant protein present in primary granules of circulating polymorphonuclear cells. It is a

member of the human peroxidase family haeme-containing enzymes that play a role in host

defence against infection, and is the major enzymatic source of leukocyte-generated oxidants,

released by activated neutrophils and used as a marker of leukocyte recruitment and function

and subsequent inflammation (175). Although not specifically in PsA, MPO has been shown to

be elevated in SF and serum derived from RA patients (175) and has been linked to the

maintenance of oxidative stress in both psoriasis (176) and RA (177). Biological similarities

between PsA and RA have been previously described, especially among PsA patients with

polyarticular peripheral arthritis (150, 171). Increased expression of MPO may also represent an

important mediator of PsA. Furthermore, CD5-like protein (CD5L) has been described as an

alternative ligand for CD5, a lymphoid-specific membrane glycoprotein that is constitutively

60

expressed in all T cells, but expressed in highest levels in activated T-cells (178). Recently,

circulating CD5 levels were shown to be increased in RA and systemic lupus erythematosus

(179), and currently we have confirmed the elevation of its ligand, CD5L, in PsA SF. Although

the functional relevance of CD5L is unknown, it may play a role in the stimulation and

regulation of the immune system (179). Moreover, a set of recent experiments have provided

evidence showing that CD5 stimulation, favors Th17- over Th1-driven polarization of naïve T-

cells (180); the functional relevance of CD5L in this has not been investigated yet. This is

relevant since in both psoriatic skin and synovium, while CD8+ T-lymphocytes predominate,

the important role played by the Th17 subset of CD4+ T-cells in psoriatic disease has recently

also become apparent (57-59, 181). Together, these findings corroborate our hypothesis, that the

proteins we have identified are relevant to PsA and the underlying mechanisms that potentiate

this disease.

We also uncovered novel putative mediators, which have yet to be described in arthritides,

which include BASP1, H2AFX, and ORM1. Interestingly, of these, only ORM1 has been

previously associated with psoriasis, where ORM1 was increased in the plasma of psoriatic

patients. The specific function of this protein has not been determined yet, although it is

believed to be involved in aspects of immunosuppression (182). We speculate that ORM1 may

have a protective role in suppressing the immune system to decrease inflammation.

Despite these interesting findings, there are some aspects in the design of this study that need to

be further clarified. First, the “control” synovial fluid originated from joints of patients with

early OA, and not patients with psoriasis without PsA. We decided to use these samples since

61

SF from psoriasis patients is difficult to obtain. Moreover, it has been shown that early OA

represents an appropriate comparison, to our inflammatory PsA SF (147).

Second, pooling of samples in the discovery phase of a proteomic study could potentially mask

meaningful discrepancies among the different individual SF proteomes. While pooling of

biological replicates does not allow for statistical comparison, it does produce sufficient sample

and increases the likelihood of identifying proteins that are otherwise undetectable (low

abundance) in individual samples, therefore allowing a more extensive proteomic coverage of

the disease’s heterogeneity. In the current study, we pooled biological replicates and performed

SCX fractionation of each pool in triplicate. With the discovery step, we intended to generate a

comprehensive SF dataset to identify potential mediators and select candidates for SRM.

Therefore, by pooling samples and subsequently quantifying proteins in individual samples, we

have taken advantage of two complementary approaches in order to obtain more meaningful

results.

Third, many groups have previously utilized albumin depletion prior to in-gel separation (97) ,

and subsequent proteomic analysis, in order to simplify the SF protein content, and reduce the

dynamic range of proteins. We chose to omit this step for the following reasons: I) albumin is a

fundamental carrier protein, and therefore, its depletion would result in loss of potentially

important proteins from our analysis (97), and II) including a step of immune depletion to our

analysis has the potential of increasing the percent error and reducing the reproducibility of our

experiments, both of which could increase our false discovery rate (133). In fact, in a pilot study

where we assessed the protein recovery following albumin depletion, we determined that our

protein recovery was 20-40% lower (data not shown).

62

As discussed previously, quantification of protein levels has been a major challenge in

proteomics (136). We utilized a label-free approach in order to quantify, and identify

upregulated proteins in PsA SF. In general, the advantage of label-free approaches over

chemical labeling lies in the low cost and the high number of samples that can be easily

included in the experiment. Potential disadvantages are lower reproducibility, which may

compromise detection of smaller quantitative changes between samples. The lower

reproducibility mostly results from the fractionation of peptides prior to LC-MS/MS analysis,

and the subsequent pooling of eluted fractions, which can result in unequal/uncontrolled

pooling, as was the case in the present study. For example, based on our SCX chromatographic

spectra, we noticed several PsA fractions contained higher protein amounts, and pooling of

these fractions could hinder the identification of low-abundance proteins; the high-protein

fractions were therefore analyzed individually in the PsA group, resulting in the higher number

of SCX fractions (15, compared to 10 in early OA, as described in the Methods section). To

ensure accuracy and reproducibility of the SCX chromatography, following LC-MS/MS

analysis, several peptides were chosen at random, and their occurrence was monitored across

fractions corresponding to the PsA and early OA pools. Although the peptide profile was not

identical in each fraction, as several peptides in the PsA group spanned multiple fractions,

generally the peptide having the highest abundance was found in the same fraction when

comparing early OA and PsA groups. Additionally, the reproducibility of LFQ experiments is

also largely based on the timeframe of the experiment (97); therefore, in the present study, we

standardized the entire pipeline, from sample collection and processing, to instrument setup and

calibration. Despite the shortcomings of the fractionation and pooling strategy, we did verify the

63

overexpression of particular proteins using specific SRM assays, which provides validity to our

entire strategy.

Overall, this study represents the most comprehensive proteomic analysis of PsA synovial fluid,

to date. We discovered and verified 12 proteins significantly elevated in PsA SF, compared to

early OA SF. Interestingly, the majority of these proteins are part of functional pathways that

are known to be dysregulated in psoriasis or PsA. These proteins may serve as potential

mediators of the pathogenesis of PsA, and should be further investigated in functional

experiments. Also, a large-scale validation of these proteins in serum is essential, in order to

investigate these proteins as putative biomarkers and/or therapeutic targets for the detection and

treatment of PsA.

64

Chapter 3

The data presented in this chapter has been published in Clinical Proteomics:

Cretu D, Liang K, Saraon P, Batruch I, Diamandis EP, Chandran V. Quantitative tandem mass-

spectrometry of skin tissue reveals putative psoriatic arthritis biomarkers. Clin

Proteomics.2015;12:1

65

Quantitative proteomic analysis of skin biopsies from

patients with psoriasis and psoriatic arthritis

3.1 Introduction

Psoriatic arthritis (PsA) is a distinct inflammatory arthritis, which takes its name from its

association with the cutaneous, autoimmune inflammatory disease, psoriasis. It occurs in 30% of

psoriasis patients, with a predicted prevalence of up to 1% in the general population. PsA is a

complex, potentially disabling musculoskeletal disorder often arising early in age. Patients with

PsA have an increased risk for a spectrum of co-morbidities, such as obesity, metabolic

syndrome, diabetes, and cardiovascular disease (31, 32). The diagnosis of PsA presents a

challenge, largely due to its heterogeneous clinical presentation (80, 81); however, early

diagnosis of PsA is essential for prevention of joint damage and disability (88, 89).

The key to early diagnosis is a better recognition of PsA in patients with psoriasis, since the

presence of psoriasis indicates a high risk of the presence or future development of PsA (31).

Soluble biomarkers represent the ideal means for screening patients for PsA. With

improvements in high-throughput genomic platforms, a number of putative markers, ranging

from susceptibility genes, to mRNA profiles have been proposed (9, 41, 42, 106, 151, 152, 183-

185) however there exists no single (or panel) of specific markers, or mediating factor(s). Much

of the research, therefore, focuses on the discovery and validation of PsA biomarkers (186-189).

Identifying these factors would not only facilitate diagnosis and prognosis of PsA, but provide

further insight into disease pathogenesis. Mass spectrometry (MS)-based quantitative proteomic

approaches are well-suited for the discovery of protein mediators and biomarkers of disease

66

(190, 191). Specifically, label-free quantification methods have recently been optimized, where

quantification is based on the differential peak intensity of the peptides in each MS scan (125,

157). Such experiments often result in tens to hundreds of candidate biomarkers that must be

subsequently verified in serum (97). Therefore, in this context of biomarker discovery, high-

throughput proteomics using mass spectrometry is a powerful tool for obtaining disease-specific

proteome profiles of virtually any biological material. Human plasma represents a diverse

proteome and is an excellent source of protein mediators of disease, but proteins secreted by

tissues are diluted in blood, and are often undetectable by current MS methods. Much interest

has been given to the analysis of proximal fluids and tissues, such as synovial fluid and skin.

These represent logical compartments for PsA biomarkers since they are derived directly from

the diseased site and function in the exchange of proteins between the affected site and the

systemic circulation.

Cutaneous psoriasis develops simultaneously or precedes the onset of PsA in up to 90% of PsA

patients, and in order to move forward in the search for PsA screening biomarkers, we must

consider the preceding cutaneous psoriasis stage in PsA patients. Therefore, comparative

analysis of skin between patients with PsA and those with psoriasis but without PsA (PsC)

represents a reasonable experimental workflow. In a pilot study, our group demonstrated that

proteins elevated in the inflamed skin, are likewise upregulated at the serum level, and may

serve as putative markers of PsA (160).

In the current study, we performed label-free quantitation of skin proteins from PsA and PsC

patients. Using selected reaction monitoring assays (SRM) we confirmed the elevation of some

proteins in an independent set of samples from patients with PsA. Following a small-scale

67

validation in serum using ELISAs, we confirmed a significant elevation of β5 Integrin (ITGB5)

in the serum of PsA patients. Periostin (POSTN) also showed a trend towards association. These

proteins may not only serve as potential PsA biomarkers, but may also shed new light into the

pathogenesis of PsA.

68

3.2 Methods

3.2.1 Skin proteomic analysis

3.2.1.1 Clinical samples

Samples were collected with informed consent after institutional review board approval was

obtained from the University Health Network. For the discovery phase, skin biopsies were

obtained from 10 PsA (6 males, 4 females; age range 38-73) and 10 PsC patients (6 males, 4

females; age range 28-77 years) (Set I). PsA patients had psoriasis and satisfied the CASPAR

classification criteria (27), while the PsC patients were assessed by a rheumatologist, to exclude

arthritis. Patients were not undergoing treatment with methotrexate (MTX) or anti-TNF agents.

One 6mm punch biopsy was obtained from unaffected (non-lesional) skin, and one from

affected (lesional) skin from each PsA and PsC patient, amounting to a total of 40 samples.

For the verification (quantification) phase, an independent set of skin samples was acquired

from 5 PsA patients (all males; age range 49-63 years), and 5 PsC patients (3 males, 2 females;

age range 49-67) (Set II). Inclusion criteria were the same as described above.

For the small-scale validation, serum samples were obtained from 33 PsA patients (22 males, 11

females; age range 21-76), and 15 PsC controls (9 males, 6 females, age range 28-77). Inclusion

criteria were the same as described above.

69

3.2.1.2 Pre-analytical sample processing

Skin samples were snap-frozen in liquid nitrogen and stored at -80°C until use. Samples were

suspended in 0.05% RapiGest buffer (Waters, Milford, MA, USA), and the tissue was

homogenized and sonicated for protein extraction. The tissue lysates were spun at 11,000g and

total protein was measured by the Bradford assay (Pierce Biotechnology, Rockford, IL, USA),

on the resulting supernatants. Equal protein amounts from each sample were pooled to create

eight different pools of five samples each [2 PsA lesional (PsA L), 2 PsA non-lesional (PsA N),

2 PsC lesional (PsC L), and. 2 PsC non-lesional (PsC N)]. Proteins in each pool were denatured

and reduced with 5 mM dithiothreitol at 60°C for 45 minutes and alkylated with 15 mM

iodoacetamide, in the dark at room temperature for 45 minutes. Sequencing grade trypsin

(Promega, Madison, WI, USA) was added in a 1:50 (trypsin: protein) ratio, and allowed to

digest for 18 hours at 37°C. Trifluoroacetic acid (1%) was added to cleave RapiGest and inhibit

trypsin activity, and samples were centrifuged at 11,000g for 20 minutes prior to high

performance liquid chromatography (HPLC) using strong cation exchange (SCX) to reduce

peptide mixture complexity.

3.2.1.3 HPLC-SCX

Digested samples were diluted 1:2 in mobile phase A SCX buffer (0.26 M formic acid (FA),

10% acetonitrile (ACN); pH 2.5) and loaded directly onto a 500 μL loop connected to a

PolySULFOETHYL A column (2.1 mm inside diameter × 200 mm length; 5 μm particle size;

200 °A pore size; The Nest Group Inc., MA, USA), containing a silica-based hydrophilic,

anionic polymer (poly-2-sulfotheyl aspartamide). An Agilent 1100 HPLC system was used for

70

fractionation. A 60 minute gradient was employed with a linear gradient starting at 30 minutes

and consisting of mobile phase A and mobile phase B (0.26 M FA, 10% ACN, 1 M ammonium

formate; pH-4.5) for elution of peptides (flow rate 200uL/min). The fractionation was monitored

at a wavelength of 280 nm and performed in duplicate. Fractions were collected every two

minutes from 20 to 55 minutes, and those with a low peak absorbance were pooled, resulting in

a total of 12 fractions per sample. This amounted to a total of 96 SCX fractions, which were

then subjected to liquid chromatographic and tandem mass spectrometric analysis (LC-MS/MS).

SCX column and system performance was ensured by running a quality control peptide mixture

consisting of 1ug/uL Alpha Bag Cell peptide, 1ug/uL Fibrinogen fragment, 5ug/uL Human

ACTH, and 5ug/uL ACE Inhibitor (American Protein Company, CA, USA) after every sample.

3.2.1.4 LC-MS/MS

The SCX fractions were purified through C-18 OMIX Pipette Tips (Agilent Technologies,

Germany), to remove impurities and salts, and eluted in 5 μL of 65% MS buffer B (90% ACN,

0.1% FA, 10% water, 0.02% trifluoroacetic Acid (TFA)) and 35% MS buffer A (5% ACN,

0.1% FA, 95% water, 0.02% TFA). The samples were diluted to 85 μL in MS buffer A and

injected into a nano-LC system (Proxeon Biosystems, FL, USA) connected online to an LTQ-

Orbitrap (Thermo Fisher Scientific, USA). A 90 minute linear gradient reversed phase

chromatography using MS buffer A and MS buffer B, was performed at a flow rate of 400

nL/min to resolve peptides on a C-18 column (75uM x 5 cm; Proxeon Biosystems, FL). The MS

parameters were: 300 Da minimum mass, 4000 Da maximum mass, automatic precursor charge

selection, 10 minimum peaks per MS/MS scan; and 1 minimum scan per group. XCalibur

software v.2.0.7 (Thermo Fisher Scientific, USA) was used for data acquisition.

71

3.2.1.5 Protein identification and quantification

Raw files corresponding to PsA L, PsA N, PsC L, and PsC N data sets were uploaded into

MaxQuant v.1.2.2.2 (www.maxquant.org) (139) and searched with Andromeda (built into

MaxQuant) (138) against the non-redundant IPI.Human v.3.71 database (86, 309 sequences;

released March 2010). Search parameters included a fixed carbamidomethylation of cysteines

and variable modifications of methionine oxidation and N-terminal acetylation. Data was

initially searched against a “human first search” database with a parent tolerance of 20 ppm and

a fragment tolerance of 0.5 Da in order to calculate and adjust the parent tolerance to 5 ppm for

the search against the IPI.Human fasta file. During the search, the IPI.Human fasta database was

randomized and the false discovery rate was set to 1% at the peptide and protein levels. Data

was analyzed using “Label-free quantification” checked, and the “Match between runs” interval

was set to 2 min. Proteins were identified with a minimum of one unique peptide. “LFQ

Intensity” columns corresponding to the extracted ion current (XIC) value of each protein in

replicate PsA L, PsA N, PsC L, and PsC N were averaged, and used to calculate PsA/PsC

lesional and non-lesional ratios (fold change; FC).

3.2.1.6 Bioinformatics analysis

To detect upregulated proteins in the PsA L, one-sided Student’s t-tests were used between PsA

L and PsC L, and PsA N and PsC N after log transformation of XIC values. Only proteins

displaying significant differences [False discovery rate (FDR) <0.2] were used for further

analysis. Averages of the replicate XIC values were calculated for each PsA L, PsA N, PsC L,

and PsC L, and ratios (FC) of PsA L/PsC L, and PsA N/PsC N were computed.

72

Gene names of the upregulated proteins were checked against gene [BioGPS

(http://biogps.org/#goto=welcome) (162)], and protein [Human Protein Atlas

(http://www.proteinatlas.org/) (163)] databases, to identify proteins with strong expression in

PsA-associated tissues and cell types (skin, bone, immune cells).

The Plasma Proteome Database (http:// www.plasmaproteomedatabase.org/) (126) was

employed to identify proteins present in high-abundance in serum, which could represent

potential contaminants. Selected reaction monitoring assays were developed for the top

upregulated proteins in the lesional PsA group and housekeeping (ACTB, TUBB) proteins, and

relative protein quantification was performed in individual skin samples to confirm their

elevation in PsA skin.

3.2.2 Verification of identified proteins using SRM

SRM methods were developed for verification of protein ratios in skin, as described in Chapter

2.

3.2.2.1 SRM assay development

PeptideAtlas (http://www.peptideatlas.org/) was utilized to select 3–4 tryptic peptides for the

proteins of interest. The uniqueness of peptides was verified by BLAST

(https://blast.ncbi.nlm.nih.gov/Blast.cgi). To identify peptide fragments (transitions) to monitor,

in silico peptide fragmentation was performed using Pinpoint software (Thermo Fisher

Scientific, USA) and 5–6 transitions were selected for each peptide. For method optimization,

digested pooled samples of PsA lesional skin, used in our LC-MS/MS analysis, were loaded

onto a C-18 column (Proxeon Biosystems, FL, USA) coupled to a triple quadrupole mass

73

spectrometer (TSQ Vantage; Thermo Fisher Scientific, USA), and approximately 2500

transitions were monitored in 25 subsequent runs. Three transitions were used for subsequent

quantification.

3.2.2.2 Skin sample preparation

Fifty µg of total protein of each skin sample were denatured by heat, reduced, alkylated, and

trypsin-digested as described previously. In sample set II, 500 fmol of a heavy-labeled peptide

(Thermo Fisher), were also added as an internal standard. The heavy peptide sequence was

LGPLVEQGR, where the C-terminal arginine was labeled with 13C and 15N. The resulting

peptides were purified through C-18 OMIX Pipette Tips (Agilent), and eluted in 3µL of 65%

MS buffer B and 35% MS buffer A. Samples were diluted to 40µL of MS buffer A, randomized,

and loaded onto a C-18 column coupled to a triple quadrupole mass spectrometer.

3.2.2.3 SRM Assays

SRM assays were developed on a triple-quadrupole mass spectrometer (TSQ Vantage) using a

nanoelectrospray ionization source (nano-ESI, Proxeon Biosystems, FL), as previously

described in Chapter 2. Briefly, a 60-minute, three-step gradient was used to load peptides onto

the column via an EASY-nLC pump (Proxeon Biosystems, FL), and peptides were analyzed by

a multiplexed SRM method. Quantification, in skin set I was performed after normalization

against a set of 4 peptides corresponding to 2 housekeeping proteins, to offset technical

variations. Each sample was analyzed in duplicate, using a 60-minute method, whereby 153

transitions were monitored. Quantification in SF set II was performed following normalization

against the spiked-in heavy-labeled peptide, as described earlier. Each sample was analyzed in

74

duplicate, using a 60-minute method, whereby 156 transitions were monitored. Reproducibility

of the SRM signal was confirmed by running a quality control solution of 0.1 fmol/μL BSA

after every 10 runs.

3.2.2.4 SRM protein quantification

Raw files recorded for each sample were analyzed using Pinpoint software (Thermo Fisher)

(144), and peptide XIC were extracted. Pinpoint was used for identification and visualization of

transitions. In the first skin set, in order to control for technical variation between the samples,

XIC corresponding to each endogenous peptide replicate were divided by the XIC

corresponding to the average of the two housekeeping peptides. This value was then averaged

amongst the two replicate runs, to obtain “Normalized XIC”. In skin set II, in order to control

for technical variation between the samples and obtain a more robust quantitative value for the

proteins of interest, the XIC value corresponding to each endogenous peptide, was normalized

to the XIC value corresponding to the spiked-in heavy peptide, in order to obtain a light:heavy

(L:H) ratio. Since we added a known amount of heavy peptide to our samples prior to analysis,

we used the L:H ratio to calculate the relative concentration of each endogenous peptide

corresponding to the proteins of interest.

3.2.3 Validation of verified proteins

3.2.3.1 Enzyme-linked immunosorbent assays

The concentration of ITGB5 in serum was measured using an in-house developed protocol.

Sheep anti-human ITGB5 polyclonal antibody (R&D Systems, Minneapolis MN, USA; Cat.#

75

AF3824) was immobilized in a 96-well clear polystyrene plate by incubating 100 μL of

0.75 ng/μL capture antibody in PBS (137 mM NaCl, 2.7 mM KCl, 8.1 mM Na2HPO4, 1.5 mM

KH2PO4, pH 7.4) overnight. The plates were washed three times with wash buffer (5 mmol/L

Tris, 150 mmol/L NaCl, 0.05% Tween® 20, pH 7.8), after which the plate was blocked by

adding 300 μL of 1% BSA in PBS to each well and incubated with shaking at room temperature

for 60 min. The plates were then washed three times with wash buffer and incubated with

100 μL per well of ITGB5 recombinant protein standards (R&D Systems), or serum samples

with shaking at room temperature for 2 h. ITGB5 standards and serum samples were diluted in

1% BSA in PBS, with all serum samples diluted 10-fold. After incubation, the plates were

washed three times with wash buffer and incubated with 100 μL per well of biotinylated sheep

anti-human ITGB5 detection antibody (R&D Systems; Cat.# BAF3824) (0.25ng/μL detection

antibody in 1% BSA in PBS) with shaking at room temperature for 2 h. After washing the plates

three times with wash buffer, 100 μL of streptavidin-conjugated horseradish peroxidase solution

(diluted 200-fold in 1% BSA in PBS) was added to each well and incubated for 15 min with

shaking at room temperature. A final wash of three times with washing buffer was followed by

the addition of 100 μL of 3,3′,5,5′-Tetramethylbenzidine (Sigma Aldrich, St, Louis, MO, USA)

per well and incubated with shaking at room temperature for 10 min. The chromogenic reaction

was stopped with the addition of 50 μL of 2 mol/L hydrochloric acid solution per well.

Subsequently, the absorbance of each well was measured with the Wallac Envision 2103

Multilabel Reader (Perkin Elmer, MA) at 450 nm, standardized to background absorbance at

540 nm. Final serum concentrations were calculated by multiplying by the dilution factor.

76

The concentration of POSTN in serum was measured using a custom multiplexed Luminex

Screening Assay (R&D Systems; Cat.# LXSAH), according to manufacturer’s instructions.

Serum was diluted 100-fold.

3.2.3.2 Statistical analysis

The Student’s t-tests were performed to analyze the LC-MS/MS data using R statistical software

(http://www.r-project.org/). The remaining statistical analyses were performed using Graph Pad

Prism v.6.0 for Mac (GraphPad Software, CA, USA). Results were analyzed using the

nonparametric Mann-Whitney U test. P-values less than 0.05 were considered statistically

significant. Spearman's rank correlation coefficient was used to assess the correlations between

serum levels of POSTN, and ITGB5. Concentrations are reported in the text as mean ± standard

deviation (SD).

77

Figure 3.1 Summary of the biomarker discovery, verification, and preliminary validation

experimental workflow in skin

1922

138

78

3.3 Results

3.3.1 Delineating the PsA skin proteome

Our LC-MS/MS analysis yielded a list of 1922 quantifiable proteins. Student’s t-tests were

utilized to identify upregulated proteins between the PsA and PsC lesional groups, and this

yielded a total of 62 proteins [false discovery rate (FDR) <0.2]. Generally, these proteins

exhibited a ratio above 4.0. In parallel, a comparison between PsA and PsC non-lesional skin,

demonstrated elevated expression of 131 proteins (FDR<0.2). In comparing the proteins

identified from the two independent analyses (PsA L vs PsC L, and PsA N vs. PsC N), only 7 of

these proteins are common.

Since our scope was to identify proteins elevated in lesional PsA skin, we decided to focus on

the 62 proteins overexpressed in the PsA lesional skin, since they are most likely to represent

mediators and potential biomarkers of PsA. We further focused on proteins displaying strong

expression in skin, bone, and immune regulatory cells, but excluded immunoglobulins,

unknown proteins, and high abundance serum proteins from further analysis. Table 3.1 presents

the final list of 47 proteins, the corresponding fold change (FC) between PsA and PsC samples,

and the P-Value and FDR associated with each protein. These were then assessed and

quantified, using a multiplexed selected reaction monitoring assay, in individual skin samples

pertaining to the discovery set (Set I), as well as in an independent set of 10 samples (Set II).

79

Table 3.1 Fold change (FC) of 47 elevated proteins identified by LC-MS/MS in PsA skin

when compared to PsC skin

Protein Name Gene Name

PsA L :PsC

L FC*

P-

Value*** FDR****

Periostin Osteoblast Specific Factor POSTN 2.3 0.006 0.18

Adenylate Kinase 1 AK1 2.4 0.004 0.16

Heat Shock 70kDa Protein 5 HSPA5 2.5 0.008 0.19

WNT1 inducible signaling pathway protein 2 WISP2 2.5 0.002 0.15

Eukaryotic Translation Initiation Factor 2A EIF2A 2.9 0.007 0.18

Activator Of Multicatalytic Protease Subunit 3 PSME3 3.2 0.003 0.16

Alpha-crystallin B chain CRYAB 3.9 0.006 0.18

Proteasome 26S Subunit ATPase 2 PSMC2 3.9 0.001 0.12

Platelet-activating factor acetylhydrolase IB PAFAH1B2 4.5 0 0.12

Keratin 18 KRT18 4.6 0.006 0.18

Carboxypeptidase N 2 CPN2 5.7 0.002 0.15

60S ribosomal protein L19 RPL19 6.6 0.001 0.12

Signal Recognition Particle SRP14 6.8 0.003 0.16

Four And A Half LIM Domains 1 FHL1 7 0.006 0.18

Proline Glutamine-Rich Splicing Factor SFPQ 7.1 0.007 0.18

Asporin ASPN 8.9 0.007 0.18

Gamma-Glutamyltransferase 5 GGT5 9 0.006 0.18

Cytochrome B5 Reductase 2 CYB5R2 9.1 0.006 0.18

Cytoplasmic FMR1 Interacting Protein 1 CYFIP1 10 0.003 0.16

Ring Finger Protein 113A RNF113A 10.4 0.004 0.17

Valyl-TRNA Synthetase VARS 11.7 0.005 0.18

Hydroxysteroid (17-beta) dehydrogenase 4 HSD17B4 15.2 0.003 0.16

Alpha Synuclein SNCA N/A** 0.003 0.16

Heat Shock 70kDa Protein 4-Like HSPA4L N/A 0.003 0.16

26S Proteasome Regulatory Subunit P40.5 PSMD13 N/A 0.004 0.17

KDEL Motif-Containing Protein 2 KDELC2 N/A 0.004 0.17

DNA Polymerase II Subunit A POLE N/A 0.005 0.18

Beta Integrin 5 ITGB5 N/A 0.006 0.18

Armadillo Repeat Containing 1 ARMC1 N/A 0.007 0.18

Transmembrane Emp24 Protein Transport

Domain 7 TMED7 N/A 0.007 0.18

Complement C1q CIQC N/A 0 0.12

Isoform 1 of UPF0505 protein C16orf62 C16orf62 N/A 0 0.12

Sushi repeat-containing protein SRPX N/A 0.001 0.12

Mercaptopyruvate Sulfurtransferase MPST N/A 0.001 0.12

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Leucine zipper and CTNNBIP1 domain

containing LZIC N/A 0.001 0.12

Mitogen-Responsive Phosphoprotein Disabled

Homolog 2 DAB2 N/A 0.001 0.12

Angiopoietin-like 2 ANGPTL2 N/A 0.001 0.12

Glia maturation factor gamma GMFG N/A 0.001 0.12

Renin binding protein RENBP N/A 0.001 0.12

Ubiquitin-conjugating enzyme E2L 6 UBE2L6 N/A 0.001 0.12

Protein phosphatase 2A activator, regulatory

subunit 4 PPP2R4 N/A 0.002 0.15

Heat shock factor binding protein 1 HSBP1 N/A 0.002 0.15

Glyoxylate reductase/hydroxypyruvate reductase GRHPR N/A 0.002 0.15

Eukaryotic translation initiation

factor 2B, subunit 4 delta EIF2B4 N/A 0.003 0.16

Sorting nexin 1 SNX1 N/A 0.003 0.16

Acyl-CoA Oxidase 1 ACOX1 N/A 0.001 0.12

G protein pathway suppressor 1 GPS1 N/A 0.006 0.18 *FC represents the ratio of the mean XIC values of 10 skin samples per group (PsA L vs. PsC L) **N/A indicates that a ratio could not be compiled since the protein was absent in PsC skin ***P-Values were calculated using the student's t-tests

****FDR represents the false discovery rate of each protein

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3.3.2 SRM verification of putative markers in individual skin

samples

We developed a multiplexed SRM assay to verify the differences in protein expression between

lesional PsA and PsC skin. Assays were developed for 47 peptides representative of the 47

proteins with increased expression in the skin of PsA patients, as well as 4 peptides representing

2 housekeeping proteins (ACTB, TUBB). Consistent with our LC-MS/MS analysis of the

pooled samples, overexpression of 12 out of the 47 proteins was also verified in the individual

PsA lesional skin samples (Set I), when compared to the lesional PsC samples. Each sample

consisted of two technical replicates. The mean FC of each confirmed protein in the lesional and

non-lesional PsA and PsC skin samples, as well as the associated P-values are depicted in Table

3.2. The proteins included C16ORF62, SNCA, LZIC, SRP14, ITGB5, POSTN, SRPX, FHL1,

PPP2R4, CPN2, GPS1, and PAFAH1B2. The distribution of these, and the housekeeping

proteins across the Set I skin samples, are represented in Figure 3.2.

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Figure 3.2 Distribution of significant markers and housekeeping proteins across the Set I

PsA and PsC skin samples

Dots represent skin samples from individual subjects; thin horizontal lines depict the mean, and

vertical lines the SD. **** indicates P<0.0001; ***P<0.001; **P<0.01; *P<0.05; ns: non-significant.

83

Table 3.2 Summary of the fold change of candidate markers in skin Set I and Set II

*The description of Set I and Set II are given in the experimental methods section. **Fold change (FC) represents the ratio of means of 10 (Set I) or 5 (Set II) skin samples per

group. Data are based on normalized XIC ratios, as described in the experimental methods

section. PsA, psoriatic arthritis; PsC, cutaneous psoriasis ***P-Values were calculated using the non-parametric Mann-Whitney U test.

Set I Set II

Lesional

Non-lesional Lesional

Protein

Name

PsA:PsC

FC**

P-Value*** PsA:PsC

FC

P-Value PsA:PsC

FC

P-Value

CPN2 17.4 <0.001 2.4 0.030 1.9 0.032

GPS1 6.0 0.014 1.2 0.385 17.9 0.008

C16ORF62 6.0 <0.001 5.3 0.007 3.9 0.667

FHL1 4.6 <0.001 3.1 0.021 2.2 0.016

SRPX 3.8 0.043 3.3 0.014 5.0 0.008

SNCA 3.6 <0.001 1.7 0.089 3.8 0.095

POSTN 3.5 0.001 2.8 0.013 7.5 0.032

PAFAH1B2 3.3 0.004 2.0 0.104 0.8 0.667

SRP14 3.0 0.019 1.3 0.570 2.4 0.016

ITGB5 2.7 0.006 3.5 0.017 4.2 0.032

PPP2R4 2.2 0.043 2.0 0.678 3.9 0.008

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Additionally, we further confirmed the elevation of 8 of the aforementioned 12 proteins, in an

independent set of 5 PsA, and 5 PsC lesional skin samples (Set II). In this case, we utilized a

heavy-labeled peptide in order to obtain the absolute concentration of these peptides in the skin.

The proteins included SRP14, ITGB5, POSTN, SRPX, FHL1, PPP2R4, CPN2, and GPS1. The

mean FC and P-values corresponding to each protein from SF set II are also depicted in Table

3.2. Figure 3.3 shows the distribution of the 8 proteins across the Set II skin samples, as well as

of the housekeeping proteins. Since only 8 proteins were confirmed in the independent sample

set, we decided to continue all further analyses using these potential markers.

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Figure 3.3 Distribution of significant markers across the Set II PsA and PsC skin samples

PsA

PsC

0

20

40

60

80

SRP14

fmol/ul

*

PsA

PsC

-500

0

500

1000

1500

2000

POSTN

fmol/ul

*

PsA

PsC

0

20

40

60

80

100

PPP2R4

fmol/ul

**

PsA

PsC

0

20

40

60

ITGB5

fmol/ul

*

PsA

PsC

0

20

40

60

80

100

CPN2

fmol/ul

*

PsA

PsC

0

50

100

150

FHL1

fmol/ul

*

PsA

PsC

0

1000

2000

3000

SRPX

fmol/ul

**

PsA

PsC

0

500

1000

1500

2000

2500

GPS1

fmol/ul

**

PsA

PsC

0

20

40

60

ACTB

fmol/ul

PsA

PsC

0

10

20

30

TUBB

fmol/ul

Dots represent skin samples from individual subjects; thin horizontal lines depict the

mean and vertical lines the SD. **** indicates P<0.0001; ***P<0.001; **P<0.01;

*P<0.05; ns: non-significant.

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3.3.3 Small-scale ELISA validation in serum

To validate the expression of these markers in serum of PsA patients, we measured the levels of

ITGB5 and POSTN in the serum of 15 PsC and 33 PsA patients. We were unable to measure

SRP14, SRPX, FHL1, PPP2R4, CPN2, and GPS1, due to unavailability of ELISA kits,

antibodies, or protein standards. As shown in Figure 3.4, ITGB5 was significantly elevated in

PsA, when compared to PsC patients (1.19±0.5 compared to 0.77±0.6; p<0.01). The

concentration of POSTN was not significantly different between the two groups, but the mean

level in PsA serum were numerically higher compared to PsC serum (17.71±6.4 compared to

14.53±6.2). The mean FC and distribution of POSTN and ITGB5 in the PsA and PsC serum, is

represented in Figure 3.4.

Figure 3.4 Distribution of markers across PsA (n=33) and PsC (n=15) serum sets

Dots represent serum samples from individual subjects; thin horizontal lines depict the mean,

and vertical lines the standard deviation; FC represents the ratio of the mean concentration

values corresponding to 33 PsA and 15 PsC serum samples. ** indicates P<0.01; ns: non-

significant. PsA, Psoriatic Arthritis; PsC, Cutaneous Psoriasis

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3.3.4 Correlation amongst markers

Spearman’s rank correlation coefficient was used to assess the correlation amongst markers for

the PsA, and PsC serum groups. ITGB5 correlated significantly with POSTN in PsC serum

(Spearman r = 0.637, P = 0.013), and in PsA serum (Spearman r = 0.433, P = 0.012). Figure 3.5

displays the correlation between POSTN and ITGB5 in all samples (Spearman r = 0.472,

P<0.001).

Figure 3.5. Correlation between ITGB5 and POSTN across the PsA and PsC serum sets.

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3.4 Discussion

Recent advances in mass spectrometry-based proteomics have facilitated the identification of

putative novel biomarkers and mediators from a variety of tissues, and for various clinical

applications (165, 190, 191). In this study, we conducted high-throughput quantitative

proteomic analyses to identify differentially expressed proteins between skin derived from PsA

and PsC patients. According to our filtering criteria, forty-seven proteins were elevated in the

PsA group, when compared to the PsC group. Eight proteins were verified using a multiplexed

selected reaction monitoring assay, in an independent skin sample set. None of these potential

markers has been described before in the context of PsA or psoriasis. Since serum measurement

of these proteins using SRMs is not efficient due to the high complexity of the fluid, we utilized

available enzyme-linked immunosorbent assays to measure two biomarker candidates in serum

(ITGB5, and POSTN).

Cumulative evidence strongly supports the involvement of Interleukin-23/IL-17 axis in the

pathogenesis of PsA (57-59, 120), and a number of compounds that target components of these

pathways have been recently used in PsA clinical trials (24, 60). IL-23 acts synergistically with

IL-6 and TGF-β to promote rapid Th17 development and IL-17 release (121, 122), which, in

turn, plays a central role in sustaining chronic inflammation (121). Both POSTN and ITGB5

have been implicated, in some manner with this pathway.

Periostin is a matricellular protein belonging to the fascilin family (192). It interacts with several

integrin molecules on cell surfaces, one of which is α5β5 Integrin, and provides signals for tissue

development and remodeling (193). More specifically, it is thought that POSTN interacts with

α5β5 to induce pro-inflammatory cytokine production via the Akt/NF-κB signaling pathway

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(194). In support of this, deficiency of POSTN or inhibition of α5 integrin (ITGA5) prevented

development or progression of skin inflammation in an Atopic Dermatitis mouse model (193).

Additionally, POSTN has been shown to also act on immune cells, leading to their enhanced

transmigration, chemotaxis, and adhesion (195), all of which further implicate this molecule in

inflammatory processes relevant to PsA.

Apart from acting as a POSTN ligand (along with ITGA5), ITGB5 has also been described in

rheumatoid arthritis, where it serves as a ligand for Cyr61 (195). Cyr61 is a molecule secreted

by fibroblast-like synoviocytes in the joint, and stimulates IL-6 production via ITGA5-

ITGB5/Akt/NF-κB signaling pathway (196). As described earlier, IL-6 production acts with IL-

23 and TGF-β to trigger Th17 differentiation and IL-17 production (121, 122) in inflammatory

processes as they also occur in PsA. As a result, we believe POSTN and ITGB5 are attractive

molecules to investigate further as PsA biomarkers.

Following a small-scale serum validation of POSTN and ITGB5, we determined that only

ITGB5 was significantly elevated in PsA serum, although POSTN also showed a trend.

Additionally, Spearman correlation showed that serum ITGB5 correlates well with POSTN (r =

0.472, p<0.001), which may indicate that these two markers may, in the future, be used as part

of a panel of markers to screen for PsA in PsC patients. The work reported here has some

limitations. First, pooling of the skin samples in the discovery phase has both benefits as well as

drawbacks. While pooling allows a more extensive coverage of the disease’s heterogeneity by

increasing the likelihood of identifying proteins that are otherwise undetectable in individual

samples, it may also mask meaningful discrepancies among the different individual skin

proteomes. To minimize this effect, samples were pooled to obtain two distinct pools in both,

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PsA and PsC sample groups. Additionally, all pool-derived candidates were further examined in

all individual samples using a targeted and more sensitive mass spectrometry technique. Thus,

by pooling samples as well as verifying protein expression in individual skin samples, we have

taken advantage of two complementary approaches in order to obtain more meaningful results.

Second, the levels of some of the proteins, notably CPN2, differed considerably between the

two verification sets. This could be due to the fact that Set I samples were the original discovery

samples, and thus the large difference seen in Set I verification is expected. Additionally, the

difference observed could simply be due to chance, which outlines the need to perform

verification in hundreds of samples in order to gauge the true distribution of the markers.

Unfortunately, skin biopsies were hard to obtain, and thus we were limited to only 10 and 5

samples for Set I and Set II, respectively.

Third, the identified markers were only tested on a small number of serum samples (n=48), and

only 20 of these were distinct from the Set I and Set II sample sets. Therefore, to assess their

distribution in serum, these markers still need to be tested in a large-scale verification study. In

addition, as discussed previously, only two of the existing eight proposed markers have been

investigated in serum due to the lack of ELISAs or antibodies. Hence, strategies to enable

measurement of the remainder of the markers will have to be employed in the future.

Taken together, our current observations are consistent with the notion that label-free

quantitative LC-MS/MS can be utilized to quantify proteins in tissues, which are shed into the

circulation and serve as serum markers of PsA. Further studies to validate these findings are

underway, in a larger and independent serum cohort. Investigating these proposed markers

91

further, may not only result in the identification of PsA screening biomarkers, but may also

uncover aspects of PsA pathobiology that are currently unknown

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Chapter 4

93

Validation of three candidate PsA biomarkers in serum

4.1 Introduction

PsA is an inflammatory arthritis associated with the chronic inflammatory skin disease,

psoriasis. The estimated population prevalence of PsA is 0.25%, and it affects approximately

30% of patients seen in dermatology clinics (92, 197, 198). PsA is classified according to the

CASPAR classification criteria (27), which provide high specificity and sensitivity when

applied by rheumatologists (80, 81).

Patients with PsA have progressive joint damage, reduced quality of life, work-related

problems, and more comorbidities when compared to psoriasis patients (200). Additionally,

disease severity at the time of presentation is a risk factor for mortality (201, 202). There is

increasing evidence that early diagnosis and management of PsA leads to better patient

outcomes, and the key to early diagnosis is better recognition of PsA in patients with psoriasis

(79, 87-89).

The presence of cutaneous psoriasis indicates a high risk for developing PsA. Studies conducted

in dermatology clinics have shown high prevalence of undiagnosed PsA in psoriasis patients

(32, 72, 198, 199). This is due to the fact that identifying inflammatory musculoskeletal disease

in patients by non-rheumatologists is difficult since the symptoms are non-specific, and acute-

phase reactants may be normal (80). While there are clinical features that can be utilized to

predict PsA, such as nail, scalp, intergluteal/perianal psoriasis, and psoriasis severity, identifying

soluble biomarkers that can identify PsA in psoriasis patients, may help in early diagnosis, and

subsequent prevention of disability and improvement in quality of life (198, 200, 92, 80, 79).

94

With the aim of identifying new PsA biomarkers, we previously performed proteomic analysis

of synovial fluid and skin biopsies obtained from PsA patients, using LC-MS/MS, and identified

20 putative PsA biomarkers. Of these, the current study details the validation of six candidates,

M2BP, MPO, ITGB5, CD5L, MMP3, and CRP. These were selected based on the availability of

commercial ELISA kits or antibodies, as well as preliminary verification studies reported in a

previous publication.

95

4.2 Methods

4.2.1 Clinical samples

Consent was obtained from all study subjects. Research ethics board approval was obtained

from the University Health Network. Serum samples were obtained from 100 cases with PsA,

100 with PsC, and 100 healthy controls. Patients with PsA had psoriasis, satisfied the CASPAR

classification criteria (27), and had active PsA, defined by at least 3 swollen and tender joints.

PsC patients had chronic plaque psoriasis and were assessed by a rheumatologist to exclude

PsA. Controls represented individuals who did not have psoriasis or inflammatory arthritis.

Patients with PsA and PsC were group matched for age, sex, and psoriasis severity and duration.

Controls were matched for age and sex. The demographics, disease characteristics, and

biomarker levels of recruited patients are demonstrated in Table 4.1.

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Table 4.1 Demographics, disease characteristics, and serum biomarker levels in studied

cohorts

Characteristics PsA (n=100) PsC (n=100) Controls (n=100)

Females, % 41 45 52

Agea, years 51 (12.1) 49.8 (12.6) 35.2 (10.7)

Psoriasis durationa, years 22.9 (12.5) 20.3 (13.9) -

PsA durationa, years 14.6 (12) - -

Swollen joint counta 3.2 (3.5) - -

Tender joint counta 6 (8.1) - -

Clinically damaged joint counta 7.4 (11.1) - -

PASI scorea 4.7 (6.4) 4 (3.4) -

Patients with current nail involvement, % 74 51 -

Patients on systemic agents, % 35 8 -

Biomarkers

CD5La, ng/mL 192.8 (197.1) 166.9 (147.4) 89.0 (145.1)

ITGB5a, ng/mL 1.5 (2.6) 0.7 (0.4) 0.8 (1.4)

M2BPa, ng/mL 295.6 (50) 269.6 (27.7) 232.9 (64.2)

MMP3a, ng/mL 44.3 (57.3) 22.2 (15.6) 17 (12)

MPOa, ng/mL 418.1 (270.9) 402.5 (217.3) 232.8 (126.6)

CRPa, mg/L 5.9 (2.9) 4 (3.8) 2.3 (2.1)

a Values indicate mean (standard deviation)

No patients were undergoing treatment with biologics at the time of serum collection. Three

patients with PsC were on methotrexate (MTX), while 5 were on retinoids. Thirty-five patients

with PsA were on at least one form of systemic therapy (10 MTX, 13 leflunomide, 10

sulfasalzine, 3 hydroxychloroquine, 1 azathioprine, 1 cyclosporine). Blood samples were drawn

at the time of clinical assessment, processed, and serum aliquots were stored at -80 until

biomarker measurement.

97

4.2.2 Measurement of CD5L, ITGB5, MPO, MMP3, CRP, and

M2BP

4.2.2.1 ITGB5

ITGB5 was measured according to our in-house developed protocol, as described in Chapter 3.

Briefly, sheep anti-human ITGB5 polyclonal antibody (R&D Systems, Minneapolis MN, USA)

was immobilized in a 96-well clear polystyrene plate by incubating 100 μL of 0.75 ng/μL

capture antibody in PBS (137 mM NaCl, 2.7 mM KCl, 8.1 mM Na2HPO4, 1.5 mM KH2PO4,

pH 7.4) overnight. The plates were washed three times with wash buffer (5 mmol/L Tris,

150 mmol/L NaCl, 0.05% Tween® 20, pH 7.8), after which the plate was blocked by adding

300 μL of 1% BSA in PBS to each well and incubated with shaking at room temperature for

60 min. The plates were then washed three times with wash buffer and incubated with 100 μL

per well of ITGB5 recombinant protein standards (R&D Systems), or serum samples with

shaking at room temperature for 2 h. ITGB5 standards and serum samples were diluted in 1%

BSA in PBS, with serum samples diluted 10-fold. After incubation, the plates were washed

three times with wash buffer and incubated with 100 μL per well of biotinylated sheep anti-

human ITGB5 detection antibody (R&D Systems) (0.25ng/μL detection antibody in 1% BSA in

PBS) with shaking at room temperature for 2 h. After washing the plates three times with wash

buffer, 100 μL of streptavidin-conjugated horseradish peroxidase solution (diluted 200-fold in

1% BSA in PBS) was added to each well and incubated for 15 min with shaking at room

temperature. A final wash of three times with washing buffer was followed by the addition of

100 μL of 3,3′,5,5′-Tetramethylbenzidine (TMB) (Sigma Aldrich, St, Louis, MO, USA) per well

and incubated with shaking at room temperature for 10 min. The chromogenic reaction was

98

stopped with the addition of 50 μL of 2 mol/L hydrochloric acid solution per well.

Subsequently, the absorbance of each well was measured with the Wallac Envision 2103

Multilabel Reader (Perkin Elmer, MA) at 450 nm, standardized to background absorbance at

540 nm. Final serum concentrations were calculated by multiplying by the dilution factor.

4.2.2.2 CD5L

The plates were washed three times with wash buffer, after which the plate was blocked by

adding 300 μL of 1% BSA in PBS to each well and incubated with shaking at room temperature

for 60 min. The plates were then washed three times with wash buffer and incubated with

100 μL per well of CD5L recombinant protein standards (R&D Systems; Cat.# 2797-CL-050),

or serum samples with shaking at room temperature for 2 h. CD5L standards and serum samples

were diluted in 1% BSA in PBS, with all serum samples diluted 10-fold. After incubation, the

plates were washed three times with wash buffer and incubated with 100 μL per well of

biotinylated goat anti-human CD5L detection antibody (0.1 ng/μL detection antibody in 1%

BSA in PBS) with shaking at room temperature for 2 h. After washing the plates three times

with wash buffer, 100 μL of streptavidin-conjugated horseradish peroxidase solution (diluted

200-fold in 1% BSA in PBS) was added to each well and incubated for 15 min with shaking at

room temperature. A final wash of three times with washing buffer was followed by the addition

of 100 μL of TMB per well and incubated with shaking at room temperature for 10 min. The

chromogenic reaction was stopped with the addition of 50 μL of 2 mol/L hydrochloric acid

solution per well. Subsequently, the absorbance of each well was measured with the Wallac

Envision 2103 Multilabel Reader (Perkin Elmer, MA) at 450 nm, standardized to background

99

absorbance at 540 nm. Final serum concentrations were calculated by multiplying by the

dilution factor.

4.2.2.3 M2BP

Additionally, the concentration of M2BP was measured using a commercially available ELISA

kit (R&D Systems; Cat.# DY2226) according to manufacturer’s instructions. Samples were

diluted 250-fold.

4.2.2.4 CRP, MMP3, MPO

A commercially available customized multiplexed Luminex Screening Assay (R&D Systems;

Cat.# LXSAH) was purchased for MPO, MMP3, and CRP, and measurement of these markers

was performed according to manufacturer’s instructions. Samples were diluted 100-fold.

4.2.3 Statistical analysis

The levels of most of the proteins are highly skewed to the right, and thus, all statistical analyses

were based on log2 transformed protein levels. The significance of each group (Control, PsC,

PsA) was tested for each biomarker to determine whether there was a significant difference in

protein levels for two group comparisons: PsA vs PsC, and PsA vs Control. Statistical tests were

performed while controlling for significant covariates, which could include age, sex, psoriasis

duration, sample storage duration, PASI score, or drug treatment.

Effects of markers on patients with psoriasis alone vs controls and patients with PsA vs controls

were compared using polychotomous logistic regression, a regression technique useful for

categorical responses with three or more categories. In this way, we were able to test whether

100

the associations between each marker and disease status (PsA or PsC) are the same between the

two disease groups. Univariate, and multivariate logistic regression models were used to identify

which biomarkers were independently associated with PsA, when compared to PsC. These

models were fit with disease classification as the outcome using biomarkers as explanatory

variables while controlling for sex and age. Receiver operating characteristic (ROC) curves

were constructed to investigate how biomarker data can best be used to classify patients into

disease groups. The accuracy of the classification of patients was summarized using the area

under the ROC curve. Finally, to determine if there is significant correlation between

biomarkers, the Spearman's rank correlation coefficients were computed.

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4.3 Results

The distribution of markers across the three serum sets is demonstrated below.

Figure 4.1 Distribution of markers across control (n=100), PsA (n=100), and PsC (n=100)

serum sets

Dots represent serum samples from individual subjects; thin horizontal lines depict the mean,

and vertical lines the standard deviation; PsA, Psoriatic Arthritis; PsC, Cutaneous Psoriasis

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Table 4.2 outlines the significantly different biomarker levels, when comparing PsA to PsC, or

PsA to controls. Increased levels of ITGB5 (p=7.14E-06), CRP (p=1.90E-07), MMP3 (p=7.04E-

06), and M2BP (p=8.28E-04) are present in patients with PsA, when compared to PsC.

Similarly, elevated ITGB5 (p=1.11E-11), CRP (p=2.90E-17), MMP3 (p=5.72E-06), M2BP

(p=2.01E-13), CD5L (p=5.72E-08), and MPO (p=3.66E-05), are present in PsA patients when

compared to controls.

Table 4.2 Fold change of serum biomarker levels when comparing PsA to PsC, and PsA to

Controls

PsA compared to PsC PsA compared to controls

Biomarkers Fold change (95% CI) P-value Fold change (95% CI) P-value

ITGB5, ng/mL 1.76 (1.38, 2.25) 7.14E-06 2.40 (1.88, 3.06) 1.11E-11

CRP, mg/L 2.24 (1.66, 3.02) 1.90E-07 3.90 (2.89, 5.25) 2.90E-17

MMP3, ng/mL 1.54 (1.28, 1.86) 7.04E-06 1.58 (1.30, 1.93) 5.72E-06

M2BP, ng/mL 1.11 (1.04, 1.17) 8.28E-04 1.27 (1.20, 1.35) 2.01E-13

MPO, ng/mL 1.13 (0.93,1.36) 2.25E-01 1.53 (1.25, 1.87) 3.66E-05

CD5L, ng/mL 1.17 (0.83, 1.64) 3.76E-01 2.62 (1.86, 3.68) 5.72E-08

The results of the polychotomous logistic regression analysis to identify biomarkers that have

differential associations between the three cohorts (PsA, PsC, Controls) are given in Table 4.3.

The polychotomous logistic regression allows us to simultaneously investigate the differences

between the three groups using logistic regression. The homogeneity P-values indicate the

differences of the biomarker level among the three groups (PsC, PsA, and controls) when

modeling PsC and PsA separately while controlling for age, sex, and the other biomarkers

investigated. Furthermore, the p-values associated with PsA or PsC indicate the significance of

biomarkers’ associations with PsA or PsC when using healthy controls as reference. We show

that ITGB5 (p=1.18E-05), CRP (p=1.40E-06) and to a lesser extent M2BP (p=1.97E-03), are

biomarkers that can distinguish between PsA from PsC. We also show that CD5L (p=1.49E-04),

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ITGB5 (p=2.88E-06), M2BP (p=1.48E-06), MPO (p=1.97E-04), MMP3 (p=3.18E-02) and CRP

(p=9.93E-07) are independently associated with PsA, while CD5L (p=8.71E-05), M2BP

(p=9.38E-04) and MPO (p=3.27E-06) are independently associated with PsC.

Table 4.3 Polychotomous logistic regression analysis to identify biomarkers associated

with PsA and PsC

aIndicates whether the markers have significantly different effects when modeling PsA and PsC

separately, and controlling for age, sex, and the other biomarkers listed.

bIndicates the significance of difference between PsA or PsC, and controls.

To further determine the significance of our putative PsA biomarkers, we compared the

biomarker levels in patients with PsA, to those in patients with PsC, the results of which are

demonstrated in Table 4.4. Consistent with the results previously described, ITGB5, M2BP, and

CRP are independently associated with the presence of PsA when compared to PsC. ROC

analysis of this combined model showed an AUC of 0.851 with a 95% CI of (0.799,0.904)

(Figure 4.2).

PsA PsC

Biomarkers

Homogeneity

P-valuea OR (95% CI) P-valueb OR (95% CI) P-valueb

CD5L, ng/mL 5.48E-01 1.67 (1.28, 2.17) 1.49E-04 1.57 (1.25, 1.97) 8.71E-05

ITGB5, ng/mL 1.18E-05 3.23 (1.98, 5.27) 2.88E-06 1.21 (0.89, 1.64) 2.14E-01

M2BP, ng/mL 1.97E-03 151.73 (19.64, 1172.19) 1.48E-06 9.19(2.47, 34.25) 9.38E-04

MMP3, ng/mL 1.72E-01 1.79 (1.05, 3.06) 3.18E-02 1.39 (0.86, 2.26) 1.81E-01

MPO, ng/mL 5.33E-01 2.36 (1.50, 3.72) 1.97E-04 2.64 (1.75, 3.98) 3.27E-06

CRP, mg/L 1.40E-06 2.36 (1.67, 3.33) 9.93E-07 1.16 (0.91, 1.47) 2.28E-01

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Table 4.4 Logistic regression analysis comparing patients with PsA to PsC to identify

biomarkers associated with PsA in patients with PsC

Univariate Multivariate

Biomarkers OR (95% CI) P-value OR(95% CI) P-value

CD5L, ng/mL 1.08 (0.92, 1.26) 3.63E-01 - -

ITGB5, ng/mL 4.07 (2.44, 6.81) 8.36E-08 3.82 (2.18, 6.70) 3.05E-06

M2BP, ng/mL 29.72 (5.88, 150.09) 4.05E-05 32.32 (4.90, 213.32) 3.07E-04

MMP3, ng/mL 1.59 (1.21, 2.11) 1.00E-03 - -

MPO, ng/mL 1.09 (0.83, 1.43) 5.40E-01 - -

CRP, mg/mL 1.93 (1.50, 2.48) 2.55E-07 1.96 (1.46, 2.62) 5.84E-06

Figure 4.2 ROC curve showing the AUC for the logistic regression model comparing PsA,

to PsC patients

AUC=0.851

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Additionally, after calculating Spearman’s rank correlation coefficients between the markers, we

noted that although there are significant correlations between markers, their magnitudes are not

large (Table 4.5). The largest correlation is between ITGB5 and M2BP (r= 0.24; p=3.51E-05).

Table 4.5 Correlation between markers

CD5L ITGB5 M2BP MMP3 MPO CRP

rsa Pb rs P rs P rs P rs P rs P

CD5L 1 - 0.15 - 0.04 4.79E-01 0.08 1.72E-01 0.06 3.19E-01 0.19 7.38E-04

ITGB5 0.15 1.00E-02 1 1.00E-02 0.24 3.51E-05 0.17 3.40E-03 0.14 1.34E-02 0.19 9.20E-04

M2BP 0.04 4.79E-01 0.24 4.79E-01 1 - 0.19 7.00E-04 0.23 7.43E-05 0.18 1.30E-03

MMP3 0.08 1.72E-01 0.17 1.72E-01 0.19 7.00E-04 1 - 0.19 1.20E-03 0.18 1.50E-02

MPO 0.06 3.19E-01 0.14 3.19E-01 0.23 7.43E-05 0.19 1.20E-03 1 - 0.13 2.96E-02

CRP 0.19 7.38E-04 0.19 7.38E-04 0.18 1.30E-03 0.18 1.57E-32 0.13 2.96E-02 1 -

a Indicates the Spearman’s rank correlation coefficient between markers b Indicates the significance of correlation between markers; P<0.05 is considered significant

106

4.4 Discussion

Psoriatic arthritis is an inflammatory arthritis that is associated with psoriasis, as it develops

primarily in patients already diagnosed with chronic plaque psoriasis. PsA often leads to

progressive joint damage and disability, poor quality of life, and increased mortality risk (32,

200), and early diagnosis and treatment may lead to better long term outcomes (79, 87-89). In

the past, candidate biomarkers have been selected for validation based on their assumed

importance in disease pathogenesis and the availability of assays (115), but to date, the need for

discovery of PsA biomarkers in psoriasis patients, has remained unmet. To discover novel PsA

biomarkers, we previously hypothesized that candidate markers of PsA are differentially

expressed in inflamed skin and SF, and are detectable in serum. With this in mind, we

performed high-throughput and targeted quantitative proteomics, as demonstrated in Chapters 2

and 3, to identify putative PsA biomarkers, and have verified a subset of those, in the current

study. Our results indicate that ITGB5, M2BP, and CRP are associated with PsA and may serve

as biomarkers for this disease.

Through our previously reported proteomic work, we identified increased ITGB5 levels in the

inflamed skin of PsA patients, when compared to PsC. We now show that ITGB5 levels are

elevated in PsA serum. ITGB5 has been described and investigated more extensively in synovial

fibroblasts of patients with rheumatoid arthritis, where it is believed to serve as a ligand for

Cyr61 (196). Cyr61 is a molecule secreted by fibroblast-like synoviocytes in the joint, and

stimulates Interleukin-6 (IL-6) production via ITGA5-ITGB5/Akt/NF-κB signaling pathway

(196). IL-6 production acts with IL-23 and TGB-β to trigger Th17 differentiation and IL-17

production (121, 122) in inflammatory processes, as they also occur in PsA. Here we show that

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ITGB5 is a marker of PsA in PsC patients, where it may also play a role in the inflammatory

nature of the disease.

M2BP (also known as galectin 3 binding protein) belongs to the family of scavenger receptor

cysteine-rich domain proteins, and is a secreted glycoprotein suggested to have a role in host

defense and tumour invasion (203-205). In addition to galectins, G3BP binds strongly with

extracellular matrix components type IV–VI collagens, fibronectin, and nidogen, as well as with

cell surface β1 integrins (206). M2BP has been investigated in the context of rheumatoid

arthritis. Increased levels of M2BP were found in both synovial fluid and synovial tissue at sites

of joint destruction, and M2BP levels correlated with the number of involved joints (207). It is

believed that M2BP is a marker of synovial cell activation and joint destruction (207). However,

M2BP was not elevated in the serum of patients with RA. We show that M2BP may also be

involved in the pathogenesis of PsA, as it was elevated in both the synovial fluid (208), and

serum of PsA patients, and serves as a specific marker of PsA in PsC patients.

While ITGB5 and M2BP are novel markers, CRP and MMP3 have previously been identified as

markers of PsA in several pilot studies (11, 93, 115, 116). Here, we show that both CRP and

MMP3 are increased in PsA patients, when compared to psoriasis, therefore supporting the

previous data, that MMP3 and CRP are markers of PsA in patients with PsC.

Contrary to our previous findings outlined in Chapter 2, MPO and CD5L were not found to be

elevated in patients with PsA, when compared to PsC. Similar results were obtained in a study

by Ademowo et al. (209), whereby a panel of markers were identified and validated in synovial

tissue biopsies, but when tested in serum, their findings could not be translated. The discrepancy

in protein levels between tissues and serum could largely be due to the different analytical

108

techniques utilized to measure the markers. In targeted mass spectrometry, as discussed in

Chapter 1, proteins are denatured, reduced, alkylated, and digested into peptides, some of which

are then monitored. Unless specified, post translational modifications are not taken into account

during multiple reaction monitoring assays. ELISAs are more sensitive in that respect. Due to

the dependence on antibody-antigen interaction, a post translational modification of a protein

may mask the epitope, thus limiting antigen-antibody binding, and lowering the signal.

Alternatively, since CD5L and MPO were both derived from the comparison of SF from PsA

and early OA, our results may in fact reflect that, and could indicate that these markers may

perform better in differentiating PsA from OA, and not PsA from PsC.

Thus, our results demonstrate that soluble biomarkers, previously identified through our

proteomic studies, have the ability to distinguish PsA from PsC. Additionally, after examining

the correlation between markers, we noted a significant, but low magnitude correlation between

markers, which indicates that these biomarkers are independently associated with PsA.

Moreover, logistic regression analyses provided evidence that these markers are independently

associated with PsA. The diagnostic and prognostic roles of these biomarkers remain to be

investigated in future longitudinal studies in patients with early PsA, and PsC alone.

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Chapter 5

110

Summary and future directions

5.1 Summary of findings and implications

The preceding chapters of this thesis have described the necessity for identifying and developing

biomarkers for psoriatic arthritis, in psoriasis patients. Prior to the work presented herein,

candidate biomarkers have been selected for validation based on their assumed importance in

PsA pathogenesis and the availability of assays, but to date, the discovery of clinically useful

PsA biomarkers in psoriasis patients, has remained unmet. As such, in chapters two and three of

this thesis we aimed to identify novel PsA candidate biomarkers, using quantitative mass-

spectrometry-based proteomics. These represent the first studies that delineate the proteomes of

synovial fluid and skin from patients with PsA, and led to the identification of 20 candidate PsA

biomarkers. Seventeen of these markers were novel.

Following the discovery and verification of biomarkers, the candidate markers must undergo

validation in serum samples. In chapter 4, I utilized enzyme-linked immunosorbent assays to

measure the concentration of six of the proposed markers in a well-defined cohort of 300

individuals. I demonstrated that two novel markers, M2BP, ITGB5, and the well-known CRP

are associated with PsA and may serve as biomarkers for this disease.

Therefore, through this work I was able to develop and utilize a standard biomarker discovery

pipeline to identify novel biomarkers of psoriatic arthritis, in psoriasis patients. Additionally,

and as important, these biomarkers may serve as interesting factors that could play important

roles in the pathogenesis of PsA

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5.2 Limitations and considerations

Despite the interesting findings described, the project has several limitations. First, I utilized a

label-free approach in order to quantify, and identify upregulated proteins in PsA target tissues.

Potential disadvantages are lower reproducibility, which may compromise detection of smaller

quantitative changes between samples. The lower reproducibility mostly results from the

fractionation of peptides prior to LC-MS/MS analysis, and the subsequent pooling of eluted

fractions, which can result in unequal/uncontrolled pooling. The reproducibility of LFQ

experiments can be maximized by standardizing the entire pipeline, from sample collection and

processing, to instrument setup and calibration, as I have done in the present study.

Second, pooling of samples in the discovery phase of this study could have potentially masked

meaningful discrepancies among the compared proteomes. To minimize this, I confirmed all

pool-derived using SRM in individual samples. By pooling biological samples as I have done, I

obtained sufficient sample and increased the likelihood of identifying proteins that would

otherwise be undetectable in individual samples, therefore allowing a more extensive proteomic

coverage of the disease’s heterogeneity. Despite the shortcomings of the fractionation and

pooling strategies, I do confirm the elevation of several markers using SRM assays, which

provides validity to my entire strategy.

Third, the “control” synovial fluid described in Chapter 2, originated from joints of patients with

early OA. Early OA synovial tissue has been shown to be inflammatory in nature, with

increased mononuclear infiltration and overexpression of mediators of inflammation (210), so it

does not represent the ideal comparison, as SF from PsC patients would. My use of early OA SF

could have masked important inflammatory mediators otherwise not present in PsC SF, thus

112

interfering with the identifications of PsA markers in PsC patients. Additionally, as described,

PsA is a heterogeneous disease with many clinical phenotypes, and by obtaining SF only from

large joints, I narrowed my analysis to only one clinical phenotype, and excluded the distal

interphalangeal predominant and spondylitis forms of PsA. Therefore, my findings may only be

applicable to the asymmetric or symmetric forms of PsA.

Fourth, to ensure I chose protein markers that were specific to the pathogenesis of PsA, I filtered

my proteins, as described in Chapter 2 and 3. I narrowed my results to include only upregulated

proteins, proteins that are not present in high abundance in the serum, and proteins expressed or

present in cells/tissues associated with PsA. This represents another limitation, since by doing

so, I may have disregarded potentially important proteins of interest. For example, SAA (which

was excluded due to its high abundance in serum in Chapter 2), has been shown to have

important biological effects in driving inflammation in rheumatoid arthritis (211, 212).

Finally, the three biomarkers that were validated only included one of the previously identified

PsA biomarkers by Chandran et al. (115), which may be due to the different and much larger

validation cohort that was utilized in the current study. This fact highlights the importance of

multiple and multi-site validation cohorts, in determining the clinical utility of the possible PsA

biomarkers that have been named in this study and others.

Despite the limitations, the project carries several strengths. The first lies in the quality and

source of the samples analyzed. Any biomarker discovery pipeline relies on the use of well-

characterized, high-quality samples. It is ideal to collect and store samples following a

standardized protocol, which is extremely important during the biomarker development process

considering that a candidate biomarker needs to be repeatedly verified /validated with large and

113

independent sample sets. The synovial fluid, skin, and serum samples used in the current studies

were obtained from one institution and underwent strict sample handling procedures.

Second, the discovery phase of our biomarker development pipeline, was followed by SRM-

based verification of a large number of candidates, which was followed by ELISA-based

validation of three markers in serum samples from a well-defined cohort of 300 patients. The

work presented in this thesis represents the most comprehensive biomarker development project

conducted to date for PsA.

114

5.3 Future directions

In this thesis, 20 proteins were presented that may represent PsA biomarkers, and only 7 of

those were tested in serum, largely due to the lack of commercially available antibodies or

immunoassay kits. Apart from CRP, S100A9, and MMP3, none of the markers proposed have

been previously measured in serum from PsA patients. Therefore, an important next step would

be to develop monoclonal and polyclonal antibodies to allow the measurement of the remaining

candidates in PsA, PsC, and healthy serum.

Protein verification/validation was only performed on upregulated proteins, proteins specific to

PsA cells/tissues, and proteins not present in high abundance in the serum. The proteins I

excluded are also of importance, as they may represent mediators/biomarkers of PsA, and in

future studies, these should be further investigated to delineate their involvement (or lack of

involvement) in PsA.

As described, the validation cohort consisted of patients with PsA, PsC, as well as healthy

controls. Since the ultimate scope is to identify markers of early PsA in psoriasis patients, future

longitudinal studies consisting of patients with early PsA, and psoriasis alone are warranted to

evaluate the diagnostic and prognostic roles of these biomarkers.

Since PsA is a largely heterogeneous disease, it is unlikely that a single biomarker will be

sufficient in predicting PsA in psoriasis patients. There are a number of markers, as exemplified

earlier, that have been tested in pilot studies, and are still awaiting validation in large patient

cohorts. Therefore, another important step would be to evaluate these, alongside those markers

discovered and described in this thesis, in order to compose panels which are likely more

115

informative and reliable. Also, there are some clinical features in psoriasis that indicate higher

risk for developing PsA, such as nail psoriasis, scalp, intergluteal/perianal psoriasis, and severe

psoriasis. Combining these clinical features, with biomarker panels may represent the ideal

predictive model to identify psoriatic arthritis.

Lastly, the work presented in this thesis demonstrated two newly-identified PsA-associated

markers: ITGB5, and M2BP. Studies examining the mechanism resulting in the elevation of

these proteins will be important, as they may provide novel insights into PsA pathogenesis.

116

5.4 Conclusion

Early diagnosis and management of PsA is known to lead to better long-term patient outcomes

(79, 87-89). Unfortunately identifying inflammatory musculoskeletal disease early in patients

with psoriasis is a very difficult task, often due to a lack of validated biomarkers. Therefore,

much of the recent research in PsA, has focused on identifying, serum-based biomarkers, which

could be used to screen psoriasis patients, in order to identify those that may have or will

develop PsA. The work described in this thesis has outlined a mass-spectrometry-based

approach to PsA biomarker discovery, and has resulted in the validation of two novel

biomarkers, adding to the growing list of PsA markers. Large multi-site validation cohorts are

imperative in finally identifying clinically relevant PsA biomarkers.

117

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