Molecular Biomarker Discovery in Psoriatic Arthritis

by

Remy Angela Pollock

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

Institute of Medical Science University of Toronto

© Copyright by Remy Angela Pollock 2016

ii

Molecular Biomarker Discovery in Psoriatic Arthritis

Remy Angela Pollock

Doctor of Philosophy

Institute of Medical Science

University of Toronto

2016

Abstract

Aim: Psoriatic arthritis (PsA) is an inflammatory arthritis of unknown etiology that develops in

approximately 30% of individuals with psoriasis. No objectively measurable biomarker has been

identified for PsA, due in part to insufficient knowledge of its etiopathogenesis. This work aims

to identify candidate biomarkers of PsA by studying its underlying transcriptomic and

epigenomic mechanisms.

Methods: Psoriasis (PsC) and PsA patients from a prospective cohort were analyzed. Whole

blood, serum, and semen samples were obtained from subsets of patients and unaffected controls

for transcriptomic, protein, and epigenomic analyses, respectively. Potential epigenetic

mechanisms were also analyzed using self-reported family history data from the entire PsC and

PsA cohort to further explore the parent-of-origin effect.

Results: Transcriptomic analyses identified several genes involved in innate immunity,

particularly toll-like receptor signalling as differentially expressed in whole blood of PsA and

PsC patients. Four candidate gene expression biomarkers CXCL10, NOTCH2NL, HAT1, and

SETD2 were replicated in an independent cohort of PsC and PsA patients. Soluble CXCL10 was

significantly elevated in baseline serum samples of psoriasis patients who later developed PsA

compared to patients who did not develop PsA. Excessive paternal transmission was found in

iii

PsC and PsA patients, as well as genetic anticipation manifesting as increased disease severity

during male transmission. DNA methylation profiling of sperm cells revealed several germ line

variations associated with psoriasis and PsA occurring near or within genes involved in

inflammatory and immune system processes, including HCG26 within the major

histocompatibility complex.

Conclusions: Whole blood transcriptomic and serum protein analysis identified the chemokine

CXCL10 as a putative predictive biomarker of PsA in PsC patients. Evidence of a parent-of-

origin effect, genetic anticipation, and the identification of germ line DNA methylation

variations in patients suggest a role for epigenetic mechanisms in psoriatic disease

etiopathogenesis, and a potential new avenue of biomarker discovery.

iv

Acknowledgments

First and foremost, I would like to express my gratitude to my supervisor and mentor Dr. Dafna

Gladman for inspiring me to pursue research in this field and the endless guidance and support

she provided over the past several years. To my committee members, Drs. Cathy Barr, Jo Knight,

and Art Petronis, thank you for providing direction, insight, knowledge, and encouragement

during the completion of my degree. I would also like to thank my predecessors Drs. Vinod

Chandran and Lihi Eder, for setting high standards of scholarship that I continuously strive to

emulate; colleagues Fawnda Pellett and Fatima Abji for their technical expertise and support in

designing and performing laboratory-based analyses; collaborators Drs. Proton Rahman and Kun

Liang for providing data and analytical advice; Anne MacKinnon and the staff of the University

of Toronto Psoriatic Arthritis Program for administrative and clinical support; and finally, the

patients of the University of Toronto Psoriatic Arthritis Program whose contributions made these

studies possible.

I would like to acknowledge the Canadian Institutes of Health Research for funding my work

through the Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Research

Awards, as well as the Arthritis Research Foundation, National Psoriasis Foundation, and

Krembil Foundation for providing funds for the germ line methylation study.

Lastly, I would like to thank my parents, Hume and Bella, who encouraged me from a young age

to pursue science, and my fiancé Colin, whom I met on the first day I started this degree, and

whose confidence in me and unconditional support from that day forward made all the

difference.

v

Contributions

For Chapter 3, I was responsible for processing and extracting the RNA samples, performing

secondary bioinformatics analyses, data mining, and performing the technical validation by

qPCR. I was also responsible for the statistical analysis of the nCounter® data, purification of

leukocyte subsets, RNA extraction from purified cells, and measurement of candidate gene

expression by qPCR. I generated all figures in Chapter 3 with the exception of Figures 3.1 and

3.2, which were created by Kun Liang (Department of Statistics and Actuarial Science,

University of Waterloo). For Chapter 4, I contributed to the processing and biobanking of serum

samples from psoriasis patients, performed all of the statistical analyses and interpretation of

CXCL10 protein expression data, and was responsible for designing and acquiring data for the

gene expression experiments. Figures in Chapter 4 were created by Fatima Abji (Psoriatic

Arthritis Program, Toronto Western Research Institute), with the exception of Figure 4.3 which I

created. For Chapter 5, I was responsible for gathering all of the family history data from various

clinical databases, verifying data with the baseline research protocols, family history

questionnaires, patient charts, or by telephone interviews with patients, and performing all data

analysis and interpretation. For Chapter 6, I was responsible for patient recruitment, sample

collection and processing, DNA extraction and preparation of DNA samples for arrays. I also

performed all steps of data quality control, preprocessing, statistical/bioinformatics analyses, and

created all figures in this chapter.

vi

Table of Contents

Acknowledgments .......................................................................................................................... iv

Contributions ................................................................................................................................... v

Table of Contents ........................................................................................................................... vi

Abbreviations ............................................................................................................................... viii

List of Tables .................................................................................................................................. x

List of Figures ............................................................................................................................... xii

List of Appendices ....................................................................................................................... xiv

Chapter 1 Literature Review ........................................................................................................... 1

1.1 Psoriasis .............................................................................................................................. 1

1.2 Psoriatic Arthritis ................................................................................................................ 7

1.3 Tools for Diagnosing PsA ................................................................................................. 14

1.4 Molecular Biomarkers of PsA .......................................................................................... 23

Rationale, Hypotheses and Specific Aims ................................................................... 47

2.1 Rationale ........................................................................................................................... 47

2.2 Hypotheses and Specific Aims ......................................................................................... 48

............................................................................................................. 50

3.1 Introduction ....................................................................................................................... 50

3.2 Materials and Methods ...................................................................................................... 51

3.3 Results ............................................................................................................................... 55

3.4 Discussion ......................................................................................................................... 82

.................................................................... 86

4.1 Introduction ....................................................................................................................... 86

4.2 Materials and Methods ...................................................................................................... 87

vii

4.3 Results ............................................................................................................................... 90

4.4 Discussion ......................................................................................................................... 99

Further Evidence Supporting a Parent-of-Origin Effect in Psoriatic Disease ............ 103

5.1 Introduction ..................................................................................................................... 103

5.2 Patients and Methods ...................................................................................................... 104

5.3 Results ............................................................................................................................. 105

5.4 Discussion ....................................................................................................................... 112

.................................... 115

6.1 Introduction ..................................................................................................................... 115

6.2 Methods ........................................................................................................................... 117

6.3 Results ............................................................................................................................. 121

6.4 Discussion ....................................................................................................................... 141

General Discussion ..................................................................................................... 146

7.1 Limitations ...................................................................................................................... 158

7.2 Conclusions ..................................................................................................................... 163

7.3 Future Directions ............................................................................................................ 164

Appendix ..................................................................................................................................... 167

References ................................................................................................................................... 178

viii

Abbreviations

APCA Anti-citrullinated peptide antibody

AS Ankylosing spondylitis

AUC Area under the curve

Avy Agouti viable yellow

AxinFU Axin fused

BMI Body mass index

CASPAR Classification of Psoriatic Arthritis

CD Cluster of differentiation

CI Confidence interval

CpG Cytosine-guanine dinucleotide

CRP C-reactive protein

CXCL10 C-X-C motif ligand 10

DEG Differentially expressed gene

DMARD Disease-modifying anti-rheumatic drug

DMR Differentially methylated region

DNMT DNA methyltransferase

DZ Dizygotic

ELISA Enzyme-linked immunosorbent assay

ESR Erythrocyte sedimentation rate

FC Fold change

FDR False discovery rate

GAPDH Glyceraldehyde 3-phosphate dehydrogenase

GWAS Genome-wide association study

H3K4/9/27/36 Histone 3 lysine 4/9/27/36

HAT1 Histone acetyltransferase 1

HCG26 HLA complex group 26

HLA Human leukocyte antigen

HNPCC Hereditary non-polyposis colorectal cancer

IAP Intracisternal A particle retrotransposon

ICR Imprinting control region

IFN Interferon

Ig Immunoglobulin

IL Interleukin

IQR Interquartile range

KIR Killer cell immunoglobulin-like receptor

MAF Minor allele frequency

M-CSF Monocyte colony stimulating factor

mDC Myeloid dendritic cell

MHC Major histocompatibility complex

MICA/B MHC Class I polypeptide-related sequence A/B

MMP Matrix metalloproteinase

mRNA Messenger RNA

MS Multiple sclerosis

MTX Methotrexate

MZ Monozygotic

ix

ncRNA Non-coding RNA

NF-κB Nuclear factor kappa B

NK Natural killer cell

NOTCH2NL Notch 2 N-terminal like

NSAID Non-steroidal anti-inflammatory drug

OCP Osteoclast precursor

OR Odds ratio

PASE Psoriatic Arthritis Screening and Evaluation Tool

PASI Psoriasis area and severity index

PAQ Psoriasis Assessment Questionnaire

PBMC Peripheral blood mononuclear cell

pDC Plasmacystoid dendritic cell

PEST Psoriasis Epidemiology Screening Tool

PE Phycoerythrin

PsA Psoriatic arthritis

PsC Cutaneous psoriasis without arthritis

qPCR Quantitative PCR

RA Rheumatoid arthritis

RANKL Receptor activator of NF-κB ligand

RF Rheumatoid factor

RNA Ribonucleic acid

ROC Receiver operating characteristics

SD Standard deviation

SETD2 SET domain containing 2

SLE Systemic lupus erythematosus

SNP Single nucleotide polymorphism

T1D Type 1 diabetes

Th1/2/17 T helper type 1/2/17

TLR Toll-like receptor

TNFα Tumour necrosis factor alpha

ToPAS Toronto Psoriatic Arthritis Screen

TP, TN, FP, FN True positive, true negative, false positive, false negative

x

List of Tables

Table 1.1. Performance characteristics of diagnostic tools.

Table 3.1. Demographic and clinical characteristics of the discovery and replication cohorts.

Table 3.2. Enriched biological annotations among the 494 differentially expressed genes between

PsA and PsC.

Table 3.3. Top differentially expressed genes between PsA and PsC from primary microarray

analyses.

Table 3.4. Differentially expressed genes between PsA compared to PsC identified by TLR

signaling and chromatin modification targeted qPCR arrays.

Table 3.5. Candidate genes selected for replication testing in an independent cohort by

nCounter® technology.

Table 3.6. Correlations between gene expression and clinical variables from Table 3.1 that differ

between discovery and replication cohorts.

Table 3.7. Comparison of clustered and unclustered PsA patients in the validation cohort.

Table 4.1. Demographic and clinical characteristics of the study subjects at baseline.

Table 4.2. Baseline CXCL10 as a predictor of PsA converter status.

Table 4.3. Baseline CXCL10 compared to clinical predictors of conversion of PsA.

Table 5.1. Cross tabulation of disease status in fathers and mothers of all probands.

Table 5.2. Cross tabulation of disease status in fathers and mothers of the PsA probands.

Table 5.3. Cross tabulation of disease status in fathers and mothers of the PsC probands.

Table 5.4. Results of univariate logistic regression models examining the association between

paternally-transmitted disease and clinical and genetic variables in PsA patients from

Newfoundland.

Table 5.5. Significant results from multivariable logistic regression models examining the

association between paternally-transmitted disease and clinical and genetic variables, adjusted

for sex of the proband.

Table 6.1 Demographic and clinical characteristics of the study subjects.

Table 6.2 Biological functional enrichment analysis of all genes found to be differentially

methylated sperm cells.

Table 6.3 Top hyper and hypomethylated genes from each of the groupwise comparisons and

genes most relevant to psoriatic disease.

xi

Table 6.4 Association of HCG26 methylation in sperm with PsA compared to psoriasis patients

and controls after adjustment for HLA-B and HLA-C.

Table 6.5. Association of rs2385226 alleles and genotypes with an extended sample of psoriatic

disease patients.

xii

List of Figures

Figure 1.1. Example ROC curves illustrating AUCs of 0.5 (Reference Line), 0.67 (Hypothetical

Biomarker), and 1.0 (Perfect Biomarker).

Figure 1.2. Principle of the NanoString nCounter® gene expression profiling technology

(Standard chemistry).

Figure 1.3. Principle of microsphere-based immunoassays.

Figure 3.1. Significant clinical, demographic, and technical factors affecting gene expression.

Figure 3.2. Scatter plot of each differentially expressed gene found in PsA vs. Controls, using the

log Fold Change (FC) values from PsA vs. PsC plotted against PsC vs. Controls.

Figure 3.3. Concordance between microarray and qPCR or NanoString fold change

measurements in the discovery (microarray) samples.

Figure 3.4. Two-way hierarchical clustering of nCounter® gene expression data from the

replication cohort, with the PsA cluster shown.

Figure 3.5. Mean normalized Ct value and fold change (FC) of the 4 replicated genes in isolated

leukocyte subpopulations.

Figure 4.1. Scatter dot plot of baseline serum CXCL10 levels from 46 converters and 45 non-

converters.

Figure 4.2. Scatter dot plot of paired CXCL10 serum concentrations from 23 PsC patients before

and after the development of PsA.

Figure 4.3. CXCL10 gene expression in peripheral whole blood (Blood PsA, n=4), synovial fluid

cells of PsA patients (SF PsA, n=8), and synovial fluid cells of gout patients (SF Gout, n=6).

Figure 4.4. Scatter dot plot of baseline CRP serum levels from 46 converters and 45 non-

converters.

Figure 4.5. Scatter dot plot of paired CRP serum levels from 23 PsC patients before and after the

development of PsA.

Figure 6.1 Identification of outliers by hierarchical clustering of pre-processed array data.

Figure 6.2 Summary of probe filtering steps beginning with 485,577 probes.

Figure 6.3 Summary of differentially hyper- and hypomethylated CpG sites in sperm cells.

Figure 6.4 Differentially methylated CpG sites in sperm cells by genomic location relative to

nearby genes and CpG islands.

xiii

Figure 6.5 Two-dimensional hierarchical clustering of all differentially methylated CpG sites

identified in sperm

Figure 6.6. Group-wise (A) and individual (B) differences in methylation levels of the three CpG

sites within HCG26 associated with PsA compared to psoriasis and controls.

xiv

List of Appendices

Appendix 1. PCR primers used to measure validated gene expression biomarkers.

Appendix 2. Histograms depicting the distribution of CXCL10 serum concentrations.

Appendix 3. Scatter dot plot of paired CXCL10 serum expression from 16 PsC patients at

baseline, follow-up and after the development of PsA.

Appendix 4. Psoriasis and psoriatic arthritis family history questionnaire.

Appendix 5. Methylation-specific PCR assessing bisulfite conversion efficiency.

Appendix 6. Full list of differentially methylated genes in psoriasis patients vs. controls

(p<0.05).

Appendix 7. Full list of differentially methylated genes in PsA patients vs. controls (p<0.05).

Appendix 8. Full list of differentially methylated genes in PsA patients vs. psoriasis patients

(p<0.05).

1

Chapter 1

Literature Review

1.1 Psoriasis

1.1.1 Epidemiology and clinical phenotypes

The ancient Roman encyclopedist Celsus (25 BCE–50 CE) was the first to describe an impetigo-

like skin disease characterized by roughness and scales [1]. Today, this disease is known as

psoriasis—a chronic, immune-mediated disorder of the skin that is prevalent worldwide.

Although rarely life threatening, it is associated with increased morbidity, mortality, and reduced

quality of life, and places a considerable burden on health care systems and society in general

[2]. Psoriasis is most common among Caucasians, with an estimated prevalence ranging from

0.6-6.5% of Europeans and 0.5-4% of North Americans. In Great Britain and the United States,

the incidence of psoriasis appears to be increasing over time, with an estimated rate of around 60

per 100,000 person years in the 1980s that increased to around 107 per 100,000 person years in

1999 [3]. Estimates of the prevalence and incidence of psoriasis are similar for both males and

females [4].

Psoriasis is a chronic disease that follows an unpredictable clinical course characterized by

variable disease severity and periods of remissions and flares. Individuals who develop psoriasis

before the age of 40 (type I psoriasis) tend to have more severe disease that is familial in nature

compared to those who develop psoriasis after the age of 40 (type II psoriasis) [5]. In either case,

it is characterized by hyperproliferation of the epidermis, incomplete differentiation of

keratinocytes, and an inflammatory infiltration of the epidermis and papillary dermis [2].

Psoriasis can present anywhere on the body, but most typically occurs on the trunk, limbs, scalp,

elbows, knees, or in the body folds. There are a variety of clinical presentations including:

2

1. Chronic plaque psoriasis (vulgaris), the most common form affecting 85-90% of patients,

characterized by symmetrical, silvery-white, scaly, coin-sized plaques

2. Guttate psoriasis, consisting of a few to several small lesions

3. Flexural or inverse psoriasis, characterized by red, shiny plaques occurring in

inframammary, perineal, and axillary body folds

4. Erythrodermic psoriasis, an unstable psoriasis that results from extensive plaque psoriasis

or environmental exposures

5. Generalized pustular psoriasis (von Zumbusch), which involves red, painful, inflamed

pustules and may require hospitalization [6]

In approximately 40% of patients, psoriasis can also affect the nails, resulting in yellowish

discolouration, pitting, ridges, and onycholysis, characterized by detachment of the nail from the

nail bed [5, 6].

1.1.2 Etiology and Pathogenesis

1.1.2.1 Genetic Factors

Psoriasis has a multifactorial etiology, resulting from a complex interaction of several inherited

genetic risk factors, environmental exposures, and epigenetic factors. The importance of genetic

factors is evidenced by familial aggregation of the disease. The recurrence risk ratio for psoriasis,

which is an estimate of the prevalence of a disease within family members relative to the

prevalence in the general population, is 4-10 for the relatives of psoriasis patients [7].

Furthermore, the disease concordance rate for genetically identical monozygotic (MZ) twins is

considerably higher (33-72%) than for more genetically dissimilar dizygotic (DZ) twins (12-

23%) [8-10]. Both dominant and recessive inheritance have been proposed, however it is now

widely acknowledged that psoriasis lacks a clear Mendelian pattern of inheritance typical of

single gene disorders [7], and more likely has a complex genetic architecture.

3

Linkage, sequencing, and fine mapping studies have helped to establish that the human leukocyte

antigen (HLA) C gene allele *0602 (PSORS1), located in the major histocompability complex

(MHC) Class I region on chromosome 6p21.3, shows the strongest association with psoriasis

compared to healthy controls [11-13]. HLA-C*0602 is particularly associated with early-onset,

severe forms of the disease (type I psoriasis) [14]. Alleles of the adjacent HLA-B gene, namely

HLA-B*13, B*38, and B*39, as well as the nearby gene MICA are also strongly associated with

psoriasis independently of HLA-C*0602, particularly MICA*016 [15-17]. Other loci in the MHC

associated with psoriasis independently of PSORS1 lie close MICB, HLA-A, and HCG9 [18, 19].

Genome-wide association studies (GWAS), Immunochip studies, and meta-analyses have

identified an additional 41 single nucleotide polymorphisms (SNPs) spread throughout the

genome that are associated with psoriasis and reach genome-wide significance (p<5x10-8) among

individuals of European descent [16, 20-26]. Less than 25% of these variants are found in coding

regions or are in linkage disequilibium with coding variants, and it is possible that they function

in the regulation of nearby genes [25, 26]. Many of these variants can be grouped into a

pathogenic model of psoriasis comprised of distinct signaling networks affecting skin barrier

function (i.e., LCE3, GJB2), innate immune responses involving NF-κB and interferon (IFN)

signaling (i.e., TNFAIP3, TNIP1, NFKBIA, REL, FBXL19, TYK2, NOS2), and adaptive immune

responses involving CD8+ T lymphocytes and interleukin (IL)-23/IL-17-mediated lymphocyte

signaling (i.e., HLA-C, IL12B, IL23R, IL23A, TRAF3IP2, ERAP1) [27].

1.1.2.2 Environmental Factors

Studies of MZ and DZ twins have estimated that genetic factors can explain approximately 66-

68% of the variation in psoriasis susceptibility [9, 28]. Some of the remaining variation may be

attributed to additional rare genetic variants with large effect sizes, and non-shared

environmental factors such as physical trauma to the skin, known as the Koebner phenomenon,

which can result in psoriatic plaques directly at the sites of trauma. Other environmental factors

associated with psoriasis include emotional stress, streptococcal pharyngitis infection (in guttate

4

psoriasis specifically), HIV infection, humidity, cold weather, diet, obesity, smoking, and

medications such as beta-blockers, lithium, anti-malarials, and interferon [2, 3].

1.1.2.3 Epigenetic Factors

Beyond genetic and environmental risk factors, there is some evidence that epigenetics might

also be involved in the etiology of psoriasis from the observation of a parent-of-origin effect.

Parent-of-origin effects refer to the differential risk or pathogenicity of a disease that depends on

the sex of the disease-transmitting parent. A greater tendency for psoriasis to be inherited from

affected males compared to females has been replicated in large, independent cohorts of psoriasis

patients from the Faroe Islands [29] and Scotland [30]. In the Faroe Islands, a greater percentage

of children of psoriatic males than psoriatic females were found to develop psoriasis (28.4% vs.

20.8%, p<0.007). If analyzed with respect to affected grandchildren, the proportion of affected

grandfathers was found to be significantly greater than the proportion of affected grandmothers

(65% vs. 35%, p<0.004) [29]. In Scotland, the proportion of psoriasis probands reporting an

affected father was significantly higher than those reporting an affected mother (13% compared

to 11%, p=0.044). Furthermore, probands reporting an affected father had a significantly greater

reduction in age of onset compared to probands reporting an affected mother (24.1 vs. 10.9-year

reduction, p=0.009), providing evidence of genetic anticipation [30].

Genomic imprinting is one molecular mechanism that has been hypothesized to explain parent-

of-origin effects. Genomic imprinting is mediated by epigenetic marks and involves differential

marking of alleles in the oocyte and sperm. These marks are maintained in the next generation,

resulting in parent-of-origin specific gene expression. The hypothesis of genomic imprinting in

psoriasis was first put forth to explain the results of a Dutch study comparing the birth weight of

children of psoriatic fathers and mothers to children of healthy controls [29]. After adjustment

for confounding factors such as sex of the child, birth rank, pregnancy duration, maternal

complications or disease, smoking, drinking, and twinning, children of psoriatic fathers were

found to be significantly heavier than children of psoriatic mothers (270g difference, p<0.004),

and children of healthy controls (168g difference, p<0.01). The study was conducted a year after

5

the identification of the first imprinted genes in the mouse, H19, Igf2, and Igf2r, which are

related to fetal overgrowth. Thus, the authors hypothesized that their observation in psoriasis was

due to imprinting of a major psoriasis-related gene resulting in overexpression of a growth factor

from the paternal genome [29].

1.1.2.4 Pathogenic Model

The current pathogenic model of psoriasis can be broken down into initiation, amplification, and

effecter phases [31]. In the initiation phase, a genetically and/or epigenetically susceptible

individual is exposed to an environmental trigger, resulting in a pathological cascade of cells and

effector molecules that can take alternative routes to yield characteristic yet diverse clinical

manifestations. The antimicrobial peptide LL-37 (cathelicidin), a component of the innate

immune system, is produced by injured keratinocytes and binds to nucleic acid fragments to

activate skin resident plasmacystoid dendritic cells (pDCs) through toll-like receptors (TLRs),

leading to IFN alpha (IFNα) production [31, 32]. This leads to the activation of several innate

immune cells, which produce additional pro-inflammatory cytokines such as tumour necrosis

factor alpha (TNFα), IL-1, and IL-6. The proinflammatory milieu activates dermal myeloid

dendritic cells (mDCs), which migrate to regional skin-draining lymph nodes where they

stimulate T cell activation by presentation of an unknown antigen and secretion of cytokine

mediators IL-12 and IL-23 [32]. Activated effector T cells differentiate into cytotoxic CD8+, and

CD4+ T helper type 1 (Th1) and type 17 (Th17) effector cells, as well as a poorly-defined IL-22-

producing T cell subset, which home to the skin to perpetuate and amplify skin inflammation [2,

31].

In the amplification phase, Th17 cells infiltrating the skin produce IL-17A and IL-17F, which

stimulate chemokine production by keratinocytes resulting in neutrophil attraction and

amplification of inflammation. The production of IL-22 induces epidermal hyperplasia and

abnormal keratinocyte differentiation resulting in the characteristic scaling of psoriasis. The

effector phase involves complex cross-talk between keratinocytes, dermal fibroblasts, and

resident and infiltrating immune cells, and involves increased expression of chemokines such as

6

CXCL8, 9, 10, and 11, CCL20, S100 proteins such as S100A8 and S100A9, and signaling

molecules such as transforming growth factor beta, keratinocyte growth factor, epidermal growth

factor, and fibroblast growth factor [31, 32].

1.1.3 Treatment

Patients diagnosed with mild psoriasis are prescribed topical agents such as emollients,

corticosteroids, the vitamin D analogue calcipotriene, coal tar, keratolytic agents, and anthralin.

Some benefit from targeted narrowband and broadband ultraviolet B phototherapy. For patients

diagnosed with more extensive disease, ultraviolet B irradiation or psoralen plus ultraviolet A

therapies are prescribed, as well as systemic therapies such as the immunosuppressive drug

methotrexate (MTX), cyclosporine, and the vitamin A analogue acitretin. An increased

understanding of the cellular pathogenesis of psoriasis has led to the development of several

successful targeted biologic therapies for patients with moderate to severe disease. These include

alefacept, a fusion protein that inhibits CD4+ and CD8+ T cell activation by blocking the

interaction of CD2 and the co-stimulatory molecule LFA-3, and by inducing apoptosis of

memory effector T cells; adalimumab, a human monoclonal antibody against TNFα; infliximab,

a human-mouse chimeric monoclonal antibody against TNFα; and etanercept, a fusion protein of

the TNF receptor to the immunoglobulin (Ig) G1 constant chain that functions as a decoy

receptor for TNFα. Besides anti-TNF agents, the anti-IL-12 and IL-23 monoclonal antibody

ustekinumab is also used in the treatment of psoriasis. Ustekinumab binds the p40 subunit of

these cytokines to prevent activation of their receptors [32]. The phosphodisesterase 4 inhibitor

apremilast, approved by Health Canada in 2014 for the treatment of psoriasis, modulates cyclic

AMP metabolism and suppresses production of inflammatory mediators TNFα, IL-17, and IL-

23, and increases production of anti-inflammatory mediators such as IL-10. Lastly, the anti-IL-

17A human monoclonal antibody secukinumab was recently approved in Canada for the

treatment of moderate to severe plaque psoriasis.

7

1.2 Psoriatic Arthritis

1.2.1 Epidemiology and Clinical Phenotypes

While it primarily affects the skin, psoriasis can also target diverse tissues such as the gut, eye,

and musculoskeletal system, resulting in associated features such as inflammatory bowel disease,

uveitis, and arthritis [3, 5]. Chronic inflammation in psoriasis patients can also increase the risk

of comorbidities such as metabolic syndrome and cardiovascular disease. Of the various

associated features of psoriasis, psoriatic arthritis is the most common, with an estimated

prevalence ranging from 6-42% among psoriasis patients [3]. The English physician Thomas

Bateman (1778-1821) was the first to associate psoriasis and arthritis in his 1813 book “Practical

Synopsis of Cutaneous Diseases”. Subsequent works published by French dermatologists Jean

Louis Alibert (1766-1837), Pierre Rayer (1793-1867), and Ernest Bazin (1807-1878) contain

additional references to a cutaneous-articular condition [1]. However, it was not until 1964 that

the specific form of arthritis that develops in psoriasis patients, known as psoriatic arthritis

(PsA), was recognized by the American Rheumatism Association (now known as the American

College of Rheumatology) as a clinical entity distinct from rheumatoid arthritis due to its

appearance in individuals with psoriasis and lack of association with rheumatoid factor [33].

Within the more than 100 types of arthritis, PsA belongs to a family known as the seronegative

spondyloarthropathies, which includes ankylosing spondylitis (AS), reactive arthritis,

inflammatory bowel disease-associated arthritis, juvenile idiopathic arthritis, and undifferentiated

spondyloarthropathy. Seronegative spondyloarthropathies are strongly associated with the MHC

gene HLA-B allele 27 (HLA-B*27). PsA usually manifests in the third or fourth decade of life,

and develops after psoriasis onset in 70% of cases, but can appear concomitantly with psoriasis

in 15% of cases or before psoriasis in the remaining 15% of cases [34]. The incidence of PsA in

psoriasis patients is constant over time, which means that the risk of a psoriatic individual

developing PsA remains the same throughout the course of disease [35]. PsA affects peripheral

and axial joints such as the spine and sacroiliac joint, and was initially classified into five clinical

patterns or subgroups described by Moll and Wright in 1973:

8

1. Asymmetric oligoarthritis

2. Symmetric polyarthritis

3. Predominant distal interphalangeal joint arthritis

4. Spondylitis

5. Arthritis mutilans [36]

In their series of patients, Moll and Wright observed that asymmetric oligoarthritis is the most

common form of PsA, seen in approximately 70% of patients, followed by symmetric

polyarthritis, seen in 15% of patients. However, subsequent studies have shown that peripheral

polyarthritis is more common than oligoarthritis, which is particularly evident as patients are

followed longitudinally and observed to evolve from asymmetric oligoarthritis to symmetric

polyarthritis as joint damage is accrued [37]. The remaining subgroups are rare, each comprising

only 5% of patients. Arthritis mutilans is the most severe form of PsA, and involves osteolysis,

joint destruction, and eventual deformity [33, 36, 38]. Although PsA affects men and women

equally, men are more likely to develop spondylitis and severe radiographic damage in

peripheral joints compared to women [39]. PsA subgroups are not mutually exclusive, as 30-50%

of patients may have, for example, asymmetric oligoarthritis with concomitant spondylitis.

In addition to the clinical manifestations described above, around 48% of PsA patients

experience dactylitis, or swelling of an entire finger or toe signaling inflammation in the joints,

tendons, bones, and soft tissues within the digit. Many also experience tenosynovitis, or

inflammation of the tendon sheath in the hands, wrists and ankles, as well as enthesitis, a

hallmark of PsA observed in 40% of patients. Enthesitis refers to inflammation of the entheses,

or the sites where the ligaments and tendons attach to the bone [40]. In PsA patients, enthesitis

frequently occurs at the back and bottom of the heel where the Achilles tendon and plantar fascia

connect to the calcaneus bone. As described below, the entheses are thought to be of central

importance to the initiation of the pathogenic disease process in PsA [41].

9

PsA is a chronic, progressive disease. A small fraction of patients (18-24%) achieve clinical

remission, however this lasts for 2.6 years on average and relapses are common [42, 43]. It is

now apparent that PsA is more severe than previously thought, and can lead to progressive joint

damage and disability, as well as increased mortality [40]. The progressive nature of PsA is

evident from an increased frequency of patients with greater than or equal to 5 damaged joints at

follow-up over 5 years (19% to 41%) [44]. While PsA patients experience similar causes of

death compared to the general population, they have a 60% higher mortality risk compared to the

general population [45]. Patients with severe disease, defined by higher disease activity and

higher number of damaged joints, are prone to this increased mortality [46]. Moreover, PsA

patients have an increased risk of other comorbidities such as cardiovascular disease, type 2

diabetes, neurologic conditions, gastrointestinal disorders, and liver disease [47], and

demonstrate a reduced quality of life and physical function compared to the general population

[40].

1.2.2 Etiology and Pathogenesis

1.2.2.1 Genetic Factors

Like psoriasis, PsA has a multifactorial etiology and results from the interaction of genetic,

environmental, and possibly epigenetic risk factors. Genetic factors are evident from the high

(7.6%) prevalence of PsA among first-degree relatives of PsA probands, and a recurrence risk

ratio of 30-35 [48]. The PsA concordance rate for monozygotic twins is 11%, while the dizygotic

twin concordance rate is 4.5% [49]. Like psoriasis, both dominant and recessive inheritance have

been proposed for PsA but it is clear that neither apply, thus PsA is also considered a

multifactorial genetic disease [7].

Comparisons of psoriasis and PsA patients in genetic studies of the MHC have shown that PsA is

more strongly associated with alleles of HLA-B. Like other seronegative spondyloarthropathies,

PsA is specifically associated with HLA-B*27, as well as B*08, and B*38 [50]. Loci associated

10

with PsA independent of HLA-B and C include SNPs in MICB and the TNFA-238 polymorphism

[51]. On chromosome 19q13.4, polymorphisms in activating forms of the killer cell

immunoglobulin-like receptor (KIR) genes KIR2DS1 and KIR2DS2 are associated with PsA

compared to healthy controls [52-54]. Together, these genetic susceptibility factors cannot

explain all cases of PsA, and it is possible that unidentified rare variants contribute additional

genetic susceptibility.

1.2.2.2 Environmental Factors

Several environmental factors are associated with the development of PsA. After adjustment for

age, sex, education level, and psoriasis severity and duration, it was found that occupations

requiring heavy lifting and infections requiring antibiotics were positively associated with PsA,

whereas smoking was protective [55]. It has also been found that trauma (known as the ‘deep’

Koebner phenomenon) [55, 56], changing residence, rubella vaccination [56], and family history

of PsA [57] are associated with the development of PsA in psoriasis patients.

1.2.2.3 Epigenetic Factors

Investigations of the parent-of-origin effect in PsA have shown conflicting results. In the Scottish

study of the parent-of-origin effect in psoriasis patients, no evidence of a parent-of-origin effect

was found among 900 PsA probands [30]. This contrasts findings from a Canadian cohort of 95

PsA probands, in which significantly more probands reported an affected father than an affected

mother (65% vs. 35%, p=0.001) [58]. A genome-wide linkage study of 100 Icelandic PsA

patients provided putative genetic evidence of genomic imprinting in PsA, identifying linkage to

a marker on chromosome 16q near the NOD2 locus after limiting the analysis to probands with

paternally-inherited disease (LOD score of 4.19, p=5.31x10-6) [59]. Evidence for a parent-of-

origin effect in PsA is therefore inconsistent, and if the phenomenon is present, it is unknown if it

is as strong an effect as seen in psoriasis patients. Chapter 5 of this thesis addresses this question

and provides further evidence supporting a parent-of-origin effect in both psoriasis and PsA,

demonstrating that it may be associated with genetic anticipation.

11

1.2.2.4 Pathogenic Model

The traditional pathogenic model linking psoriasis and PsA posited that both diseases are

autoimmune in origin and result from defects in the adaptive immune system, similar to classical

autoimmune diseases systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). In this

model, a shared autoantigen expressed in both the skin and the joint’s synovial membrane and

cartilage elicits chronic autoreactive T cell-driven inflammation, with dysregulation occurring in

the primary or secondary lymphoid organs [60]. Histological evidence of CD8+ T cell

populations in both inflamed skin and synovium of PsA patients supports this mechanism, as

does the strong association of both psoriasis and PsA with variants of HLA Class I genes HLA-C

and HLA-B, respectively, which both function in antigen presentation. However, this model is

problematic because synovial T cells do not exhibit auto-reactivity, and no self-antigen has ever

been identified [61].

As a result, an alternative model of PsA has been proposed, which instead of dysregulation

occurring in the primary or secondary lymphoid organs, places the entheses at the initiation site

of inflammation [60]. This model can be similarly divided into three phases [31]. In the initiation

phase, biomechanical strain, enthesial microtrauma or dysregulated tissue homeostasis attracts

inflammatory cells to the adjacent synovium and bone marrow, because the entheses itself is

relatively resistant to vascular and immune cell invasion. These inflammatory cells include

immature pDCs, which have been found in the synovial fluid of PsA patients, and which produce

the pro-inflammatory cytokine IFNα [31].

The amplification phase likely involves IL-17-secreting Th17 cells, as they have been found in

increased numbers in peripheral blood mononuclear cells (PBMCs) of PsA patients compared to

patients with RA and are enriched in the joints, suggesting migration to the sites of injury. IL-17

and Th17 levels have been found to correlate with systemic disease activity. Activated T cells

likely contribute to the enhanced production of cytokines in both the synovial fluid and synovial

cultures from PsA patients [62]. These cytokines include IL-1β, IL-2, IL-10, IFN-α and TNF-α,

which induce proliferation and activation of synovial and epidermal fibroblasts, leading to the

12

fibrosis reported in patients with longstanding PsA [63, 64]. Several innate immune lymphocytes

also participate in inflammation in the amplification phase, including natural killer (NK) cells

and γδ T cells. Both NK and NK-T cells have been described in increased numbers in psoriatic

plaques and in synovial tissues from PsA patients [65]. TNFα is also produced by different cell

types in the synovium, such as monocytes. Histological studies of synovium of PsA and other

spondyloarthropathies have shown a common pathology consisting of increased vascularity, as

well as infiltration by neutrophils and CD163+ M2 macrophages [66].

In contrast to psoriasis, in which the effecter phase results in no permanent damage to the skin,

permanent joint damage can occur in PsA through loss of cartilage and bone erosion.

Interestingly, the opposite process of new bone formation can also occur in PsA, as evidenced by

the presence of enthesophytes and syndesmophytes that can lead to ankylosis [31]. The role of

innate and adaptive immune mechanisms involved in the processes of joint destruction and new

bone formation are not well known. Cartilage loss during inflammation is associated with

upregulation of various tissue destructive enzymes such as the matrix metalloproteinases

(MMPs) and ADAMTS protease, which are regulated by IL-1 and TNFα [31]. Osteoclasts,

which break down calcified bone, might be involved. These cells differentiate from monocytic

osteoclast precursors (OCPs) upon exposure to monocyte colony stimulating factor (M-CSF) and

receptor activator of NF-κB ligand (RANKL). RANKL is produced by chondrocytes and Th17

cells under inflammatory conditions, and binds to its receptor RANK, which is expressed on the

surface of OCPs. OCPs have been found in increased numbers in the circulation and synovial

lining of PsA patients compared to healthy controls [67]. In PsA, it has been proposed that

monocytes activated by TNFα migrate to the synovium, where they are exposed to M-CSF and

RANKL, differentiate into OCPs, and promote osteolysis and bone resorption [67]. Less is

known about the mechanisms of new bone formation in PsA patients, although it has been shown

that TNFα and IL-1 can upregulate bone and cartilage anabolic cytokines like bone

morphogeninc protein as well as antagonists of the Wnt pathway, an important signaling

pathway in the regulation of bone metabolism [31].

13

Overall, due to the lack of evidence of classical autoimmune mechanisms, both psoriasis and

PsA are currently viewed as ‘autoinflammatory’ diseases that result from tissue-specific

dysregulation and are characterized by both adaptive and innate immune components. However,

the exact contributions of specific immune cell populations, how chronic inflammation is

sustained, the role of epigenetic factors in disease etiology, and the precise link between skin and

joint disease remain poorly understood.

1.2.3 Treatment

Treatment recommendations for PsA have been developed by the European League Against

Rheumatism [68] and the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis

[69]. In both sets of recommendations, treatment begins with non-steroidal anti-inflammatory

drugs (NSAIDs), or if few joints are involved, with intra-articular glucocorticoid injections.

However, neither NSAIDs nor corticosteroids can prevent progression to destructive joint

disease, which may occur in up to 50% of PsA patients. For patients who do not respond to these

first-line therapies, and patients with adverse prognostic factors such as five or more actively

inflamed joints, high functional impairment or damage, disease-modifying anti-rheumatic drugs

(DMARDs) are required [68]. DMARDs can potentially prevent joint damage [69]. Commonly

used DMARDs include MTX, sulfasalazine, lefluonamide, cyclosporine, and azathioprine [32].

MTX is the most commonly used DMARD, although clinical trial evidence for its effectiveness

in treating skin and joint manifestations in PsA is scarce.

If the treatment target of clinical remission or low disease activity is not achieved with more than

one DMARD, if the patient develops toxicity, or if they have predominantly axial disease or

severe enthesitis, biologic therapies are considered [68]. Many of the same biologic drugs used to

successfully treat skin disease are approved in Canada for the treatment of joint disease,

including adalimumab, etanercept, and infliximab, as well as the human monoclonal anti-TNFα

antibodies golimumab and certolizumab [32]. These biologics can be used in combination

therapy with a DMARD, which is oftentimes MTX.

14

1.3 Tools for Diagnosing PsA

1.3.1 Early Diagnosis

Early diagnosis of PsA is beneficial to the patient, while delays can be detrimental, as evidenced

by the fact that patients who wait to consult a rheumatologist more than 6 months after the onset

of symptoms have significantly more peripheral joint erosions evident in radiographs, and worse

health assessment questionnaire scores than those who consult a rheumatologist within 6 months

of experiencing the first symptoms [70]. PsA patients who attend a specialized PsA clinic more

than 2 years after PsA diagnosis show a higher rate of clinical damage progression than patients

who attend the same clinic within 2 years of diagnosis, suggesting that early monitoring and

appropriate treatment is beneficial [71]. It is clear that the presence of PsA in psoriasis patients

needs to be recognized soon after PsA onset in order to begin treatment to control the

inflammatory process and prevent poor clinical outcomes. Unfortunately, there is a general lack

of awareness of the disease, which when compounded by the heterogeneity of disease

presentation, and the absence of diagnostic tools for use by primary care physicians and

dermatologists, exacerbates the clinical problem [72]. Over the past 30 years, the development of

clinical and laboratory tools to aid in the diagnosis of PsA in psoriasis patients has grown into an

extremely active field of research that will be reviewed in the following sections.

1.3.2 Characteristics of Diagnostic Tools

An ideal diagnostic tool must have several characteristics, such as high sensitivity and

specificity, and a high overall ability to discriminate between the presence and absence of

disease. A 2x2 table can be constructed in which the true disease status (presence or absence of

disease) is divided into categories based on the diagnostic test result (positive or negative) (Table

1.1). If the test result is measured as a continuous variable, it must be dichotomized by

establishing an arbitrary cutoff at which patients are classified as positive or negative for PsA.

Patients with the disease who test positive are then classified as true positives (TP), and patients

without the disease who test negative are classified as true negatives (TN). Patients with the

15

disease who test negative are classified as false negatives (FN), and patients without the disease

who test positive are classified as false positives (FP) [73].

The sensitivity or true positive rate of the diagnostic test is defined as the proportion of true

positives that are classified as such, divided by the total number of patients with the disease

(sensitivity = TP/[TP+FN]), and is the probability of the test being positive when the disease is

present. The specificity or true negative rate of a diagnostic test is defined as the proportion of

true negatives classified as such, divided by the total number of individuals without the disease

(specificity = TN/[TN+FP]), which gives the probability of the test being negative when the

disease is absent.

For continuous test results with overlapping distributions among true positive and true negative

individuals, the classification of patients into TP, TN, FP, and FN, and hence the sensitivity and

specificity, are dependent on the cutoff value that determines positive and negative test

categories. The cutoff value can be varied in order to determine the effect on the sensitivity and

specificity, and this can be plotted to generate a receiver operating characteristics (ROC) curve

(Figure 1.1). A calculation of the area under this curve (AUC) is a measure of the overall

discriminatory ability of the biomarker, and can range from 0.5 (no better than chance alone) to

1.0 (perfect discriminatory ability). For diagnostic tests in which positive results are above the

cutoff value, and negative results are below, increasing the cutoff value will increase the

specificity and proportionally decrease the sensitivity of the test, while decreasing the cutoff

value will increase the sensitivity and decrease the specificity of the test. Diagnostic tests

generally aim to achieve both high sensitivity and specificity by balancing this trade-off [73].

However, in diseases such as PsA where there are safe and effective therapies and the poor

consequences of treating an FP patient are few, sensitivity often takes priority [74] as it is

prudent to identify as many potential patients with PsA in order to expedite diagnosis and

prevent accrual of irreversible joint damage.

16

Table 1.1. Performance characteristics of diagnostic tools.

Disease Status

Present Absent

Test Result Positive True Positive (TP) False Positive (FP)

Negative False Negative (FN) True Negative (TN)

True Positive Rate

= TP/TP+FN

“Sensitivity”

True Negative Rate

= TN/TN+FP

“Specificity”

17

Figure 1.1. Example ROC curves illustrating AUCs of 0.5 (Reference Line), 0.67 (Hypothetical

Biomarker), and 1.0 (Perfect Biomarker).

18

1.3.3 The CASPAR Criteria

Currently, the diagnosis of PsA is based on a combination of history and physical examination

by a rheumatologist, as well as radiographic imaging. The Classification of Psoriatic Arthritis

(CASPAR) criteria was published in 2006, and has become the most widely used tool to classify

PsA and aid in its diagnosis [75]. The CASPAR criteria consist of:

Inflammatory musculoskeletal disease of the joints, spine, or entheses, as well as 3 points from

the following:

1. Evidence of psoriasis: current psoriasis (2 points), OR personal OR family history of

psoriasis among first or second-degree relatives (1 point)

2. Psoriatic nail disease (onycholysis, pitting, and hyperkeratosis) (1 point)

3. Negative test for rheumatoid factor (1 point)

4. Current dactylitis, or personal history of dactylitis as recorded by a rheumatologist (1

point)

5. Radiographic evidence of juxta-articular new bone formation in the hand or foot (1 point)

The CASPAR criteria performs with a specificity of 98.7% and sensitivity of 91.4% [75], and

>99% when applied by a rheumatologist [76]. However, given the high prevalence of psoriasis, it

is logistically impossible for every psoriasis patient to be examined by a rheumatologist. As a

result, there is a high rate of undiagnosed PsA in dermatology clinics [77] and likely in the

general population.

1.3.4 Screening Questionnaires

Several screening questionnaires have been developed in the hopes of improving the ease and

quickness with which PsA is diagnosed. Screening questionnaires are advantageous as they do

not require physical examination and are easy to implement in large numbers of psoriasis

patients in dermatology and primary care clinics. Patients identified as having a high probability

of PsA can then be referred to a rheumatologist for a definitive diagnosis and treatment [78]. The

first questionnaire to be developed in 1997 was the Psoriasis Assessment Questionnaire (PAQ),

which performed with a sensitivity of 60% and specificity of 73% in a hospital and community-

19

based psoriasis cohort of 276 individuals [79]. The Psoriatic Arthritis Screening and Evaluation

Tool (PASE) was developed in a combined dermatology-rheumatology clinic at Brigham and

Women’s Hospital in Boston, and functions with a sensitivity of 93% and specificity of 80% in

patients with active disease [80, 81]. The Toronto Psoriatic Arthritis Screen (ToPAS) was

developed at the University of Toronto Psoriatic Arthritis Clinic, and has been validated in

clinics for PsA, psoriasis, general dermatology, general rheumatology, and family medicine. It

was found to perform with an overall sensitivity of 87% and specificity of 93% [82]. Finally, the

Psoriasis Epidemiology Screening Tool (PEST) was developed in Bath, England as a

modification of the PAQ with additional questions about spondyloarthritis and dactylitis. In a

community-based psoriasis sample, the PEST performed with 92% sensitivity and 78%

specificity [83].

A recent study compared the latter three PsA screening questionnaires head-to-head by

administering them to 938 psoriasis patients from secondary care dermatology clinics who were

not previously diagnosed with PsA [84]. In 657 patients who completed all three questionnaires

and were examined by a rheumatologist, 47% were diagnosed with PsA using the gold standard

CASPAR criteria. The PASE performed with a sensitivity, specificity and area under the curve

of 74.5%, 38.5%, and 0.594, the PEST with 76.6%, 37.2%, and 0.610, and the ToPAS with

76.6%, 29.7%, and 0.554. All three screening questionnaires had sensitivities lower than initially

reported, likely because they were being tested in a new population and dataset. The low

specificities of all three questionnaires was the result of high false positive rates, as they

identified several psoriasis patients with osteoarthritis, degenerative arthritis, fibromyalgia,

hypermobility syndrome, avascular necrosis, connective tissue disorder, trauma, and gout [84].

In summary, all three questionnaires still showed adequate sensitivities for the purposes of

screening psoriasis patients, however their specificities and overall performance as diagnostic

tests were low. Further refinement and validation in large epidemiological cohorts is necessary if

they are to be implemented in clinical practice.

20

1.3.5 Biomarkers

A biomarker is defined as an objectively measurable characteristic that indicates a normal

biological process, pathogenic process, or pharmacological response [85]. Based on their

potential clinical uses, there are 4 main types of biomarkers: diagnostic markers, disease activity

markers, drug effect markers, and drug kinetic markers [74]. Diagnostic markers can be used to

determine the presence or absence of disease, assess the degree of disease progression, or

forecast disease severity or expression and suggest the most appropriate therapy. Disease activity

markers can be used to assess the current severity of disease and thus are useful in monitoring

response to treatment. Drug effect markers are typically related to processes directly modulated

by pharmacological therapies, and can be measured to assess the effect of a drug and establish

the required dosage. Finally, drug kinetic markers are typically genetic variants in drug

metabolizing enzymes or transporters, and are studied to assess the causes of inefficacy or

adverse drug effects [74]. Although all aforementioned types of biomarkers could be applied in

the clinical management of PsA, presently, diagnostic biomarkers for the presence of PsA in

psoriasis patients, and prognostic markers of joint damage constitute the most urgent unmet

clinical needs, and as such, they are the most actively pursued in PsA biomarker research [86].

Diagnostic biomarkers have been established in several disease areas such as infectious disease,

cardiovascular disease, cancer, genetic disorders, and auto-immune and inflammatory conditions

such as RA, AS, and SLE. In RA, both anti-citrullinated peptide antibody (APCA) and

rheumatoid factor (RF), an autoantibody against the Fc domain of IgG, are used in its diagnosis

and prognosis. In AS, positivity for carriage of the HLA-B*27 allele serves as a diagnostic

genetic biomarker. In SLE, anti-nuclear antibody and anti-double stranded DNA antibodies are

used for diagnosis. In these examples, biomarkers are informative of certain aspects of the

pathogenic processes of disease. The acute phase reactants C-reactive protein (CRP) and

erythrocyte sedimentation rate (ESR), on the other hand, function as non-specific markers of

inflammation that are not related to specific pathogenic factors, and are used in several auto-

immune and inflammatory disorders to assess disease activity [87].

21

In PsA, RF positivity is an exclusion criteria in the CASPAR classification, thus unsurprisingly,

it is found in only 2-16.5% of PsA patients and is a poor diagnostic marker of PsA. Similarly,

APCA positivity is found in only 5-16% of PsA patients. ESR and CRP are normal in 50% of

PsA patients with active disease [86]. However, highly sensitive CRP is significantly elevated in

PsA patients compared to patients with psoriasis alone, and thus might be a biomarker of the

increased inflammatory burden of PsA [88]. ESR and CRP might be better markers of disease

activity, as shown by their correlation with number of involved joints. Furthermore, CRP along

with scores from the Health Assessment Questionnaire Disability Index has been shown to be

predictive of clinical improvements with anti-TNFα biologic drugs in patients with peripheral

polyarthritis [89].

Several clinical variables have been examined as possible predictors of PsA in patients with

psoriasis. These include the presence of psoriatic nail lesions [90, 91], scalp, intergluteal, or

perianal psoriasis [90], use of corticosteroids [92], psoriasis severity as measured by psoriasis

area and severity index (PASI) score [93], obesity, lower level of education [57], and subclinical

enthesitis [94]. As reviewed above, environmental exposures have also been examined as

predictors of PsA. It has been found that trauma, a change of residence, rubella vaccination,

heavy lifting, infections and family history of PsA may be predictive of PsA. However, odds

ratios for the association of clinical variables and environmental exposures with PsA are

typically low, implying small effect sizes that may be of little practical use.

As reviewed, genetic risk factors for PsA have also been examined. Thus far, the strongest

genetic predictor of PsA is HLA-B*27 carriage, with odds ratio estimates ranging from 2.6 to 5.2

for the association with PsA compared to psoriasis patients [95, 96]. Although it is strongly

associated with PsA, the frequency of HLA-B*27 positivity in PsA patients ranges from 16-35%

depending on the population in question, suggesting that it would perform with low sensitivity as

a general biomarker for PsA. However, the frequency and association of HLA-B*27 positivity is

higher in patients with the isolated axial form of PsA, suggesting that it may be suitable as a

biomarker specific to spondyloarthritis.

22

Although the current knowledge of the cellular pathogenesis of PsA is scant, cellular biomarkers

related to the pathogenic process have been discovered. Circulating OCPs, which are thought to

be involved in joint destruction and cartilage loss in PsA, were found to increase in psoriasis

patients during their transition to PsA [97]. Furthermore, by flow cytometric staining of

monocytes with anti-CD14 and anti-dendritic cell-specific transmembrane protein antibodies,

OCPs have been found to be elevated in the peripheral blood of psoriasis patients who later

developed PsA compared to psoriasis patients who did not develop PsA [98].

23

1.4 Molecular Biomarkers of PsA

1.4.1 Biomarker Discovery Pipeline

In addition to the genetic and cellular markers of PsA already discussed, several powerful

molecular approaches have emerged that enable examination of gene expression, protein

expression, and their regulation through DNA methylation. These approaches can be categorized

as “hypothesis-driven”, wherein candidate loci are chosen for analysis based on prior evidence,

or “hypothesis-generating”, which employs a broader strategy of examining the entire genome

and systematically narrowing down candidate loci to discover novel associations with disease

and refining or extending initial hypotheses [99]. Hypothesis-generating approaches have the

additional benefit of potentially providing new insights into the etiology of disease and its

pathogenic mechanisms.

Hypothesis-generating approaches typically follow a common pipeline of experimental steps

comprised of discovery, technical verification or confirmation, and replication and validation

phases. In the discovery phase, comprehensive transcriptomic, proteomic, or epigenetic profiling

is performed in human tissues, biological fluids, cultured cells, or cell supernatants, and tens to

thousands of differentially expressed or marked genes or proteins are identified. Identified genes

or proteins are then annotated and data mined using bioinformatics analyses, literature searches,

or other rational criteria to generate a shortened, prioritized list of tens of candidates for

verification. In the verification phase, candidates are measured using a high accuracy technique,

ideally the gold standard for the molecule of interest, in the same samples used for discovery in

order to confirm the initial findings. In the replication phase, verified candidates are tested in a

larger, independent set of samples to assess the replicability and generalizability of the initial

findings to a broader population. At this stage, ROC analysis can be performed to estimate the

performance characteristics of the candidate biomarkers, and the majority of candidates are

usually discarded due to low discriminatory ability and lack of statistical significance. Finally,

validation phases consist of testing top candidates in additional large populations to determine

their practicality and clinical usefulness [100].

24

1.4.2 Gene Expression Biomarkers

The human transcriptome refers to the entire collection of RNA molecules encoded by the

human genome. It consists of approximately 30,000-40,000 RNA-coding genes that include

messenger RNAs (mRNAs), which encode protein products, and non-coding RNAs (ncRNAs),

which play structural or regulatory roles in the cell. The exact number and identity of mRNAs

varies across tissues and stages of cell differentiation. Furthermore, more than 90% of mRNAs

and 30% of ncRNAs undergo alternative splicing, during which different exons of an RNA

transcript are combined to create unique mature mRNAs. If these are taken into account, the

current annotated human transcriptome consists of 111,451 unique mRNA and 101,347 unique

ncRNA transcripts [101].

Profiling gene expression in human tissues is one approach taken for biomarker discovery and

for gaining understanding of disease pathogenesis, because of its ability to investigate the

convergent effects of genetic variants on the expression of single transcripts and groups of

functionally related transcripts. Peripheral blood gene expression profiling has been used

extensively in autoimmune and inflammatory disorders, such as type I diabetes (T1D), in which

expression profiling identified a signature upregulated by IL-1β that distinguished patients from

unaffected controls and at-risk relatives of patients from controls [102], as well as a signature of

IFN responsive genes identified in pre-diabetic individuals, supporting a pathogenic mechanism

similar to SLE and Sjogren’s syndrome [103-105]. In multiple sclerosis, blood expression

profiling identified differentially expressed genes involved in T-cell activation, which supports

the involvement of autoreactive T cells its pathophysiology [106, 107]. Moreover, gene

expression profiling has yielded numerous potentially clinically useful patented biomarkers or

gene expression signatures including, but not limited to, those for diagnosing and monitoring

treatment efficacy in Alzheimer’s disease, diagnosing autism spectrum disorders, and diagnosing

high-risk human papilloma virus infection. Recently, a gene expression biomarker signature

called PAM50, the basis of the Prosigna® test, gained US Food and Drug Administration and

Health Canada approval to be used for clinical prognosis of 10-year risk of distant recurrence of

invasive breast cancer.

25

1.4.2.1 Techniques for Analyzing Gene Expression

Hybridization-based gene expression microarrays enable locus-by-locus detection of expression

across a large fraction of the human transcriptome, and are thus well suited for biomarker

discovery. The Agilent 4x44k v2 microarray introduced in 2009 is one such example, covering

approximately 41,000 different transcripts from 27,958 Entrez Gene mRNAs. The Agilent 4x44k

platform was designed based on RefSeq Build 36.3 and uses 60-mer oligonucleotide probes

printed onto glass slides using a process analogous to inkjet printing. Probe design takes into

account multiple alternatively spliced transcripts so many genes are represented by more than

one probe. The Agilent 4x44k platform has been shown to have a high sensitivity of 1 transcript

per cell per million cells, and a large dynamic range covering 3 orders of magnitude. In addition,

it is amenable to a two-colour experimental design wherein each sample is separately labeled

with a fluorophore such as Cy5 and co-hybridized to the same array with a reference RNA

sample labeled with Cy3. Gene expression is quantified by measuring the amount of

fluorescence signal of each gene in each sample, normalized to the fluorescence signal within the

reference sample.

Techniques for analyzing gene expression on a smaller scale, which is practical for verification

and validation steps, includes the gold standard real-time PCR, which measures expression of

individual loci using locus-specific PCR primers coupled with the non-specific DNA binding dye

SYBR green or locus-specific Taqman® probes to quantitate the amount of RNA molecules

present in a sample. Commercial low-density targeted real-time PCR arrays have also been

developed that allow for simultaneous quantitation of tens of genes belonging to related

biological functions or pathways, which is suitable for small-scale discovery or microarray

verification.

Real-time PCR-based techniques rely on reverse transcription of RNA molecules and PCR

amplification, which may not be possible in degraded RNA samples, may introduce

amplification biases and lead to experimental artifacts, and can be time consuming and cost

prohibitive for large numbers of genes or samples. In 2008, an amplification-free digital gene

26

expression profiling platform was described [108]. The NanoString nCounter® system uses a

pair of sequence-specific reporter and capture probes. Reporter probes are 50-mer oligos

complementary to the RNA of interest, linked to a unique string of 7 fluorophore-labeled RNA

segments that serves as a molecular barcode. Capture probes are 50-mer oligos that carry a biotin

label at their 3’ end (Figure 1.2). RNA test samples are hybridized to probes, excess probes are

removed, and the resultant tripartite structures are captured with a streptavidin-coated slide. An

electric current is then applied to elongate and align each RNA molecule, which are imaged and

each molecular barcode quantified to yield gene expression counts. The NanoString nCounter®

system enables multiplexed measurement of up to 800 RNA molecules in a single sample and

correlates highly with gold-standard real-time PCR measurements (R2=0.95). Furthermore, the

nCounter® has an extremely high sensitivity of 0.2-1 RNA molecule per cell, and a broad linear

dynamic range of over 500-fold [108].

27

Figure 1.2. Principle of the NanoString nCounter® gene expression profiling technology

(Standard chemistry).

28

1.4.2.2 Gene Expression Studies in PsA

Several gene expression microarray studies in PsA have been performed and have provided

insights into its immune-mediated pathogenesis as well as candidate biomarkers. The earliest

study analyzed expression differences in peripheral blood mononuclear cells (PBMCs) between

patients with AS, undifferentiated spondyloarthropathy, RA, PsA, and healthy controls using a

588-gene array [109]. Expression of the chemokine receptor CXCR4 was validated to be

significantly increased >5 fold in all types of arthritis compared to controls. In another study, the

proinflammatory genes S100A8, S100A12 and thioredoxin were increased in PsA patients

compared to healthy controls. Genes involved in MAP kinase signaling, B cell maturation,

activation, and signaling, antigen presentation (HLA-E, -B, -DQA, -DMA), ubiquitination,

apoptosis, and RNA trafficking were decreased in PsA patients compared to healthy controls.

NUP62 was the strongest gene expression biomarker of PsA, correctly classifying 95% of PsA

patients separately from controls [110]. A subsequent study using whole blood RNA collected in

PAXgene stabilizing tubes identified 310 differentially expressed genes in PsA with >2-fold

difference, most of which were not found in RA and SLE, suggesting disparate pathogenic

mechanisms. ZNF395 and phosphoinositide-3-kinase 2B could discriminate between PsA and

healthy controls by logistic regression, suggesting that gene expression can be applied to PsA

diagnosis. Differentially expressed genes were implicated in functions such as apoptosis, cell

adhesion, cytokine/chemokine signaling, G-protein signaling and adaptive immunity. A subset of

genes was also found to correlate with ESR, and thus may be reflective of inflammation [111].

A more recent microarray study examined whole blood changes in gene expression in PsA

patients, and PsA patients receiving MTX or anti-TNF biologic treatment. Compared to healthy

controls, 128 genes were differentially expressed in PsA patients. These genes were involved in

processes such as cell proliferation, apoptosis, keratinocytes, basophiles, cell adhesion, and

inflammation. Fifty-five genes were differentially expressed in PsA patients taking MTX, and

these were involved in processes including cell proliferation, T cell functioning, cytokines, and

antigen presentation. In PsA patients taking anti-TNFs, 188 genes were differentially expressed,

including genes with the same functions to those differentially expressed in MTX-treated

patients, as well as genes involved in keratinocytes, apoptosis, angiogenesis, viruses, osteoclasts,

29

and neutrophils [112]. Lastly, a recent hypothesis-driven study used real-time PCR to assess

differences in PBMC expression of genes involved in bone remodeling between PsA patients and

controls. Expression of bone morphogenetic protein 4 (BMP-4), a TGF-B family protein

involved in new bone formation was positively correlated with patient assessed disease activity,

while Runx2, a master transcription factor controlling osteoclast differentiation, was found to be

negatively correlated with enthesial pain [113].

1.4.2.3 Limitations of Previous Gene Expression Studies

Previous studies have provided ample evidence that gene expression profiling is a robust

technique for uncovering aspects of disease pathogenesis and discovering candidate biomarkers

of PsA. However, studies performed thus far have been limited to comparisons of PsA to other

inflammatory arthropathies or healthy controls. Such comparisons are confounded by the

concomitant skin and joint manifestations of PsA, making it difficult to draw conclusions about

the relationship between gene expression and joint disease specifically. In order to gain insight

into the specific pathogenic mechanisms and biomarkers of PsA, it is necessary to directly

compare gene expression profiles of PsA and psoriasis patients without arthritis, which has not

been done previously. Chapter 3 of this thesis describes the first whole transcriptome comparison

of PsA patients, psoriasis patients, and healthy controls using Agilent 4x44k microarrays, the

identification of candidate gene expression biomarkers of PsA and their subsequent verification

and validation by real-time PCR and nCounter® technology, as well as an assessment of

biomarker performance in an independent set of patients.

1.4.3 Protein Biomarkers

Proteins are attractive biomarkers for PsA because they are quantifiable in easily accessible

tissues such as blood serum or plasma. Numerous clinical laboratory tests are currently based on

measuring protein levels, so new protein-based biomarker tests can be integrated into routine

clinical laboratory workflows without difficulty. Unfortunately, high-throughput proteomic

techniques lag somewhat behind transcriptomic techniques due to, until recently, the

30

unavailability of a comprehensive map of the human proteome. The first draft of the Human

Proteome Map was published in 2014 and consists of proteins from 30 human tissues encoded by

17,294 genes, or approximately 84% of known protein-coding genes [114].

1.4.3.1 Techniques for Analyzing Protein Expression

Mass spectrometry is the most common high throughput analytical method for proteins. In mass

spectrometry-based techniques, a protein sample is first fractionated and highly abundant

proteins are removed. Remaining proteins are proteolytically cleaved into peptides, ionized, and

identified and quantified using their mass to charge ratios. An alternative route is to use gene

expression microarrays as a surrogate for proteomic studies, providing the initial the discovery

step. Candidate genes identified as differentially expressed at the RNA level can then be verified

and validated at the protein level in subsequent experimental steps.

Hypothesis-driven approaches are more common in protein biomarker research, and typically

employ the gold standard low-throughput enzyme-linked immunosorbent assays (ELISAs) to

measure levels of soluble protein in patient serum. Microsphere-based immunoassays are a

newer, medium-throughput alternative for measuring soluble proteins (Figure 1.3). Microsphere-

based immunoassays are based on analyte-specific capture antibodies conjugated to coloured

microspheres. When added to a serum sample, capture antibodies bind the analyte of interest,

and after addition of a biotinylated secondary antibody, form a complex analogous to a sandwich

ELISA. Upon addition of phycoerythrin-conjugated streptavidin, a fluorescent signal is

generated. Using a dual-laser platform such as the Luminex 200, both the magnitude of the

fluorescent signal (proportional to the amount of analyte present in the sample) and the colour of

the bead labeling the capture antibody (indicating the specific analyte being tested) can be

determined. Advantages of microsphere-based immunoassays include the absence of steric

factors, since the capture antibody is not physically bound to the test plate, higher accuracy,

sensitivity, and multiplexing capabilities.

31

Figure 1.3. Principle of microsphere-based immunoassays.

32

1.4.3.2 Protein Biomarker Studies in PsA

Thus far few published studies have performed liquid chromatography followed by tandem mass

spectrometry to analyze the whole proteome of PsA patients. In one study that used pooled

psoriatic skin samples from 10 PsA and 10 psoriasis patients, 47 upregulated proteins in PsA

patients were identified. Eight of these proteins were confirmed as differentially expressed in

psoriatic skin from an independent set of 5 PsA and 5 psoriasis patients, and 2 proteins, ITGB5

and POSTN were further confirmed in the serum of an independent set of 33 PsA and 15

psoriasis patients using ELISA and microsphere-based immunoassays [115]. In a second study,

MS was used to investigate the synovial tissue proteome in PsA patients who did and did not

respond to biologic treatments. A panel of 57 proteins was found to be predictive of response to

treatment with an AUC of 0.76 [116].

Traditional ELISA studies have found that serum levels of the cytokine IL-6 are significantly

higher in PsA patients compared to patients with psoriasis alone, and correlate with joint count,

ESR, CRP, and serum levels of IL-2Ra [117]. Levels of hs-CRP, osteoprotegrin (OPG, also

known as TNFRSF11B), matrix metalloproteinase 3 (MMP3), and the ratio of C-propeptide of

type II collagen (CPII) to collagen fragment neoepitopes Col2-3/4 (C2C) are also significantly

associated with PsA compared to patients with psoriasis alone. When used in a combined ROC

analysis, these latter 4 proteins perform with an AUC of 0.904 [88]. This result awaits validation

in additional cohorts.

Microsphere-based immunoassays have also been used to profile various inflammatory cytokines

in psoriasis serum, identifying higher levels of IFN-γ, IL-1RA, IL-2, IL-23, and LL-37 in

patients compared to control serum. Cytokine levels are positively correlated with PASI score

[118]. Microsphere-based immunoassays have also been used to show increased serum levels of

interleukin IL-10, IL-13, IFNα, epidermal growth factor (EGF), vascular endothelial growth

factor (VEGF), fibroblast growth factor [CCL3 macrophage inflammatory protein (MIP)-1a],

CCL4 (MIP-1) and CCL11 (Eotaxin), and decreased serum levels of granulocyte-colony

stimulating factor in PsA patients compared to controls [119].

33

1.4.3.3 Limitations of Previous Protein Studies

The majority of protein biomarker studies in PsA have relied on limited knowledge of PsA

pathogenesis to select candidate proteins to test using low-throughput assays. Thus far, only two

hypothesis-generating proteome-wide studies have been performed in PsA. No study has

examined whether candidate gene expression biomarkers can be translated into soluble protein

biomarkers of PsA, or whether soluble proteins can predict which psoriasis patients are destined

to develop PsA. Chapter 4 of this thesis describes a novel use of microsphere-based

immunoassays to examine the predictive ability of a candidate gene expression biomarker of

PsA, measured at the protein level, by profiling serum differences in pre-and post-PsA

conversion samples from longitudinally-followed psoriasis patients who developed PsA, and a

comparison to baseline samples from psoriasis patients who do not develop PsA.

1.4.4 DNA Methylation Biomarkers

1.4.4.1 General Epigenetic Principles

Epigenetics refers to partially stable modifications of DNA and histone proteins that are

meiotically or mitotically heritable and function in genomic regulation. The most common DNA

modification is methylation of cytosine residues occurring in the context of cytosine-guanine

dinucleotides (CpGs). Post-translational histone modifications are numerous and complex, and

include acetylation, methylation, phosphorylation, ubiquitination, and sumoylation of lysine,

arginine, serine, or threonine residues. DNA and histone modifications act synergistically to

regulate functions such as DNA repair, replication, and gene expression [120].

Methylation at a particular CpG dinucleotide is a binary mark that is averaged across the

sampled cells to yield a continuous value from zero to 100% cellular methylation. CpG

dinucleotides are known to be concentrated in repetitive sequences and regions overlapping with

gene promoters, called CpG islands. Methylation density at these locations inversely correlates

34

with gene expression, serving to repress transcription by hindering transcription factor binding,

and by recruiting various methyl-binding domain proteins, which assemble large complexes that

deacetylate histones to condense chromatin. CpG methylation is catalyzed by enzymes called

DNA methyltransferases (DNMTs), which use S-adenosylmethionine as a methyl donor to

maintain methylation marks during DNA replication (DNMT1) or create new methylation marks

(DNMT3A and 3B) [121]. CpG methylation patterns are partially responsible for establishing

and maintaining the cellular identity of each type of human cell, and are thus cell-type specific,

but dynamically change over time through human development [120].

Proper setting of DNA methylation marks is essential to the normal functioning and regulation of

the human genome. Meiotically and/or mitotically heritable gains or losses of DNA methylation,

called epigenetic mutations or ‘epimutations’, can result in a change in gene activity that may be

deleterious to an organism. Epigenetic marks are far more plastic and dynamic than the DNA

sequence itself, and can be influenced by environmental or stochastic factors internal and

external to an organism [122]. This metastability results from the low fidelity and efficiency of

DNMT1, which when associated with transcription factor complexes involved in tissue-specific

gene regulation, can lead to tissue and locus-specific epigenetic deregulation [120].

Epigenetics is of considerable interest in complex diseases in which genetic variants cannot

explain 100% of disease susceptibility, due to its potential to provide insight into the origins and

progression of complex diseases, as well as candidate biomarkers for diagnosis and prognosis.

Many features of common complex diseases such as autoimmune and autoinflammatory diseases

like psoriasis and PsA strongly suggest that epigenetic deregulation plays a role in disease

etiology and pathogenesis. The fluctuating disease course of psoriasis and PsA suggests dynamic

changes in gene regulation, while the low disease concordance rates among MZ twins with

psoriasis and PsA suggests that non-shared environmental factors might influence the genome,

possibly through epigenetic mechanisms. The epigenetics of cancers has been extensively

studied, but in contrast, relatively few studies have examined the epigenetic origins of

autoimmune or autoinflammatory conditions. Those that have been performed have focused

mainly on SLE and RA [120]. There is increasing recognition of the importance of epigenetics in

35

the differentiation and functioning of immune cells, including those implicated in the

pathogenesis of autoinflammatory disorders [123]. Studies in RA have demonstrated

hypomethylation of IL-6 in peripheral blood mononuclear cells of patients associated with

hyperactivation of inflammation [124], retrotransposable long interspersed nuclear element 1

(LINE-1) associated with invasive RA synovial fibroblasts [125], and hypermethylation of the

death receptor-3 (DR-3) locus associated with resistance to apoptosis in RA monocytes [126].

Several patented biomarkers based on DNA methylation exist for the diagnosis of small cell lung

cancer, the detection of cancer metastases, and the diagnosis or prediction of post-partum

depression and colorectal cancer, to name a few.

1.4.4.2 Epigenetic Inheritance and the Parent-of-Origin Effect

As discussed in Chapters 1.1 and 1.2, it has been proposed that the parent-of-origin effect

observed in psoriasis, and possibly PsA, might indicate a role for epigenetic phenomena such as

genomic imprinting. Genomic imprinting occurs in mammals, insects, and flowering plants, and

refers to monoallelic expression of a gene that depends on the parental origin of the allele.

Genomic imprinting results from different epigenetic states of maternal and paternal alleles that

were established in the parental gametes, inherited, and maintained in adult somatic tissues in the

next generation [127]. In mice, establishment of parental imprints begins as primordial germ

cells start to differentiate around embryonic day 7.25 (E7.25). At this stage, epigenetic marks and

imprints inherited from the previous generation are erased by extensive epigenetic

reprogramming that involves loss of histone modifications and DNA methylation. The

mechanisms governing the erasure of DNA methylation marks are poorly understood, but may

involve the actions of the cytidine deaminase AID and the methyl binding domain protein 4

(MBD4), which is a mismatch-specific thymine glycosylase [128], as well as through conversion

of 5-methylcytosine to 5-hydroxymethylcytosine by TET proteins [129]. After sex determination

occurs at E12.5, DNA methylation patterns specific to oocytes or sperm, as well as the

appropriate imprints are re-established [128]. Imprinted genes are clustered in domains of up to

several megabases of DNA, each controlled by an imprinting control region (ICR). Re-

establishment of parental imprints at ICRs is mediated by DNMT3A as well as DNMT3L, a

DNA methyltransferase-like protein that lacks catalytic activity and likely serves to recruit

36

DNMT3A to the ICR [128, 130]. ICRs that are methylated on the maternal allele are called

maternally imprinted, with expression from only the paternal allele, whereas ICRs methylated in

the paternal allele are paternally imprinted and expressed only from the maternal allele. These

marks are maintained after fertilization and persist into the somatic cells of the next generation,

but are erased and reprogrammed once again in the primordial germ cells.

The number of imprinted genes in the mammalian embryo is currently estimated to be around

100, however recent evidence suggests that additional genes may show tissue-specific imprinting

in adult somatic tissues [128]. Disorders caused by defects in imprinted genes are generally quite

rare, appearing in 1/10,000 to 1/75,000 individuals [131]. These typically result from genetic

abnormalities and are thus termed ‘secondary epimutations’, and include large chromosomal

deletions containing imprinted genes, uniparental disomy, exposure of deleterious loss-of-

function mutations on the expressed allele, or in rare cases, genetic mutations in ICRs that impair

the erasure and resetting of imprints in the germ line. Aberrant gains or losses of DNA

methylation in the absence of underlying genetic causes, called ‘primary epimutations’, can also

result in ectopic expression of parental alleles at imprinted genes. Furthermore, a primary

epimutation (aberrant methylation of the maternal allele) in the ICR of H19 results in loss of

imprinting of both the H19 and IGF2 genes and results in Wilms tumour, while loss of

imprinting at IGF2 has been found in various forms of cancer. Genetic variants within imprinted

loci also show parent-of-origin-specific associations with risk of common diseases such breast

cancer, basal cell carcinoma, and types 1 and 2 diabetes [132, 133].

In addition to the the epigenetic reprogramming event that occurs in the primordial germ cells

during gametogenesis, which functions to erase and reset parental imprints depending on the sex

of the gestating fetus, another wave of epigenetic reprogramming occurs in the fetus immediately

following fertilization. This reprogramming event serves to prevent vertical transmission of

epimutations present in the gametes across generations. However, there is some evidence in

mammals that primary epimutations at non-imprinted genes may be able to resist epigenetic

reprogramming and be transmitted vertically, with phenotypic consequences for the next

generation. For example, in mice, insertion of an intracisternal A particle (IAP) retrotransposon

37

upstream of the agouti coat colour locus results in the creation of the agouti viable yellow (Avy)

allele whose expression is controlled by the methylation status of a cryptic promoter contained

within the IAP. Isogenic Avy mice have coats that range in colour depending on the methylation

status of the IAP, from yellow (unmethylated), to variegated (intermediate methylation), to

pseudoagouti (methylated). Following transmission of the Avy allele through the germ line of

males of all colours, the same range of 40% yellow, 45% mottled, and 15% pseudoagouti coat

colours are observed. However, following transmission through the female germ line, yellow

dams produce no pseudoagouti offspring, and pseudoagouti dams produce a higher percentage of

pseudoagouti offspring, suggesting that there is a failure to reset IAP methylation marks in the

female germ line [134]. Notably, the IAP within the Avy allele has been shown to be sensitive to

maternal nutrition, leading to changes in coat colour in the offspring [135, 136]. Methylation

status of another IAP element in the 5’ region of the axin fused (AxinFU) allele, responsible for

embryonic axis formation in mice, results in the expression of aberrant transcripts and a kinked-

tail phenotype. The methylation status of the IAP in sperm cells reflects its status in somatic

tissues. In contrast to Avy, the methylation status of the IAP in AxinFU can be inherited through

both maternal and paternal transmissions, however the penetrance of the kinked-tail phenotype is

higher following paternal transmission [137]. It has thus been suggested that parent-of-origin

effects may arise due to a differential resistance of IAPs to epigenetic reprogramming in the male

and female germ lines during gametogenesis and post-fertilization [127, 137]. Avy and AxinFUare

examples of ‘metastable epialleles’—epigenetic polymorphisms that are set stochastically,

display variable expressivity in genetically identical individuals, are environmentally-labile, and

are potentially heritable.

Other studies in rodents have provided evidence that environmentally-induced primary

epimutations might also be vertically transmitted. Low dietary folate intake in male mice is

associated with aberrant methylation of genes in sperm cells, as well as in the placenta of

offspring of folate deficient sires, suggestive of vertical transmission [138]. Exposure of

gestating female F0 rats to the endocrine disruptors dioxin and methoxychlor have been shown to

increase the incidence of kidney disease, polycystic ovary disease, and obesity in F3 progeny,

and furthermore, dioxin, methoxychlor, and vinclozolin have been shown to alter DNA

38

methylation in sperm cells of the F3 progeny [139-141], suggesting that ancestral environmental

exposures can promote vertical transmission of epimutations.

The contribution of vertical transmission of primary epimutations or metastable epialleles to

human disease is not known. Studies in hereditary nonpolyposis colorectal cancer (HNPCC)

identified aberrant methylation of the promoter of DNA mismatch repair gene MSH2 in normal

colonic mucosa and PBMCs of a family with autosomal dominant inheritance of the disease

[142]. While initially thought to be an example of a heritable primary epimutation, it was later

found to be a secondary epimutation resulting from a deletion in the upstream gene TACSTD1

causing aberrant methylation of the MSH2 promoter [143]. MLH1, another gene associated with

HNPCC, was found to be hypermethylated in colorectal cancer cell lines as well as PBMCs,

buccal cells, and normal colonic mucosa of patients, suggesting that it is a soma-wide

epimutation and thus might have arisen in the germ line [144]. Interestingly, in one family this

epimutation was passed from an affected mother to one of three sons, but there was no evidence

of the epimutation in the spermatozoa of the affected son, suggesting erasure in the male germ

line [145]. Further support for the presence of heritable epigenetic information in humans comes

from a study showing that methylation profiles are more similar between monozygotic twins

than dizygotic twins, independent of shared DNA sequence [146], as well as the identification of

HCG9 hypomethylation across multiple tissues, including post-mortem brains, PBMCs, and

sperm cells of patients with bipolar disorder [147].

1.4.4.3 Techniques for Measuring DNA Methylation

Hypothesis-generating epigenetic studies have been enabled by the advent of several

technologies for epigenome-wide interrogation of DNA methylation. These technologies can be

broadly classified into those based on next-generation sequencing or microarrays. Epigenomic

microarray experiments involve initial steps to differentiate methylated and unmethylated CpG

sites by affinity enrichment of either the methylated or unmethylated fraction of the genome, by

selective cutting of non-methylated consensus sequences using methylation sensitive restriction

39

enzymes, or by treatment with sodium bisulfite, which deaminates unmethylated cytosines to

uracil but leaves methylated cytosines intact.

The Illumina Infinium® HumanMethylation 450k Bead Chip, first described in 2011, is a

commonly used array that relies on sodium bisulfite conversion, followed by whole genome

amplification, fragmentation, and hybridization to an array [148]. The Infinium® array is

comprised of 485,577 assays covering 99% of RefSeq genes averaging 17.2 probes per gene

region, and 96% of CpG islands from the UCSC database. Additional assays represent 2kb

regions flanking CpG islands (shores), 2kb regions flanking shores (shelves), and biologically

significant non-CpG sites, DNase hypersensitive sites, and known differentially methylated

regions. Two types of assays are employed on the array. Infinium I assays are one-colour assays

consisting of a pair of unmethylated and methylated bead types for a particular CpG site, linked

to a 50-mer oligonucleotide probe whose 3’ end contains either adenine or guanine that sits atop

the CpG site. If the CpG site is unmethylated, it has been bisulfite converted to uracil, and will

be recognized by the unmethylated probe containing a 3’ adenine. Single base extension of the

probe generates a fluorescent signal. Conversely, if the CpG site is methylated, the methylated

probe containing a 3’ guanine binds and generates a fluorescent signal. Infinium II assays are

two-colour assays consisting of a single bead type that recognizes both unmethylated and

methylated CpG sites, however the 3’ end of the probe sits atop the base directly upstream of the

CpG site. Single base extension with adenine (labeled red) or guanine (labeled green) will result

in either red or green fluorescence depending on whether the CpG site was methylated or

unmethylated. These assays have a high reproducibility, and correlate well with data generated

from whole genome bisulfite sequencing [148]. Overall, the Infinium® array provides a means

of profiling DNA methylation at single CpG site resolution that is efficient, robust, and

comprehensive in terms of assessment of CpG sites that are known to be biologically relevant.

Bisulfite conversion is the basis for most locus-specific DNA methylation techniques and is

considered the gold standard. Following bisulfite conversion of whole genomes, specific

candidate regions can be interrogated by PCR using primers specific to methylated and

unmethylated sequences, or by pyrosequencing or Sanger sequencing.

40

1.4.4.4 Bioinformatics Tools for Analyzing DNA Methylation

The large amount of data generated by epigenome-wide association studies has necessitated the

development of computational tools for quality control, preprocessing, and statistical analysis of

the data. Numerous analysis packages based on the R programming language are now available

through the open-source Bioconductor project, and include the lumi and methyAnalysis packages

adapted and created, respectively, for the Illumina Infinium® HumanMethylation 450k Bead

Chip [149, 150]. The lumi package provides functions for quality control and data preprocessing

steps, and is used in conjunction with methyAnalysis, which provides functions for statistical

testing. A third package, genefilter, can be used in intermediate steps for performing probe

filtering based on user-defined criteria [151].

The default readout to quantify methylation levels from the Infinium® platform is the Beta-

value, which is a ratio of the fluorescence intensities of the methylated probe to the sum of the

methylated and unmethylated probes, or the total probes. The Beta-value of the ith measured CpG

site is given by:

Betai = yi,methy / (yi,unmethy + yi,methy)

Where yi,methy and yi,unmethy represent the intensities of the ith methylated and unmethylated probes.

Beta-values range from 0 to 1 and are interpreted as a percent methylation. Beta-values have

been shown to be heteroescedastic, that is, have unequal variances across its range of values,

particularly in the low and high methylation ranges, which makes the application of statistical

tests challenging. The lumi package defines a new class called MethyLumiM, which holds

methylation data in a matrix of M-values. M-value is the log2 ratio of the methylated probe

intensity to the unmethylated probe intensity, and has been shown to be homoscedastic across the

entire methylation range, making it more statistically valid for differential methylation analysis

[152]. The M-value of the ith measured CpG site is given by:

Mi = log2 (yi,methy / yi,unmethy)

41

However, M-values are difficult to interpret biologically, and therefore can be converted to Beta-

values for reporting. The relationship between Beta-value and M-value for the ith CpG site is a

logit transformation given by:

Mi = log2 (Betai / 1-Betai)

Quality control functions that can be employed in lumi include checking the overall sample

distributions before preprocessing using principal component analysis, checking that the M-value

distribution is bimodal using a probability density function, and checking that the colour

distributions of the red and green channels are balanced using boxplots for each sample.

Unbalanced colour distributions might arise due to differences in labeling efficiencies and

scanning properties of the dyes, and are particularly important to adjust by normalization within

and between samples if imbalanced, because for Infinium II probes, methylation levels are

estimated based on the ratio of methylated to unmethylated probes measured by each colour

[153].

Data preprocessing steps involve background correction, normalization, and probe filtering.

Background correction can be performed by subtracting the median of the negative control

probes for each colour channel. Normalization is performed through quantile normalization of

the methylated and unmethylated probes. Filtering can then be performed using the genefilter

package to remove poor quality or uninformative probes in order to reduce the number of CpG

sites carried forward for statistical analyses. Common practices include removing samples that

failed in the nth% of probes or probes that failed in the nth% of samples, removing probes that

cross-hybridize to multiple genomic locations, probes with a SNP in the CpG site or single base

extension site, and probes containing SNPs having a minor allele frequency of >1%. Another

common practice is to remove probes with the lowest variation across samples measured by

interquartile range.

42

The methyAnalysis package provides functions for identifying differentially methylated regions

based on preprocessed input data from lumi. methyAnalysis is useful for first reducing

measurement noise within DNA methylation data using sliding window smoothing that takes

into account the strong correlation between methylation status of nearby CpG sites. Next,

differential methylation can be tested using a Student’s t-test, and differentially methylated

regions (DMRs) can be identified by merging significant probes into continuous regions. Finally,

methyAnalysis enables a detailed annotation of the genes or gene elements (promoters or exons)

contained within each DMR [150].

1.4.4.5 Epigenetic Studies in Psoriasis and PsA

Global methylation describes the overall amount of 5-methylcytosine across all CpG sites in the

genome. Global methylation has been studied in peripheral blood mononuclear cells (PBMCs) in

the context of response to methotrexate (MTX) therapy in PsA patients. MTX inhibits

dihydrofolate reductase, the enzyme responsible for reducing dihydrofolate to tetrahydrofolate,

the coenzyme of folate. Folate is important in the synthesis of methioinine, the precursor of the

S-adenosylmethionine (SAM), which acts as the methyl donor in the transmethylation reaction

that produces 5-methylcytosine. MTX treatment was thus hypothesized to lead to global DNA

hypomethylation by reducing intracellular folate levels in PsA patients. Interestingly, the

opposite was found—PsA patients not receiving MTX displayed global hypomethylation

compared to patients receiving MTX and healthy controls. This suggested that (1) inflammatory

arthritis may be characterized by global hypomethylation of PBMCs, which could be related to

its pathogenesis, and (2) the therapeutic efficacy of MTX may be related to the reversal of

hypomethylation associated with inflammation [154]. A later study in psoriasis patients found

hypermethylation in both psoriatic PBMCs and lesional skin compared to controls, as well as a

positive correlation between 5-methylcytosine levels and PASI score in skin but not PBMCs

[155].

Epigenome-wide studies in psoriasis have been performed using psoriatic skin and mesenchymal

stem cells. One study found a large number of differentially methylated genes between psoriatic

43

skin and skin from healthy controls (1,108), between psoriatic and uninvolved skin from the

same patients (27 genes including MCL2, LAMA4, SYNPO, and BST2), and between uninvolved

skin from patients and controls (15 genes including ZNF454, ZNF540 and MLF1). The top 50

differentially methylated sites classified psoriatic skin from uninvolved skin with 100% accuracy

and 90% specificity [156]. Furthermore, skin biopsies obtained from psoriasis patients pre- and 1

month post-treatment with adalimumab and found that at 1 month post-treatment, methylation at

several loci began to resemble uninvolved skin. A second study also found several differentially

methylated regions between psoriatic skin and uninvolved skin from healthy controls. Two loci,

PDCD5 and TIMP2, were retested by bisulfite sequencing but only PDCD5 was found to

validate [157]. Genome-wide methylation analysis of mesenchymal stem cells of psoriasis

patients identified 96 hypermethylated and 234 hypomethylated regions [158]. The genes

CACNA2D3, CBX4, NRP2, S100A10, SRF and TCL1B were subsequently validated.

Hypermethylated genes were enriched in gene ontologies such as skin development and

epidermis morphogenesis, while hypomethylated genes were enriched in terms such as cell

communication, cellular response to stimulus, and cell migration.

Other studies examined methylation in purified helper (CD4+) and/or cytotoxic (CD8+) T

lymphocytes and whole blood. In a study comparing methylation in CD4+ and CD8+ T cells

from MZ twin pairs discordant for psoriasis, no differential methylation was identified as

methylation between affected and unaffected co-twins was highly correlated [159]. In naïve

CD4+ cells from male psoriasis patients and controls, 26 regions were significantly

hypomethylated in psoriasis patients, most of which mapped to pericentromeric regions [160].

Furthermore, 124 promoters were dramatically hypermethylated, of which 121 were on the X

chromosome. Significant hypermethylation has also been found at PPAPDC3, TP73, and FANK1

in psoriatic CD4+ cells compared to controls. Inhibition of DNMTs using 5-azacytidine

increased the expression of all three genes, suggesting that DNA methylation is the major

regulatory mechanism in CD4+ cells [161]. Most recently, a pilot study examined whole blood

methylation differences between PsA patients with paternally and maternally-transmitted

disease. Three regions on chromosome 8, and chromosome 6 loci MICA, IRIF1, PSORS1C3, and

TNFSF4 were found to be hypermethylated in paternally compared to maternally-transmitted

disease, and PSORS1C1 was found to be hypomethylated [162].

44

Hypothesis-driven studies have examined the methylation status of the SHP-1 (PTPN6) locus.

This locus was analyzed because of its role in negatively regulating cell growth and proliferation,

and evidence that epigenetic regulation of SHP-1 involves STAT3, which when constitutively

activated in mouse keratinocytes induces a psoriatic phenotype [163]. Promoter 2 of the SHP-1

locus was significantly hypomethylated in psoriasis patients, having an average methylation level

of 68.1% compared to 94.8% in skin from healthy controls (p<0.005). The p15, p21 [164] and

p16 [165] genes encode epigenetically-regulated INK4 cyclin-dependent kinase inhibitors that

function as negative regulators of the cell cycle. In hematopoietic stem cells of psoriatic patients,

lower frequencies of p15, p21, and p16 methylation, and correspondingly higher mRNA

expression compared to controls was found. p16 hypomethylation was further shown to be

associated with significantly more severe psoriasis as measured by PASI score [166].

Eukaryotic DNA is packaged into the basic unit of the nucleosome, which is an octamer of core

histone proteins H2A, H2B, H3, and H4, around which ~150 base pairs of nucleic acid are

wound. Post-translational modification of the amino acid tails of H3 and H4 is an epigenetic

mechanism that controls the affinity of the histone octamer to the nucleic acid, resulting in either

open or closed chromatin, accessibility to transcription factors, and thus gene expression. Global

histone 4 acetylation (H4ac), a modification commonly associated with transcriptional activation,

is decreased in PBMCs of psoriasis patients compared to controls and is negatively associated

with PASI score [167]. In primary T lymphocytes of PSORS1 (HLA-C*0602) positive psoriasis

patients, three loci within the PSORS1 interval contain overlapping H3K4me1 and H3K27ac

marks indicative of active enhancers—one encompassing the upstream region of HLA-C and

exons 1-3, and two within a 10kb region near the HCG27 pseudogene. However, these patterns

are similar to those observed in controls, suggesting that the pathogenic effect of PSORS1 is not

mediated by epigenetic mechanisms [168].

Five studies have identified deregulated components of the epigenetic machinery, which refers to

the enzymes that catalyze cytosine methylation and histone modifications. DNA

methyltransferase 1 (DNMT1), which is responsible for maintaining methylation patterns after

DNA replication, is significantly overexpressed in PBMCs of psoriasis patients compared to

45

controls, while important regulators of DNA methylation MDB2 and MeCP2 are significantly

under-expressed [155]. Furthermore, histone deacetylase 1 (HDAC-1), which functions to repress

gene expression, is significantly over-expressed in psoriatic skin compared to healthy control

skin [169], although other studies have found no significant difference in overall HDAC

expression and activity in psoriatic skin and PBMCs [170].

Recent studies have examined the effects of pharmacological inhibitors of epigenetic modifier

enzymes. One study showed that the HDAC inhibitor trichostatin A (TSA) suppressed

differentiation of Tregs into a pathogenic Th17-like phenotype [171], while another study

showed that JQ1, a novel pan-BET bromodomain HAT inhibitor, reduces IL-17A secretion and

the proportion of total IL-17A+ and IFNϒ+ T cells in both PsA patients and controls, with no

effect on the total IL-22+ or TNFα+ proportions [172]. Specific inhibition of HATs cAMP

responsive element binding protein (CBP) and p300 using the small molecule bromodomain

inhibitor CBP30 results in a reduced Th17 response from CD4+ T cells from PsA patients, as

evidenced by lower IL-17A, IL-17F, and GM-CSF secretion from blood and synovial fluid-

derived cells [173]. Furthermore, HDAC Sirt1 protein expression is reduced in the nuclei of

psoriatic dermal vessels and basal keratinocytes of psoriatic lesions. Inhibition of Sirt1 by HDAC

inhibitor sirtinol increases H3 and H4 acetylation and inhibits secretion of inflammatory

chemokines CXCL10, CCL2, and CXCL8, suggesting that Sirt1 may be a novel druggable target

for skin inflammation [174].

1.4.4.6 Limitations of Previous DNA Methylation Studies

Epigenetic investigations of psoriasis and PsA are in their infancy, but so far have demonstrated

that studies of DNA methylation can uncover novel candidate loci not previously identified at the

genetic level, and can thus complement our understanding of disease pathogenesis. However,

due to the responsive nature of epigenetic marks to environmental factors, including those

external and internal to the body, it is possible that some of these associations may not actually

be causal of disease, but merely a consequence of the disease itself, or confounding variables

such as lifestyle factors, age or drugs. In order to demonstrate that an epigenetic mark is causal

46

of disease, it is necessary to show that it is present prior to symptoms of disease, a situation that

could arise due to inheritance of epigenetic marks through the germ line [175, 176]. The parent-

of-origin effect provides some evidence that causal epigenetic variants may be vertically

transmitted through the germ line in psoriasis patients. Furthermore, early studies have also

demonstrated that DNA methylation can classify patients with psoriasis from controls,

suggesting that methylation signatures may serve as diagnostic or prognostic biomarkers of

psoriasis. An important question that remains largely unanswered is whether aberrant DNA

methylation occurs at specific loci in PsA, and whether it can differentiate patients with psoriasis

and PsA and serve as biomarkers of joint disease. Chapter 6 of this thesis addresses the existence

of causal DNA methylation variants in psoriasis and PsA patients.

47

Rationale, Hypotheses and Specific Aims

2.1 Rationale

Several aspects of the etiopathogenesis of PsA in individuals with psoriasis remain poorly

characterized. Although both psoriasis and PsA are viewed as ‘autoinflammatory’ diseases

resulting dysregulation of both adaptive and innate immune systems, the exact contributions of

specific immune cell populations to joint disease, and the precise link between skin and joint

disease are poorly understood. Although the observation of a parent-of-origin effect in psoriasis

suggests a role for epigenetic mechanisms in the etiology of skin disease, there is conflicting

evidence of whether a similar parent-of-origin effect is equally evident in patients with PsA.

Moreover, the contribution of vertically transmitted epigenetic phenomena to the parent-of-

origin effect has never been experimentally addressed.

It is clear that PsA must be diagnosed and treated in a timely manner in order to prevent poor

clinical outcomes such as irreversible joint damage and disability. Unfortunately, current

diagnostic methods are laborious and require referral to a rheumatologist. Diagnosis could be

expedited by the availability of biomarker tests that can be ordered by primary care physicians or

dermatologists, to enable them to recognize psoriasis patients in the early stages of PsA and

make the appropriate referrals. Current biomarkers utilized in other rheumatological conditions

are not appropriate for PsA, and to date, no suitable clinical, genetic, or soluble biomarker for

PsA has been identified.

Hypothesis-generating experiments that employ various high-throughput technologies can

simultaneously identify molecular biomarkers and glean insights into the molecular

48

etiopathogenesis of PsA in patients with psoriasis. The work presented in this thesis employs

epidemiological analyses, gene expression microarray profiling, soluble protein assays, and

DNA methylation profiling technologies to address the aforementioned understudied aspects of

PsA.

2.2 Hypotheses and Specific Aims

The first two studies presented in this thesis are concerned with the discovery and validation of

gene expression and protein biomarkers of PsA in patients with psoriasis. The first study

(Chapter 3) presented in this thesis hypothesizes that gene expression differences exist between

psoriasis and PsA patients in whole blood, that these gene expression signatures are related

specifically to joint disease and can yield insights into the pathogenesis of PsA and be used as

biomarkers of PsA in patients with psoriasis. The specific aims of the study are:

1) To characterize the whole blood gene expression differences in psoriasis and PsA patients

using Agilent 4x44k gene expression microarrays and perform bioinformatics analyses to

interpret enriched biological functions;

2) To confirm differential expression of prioritized genes by real-time PCR in the same

samples;

3) To validate candidate gene expression biomarkers of PsA in an independent cohort of

psoriasis and PsA patients by NanoString nCounter® technology;

4) To identify the cellular source of the biomarker signals by magnetic cell sorting of whole

blood and gene expression analysis.

The second study (Chapter 4) presented in this thesis hypothesizes that CXCL10, a gene

expression biomarker of PsA identified in the first study, can serve as a predictive soluble protein

biomarker of PsA in psoriasis patients prior to disease onset. The specific aims of the study are:

49

1) To measure soluble CXCL10 in a longitudinal, prospective cohort of psoriasis patients

using a microsphere-based immunoassay, and determine if baseline concentrations differ

significantly between psoriasis patients who progress to develop PsA compared to

psoriasis patients who do not develop PsA;

2) In a subset of patients who progressed to develop PsA, determine if soluble CXCL10

expression levels change after PsA diagnosis.

The third and fourth studies presented in this thesis address the contributions of epigenetic

factors to the etiology of psoriasis and PsA. The third study (Chapter 5) presented in this thesis

hypothesizes that the parent-of-origin effect previously observed in psoriasis patients is also

significant in PsA patients. The specific aims of the study are:

1) To determine whether the parent-of-origin effect is evident in a large cohort of well-

phenotyped patients with psoriatic disease (psoriasis and PsA), and determine if it is

significant in both patients with psoriasis alone and PsA;

2) To identify clinical and genetic variables significantly associated with paternally-

transmitted psoriatic disease.

Finally, the fourth study (Chapter 6) presented in this thesis hypothesizes that heritable

epigenetic mechanisms contribute to the risk of developing psoriatic disease. The specific aims

of the study are:

1) To characterize DNA methylation differences in sperm cells of psoriasis patients, PsA

patients, and unaffected controls using the Illumina Infinium® HumanMethylation 450k

Bead Chip platform;

2) To perform the complete bioinformatics and statistical analysis pipeline using lumi,

genefilter, and methyAnalysis packages.

50

Gene Expression Differences between Psoriasis Patients

with and without Inflammatory Arthritis

Remy A. Pollock, MSc(A), Fatima Abji, MSc, Kun Liang, PhD, Vinod Chandran, MD, PhD,

Fawnda Pellett, BSc, Carl Virtanen, MSc, and Dafna D. Gladman, MD, FRCPC

Published in the Journal of Investigative Dermatology as a Letter to the Editor:

J Invest Dermatol. 2015; 135(2): 620-3. doi: 10.1038/jid.2014.414.

This chapter represents the original full-length version of the manuscript.

3.1 Introduction

Psoriatic arthritis (PsA) is a seronegative inflammatory arthritis of peripheral and axial joints that

affects up to 30% of people with cutaneous psoriasis (PsC) [177]. PsA is regarded as a severe

form of PsC that contributes additional morbidity and reduces quality of life of PsC patients [40].

In the majority (70%) of patients, PsA develops following PsC onset, and is therefore modeled as

a ‘disease within a disease’ that develops due to the presence of additional arthritis-specific

environmental and genetic risk factors on the background of psoriasis [3, 55]. Recent genetic

evidence supports pathogenic mechanisms involving skin barrier function and both the innate

and adaptive immune systems [178], but the exact etiopathogenesis and the link between PsC

and PsA remain unclear.

To better understand the mechanisms underlying joint manifestations of psoriatic disease,

previous studies examined whole blood expression differences between PsA patients and patients

with rheumatoid arthritis, spondyloarthritis, and healthy controls [109, 110]. No study has

explored genome-wide RNA expression differences between PsC and PsA patients, who share

51

psoriatic skin disease but differ in the presence of inflammatory joint disease. We hypothesized

that such differences exist in whole blood, and aimed to characterize them using a combination

of well-established microarray, qPCR, and digital gene expression profiling techniques to

discover, validate, replicate, and assess their ability to act as biomarkers of PsA.

3.2 Materials and Methods

3.2.1 Patients

PsA patients were recruited from the University of Toronto PsA Clinic at Toronto Western

Hospital. PsC patients were recruited from the PsC Clinic at Toronto Western Hospital, which

was established in 2006 to assess the incidence of PsA among patients with PsC. PsA and PsC

patients were all Caucasians and medications were allowed. Controls were ethnically matched

healthy volunteers. All PsA patients were diagnosed by a rheumatologist and satisfied CASPAR

classification criteria, and all PsC patients were diagnosed by a dermatologist and examined by a

rheumatologist to exclude PsA. The University Health Network Research Ethics Board approved

the study, which was conducted according to principles of the Declaration of Helsinki and all

subjects provided written informed consent.

3.2.2 Microarrays

Peripheral whole blood was collected in PAXgene tubes and RNA was extracted using PAXgene

Blood RNA Kits (PreAnalytiX, Feldbachstrasse, Switzerland) according to the manufacturer’s

instructions. RNA integrity was assessed using an Agilent 2100 Bioanalyzer. Total RNA was

reverse transcribed using oligo dT primers containing the T7 promoter, and cRNA was labeled

with Cy5 through in vitro transcription with T7 RNA polymerase (Low RNA Input Fluorescent

Linear Amplification Kit, Agilent Technologies Inc., Mississauga, Canada). Subject samples

were co-hybridized with Cy3-labeled Human Universal Reference RNA (Stratagene, LaJolla,

CA, USA) to 4x44K Whole Human Genome Oligo Microarrays (Manufacturers ID: 14850) and

52

scanned on a SureScan High Resolution Scanner (Agilent Technologies Inc.) according to the

manufacturer’s protocol. Raw and processed data can be found in the Gene Expression Omnibus

(www.ncbi.nlm.nih.gov/geo/) (GEO accession: GSE61281).

3.2.3 Statistical Analysis

Microarray data were normalized using the Bioconductor package limma [179]. Specifically,

microarray data were background corrected, normalized within each array using the “LOESS”

smoothing method, and normalized between arrays using the “quantile” option. Gene expression

differences between groups were assessed using multiple linear regression after controlling for

significant covariates (labeling day, sex, psoriasis duration, and age of psoriasis onset). All

statistical analyses were done on log base 2 transformed data and p-values were corrected for

multiple hypothesis testing using the Benjamini and Hochberg False Discovery Rate (FDR)

[180]. DAVID Bioinformatics Resources 6.7 (National Institute of Allergy and Infectious

Diseases, National Institutes of Health) [181, 182] Functional Annotation Chart and Clustering

tools were used to identify enriched gene annotations using general annotations “chromosome”

and “cytoband” in addition to the default settings.

3.2.4 qPCR Arrays

For PCR arrays, 200ng of RNA from 19 PsA and 18 PsC patients from the discovery cohort was

reverse transcribed using the RT2 First Strand cDNA kit and amplified using RT2 SYBR

Green/ROX qPCR master mix on TLR signaling and chromatin modification enzyme RT2

Profiler PCR Arrays (SABiosciences/Qiagen, Mississauga, ON, Canada). Fold change was

quantified using the ΔΔCt method with internal array housekeeping genes and significance was

assessed by Student’s t test. Experiments were performed on an ABI Prism 7900HT (Applied

Biosystems).

53

3.2.5 Technical Validation of Microarray Data

Microarray data was validated first using Taqman qPCR assays. For Taqman assays, 1.0ug of

RNA was reverse transcribed using the SuperScript VILO kit (Invitrogen, Burlington, ON,

Canada) and 14 genes were amplified in 10 randomly selected PsA and PsC patients from the

discovery cohort using inventoried Taqman Gene Expression Assays (Applied Biosystems) and

Gene Expression Master Mix (Applied Biosystems) according to the manufacturer’s instructions.

Reactions were performed in triplicate and gene expression was quantified using the relative

standard curve method, normalized to housekeeping genes PPIB and DECR1 [183] and

expressed relative to Human Universal Reference RNA (Stratagene, LaJolla, CA, USA).

Experiments were performed on an ABI Prism 7900HT (Applied Biosystems).

3.2.6 Validation of the nCounter® Platform and Biomarker Replication

The nCounter® analysis system (NanoString Technologies, Seattle, WA, USA) was validated by

re-measuring twenty-five genes ranging of low to high expression, and small to large difference

between groups in 16 PsA and 20 PsC samples from the microarrays. Housekeeping genes PPIB

and DECR1 were included, along with 6 positive and 8 negative hybridization controls. For each

gene a 50bp capture probe linked to a biotin tag and a 50bp reporter probe linked to a florescent

barcode were designed [108]. Probes were hybridized to 100ng of total RNA, washed, purified,

and immobilized to a NanoString cartridge following the manufacturer’s instructions. Images

were processed on the nCounter® Digital Analyzer and data was analyzed with nSolver

(NanoString Technologies) following the manufacturer’s guidelines.

For biomarker replication, peripheral blood from 48 PsA and 48 PsC patients was collected in

Tempus tubes (Applied Biosystems, Streetsville, ON, Canada). Total RNA was extracted and

DNAse digestions were performed using the Tempus Spin RNA Isolation Kit (Applied

Biosystems) following the manufacturer’s instructions. Digital gene expression profiling was

performed using a custom NanoString gene expression codeset containing 18 candidate genes

and housekeeping genes PPIB and DECR1. Data was analyzed as above, and differential

54

expression was assessed by multiple linear regression adjusting for differences in age, sex,

psoriasis duration, and PASI between groups.

ROC analysis of single biomarkers was performed in SPSS v22. ROC analysis of combined

biomarkers was performed as described previously [88]. Discovery and validation cohorts were

compared with respect to demographic and clinical variables by Student’s t-test, Wilcoxon rank-

sum test, or chi-squared test where appropriate. Pearson correlation and linear regression were

used to assess the associations between normalized gene expression levels and clinical measures

of disease activity, and between normalized gene expression levels and demographic and clinical

variables that differed between discovery and validation cohorts.

3.2.7 Purification of Leukocyte Subpopulations and Gene Expression Analysis

Three tubes of whole blood were drawn from 10 PsA and 10 PsC patients not receiving biologic

therapy in sodium heparin coated vacutainers. Cells were layered on Ficoll-Paque (GE

Healthcare) to isolate peripheral blood mononuclear cells. Total T lymphocytes were isolated by

positive selection using anti-CD3 microbeads for magnetic-activated cell sorting (MACS,

Miltenyi Biotec), and NK cells were subsequently isolated from the CD3-negative fraction by

positive selection for CD56. Monocytes were isolated by positive selection for anti-CD14.

Purified cell pellets were stored at -80oC until RNA extraction was performed using the RNeasy

Kit (Qiagen). Extracted RNA (75ng) was reverse transcribed using the Maxima 1st Strand Kit

(ThermoFisher), and CXCL10, HAT1, NOTCH2NL, and SETD2 were amplified with Platinum

Taq master mix containing SYBR green on an ABI Prism 7900HT (Applied Biosystems). PCR

primers are shown in Appendix 1. Fold change was quantified using the ΔΔCt method with

GAPDH as the housekeeping gene.

55

3.3 Results

3.3.1 Subject Selection and Exploration of Technical, Clinical, and Demographic Covariates

For microarray analyses, 52 Caucasian individuals were included—40 psoriatic disease patients

(20 PsA and 20 PsC) and 12 healthy controls. PsA and PsC patients were matched for psoriasis

area severity index (PASI) and psoriasis duration, and controls were matched for age, sex, and

ethnicity. Details of the demographic and clinical characteristics of the study subjects are given

in Table 3.1. We explored several potential clinical, demographic, and technical factors affecting

differential gene expression between PsA, PsC and controls, and found that experimental batch

(labeling day) strongly affected expression, and sex moderately affected expression. Between

PsA and PsC patients, psoriasis duration and age of psoriasis onset moderately affected

expression (Figure 3.1). These factors were included in the final multiple linear regression

model. Age, Psoriasis Area Severity Index (PASI), medications (prednisone, methotrexate, or

biologics), array slide number, and microarray slide position did not substantially affect gene

expression and were not included in the final model.

56

Table 3.1. Demographic and clinical characteristics of the discovery and replication cohorts.

Discovery Cohort Replication Cohort

PsC

(n=20)

PsA

(n=20)

Controls

(n=12)

PsC

(n=48)

PsA

(n=48)

Females 10 (50%) 10 (50%) 7 (58%) 23 (48%) 23 (48%)

Age 44.4 (11.8) 48.1 (10.4) 45.9 (13.2) 46.2 (12.2)

Age of diagnosis of psoriasis1 24.6 (11.0) 24.8 (13.5) - 28.8 (16.9) 29.8 (15.1)

Age of diagnosis of PsA1 - 31.9 (13.4)** - - 40.8 (13.4)**

Duration of psoriasis1 19.8 (14.1) 23.3 (11.4)* - 17.7 (15.4) 16.8 (13.4)*

Duration of PsA2 - 17.0 (10.0-

22.3)** - -

2.0 (1.0-

10.0)**

PASI2 3.8 (2.4-

6.1) 4.7 (2.5-6.5) -

5.9 (2.2-

10.6) 2.5 (0.9-6.6)

Number of swollen and/or

tender joints2,3 - 5.5 (3.3-10.8)* - - 3.5 (1.3-6.8)*

Number of swollen joints2,3 - 3.0 (1.0-5.0)** - - 1.5 (0-3.0)**

Leukocyte count1 - 7.7 (2.7) - - 8.0 (3.0)

Platelet count1 - 269.8 (57.1) - - 278.1 (91.2)

Neutrophil count2 - 4.9 (4.9-5.9) - - 4.8 (4.0-6.5)

Lymphocyte count1 - 1.7 (0.3)* - - 1.9 (0.6)*

Monocyte count1 - 0.6 (0.2) - - 0.6 (0.2)

Eosinophil count1 - 0.2 (0.1) - - 0.2 (0.1)

ESR1 - 16.3 (15.1) - - 14.3 (12.8)

HLA-B*27 positive 0 (0%) 5 (25%) - 3 (6%) 5 (10%)

HLA-C*06 positive 8 (40%) 6 (50%) - 18 (38%) 12 (25%)

Number of patients with axial

disease4 - 7 (35%) - - 7 (29%)

57

Number of patients on NSAIDs 1 (5%) 13 (65%) - 0 (0%) 31 (65%)

Number of patients on

DMARDs 1 (5%) 14 (70%)* - 1 (2%) 23 (48%)*

Number of patients on biologics 1 (5%) 2 (10%)* - 0 (0%) 0 (0%)*

PASI, psoriasis area severity index; ESR, erythrocyte sedimentation rate; NSAIDs, non-steroidal anti-

inflammatory drugs; DMARDs, disease modifying anti-rheumatic drugs (methotrexate, lefluonamide,

sulfasalazine, azathioprine, retinoid, or oral steroids) *Difference between discovery and replication cohorts is

significant at p<0.1; **Difference between discovery and replication cohorts is significant at p<0.05; 1Mean

(standard deviation); 2 Median (25th-75th percentiles); 3Tender and damaged joints were determined clinically

in 68 joints, swollen joints in 66 (excluding hips); 4Satisfying radiographic New York criteria for ankylosing

spondylitis.

58

Figure 3.1. Significant clinical, demographic, and technical factors affecting gene expression.

p value p value

p value p value

Labeling Date Sex

59

3.3.2 Global Gene Expression Trends Provide Insight into the Relationship between PsC and PsA

Four hundred and ninety-four (494) genes were differentially expressed between PsA and PsC

patients (24% up-regulated and 76% down-regulated), but no genes were found to be

differentially expressed between PsC and controls at a Benjamini and Hochberg False Discovery

Rate (FDR) < 0.05. SP100 (FDR=0.16) was the only gene that neared significance between PsC

and controls. We speculate that this was due to a weak whole blood signature of cutaneous

disease that was “drowned out” by the cellular heterogeneity of whole blood. One thousand one

hundred and twenty-five (1,125) genes were differentially expressed between PsA patients and

healthy controls (56% up-regulated and 44% down-regulated), which encompassed 230/494

(47%) of the same genes found in PsA versus PsC. The list of arthritis-specific genes (PsA vs

PsC) included 12 genes identified in a previous study comparing PsA patients and controls [110],

while the PsA versus controls list included 37 genes from the same study.

From the current model of PsA as a ‘disease within a disease’ it follows that PsA would share

common skin-related disease processes with PsC, but have additional arthritis-related processes.

We therefore expected that the number of differentially expressed genes between PsA and

controls (1,125, comprised of both skin and arthritis-related genes) would roughly equal the sum

of the number of differentially expressed genes between PsA and PsC (494 arthritis-related

genes) plus the number of differentially expressed genes between PsC and controls (0). Instead,

due to the absence of differential expression between PsC and controls, there were several

hundred more differentially expressed genes between PsA and controls than the other two

comparisons combined. To understand the relationship between these comparisons, we plotted

the log2 of the fold change (logFC) for PsA versus PsC against the logFC for PsC versus

controls, for each differentially expressed gene found in PsA versus controls (Figure 3.2). The

majority of genes fell within the first and third quadrants, indicating that genes increased or

decreased in PsC relative to controls are changed in the same direction in PsA relative to PsC.

Fold changes in PsA versus PsC were typically larger in magnitude than fold changes in PsC

versus control, as indicated by where fold changes fall relative to the diagonal line in Figure 3.2.

60

Figure 3.2. Scatter plot of each differentially expressed gene found in PsA vs. Controls, using

the log Fold Change (FC) values from PsA vs. PsC (joint disease signature) plotted against PsC

vs. Controls (skin disease signature). The majority of genes fall within the first and third

quadrants, indicating that genes increased or decreased in PsA relative to PsC are changed in the

same direction in PsC relative to controls. Fold changes in PsA versus PsC were typically larger

in magnitude than fold changes in PsC versus control.

61

3.3.3 Annotation of Differentially Expressed Genes Identifies Key Processes in PsA

Upregulated genes in PsA patients compared to PsC patients were significantly enriched in gene

products that are membrane-anchored, ribonucleoprotein-associated, are involved in cell

proliferation, have cytokine activity, or are related to the tumour necrosis factor family of

cytokines (Table 3.2). Downregulated genes in PsA patients compared to PsC patients were

enriched in gene products that localize to the nuclear lumen, are involved in RNA splicing,

chromatin modification and chromatin-mediated transcriptional regulation (containing PHD-type

2 zinc finger and bromodomain motifs present in DNA and RNA binding proteins), or have

DNA/RNA helicase activity. A thorough manual annotation of the top upregulated genes

between PsA and PsC showed that they play key roles in innate immune processes such as TLR

signaling (LY96, ABCA1, TICAM1), NK cell activation (CD58, CLEC2B), and gene expression

regulation by NF-kB (BCL2A1), while the top downregulated genes are involved in regulating

osteoclastogenesis (TGFBR3, NOTCH2NL), epidermal development (CSTA), cell-cell

recognition, signaling and movement (EZR, MSN), and chromatin modification (SETD2,

SMARCA4), and other processes (Table 3.3).

62

Table 3.2. Enriched biological annotations among the 494 differentially expressed genes

between PsA and PsC.

Category Term Fold

Enrichment P Value

Up-regulated

Genes

GOTERM_MF_FAT Cytokine activity 9.2 0.002

SP_PIR_KEYWORDS Ribonucleoprotein 6.8 0.006

GOTERM_CC_FAT Anchored to membrane 5.8 0.029

GOTERM_BP_FAT Cell proliferation 3.8 0.039

INTERPRO Tumour necrosis factor 40.0 0.049

Down-

regulated

Genes

GOTERM_CC_FAT Nuclear lumen 3.3 1.44x10-18

SP_PIR_KEYWORDS RNA splicing 7.8 1.19x10-11

SP_PIR_KEYWORDS Helicase 8.5 1.04 x10-8

GOTERM_BP_FAT Chromatin modification 4.3 1.96 x10-6

63

Table 3.3. Top differentially expressed genes between PsA and PsC from primary microarray

analyses.

Gene Name Fold

Change Function

Up-

regulated

Genes

LY96 Lymphocyte antigen

96 2.2

Associates with TLR4 and confers

responsiveness to bacterial lipopolysaccharide

CSTA Cystatin A 2.5 Cysteine protease inhibitor involved in

epidermal development and maintenance

CLEC2B C-type lectin domain

family 2, member B 2.6

Natural killer cell antigen recognized by

activating receptor NKp80

CLEC4D C-type lectin domain

family 4, member D 2.4 Activating receptor for myeloid cells

BCL2A1 BCL2-related protein

A1 2.3 Antiapoptotic gene regulated by NF-kB

LPAR6 Lysophosphatidic

acid receptor 6 2.3

Located in an intron of the retinoblastoma

susceptibility gene

ABCA1

ATP-binding

cassette, sub-family

A, member 1

2.1

LPS efflux from macrophages to accelerate

recovery from LPS-induced tolerance and

dampen inflammation by suppressing TLR4-

mediated TNFa release

CD58

Lymphocyte

function-associated

antigen 3

1.6 T and NK cell adhesion and activation

TNFSF10

Tumor necrosis

factor (ligand)

superfamily, member

10

1.5

Cytokine and ligand of osteoprotegrin (OPG),

binding blocks OPG interaction with various

death receptors

Down-

regulated

Genes

SETD2 SET domain

containing 2 0.62

Histone 3 lysine 36 methyltransferase that opens

chromatin to activate gene expression

TICAM1 Toll-like receptor

adaptor molecule 1 0.86

Adaptor protein that binds TLR3 and activates

IFN beta via NF-KB during antiviral immune

response

TGFBR3 Transforming growth

factor B receptor 3 0.47

Co-receptor with other TGF-beta receptors,

soluble form is shed and may inhibit TGF-beta,

a cytokine that promotes osteoclastogenesis

64

NOTCH2N

L

Notch homolog 2 N-

terminal like protein 0.54

Inhibitor of NOTCH2 signaling in

osteoclastogenesis

MSN Moesin 0.60 Binds to LY96 and TLR4 to aid in LPS

recognition

EZR Ezrin 0.62 Cell-cell recognition, signaling and movement

XRCC6

X-ray repair

complementing

defective repair in

Chinese hamster

cells 6

0.70 Antibodies to XRCC6 found in some systemic

lupus erythematosus patients

SMARCA4

SWI/SNF related,

matrix associated,

actin dependent

regulator of

chromatin, subfamily

a, member 4

0.73

Component of the mammalian SWI/SNF

chromatin remodeling complex that associates

with NF-kB

65

3.3.4 qPCR Validates Array Findings and Identifies Additional Differentially Expressed Genes

As a technical confirmation of the measurement accuracy of the microarrays, 14 biologically and

statistically significant genes identified in the discovery samples were re-measured by Taqman or

SYBR assay using targeted qPCR arrays (Figure 3.3). qPCR measurements replicated the

directionality and magnitude of change found by the microarrays (r=0.98, p<0.001). The greater

sensitivity of qPCR arrays allowed us to further refine the differential expression of additional

genes involved in TLR signaling and chromatin modifications, two processes implicated in PsA

by microarray analyses. The following genes related to TLR signaling were identified as

differentially expressed between PsA and PsC (FDR<0.05): TBK1 (upregulated), IRAK2, RELA

(p65), CHUK, NF-kB1 (p50), HSPA1A, IKBKB, NFRKB, BTK, ELK1, MAP3K1, HSPD1,

MAP4K4, REL (c-Rel), IRF1, PPARA, and TAB1 (downregulated). CXCL10 (upregulated) was

differentially expressed before correction for multiple testing (fold change=1.5, p=0.03). We also

identified 43 additional differentially expressed genes between PsA and PsC related to chromatin

modifications, including: HAT1, PRMT8 (upregulated), HDAC3, HDAC1, SETD1A, EHMT2,

MLL, and SMYD3 (downregulated) (Table 3.4).

66

Figure 3.3. Concordance between microarray and qPCR (squares) or NanoString (diamonds)

fold change measurements in the discovery (microarray) samples. Genes showing discordant fold

change directions are marked by x.

ID

Microarray

Fold Changea

qPCR

(array/Taqman)

Fold Changeb

NanoString

nCounter®

Fold

Changec

BCL2A1 2.28 1.90 2.32

CLEC2B 2.56 2.34 2.13

LY96 2.23 1.81 2.29

SETD2 0.62 0.68 0.88

TRIF 0.86 1.00 0.83

TGFBR3 0.47 0.69

CLEC4D 2.43 1.63

CSTA 2.29 1.89

P2RY5 2.27 1.67

NFKB1 0.76 0.81

HDAC1 0.84 0.75

MLL5 0.82 0.76

NCOA6 0.74 0.74

MLL 0.62 0.62

CD58 1.57 1.37

EZR 0.62 0.65

MSN 0.60 0.66

N2N 0.54 0.88

-1.5

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5

Log2 Array Fold Change

Lo

g2

Nan

oS

trin

g/q

PC

RF

old

Ch

an

ge

qPCRr=0.98, p<0.001

NanoStringr=0.96, p<0.001

67

PARP1 0.74 0.69

PUM1 0.56 0.71

SMARCA4 0.73 0.77

SYNCRIP 0.66 0.84

XRCC6 0.72 0.78

PRMT6 0.64 1.09

CD14 0.72 0.82

CXCL10 1.45 1.92

EHMT2 0.64 0.80

HAT1 1.77 1.79

SETD1A 0.67 0.72

SMYD3 0.71 0.97

MyD88 0.91 1.02

TLR2 0.94 1.23

TLR7 0.89 0.97

TRAM 0.95 1.15 aBased on 20 PsA vs 20 PsC from the microarray cohort

bBased on 10 PsA vs 10 PsC from the microarray cohort (Taqman) or 19 PsA vs 18 PsC from the

microarray cohort (qPCR arrays)

cBased on 16 PsA vs 20 PsC from the microarray cohort

68

Table 3.4. Differentially expressed genes between PsA compared to PsC identified by TLR

signaling and chromatin modification targeted qPCR arrays.

Gene Name

Fold Change

PsA vs PsC

FDR Gene Name

Fold Change

PsA vs PsC

FDR

IKBKB 0.71 0.006 SETD3 0.74 0.001

LY96 2.07 0.006 NCOA3 0.74 0.006

RELA 0.68 0.004 NSD1 0.74 0.002

TBK1 1.42 0.004 SUV39H1 0.73 0.023

IRAK2 0.62 0.003 DNMT3A 0.73 0.008

NFRKB 0.72 0.008 KDM4C 0.73 0.023

BTK 0.76 0.011 DNMT1 0.73 0.004

NFKB1 0.79 0.012 CSRP2BP 0.73 0.044

ELK1 0.75 0.016 USP22 0.73 0.012

MAP3K1 0.76 0.023 RNF20 0.73 0.001

HSPA1A 0.70 0.024 KAT7 0.73 0.006

HSPD1 0.79 0.038 HDAC8 0.72 0.009

MAP4K4 0.67 0.038 ASH1L 0.72 0.016

REL 0.78 0.045 KDM4A 0.72 0.005

IRF1 0.77 0.044 SUV420H1 0.72 0.001

PPARA 0.78 0.047 KDM1A 0.72 0.002

69

TAB1 0.77 0.048 KAT6A 0.72 0.026

HAT1 1.77 0.002 PRMT3 0.72 0.004

PRMT8 1.39 0.025 SETDB1 0.71 0.004

KAT8 0.82 0.020 SETD5 0.71 0.002

RPS6KA5 0.81 0.038 SMYD3 0.71 0.010

KAT5 0.80 0.021 DOT1L 0.70 0.020

USP21 0.79 0.047 SETD2 0.68 0.000

CDYL 0.79 0.034 SETD7 0.68 0.002

HDAC3 0.77 0.011 SETD1A 0.67 0.001

MLL5 0.76 0.014 WHSC1 0.67 0.004

NCOA1 0.75 0.044 MLL3 0.66 0.002

HDAC1 0.75 0.001 EHMT2 0.64 0.001

NCOA6 0.74 0.025 MLL 0.62 0.002

SETD1B 0.74 0.021 HDAC11 0.55 0.012

70

3.3.5 nCounter® Digital Expression Profiling Replicates the Expression of Four Candidate Genes in an Independent Patient Cohort

To test the feasibility of using the nCounter® digital gene expression profiling platform for

replication testing, we re-measured 25 genes that ranged from low to high expression, and small

to large fold change between PsA and PsC (genes listed in Figure 3.3). The magnitude and

directionality of nCounter® measurements correlated well with the microarray data overall

(r=0.96, p<0.001), however some genes with small differences between PsA and PsC (fold

changes of > 0.9 and < 1.2, such as PRMT6, MyD88, TLR2, and TRAM, reversed fold change

directionality on the nCounter® (Figure 3.3). This reversal was not due to differences in mRNA

isoform specificities between the microarray and nCounter® probes, which were verified to be

identical.

For replication testing, we measured several candidate genes by NanoString in a large

independent cohort of 48 PsA and 48 PsC patients (Table 3.1), but focused the analysis on 18

genes that showed larger fold changes of > 1.5 or < 0.67 on the initial microarrays or qPCR

arrays, as these genes were more likely to be biologically significant and could be measured

more accurately. Of these, 13 genes were significantly differentially expressed, of which 4 genes

(HAT1 and CXCL10 [upregulated], and NOTCH2NL and SETD2 [downregulated]) replicated the

fold change directionality observed in the discovery samples. Based on receiver operating

characteristics (ROC) area under the curve (AUC), the strongest replicated gene was

NOTCH2NL with an AUC of 0.71 (Table 3.5). In combination, the 4 replicated genes performed

synergistically with an AUC of 0.79.

71

Table 3.5. Candidate genes selected for replication testing in an independent cohort by

nCounter® technology.

Gene

Symbol

Gene Name (Alias)

Array Result1 nCounter® Result2

AUC

(95% CI)

Fold Change

PsA vs. PsC

FDR

Fold Change

PsA vs. PsC

FDR

NOTCH2NL NOTCH2 N-terminal like (N2N) 0.55 0.01 0.80 <0.001

0.71

(0.61-0.82)

HAT1 Histone acetyltransferase 1 (KAT1) 1.77 0.001 1.12 0.02

0.68

(0.58-0.79)

SETD2 SET domain containing 2 (HIF-1) 0.62 0.04 0.68 0.03

0.63

(0.52-0.74)

CXCL10 Chemokine (C-X-C motif) ligand 10 (IP-10) 1.53 0.23 1.45 0.04

0.65

(0.53-0.76)

CD58 Cluster of differentiation 58 (LFA-3) 1.57 0.04 0.77 <0.001

0.78

(0.68-0.87)

LY96 Lymphocyte antigen 96 (MD-2) 2.23 0.02 0.69 <0.001

0.78

(0.68-0.89)

G9A Euchromatic histone-lysine N-

methyltransferase 2 (EHMT2/BAT8) 0.64 <0.001 1.31 <0.001

0.74

(0.64-0.84)

BCL2A1

BCL2-related protein A1

(ACC-1)

2.28 0.03 0.73 <0.001

0.78

(0.68-0.87)

SYNCRIP Synaptotagmin binding, cytoplasmic RNA

interacting protein (HNRNPQ) 0.67 0.02 1.34 <0.001 0.75

72

AUC, area under the curve. 1 Discovery cohort (microarray analysis of 20 PsA, 20 PsC patients,

and 12 controls; or qPCR array analysis of the same 19 PsA and 18 PsC patients). 2 Validation

cohort (nCounter® analysis of 48 PsA and 48 PsC patients).

(0.65-0.87)

CLEC2B C-type lectin domain family 2, member B

(AICL) 2.56 0.02 0.79 0.003

0.79

(0.69-0.88)

PRMT6 Protein arginine methyltransferase 6

(HRMT1L6) 0.64 0.12 1.24 0.01

0.68

(0.58-0.79)

EZR Ezrin (VIL2) 0.63 0.003 1.35 0.02

0.74

(0.64-0.85)

MSN Moesin (HEL70) 0.60 0.01 1.16 0.02

0.77

(0.67-0.87)

P2RY5 Lysophosphatidic acid receptor 6 (LPAR6) 2.27 0.02 1.09 0.07

0.60

(0.40-0.64)

TGFBR3 Transforming growth factor beta receptor 3

(betaglycan) 0.47 0.02 1.22 0.09

0.64

(0.53-0.75)

TNFSF10 Tumor necrosis factor (ligand) superfamily,

member 10 (TRAIL) 1.53 0.02 0.95 0.21

0.56

(0.44-0.67)

CLEC4D C-type lectin domain family 4, member D

(CLEC-6) 2.43 0.03 1.01 0.25

0.48

(0.37-0.60)

CSTA Cystatin/stefin A (STFA) 2.55 0.02 1.01 0.27

0.52

(0.40-0.64)

73

Several other genes in Table 3.5 were highly significant in the replication cohort but showed an

opposite direction of fold change compared with the discovery cohort. We speculated that this

was due to subtle demographic or clinical differences between the two cohorts. Indeed, the

discovery PsA cohort was characterized by a significantly younger age of PsA onset, longer PsA

duration, and higher swollen joint count compared with the replication PsA cohort (Table 3.1).

Differences in PsC duration, swollen and/or tender joint count, lymphocyte count, and

nonsteroidal anti-inflammatory drug or disease-modifying antirheumatic drug use nearly reached

significance. No differences were found between the PsC cohorts. With the exception of age of

PsA onset and lymphocyte count, these clinical variables were correlated with the expression of

SYNCRIP, CD58, LY96, EZR, MSN, and P2RY5 (Table 3.6), which might explain why these

genes showed opposing fold changes in the two cohorts. Given their strong differential

expression, these genes should not be precluded from further study. However, careful attention

must be paid to clinical variables when selecting PsC and PsA patients for testing.

74

Table 3.6. Correlations between gene expression and clinical variables from Table 3.1 that differ

between discovery and replication cohorts.

DMARDs, disease-modifying anti-rheumatic drugs * Calculated from microarray data (discovery cohort);

† Calculated from nCounter® data (replication cohort).

Gene Symbol

Pearson Correlation Coefficient (r, p<0.05)

Sw

oll

en

Jo

int

Co

un

t

Ag

e o

f P

sA

PsA

Du

rati

on

PsC

Du

rati

on

Ly

mp

ho

cyt

e C

ou

nt

DM

AR

Ds

Bio

logic

s

CD58 0.32† 0.35* -0.39† -0.49*

LY96 0.47* 0.46* -0.65*

G9A

BCL2A1

SYNCRIP 0.45*

CLEC2B -0.29*

PRMT6

EZR -0.43* 0.47*

MSN -0.41*

P2RY5 0.39* -0.45*

TGFBR3

TNFSF10 -0.51*

CLEC4D

CSTA

75

3.3.6 Hierarchical Clustering Identifies a Sub-Group of PsA Patients Responsible for Differential Expression

Hierarchical clustering of the nCounter® data showed that 17 out of the 48 PsA patients

clustered together, while the remaining PsA patients clustered among the PsC patients (Figure

3.4). Clustered patients were responsible for driving the differential expression of the majority of

significant genes, including the 4 replicated genes. Clustered PsA patients (n=17) were compared

to unclustered PsA patients (n=31) with respect to genetic risk alleles HLA-B*27 and HLA-C*06,

and demographic and disease characteristics at the time of RNA collection (Table 3.7).

Compared to the unclustered PsA patients, clustered PsA patients were characterized by a higher

lymphocyte count (2.1 [0.5] clustered compared to 1.7 [0.6] unclustered, p=0.03), a higher

prevalence of axial disease (63% of clustered patients compared to 13% of unclustered patients,

p=0.02), and a shorter duration of PsA (3.5 [3.8] years clustered compared to 7.2 [7.9] years

unclustered, p=0.04).

76

Figure 3.4. Two-way hierarchical clustering of nCounter® gene expression data from the replication cohort, with the PsA cluster shown.

X-axis: patients, Y-axis: genes. Red, up-regulated, green, down-regulated in PsA compared to PsC.

PsA Cluster

77

Table 3.7. Comparison of clustered and unclustered PsA patients in the validation cohort.

Variable

Clustered PsA

n=17

Mean (SD) or # (%)

Unclustered PsA

n=31

Mean (SD) or # (%)

P value

Age 48.5 (15.3) 44.9 (10.3) 0.33

Sex (males) 8 (47%) 17 (55%) 0.61

PASI 3.2 (2.9) 5.9 (8.5) 0.12

Age of psoriasis onset 30.7 (14.9) 29.3 (15.5) 0.77

Age of PsA onset 45.5 (15.0) 38.1 (11.8) 0.07

Psoriasis duration

(years) 18.4 (16.8) 15.9 (11.3) 0.55

PsA duration (years) 3.5 (3.8) 7.2 (7.9) 0.04†

ESR 13.0 (10.0) 15.1 (14.4) 0.61

DMARDs 10 (59%) 12 (39%) 0.18

NSAIDs 13 (77%) 18 (58%) 0.20

UV therapy 1 (6%) 4 (13%) 0.64*

Pos family history 6 (43%) 10 (33%) 0.54

HLA-B*27 Pos 2 (12%) 3 (10%) 1.00*

HLA-C*06 Pos 5 (29%) 7 (23%) 0.60

Axial disease (NY 5 (63%) 2 (13%) 0.02*

78

criteria)

Swollen joints 1.5 (2.1) 3.1 (4.6) 0.18

Tender joints 4.2 (6.0) 6.8 (10.4) 0.35

Active (tender and/or

swollen) joints 5.0 (6.2) 7.0 (10.4) 0.47

Leukocytes 7.4 (2.0) 8.3 (3.4) 0.32

Platelets 283.4 (60.6) 275.1 (105.3) 0.78

Neutrophils 4.5 (1.5) 9.1 (15.2) 0.23

Lymphocytes 2.1 (0.5) 1.7 (0.6) 0.03

Monocytes 0.6 (0.1) 0.6 (0.2) 0.71†

Eosinophils 0.2 (0.1) 0.2 (0.1) 0.12†

*Fisher’s Exact Test †Satterthwaite unequal variance t-test

79

3.3.7 Clinical Measures of Skin and Joint Disease Severity are Associated with the Expression of Candidate Biomarkers

Next, we investigated the association between clinical measures of skin disease (PASI score),

joint disease (number of swollen and tender joints, presence of axial arthritis), and non-specific

inflammation (ESR), with expression levels of the four candidate genes in PsA patients.

NOTCH2NL expression was positively correlated with number of swollen joints (r=0.38,

p=0.02), and SETD2 expression was negatively correlated with ESR (r=-0.51, p=0.001). Clinical

measures remained independently associated with expression of these genes after adjustment for

sex, age, and psoriasis duration.

3.3.8 Candidate Gene Expression Signals Originate from Specific Leukocyte Subpopulations

The functional roles of the identified biomarkers can potentially provide valuable insights into

the biological basis of PsA. However, because whole blood is comprised of a mixed cell

population it is difficult to attribute gene expression changes to a particular blood cell subset. To

gain more meaningful insights into PsA, it is necessary to purify specific leukocyte subsets from

whole blood and examine gene expression changes between PsA and PsC patients in each subset.

Activated NK cells have been described in the joints of PsA patients [184], and polymorphisms

within the MICA locus which encode an activating NK cell ligand [18, 19], as well as KIR genes

which encode activating and inhibitory NK cell receptors [54], suggest a pathogenic role for NK

cells in PsA. The strong genetic association to the MHC Class I, as well as findings of increased

numbers of CD4+ [185], CD8+ [186], and Th17 cells [187] in the synovial fluid and peripheral

blood of PsA patients suggest a role for these T cell subsets in PsA pathogenesis. Lastly,

macrophages and osteoclasts, which differentiate from circulating monocytic cells, have also

been described in increased numbers in the peripheral blood and inflamed joints of PsA patients

[97, 188], suggesting that monocytes may also be involved in the pathogenesis of PsA.

The expression of the four candidate genes was measured by qPCR in T cells of 10 PsA and 7

PsC patients, NK cells of 7 PsA and 6 PsC patients, and monocytes of 6 PsA and 5 PsC patients.

Although none of the four replicated genes were significantly differentially expressed in purified

80

cells of PsC and PsA patients, fold changes observed in certain purified cell types were

consistent with those observed in whole blood. Similar to the 1.45-fold increase in CXCL10

expression observed in whole blood, CXCL10 was also increased 1.73-fold in T cells and 1.60-

fold in monocytes of PsA patients compared to PsC patients, but was relatively unchanged in NK

cells, suggesting that both T cells and monocytes contribute to the whole blood signal of

CXCL10. NOTCH2NL was decreased 0.85-fold in monocytes, consistent with the 0.80-fold

decrease previously observed in whole blood. Similarly, SETD2 was decreased 0.72-fold in

monocytes, consistent with the 0.92-fold decrease observed in whole blood, suggesting that the

NOTCH2NL and SETD2 signals both originate from monocytes. Compared to the 1.12-fold

increase of HAT1 expression in whole blood, HAT1 was decreased 0.85-fold in T cells, 0.92-fold

in monocytes, and relatively unchanged in NK cells, suggesting that its signal might originate

from a different cellular source not examined in this study (Figure 3.5).

81

Figure 3.5. Mean normalized Ct value and fold change (FC) of the 4 replicated genes in isolated leukocyte subpopulations. Error bars

represent the standard error.

CXCL10 FC Whole Blood = 1.45

HAT1 FC Whole Blood = 1.12

SETD2 FC Whole Blood = 0.92

NOTCH2NL FC Whole Blood = 0.80

FC=1.73 FC=1.04 FC=1.60

FC=1.73 FC=1.04 FC=1.60

FC=1.32 FC=1.22 FC=0.85

FC=1.16 FC=1.03 FC=0.72

FC=0.85 FC=1.03 FC=0.92

82

3.4 Discussion

The separation of skin and inflammatory arthritis-specific risk factors, biomarkers, and

pathogenic mechanisms is a major challenge in the study of psoriatic disease. To this end, we

employed well-established microarray and qPCR array technology to profile whole blood and

identified 494 differentially expressed genes between psoriasis patients with and without

inflammatory arthritis. The inclusion of unaffected controls helped us to gain further insight into

the complex relationship between PsC and PsA. The majority of genes that were altered in PsA

compared to controls were also altered in PsC relative to controls, and in the same direction as in

PsA relative to PsC. However, these genes showed much smaller fold changes in PsC compared

to controls relative to PsA compared to PsC. These findings suggest that the same genes that are

altered in PsA are also altered in PsC, but their expression is exacerbated in patients with

arthritis, supporting a model of PsA as a more severe form of PsC.

Although many of the same differentially expressed genes in PsA vs controls were altered in PsC

vs controls, they were not statistically significant. This contrasts previous studies in peripheral

blood mononuclear cells (PBMCs) of psoriasis patients that identified several genes between PsC

and controls [189, 190]. The absence of differential expression was likely a consequence of the

type of sample used in our study. Whole blood has a heterogeneous cellular composition, which

may have introduced noise and obscured the detection of subtle gene expression changes

originating from less abundant cell subsets, resulting in small mean differences between PsC and

controls. Larger differences between PsC and controls would have likely been detectable in

PBMCs or immune cell subsets important in psoriasis pathogenesis, such as T helper 17 cells,

cytotoxic T cells, natural killer cells, and monocytes.

A biological theme that emerged from the most differentially expressed arthritis-specific genes

was the involvement of innate immunity through TLR signaling, leading to dysregulation of NF-

kB and associated chromatin remodeling complexes in PsA. The role of innate immunity

83

suggested by our data is consistent with evidence that bacterial infections, viral infections, or

tissue damage (called the ‘deep’ Kobner phenomenon) can lead to the development of PsA in

PsC patients [3, 55]. Microarrays identified upregulated expression of LY96 (MD-2), a cell-

surface protein that assocates with TLR4 [191], while qPCR arrays further identified the

downregulation of IKBKB, a kinase downstream of TLR4 activation that phosphorylates IkBa,

targeting it for degradation and removing its inhibition of NF-kB. The NF-kB subunits NF-kB1

(p50), c-Rel (Rel), and RelA (p65) were themselves down-regulated in PsA compared to PsC,

along with several members of the SWI/SNF chromatin remodeling complex (SMARCA4,

ARID1B, SMARCC1 and SMARCC2), which is involved in the correct targeting of NF-kB to

various inflammatory genes [192]. The SWI/SNF complex is an ATP-dependent multi-molecular

machine that is recruited by NF-kB to remodel nucleosomes and increase accessibility to its

‘slow’ response genes [192, 193]. The significance of their downregulation to the epigenetic and

subsequent transcriptional changes that may accompany the disease process in PsA is unknown.

PsA can progress to joint damage and disability if not diagnosed and treated in a timely manner.

Diagnosis is currently labour intensive, relying on examination by a rheumatologist and

radiographic imaging to recognize and distinguish PsA from rheumatoid arthritis, ankylosing

spondylitis, gout, and fibromyalgia [34]. To assess the performance of differentially expressed

genes as biomarkers of PsA, we chose NanoString technology because of its cost-effectiveness,

throughput, reproducibility, and proven applicability as a diagnostic assay [194]. Because we

found that in the whole blood of PsA and PsC patients, small fold changes were less reliably

measured than large fold changes, we limited our analysis to genes altered greater than 1.5-fold

up or down, and found that four genes (NOTCH2NL, HAT1, SETD2, and CXCL10) could be

replicated in an independent cohort of patients. NOTCH2NL was the best performing biomarker

individually, achieving an AUC of 0.71, and when the genes were combined as a panel, the AUC

improved to 0.79. Further improvement of the diagnostic performance of these transcriptomic

markers may be achieved by integrating genetic, demographic, and clinical data on PsA patients

to form a more comprehensive and robust diagnostic tool.

84

Mechanistically, NOTCH2NL is an interesting candidate biomarker as recent evidence supports

its involvement in bone homeostasis. NOTCH2NL is a 36kDa protein that is ubiquitously

expressed throughout the cell and is secreted [195]. It is highly homologous to NOTCH2, a

signaling protein and transcription factor whose activation by receptor activator of nuclear factor

kappa-B ligand (RANKL) promotes the development of osteoclasts (cells responsible for bone

resorption) from bone marrow macrophages [196]. Interestingly, NOTCH2NL can inhibit the

transcriptional activities of NOTCH2 in vitro [195]. We speculate that decreased expression of

NOTCH2NL reduces inhibition of NOTCH2, thereby promoting osteoclastogenesis and bone

erosions in PsA. Paradoxically however, we also found a correlation between lower NOTCH2NL

levels and lower swollen joint counts among PsA patients, suggesting it may play a different role

in PsA. The chemokine CXCL10 is also an interesting candidate biomarker as previous studies

have shown that it is elevated in the synovial fluid of patients with inflammatory arthritis

(including PsA) but not crystal arthritis [197], and its soluble form is elevated in PsA patients

compared to controls [198].

The identification of a tight cluster of nearly half of the PsA patients in the replication cohort is

interesting given the phenotypic heterogeneity of PsA. Five clinical patterns have been

identified: asymmetric oligoarthritis, symmetric polyarthritis similar to rheumatoid arthritis,

arthritis of the distal interphalangeal joints, spondyloarthritis (usually accompanied by peripheral

oligoarthritis), and arthritis mutilans [199]. Based on the NanoString replication data, 17 of our

PsA patients clustered into a group with a significantly higher prevalence of spondyloarthritis

(63%). The remaining 25 patients had a lower prevalence of spondyloarthritis (13%) and

clustered with the PsC patients. This enrichment of spondyloarthritis patients based on the

differential expression of genes involved in innate immunity is not unexpected, particularly

because the closely related HLA-B*27-associated spondyloarthridities, ankylosing spondylitis

and reactive arthritis, are partially innate immune-driven. Further validation of these innate

immune genes between the clinical subsets of PsA is warranted.

In summary, we have identified a gene expression signature of inflammatory arthritis (PsA) that

distinguishes it from cutaneous psoriasis alone (PsC). Our results suggest an important role for

85

innate immunity in the development of PsA, and particularly spondyloarthritis, through TLR

signaling, NF-κB, and associated chromatin remodeling complexes. NOTCH2NL, HAT1,

CXCL10, and SETD2 are potential biomarkers of PsA in PsC patients that warrant further

evaluation of their clinical utility.

86

C-X-C Motif Chemokine 10 is a Possible Biomarker for the

Development of Psoriatic Arthritis among Patients with

Psoriasis

Remy A. Pollock*, MSc(A), Fatima Abji*, MSc, Kun Liang, PhD, Vinod Chandran, MD, PhD,

and Dafna D. Gladman, MD, FRCPC

*Authors contributed equally to this work

4.1 Introduction

Cutaneous psoriasis (PsC) is a chronic inflammatory skin condition which is prevalent in 2-3%

of the population. Psoriatic arthritis (PsA), a seronegative inflammatory arthritis, develops in up

to thirty percent of psoriasis patients [35, 177, 200, 201]. PsA is a chronic condition that leads to

progressive joint destruction and is associated with pain, reduced quality of life, increased

mortality risk, and reduced work productivity [202]. PsA is currently undetected in

approximately 10-20% of PsC patients [35, 93, 200, 203]. Early diagnosis and treatment of PsA

is crucial, as the extent of joint disease at presentation can predict the progression of joint

destruction and radiological damage [204, 205]. Moreover, patients treated early in the course of

their disease fare better [70, 71]. The identification of biomarkers would facilitate early diagnosis

of PsA in patients with PsC.

We previously performed a case-control gene expression profiling study using peripheral blood

of PsA and PsC patients in order to identify candidate gene expression biomarkers of PsA [206].

Among the differentially expressed genes, chemokine (C-X-C motif) ligand 10 (CXCL10) was

consistently up-regulated in PsA compared to PsC in two independent cohorts. CXCL10, a CXC

subfamily chemokine, is secreted by multiple cell types in response to IFNγ and TNFα [207].

87

These include lymphocytes, monocytes, keratinocytes, fibroblasts and endothelial cells. CXCL10

has angiostatic properties and is characterized as an immune cytokine, activating and recruiting

leukocytes such as T cells, eosinophils, monocytes, and NK cells to sites of inflammation [208,

209].

The goal of this study was to determine if soluble CXCL10 could serve as a predictive biomarker

of PsA prior to its onset. To achieve this goal, we measured soluble CXCL10 in a prospectively-

followed longitudinal cohort of PsC patients, and determined if baseline concentrations were

significantly different in PsC patients that progress to develop PsA compared to PsC patients

who do not develop PsA. Furthermore, in a subset of converters, we also measured soluble

CXCL10 after PsA diagnosis to determine if temporal changes in CXCL10 expression

accompany the progression from PsC to PsA. We then compared CXCL10 mRNA levels in PsA

synovial fluid to whole blood samples and to gout synovial fluid cells in order to determine if

CXCL10 production is concentrated in the affected joint of PsA patients and its specificity to

PsA.

4.2 Materials and Methods

4.2.1 Study Subjects

Psoriasis patients without arthritis were recruited as part of an ongoing prospective, longitudinal

study that began in 2006, to assess the incidence of PsA and determine clinical and molecular

risk factors. A total of 620 subjects with psoriasis were screened at the time of this study.

Psoriasis patients were referred to this study if they were diagnosed for psoriasis by a

dermatologist, but did not have arthritis. All psoriasis patients were examined by a

rheumatologist at baseline to verify the absence of PsA. Patients returned for annual follow-up

visits and those who developed PsA, satisfying the ClASsification criteria for Psoriatic ARthritis

(CASPAR) criteria [75] were termed ‘converters’, while those that did not develop PsA were

termed ‘non-converters’. Patients are followed according to a standard protocol which includes a

complete history, including demographic and disease related features and physical examination,

88

including the assessment of psoriasis using the Psoriasis Area Severity Index (PASI). Baseline

serum samples were collected at the time of entry into the study and at each subsequent follow-

up visit, and bio-banked for later analysis. Converters and non-converters were matched based

on the duration of their psoriasis, which was taken from the date of diagnosis of the disease. The

study was performed in compliance with the Declaration of Helsinki and was approved by the

University Health Network Research Ethics Board.

4.2.2 Serum CXCL10 and CRP Assay

Baseline serum CXCL10 and C-Reactive protein (CRP) levels were measured in 46 converters

and 45 non-converters matched for psoriasis duration. Of these patients, 23 converters had serum

samples available at the time of PsA diagnosis and were also analyzed for CXCL10 and CRP

expression after PsA development. CXCL10 and CRP were measured using a microsphere-based

Luminex assay performed according to the manufacturer’s instructions (EMD Millipore,

Billerica, MA). Briefly, 25 µl of each serum sample was incubated with an analyte-specific

capture antibody conjugated to xMap® magnetic beads. A biotinylated detection antibody was

then added, followed by the reporter molecule streptavidin-PE. Plates were run on the Luminex®

200 platform (Luminex Corp., Austin, TX). Samples were run in duplicate and CXCL10 was

quantified relative to a 5-fold serially diluted standard provided with the kit, using a 5 parameter

logistic regression curve.

4.2.3 CXCL10 Gene Expression Analysis

Whole blood was collected in Tempus® tubes (Life Technologies, Carlsbad, CA) from 4 PsA

patients, and RNA was extracted according to the manufacturer’s instructions. Patients with PsA

(n=8) and gout (n=6) undergoing routine knee joint aspirations at Toronto Western Hospital were

recruited for collection of synovial fluid. RNA from synovial fluid cells was obtained by

centrifugation, treatment with red blood cell lysis buffer, and storage in Trizol® reagent (Life

Technologies). RNA was obtained by phenol-chloroform extraction and purification with

RNeasy® miniprep kits (Qiagen, Venlo, Netherlands). RNA was reverse transcribed using the

Maxima First Strand cDNA Synthesis Kit (Life Technologies). CXCL10 cDNA was amplified

with Platinum® SYBR® Green qPCR SuperMix (Life Technologies) using forward primer

89

5’GTGGCATTCAAGGAGTACCTC3’ and reverse primer

5’TGATGGCCTTCGATTCTGGATT3’. Reactions were performed in triplicate on an ABI 7500

HT. Ct values for CXCL10 were normalized to GAPDH to generate ΔCt values for individual

samples, which were compared between groups by Student’s t test. Fold change between groups

was determined by the ΔΔCt method wherein ΔΔCt = mean ΔCtgroup1 – mean ΔCtgroup2 and fold

change = 2-ΔΔCt.

4.2.4 Statistical Analysis

The Kolmogorov–Smirnov normality test was performed and found that CXCL10 and CRP

levels were not normally distributed in all groups. The distribution of CXCL10 concentrations

are shown in Appendix 2. CXCL10 and CRP concentrations in converters and non-converters

were compared by Mann-Whitney U test. Further analysis was performed by multivariable

logistic regression using CXCL10 concentration, age, sex, psoriasis duration, and duration of

follow-up as predictor variables with converter status (converter vs. non-converter) as the

outcome. CXCL10 and CRP concentrations were compared pre- and post-conversion by paired-

sample Wilcoxon Signed Rank test. Multivariable logistic regression was also performed to

compare the predictive abilities of CXCL10 concentration, and clinical variables previously

identified as predictors of PsA in patients with PsC, with converter status as the outcome. These

included PASI score, presence of psoriatic nail lesions, presence of scalp psoriasis, obesity

(BMI>30), education level (1-5, where 1=grade school incomplete, 2=high school incomplete,

3=high school graduate, 4=college, 5=university), and family history of PsA among first-degree

relatives.

90

4.3 Results

4.3.1 Baseline Patient Characteristics

The study period spans 2006 to 2014, during which a total of 52 psoriasis patients developed

PsA, of whom 46 patients had serum samples available at baseline (converters). These

individuals were matched to 45 PsC patients who did not develop PsA over the same psoriasis

duration (non-converters). Of the converters, 23 patients had serum samples taken at baseline

and again at the time of PsA diagnosis. The demographic and clinical characteristics are

summarized in Table 4.1. There were no significant differences in the baseline demographic and

clinical characteristics of converters and non-converters.

91

Table 4.1. Demographic and clinical characteristics of the study subjects at baseline.

Converters

N=46

Non-Converters

N=45

P Value‡

Sex (males) 25 (54.3%) 20 (44.4%) 0.35

Age (years)* 46.6 (13.2) 46.1 (12.3) 0.85

Duration of psoriasis (years)^ 15.0 (4.0-30.3) 13.0 (4.0-28.0) 0.66

Duration of follow-up (years)^ 3.0 (1.3-4.9) 3.1 (2.1-4.2) 0.38

Age of PsA onset (years)* 51.4 (13.4) N/A N/A

PASI^ 3.75 (2.8-9.7) 5.6 (2.1-9.2) 0.91

Nail lesions (presence/absence) 30 (66.7%) 25 (55.6%) 0.25

Scalp lesions (presence/absence) 32 (69.6%) 33 (73.3%) 0.69

Obesity (BMI>30) 15 (32.6%) 12 (26.7%) 0.54

Education level† 5.0 (3.0-5.0) 5.0 (4.0-5.0) 0.31

Positive family history of PsA (1st

degree relatives)

2 (4.3%) 2 (4.4%) 0.98§

* Mean (standard deviation)

^Median (interquartile range)

† Ordinal variable where 1=grade school incomplete, 2=high school incomplete, 3=high school graduate,

4=college, 5=university)

‡ Student’s t-test (continuous variables) or Pearson’s chi square test (categorical variables)

§ Fisher’s Exact Test

92

4.3.2 Baseline CXCL10 is Elevated in Converters Compared to Non-converters

First, we compared serum CXCL10 concentrations in converters and in non-converters matched

for their duration of psoriasis. As shown in Figure 4.1, CXCL10 was significantly elevated in

converters (median 493.2 pg/ml, interquartile range [IQR] 356.3-984.4 pg/ml) compared to non-

converters (median 370.7, IQR 263.3-578.2 pg/ml, p<0.005). CXCL10 was then used to predict

converter status with age, sex, psoriasis duration, and duration of follow-up time in the study as

covariates. As shown in Table 4.2, CXCL10 remained significantly associated with converter

status (OR=1.3, 95% confidence interval [CI] 1.1-1.5, p=0.004).

Figure 4.1. Scatter dot plot of baseline serum CXCL10 levels from 46 converters and 45 non-

converters. Error bars represent median ± IQR. CXCL10 was significantly higher in converters

(median 493.2, IQR 356.3-984.4 pg/ml682.8 ± 472.5 pg/ml) than in non-converters (median

370.7, IQR 263.3-578.2 419.4 ±219.8 pg/ml, p=0.005, Mann Whitney U test).

93

Table 4.2. Baseline CXCL10 as a predictor of PsA converter status. Multivariable logistic

regression using CXCL10 to predict converter status with age, sex, psoriasis duration and

duration of follow-up time in the study as covariates.

Covariate Odds Ratio 95% CI P Value

Age 1.0 0.9-1.0 0.38

Sex 1.2 0.5-2.9 0.75

Psoriasis duration 1.0 1.0-1.0 0.99

Duration of follow-up 1.0 1.0-1.0 0.45

CXCL10 1.3 1.1-1.5 0.004

4.3.3 CXCL10 is Higher in Converters at Baseline than after PsA Diagnosis

Next, we investigated the expression of CXCL10 in 23 converters pre- and post-conversion to

PsA. Of these 23 converters at the time of PsA diagnosis, 3 patients (13%) had enthesitis, 1 (4%)

had dactylitis, 7 (30%) had axial disease, 2 (9%) had 1 active (swollen and/or tender) joint

(monoarthritis), 6 (27%) had between 2-4 active joints (oligoarthritis), and 6 (27%) had 5 or

more active joints (polyarthritis). As shown in Figure 4.2, CXCL10 was significantly higher

(p<0.0001) in converters at baseline (median 927.4 pg/ml, IQR 547.6-1243 pg/ml) than after the

diagnosis of PsA (median 491.5 pg/ml, IQR 323.2-607 pg/ml). Intermediate samples taken at

follow-up (between the baseline sample and post-PsA diagnosis sample) from a subset of these

patients show that CXCL10 expression declined slightly prior to conversion to PsA, but the

difference compared to baseline levels was not statistically significant (Appendix 3).

94

Figure 4.2. Scatter dot plot of paired CXCL10 serum concentrations from 23 PsC patients before

and after the development of PsA. CXCL10 was significantly higher in patients before (median

927.4, IQR 547.6-1243 pg/ml) compared to after (median 491.5, IQR 323.2-607 pg/ml) PsA

onset (p<0.0001, Wilcoxon Signed Rank test).

4.3.4 CXCL10 mRNA Expression is High in PsA Synovial Fluid

Comparison of mRNA expression levels of CXCL10 in inflamed joints and whole blood of PsA

patients showed that CXCL10 was significantly increased 17.3-fold in synovial fluid cells

compared to blood cells of PsA patients (p=0.01). Furthermore, we sought to determine if

expression of CXCL10 is specific to inflammatory mechanisms of PsA. We compared CXCL10

expression in synovial fluid cells of PsA patients relative to patients with gout, an inflammatory

arthritis that is often difficult to differentiate from PsA. CXCL10 was significantly increased

44.3-fold in synovial fluid cells of PsA patients relative to patients with gout (p=0.001),

suggesting that this elevation is specific to PsA (Figure 4.3).

95

Figure 4.3. CXCL10 gene expression in peripheral whole blood (Blood PsA, n=4), synovial

fluid cells of PsA patients (SF PsA, n=8), and synovial fluid cells of gout patients (SF Gout,

n=6). Fold change was calculated using the ΔΔCt method (see Materials and Methods).

Significant differences were determined by comparing ΔCt values between groups. Error bars

represent mean ± SD. CXCL10 expression was increased 17.3-fold in synovial fluid compared to

blood of PsA patients (p=0.01) and 44.3-fold in synovial fluid of PsA patients compared to

patients with gout (p=0.001).

4.3.5 CXCL10 is Independent of Clinical Predictors of PsA

Several clinical variables have been suggested to be predictors of PsA in PsC patients, including

psoriasis severity, presence of scalp psoriasis, nail lesions, low level of education, obesity, and

family history of PsA. CXCL10 was compared to these clinical variables in a multivariate

analysis. CXCL10 was the only variable that significantly predicted converter status (OR=1.3,

95% CI 1.1-1.5, p=0.004) (Table 4.3). These results suggest that CXCL10 is independent of

these clinical variables and may be a soluble predictive biomarker of PsA in patients with PsC.

96

Table 4.3. Baseline CXCL10 compared to clinical predictors of conversion of PsA.

Multivariable logistic regression using CXCL10 concentration to predict converter status with

clinical predictors of conversion to PsA as covariates.

Covariate Odds Ratio 95% CI P Value

PASI 1.0 0.9-1.1 0.56

Nail lesions 1.1 0.5-3.2 0.66

Scalp lesions 1.5 0.4-3.0 0.96

Education level (1-5) 0.8 0.5-1.2 0.27

Obesity (BMI>30) 1.4 0.6-4.2 0.39

Family history of PsA 2.7 0.2-11.1 0.79

CXCL10 1.3 1.1-1.5 0.004

97

4.3.6 CRP is Elevated in Converters after PsA Diagnosis but not Compared to Non-Converters

CRP is an acute-phase reactant commonly used in clinical practice as a marker of inflammation.

We measured serum CRP expression in order to assess its predictive ability in this patient cohort.

As shown in Figure 4.4, there was no significant difference in CRP levels in converters (median

35.63 µg/ml, IQR 15.49-70.53 µg/ml) compared to non-converters (median 23.54 µg/ml, IQR

10.5-45.36 µg/ml, p=0.147). These results indicate that CRP is not a valid predictive biomarker

of PsA. We also measured CRP expression in converters before and after the diagnosis of PsA

and found a significant increase in CRP levels after PsA onset (median 36.1 µg/ml, IQR 14.74-

101.7 µg/ml) than at baseline (median 26.6 µg/ml, IQR 16.37-62.75 µg/ml, p=0.003, Figure 4.5).

Figure 4.4. Scatter dot plot of baseline CRP serum levels from 46 converters and 45 non-

converters. Error bars represent median ± IQR. CRP levels were not significantly different

between converters (median 35.63, IQR 15.49-70.53 µg/ml) and non-converters (median 23.54,

IQR 10.5-45.36 µg/ml, p=0.147).

98

Figure 4.5. Scatter dot plot of paired CRP serum levels from 23 PsC patients before and after the

development of PsA. CRP was significantly higher after PsA onset (median 36.1, IQR 14.74-

101.7 µg/ml) than at baseline (median 26.6, IQR 16.37-62.75 µg/ml, p=0.003).

99

4.4 Discussion

Identification of PsA in clinical practice is based on the combination of physician expertise,

radiographic imaging, and screening questionnaires. Due to a lack of awareness and well-

validated screening tools, PsA is often misdiagnosed or under-diagnosed. A reliable predictor of

PsA susceptibility in patients with PsC would be an invaluable tool for clinical use.

Clinical variables and environmental exposures have been examined as possible predictors of

PsA. Retrospective analyses have suggested that the presence of psoriatic nail lesions [90, 91],

scalp, intergluteal, or perianal psoriasis [90], use of corticosteroids [92], psoriasis severity (PASI

score) [93], trauma [55, 56], changing residence, rubella vaccination [56], heavy lifting,

infections [55] and family history of PsA [57] may be predictive of PsA. Prospective analysis

further identified obesity, lower level of education [210], and subclinical enthesitis [94]. Genetic

risk factors have also been examined, and thus far include human leukocyte antigen (HLA) B

alleles B*27, B*08, and B*38 [95, 96], and many other polymorphisms throughout the genome

[211].

Unlike rheumatoid factor or anti-citrullinated protein antibody in rheumatoid arthritis, no

objectively measurable soluble biomarker has been identified for PsA. The present study is, to

our knowledge, the first study to examine soluble proteins in PsC patients who converted to PsA

and PsC patients that did not develop PsA from a longitudinal prospective cohort. CXCL10 was

significantly associated with converters compared to non-converters, and this association appears

to be independent of clinical variables such as PASI score, presence of nail lesions, scalp

psoriasis, education level, obesity and positive family history of PsA. Although the present

sample size is small, these data provide preliminary evidence suggesting that CXCL10 may be a

soluble biomarker of PsA.

CXCL10, also known as interferon-γ-induced protein (IP-10), is a member of the CXC

subfamily of chemokines that display angiostatic properties. Its secretion is dependent upon

100

IFNγ and high levels are indicative of host immune response activation, particularly activation of

Th1 cells. Localized production of CXCL10 drives the recruitment of cytotoxic T cells, natural

killer (NK) cells, monocytes and dendritic cells. Recruitment of T cells to target tissues further

increases IFNγ and TNFα release, causing a positive feedback loop to stimulate additional

CXCL10 production. In addition to chemotaxis of immune cells to affected tissues, [212, 213],

CXCL10 displays pro-inflammatory properties on multiple levels, including cross-talk with other

proteins such as RANKL [214], as well as promotion of T cell adhesion to endothelial cells

[208].

Serum and/or plasma CXCL10 levels are elevated in patients with several immune-mediated

disorders including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), autoimmune

thyroiditis, type 1 diabetes, and scleroderma and it is also present in affected tissues [198, 215-

217]. Increased CXCL10 levels have also been reported in serum of PsA [198] and psoriasis

vulgaris patients [218]. A phase II clinical trial of a monoclonal CXCL10 antibody showed some

clinical efficacy in the treatment of RA [219]. Additionally, treatment with the anti-TNFα agent

etanercept also results in reduced serum levels of CXCL10 in patients with RA [220].

CRP levels were elevated in these patients after the development of PsA compared to baseline

levels. CRP levels increase following the release of cytokines such as IL-6 from macrophages

and T cells [221]. On the other hand, we found that CXCL10 was elevated in the serum of PsC

patients who later developed PsA, but following PsA onset returned to levels closer to those

observed in PsC patients who did not develop PsA. The explanation for these results is not clear

at present. One possible explanation for the reduction in CXCL10 is that over time, circulating

levels of CXCL10 are reduced and its production becomes more localized to target tissues. This

may be the result of an accumulation of activated lymphocytes and/or local cellular CXCL10

production. This is supported by the observation that CXCL10 mRNA expression is dramatically

(17.3-fold) higher in cells from synovial fluid of PsA patients than in cells from whole blood.

Also, a 44.3-fold increased expression of CXCL10 in PsA than gout suggests that this effect is

not only the result of inflammation but is important in the biology of PsA. These results are

corroborated by previous reports of high levels of CXCL10 in psoriatic skin, synovial fluid [197,

101

212], and PsA serum compared to controls, and its negative correlation with disease duration

[198, 222]. Taken together, these results support a role for CXCL10 in PsA pathogenesis, but

suggest further longitudinal studies are needed to shed light on the mechanisms involved.

Several limitations of this study should be noted. One limitation was the small number of

patients analyzed. Obtaining patients for this type of prospective study of PsA is difficult over

shorter time periods given that the annual incidence of PsA in our cohort of prospectively-

followed psoriasis patients is 3.1 (2.2-4.0) PsA cases per 100 psoriasis patients [210]. Continued

follow-up of psoriasis patients will help to increase the number of PsA converters available for

analysis in years to come. Similarly, at this time additional baseline samples from psoriasis

converters are not available from other cohorts, which preclude the possibility of performing an

independent validation of these results.

Results of the comparison of CXCL10 levels before and after conversion to PsA must be

interpreted cautiously as the 2nd measurement was performed on only 50% of the converters,

because many patients did not return for follow-up in the PsA clinic. Patients who did not return

for follow-up were significantly younger (mean [SD] age 42.4 [2.9] years versus 50.9 [2.4]

years, p=0.03), had a significantly shorter psoriasis duration (median 10.0 years [IQR 2.0-26.0]

versus 23.0 years [IQR 9.0-39.0], p=0.04), and significantly lower baseline CXCL10 levels

(median 360.2 pg/ml [IQR 293.1-407.0 pg/ml] versus 927.4 pg/ml [IQR 547.6-1242.8 pg/ml],

p<0.001) than patients who returned for follow-up. The comparison of CXCL10 levels before

versus after conversion might therefore be biased, and the observation of a drop in CXCL10

levels might apply only to this group of patients. Furthermore, the majority (18/23) of these

converters had begun treatment with NSAIDs, DMARDs, and even biologic drugs when the

second sample was taken, raising the possibility that the observed decrease of CXCL10 is due to

medications. However, it must be noted that CXCL10 levels also decreased by a similar

magnitude in 4 of the 5 converters who did not start on drugs before the 2nd sample. The

decrease in CXCL10 at follow-up is nonetheless an important observation, given the previous

reports of decreased CXCL10 at follow-up of children with type 1 diabetes [216], and the inverse

relationship between CXCL10 levels and PsA duration [198].

102

Lastly, it is possible that PsC patients included in the non-converters group may develop PsA in

the future. In this preliminary case-control analysis, this right censoring of the data was not taken

into account. In follow-up studies, more sophisticated survival analysis, which takes into account

the distribution of time to the development of PsA, will be necessary to provide robust evidence

of the ability of CXCL10 to serve as a predictive biomarker of PsA.

In summary, CXCL10 levels are significantly elevated in PsC patients who convert to PsA,

compared those who do not develop PsA. The association of CXCL10 with conversion to PsA

appears to be independent of PASI score, presence of nail lesions, scalp psoriasis, education

level, and obesity. Increased CXCL10 in PsC patients prior to PsA onset, and its subsequent

drop following PsA diagnosis might reflect an important role for CXCL10 in the pathogenesis of

PsA. Future studies will aim to elucidate the dynamics of CXCL10 expression during the

progression from PsC alone to PsA, and assess the clinical validity and biomarker performance

of CXCL10 in additional prospectively-followed PsC patients, to determine if it may be useful

alone or in combination with other clinical and molecular information to predict PsA in PsC

patients.

103

Further Evidence Supporting a Parent-of-Origin Effect in

Psoriatic Disease

Remy A. Pollock, MSc(A), Arane Thavaneswaran, MMath, Fawnda Pellett, BSc, Vinod

Chandran, MBBS, MD, DM, PhD, Art Petronis, MD, PhD, Proton Rahman, MD, MSc, FRCPC,

Dafna D. Gladman, MD, FRCPC

Originally published in Arthritis Care & Research:

Arthritis Care Res (Hoboken). 2015; 67(11): 1586-90. doi: 10.1002/acr.22625.

5.1 Introduction

Psoriatic disease refers to a family of auto-inflammatory conditions associated with psoriasis,

which includes psoriatic arthritis (PsA), a seronegative arthritis that develops in 30% of patients

with psoriasis [34, 223]. Psoriasis and PsA are thought to result from the interplay of

environmental and genetic risk factors related to skin and joint disease [3, 7, 55]. Psoriasis and

PsA affect men and women equally, although disease expression differs between the sexes, with

men developing more axial disease and radiographic damage, and women developing more

peripheral polyarthritis [39]. However, intriguingly, there is a differential pathogenicity and

expression of psoriatic disease that depends on the sex of the disease-transmitting parent. This

“parent-of-origin” effect has been investigated in several independent cohorts. In 114 psoriasis

families from the Faroe Islands, a significantly greater percentage of offspring of psoriatic

fathers developed psoriasis compared to offspring of psoriatic mothers (28% compared to 21%,

p<0.015) [224]. A study in 794 Scottish psoriasis patients similarly found that 13% of patients

had an affected father compared to 11% with an affected mother (p=0.04), and also found

evidence for a greater reduction in age of onset between generations in paternal compared to

maternal transmissions (24.1 years compared to 10.9 years, p=0.009), which is consistent with

genetic anticipation [30]. The paternal transmission bias was also demonstrated in a cohort of 95

104

Canadian PsA patients, in whom 65% had an affected father compared to 35% with an affected

mother (p=0.001) [225]. In this cohort, paternally transmitted disease was associated with a

higher frequency of skin lesions prior to arthritis, higher erythrocyte sedimentation rate (ESR),

and lower incidence of rheumatoid factor.

Previous parent-of-origin studies did not distinguish between cutaneous psoriasis without PsA

(PsC) and the presence of PsA in the probands or parents. This distinction is important, as there

is increasing evidence of disparate risk factors and pathological mechanisms underlying skin and

joint manifestations of psoriatic disease. The goal of this study was to further explore the parent-

of-origin effect a large cohort of well phenotyped patients with PsC or with PsA.

5.2 Patients and Methods

5.2.1 Patient Populations

Patients with PsA and PsC were recruited from the University of Toronto Psoriatic Arthritis

Program at Toronto Western Hospital (Toronto, Ontario, Canada). The 95 Canadian PsA patients

analyzed previously [225] are included in this cohort. Additional PsA patients were recruited

from Memorial University of Newfoundland (St. John’s, Newfoundland, Canada). All PsA

patients were diagnosed by a rheumatologist and satisfied the Classification of Psoriatic Arthritis

(CASPAR) criteria [75]. PsC patients had chronic plaque psoriasis and were examined by a

rheumatologist to exclude PsA. The study was conducted with the approval of the University

Health Network Research Ethics Board and all subjects provided written informed consent.

5.2.2 Data Acquisition and Statistical Analyses

Family history of psoriatic disease (PsC or PsA) was ascertained through a standard clinical

protocol completed by a rheumatologist or questionnaire completed by the patient (Appendix 4).

In all cases, the patient was considered the proband, and had at least one parent (father or mother

105

or both) affected with PsC or PsA. To compare proportions of paternally and maternally

transmitted disease, data were treated as pair matched data and analyzed using McNemar’s test

with continuity correction. Chi-square test was used to compare proportions of fathers and

mothers with PsA and PsC in probands with PsA. Proportions of father-son or father-daughter,

and mother-son or mother-daughter transmissions were compared using a normal approximation

to the binomial distribution.

Probands with maternally and paternally transmitted disease were compared with respect to

clinical and genetic variables at baseline by logistic regression. Univariate regressions were

performed using various clinical and genetic variables as the predictor variables and paternally

versus maternally transmitted disease as the outcome. Multiple regressions were also performed

using paternal versus maternal transmission as the predictor variable with sex of the proband,

and the interaction between parental transmission and proband sex as covariates. Clinical

variables examined were: age (less than or greater than 40 years), sex, race (Caucasian versus

other), age at first symptoms of psoriasis and PsA (less than or greater than 40 years), interval

between psoriasis and PsA (less than or greater than 10 years), medication use (DMARDs or

biologics), presence of nail lesions, total and damaged joint counts (less than or greater than

five), and presence of axial disease. Genetic variables investigated included the known PsC or

PsA susceptibility alleles HLA-C*01, C*02, C*06, C*12, HLA-B*07, B*08, B*27, B*38, B*39,

B*57, HLA-DR4, DR7, DQ*0303, and MICA-129Met. Analyses were performed in SPSS

Statistics version 22 and SAS 9.2.1.

5.3 Results

5.3.1 Characteristics of Probands Reporting a Parental History

Eight hundred and forty-nine probands reported a first-degree relative affected with psoriatic

disease (PsC or PsA), of which 532 (63%) reported an affected parent. Of these, 23 probands

reported that both parents were affected. The probands were 55.4% male and 90.0% Caucasian.

At first visit to the clinic, the mean (standard deviation) age was 42.6 (12.9) years, the mean age

106

of psoriasis onset was 25.2 (14.0) years, and 95.5% of patients had psoriatic skin lesions, 60.9%

had nail lesions, and the mean psoriasis area severity index (PASI) score was 6.0 (7.1). Three

hundred and ninety-two probands had PsA, with a mean age of PsA onset of 34.4 (12.7) years,

mean interval between onset of psoriasis and PsA of 10.1 (11.9) years, and 29.6% prevalence of

axial disease.

5.3.2 Parent-of-Origin Effect in Psoriatic Disease, PsA, and PsC

To test the null hypothesis that the proportions of affected mothers and fathers are equal, we

performed McNemar’s test on pair matched parental data (Table 5.1). Consistent with previous

reports, we found a significantly larger proportion of fathers with psoriatic disease compared to

mothers with psoriatic disease (289 [57%] of 509 discordant pairs versus 220 [43%] of 509

discordant pairs, respectively, odds ratio [OR]=1.3 and 95% confidence interval [CI] 1.1-1.6,

p=0.003).

Table 5.1. Cross tabulation of disease status in fathers and mothers of all probands.

Mothers

PsD Normal Total

Fathers

PsD 23 289 (57%)* 312

Normal 220 (43%)* 0 220

Total 243 289 532

Cell values represent the number of probands reporting each combination of parental disease

status. *Percentage of 509 discordant pairs.

107

Next, probands were divided into those with PsA or PsC only and the analysis was repeated

(Tables 5.2, 5.3). In the 392 probands with PsA (Table 5.2), the proportion of affected fathers

was significantly greater than the proportion of affected mothers (214 [57%] of 375 discordant

pairs versus 161 [43%] of 375 discordant pairs, OR=1.3, 95% CI 1.1-1.6, p=0.007). Furthermore,

the proportion of PsA probands having fathers affected with PsC as opposed to PsA (or, paternal

PsC—proband PsA pairs) was significantly larger than the proportion of PsA probands having

mothers affected with PsC (maternal PsC—proband PsA pairs) (161 [75%] of 214 paternal

transmissions had PsC, compared to 103 [64%] of 161 maternal transmissions with PsC,

p=0.02). In the 140 probands with PsC only (Table 5.3), the proportion of affected fathers was

also greater than the proportion of affected mothers, however this difference did not reach

statistical significance (75 [56%] of 134 discordant pairs versus 59 [44%] of 134 discordant

pairs, OR=1.3, 95% CI 0.9-1.8, p=0.20).

Table 5.2. Cross tabulation of disease status in fathers and mothers of the PsA probands.

Mothers

PsD Normal Total

Fathers

PsD 17 214 (57%)* 231

Normal 161 (43%)* 0 161

Total 178 214 392

Cell values represent the number of probands reporting each combination of parental disease

status. *Percentage of 375 discordant pairs.

108

Table 5.3. Cross tabulation of disease status in fathers and mothers of the PsC probands.

Mothers

PsD Normal Total

Fathers

PsD 6 75 (56%)* 81

Normal 59 (44%)* 0 59

Total 65 75 140

Cell values represent the number of probands reporting each combination of parental disease

status. *Percentage of 134 discordant pairs.

5.3.3 Differences between Patients with Paternally and Maternally-Transmitted Disease

No clinical or genetic variables were associated with paternally transmitted disease at p<0.05.

Since the Newfoundland population is known to be a founder population with a unique genetic

architecture, we performed a subset analysis of the differences between patients with paternally

and maternally-transmitted disease using only the Newfoundland probands. In the 92

Newfoundland PsA probands, there was a larger proportion of affected fathers (50 [58%] of 86

discordant pairs) than affected mothers (36 [42%] of 86 discordant pairs), a difference that did

not reach statistical significance (p=0.16). However, in this more homogeneous cohort,

paternally transmitted disease was associated with higher carriage of the PsA risk allele HLA-

B*08 (OR=3.2, 95% CI 1.1-9.7, p=0.04) and lower carriage of psoriasis risk allele MICA-129Met

(OR=0.4, 95% CI 0.1-0.9, p=0.03) (Table 5.4).

109

Table 5.4. Results of univariate logistic regression models examining the association between

paternally-transmitted disease and clinical and genetic variables in PsA patients from

Newfoundland.

Variable Odds Ratio

(Paternal Transmission)

P Value 95% Confidence

Interval

Age (<40 years) 1.14 0.77 0.48-2.73

Sex (male) 1.25 0.61 0.53-2.95

Age of PsC onset

(<40 years)

0.91 0.87 0.29-2.84

Age of PsA onset

(<40 years)

1.01 0.99 0.41-2.45

Interval (<10 years) 1.05 0.91 0.45-2.48

Nail disease 1.59 0.36 0.60-4.22

DMARDs 1.50 0.73 0.15-15.46

HLA-C*01 0.56 0.37 0.15-1.99

HLA-C*02 0.51 0.40 0.11-2.44

HLA-C*06 0.49 0.13 0.20-1.23

HLA-C*12 1.89 0.46 0.35-10.33

HLA-B*07 0.88 0.80 0.31-2.50

HLA-B*08 3.19 0.04 1.05-9.70

HLA-B*27 0.67 0.47 0.22-1.98

HLA-B*38 n/a n/a n/a

HLA-B*39 n/a n/a n/a

HLA-B*57 0.42 0.12 0.14-1.25

HLA-DR4 1.49 0.39 0.60-3.72

HLA-DR7 0.64 0.33 0.27-1.55

HLA-DQ*0303 0.43 0.11 0.15-1.22

MICA-129Met 0.37 0.03 0.15-0.93

110

5.3.4 Influence of Sex of the Proband

We observed a significant excess of father-to-son transmissions compared to father-to-daughter

transmissions (57.5% vs. 42.5%, p=0.01), but no difference between mother-to-son compared to

mother-to-daughter transmissions (52.9% vs. 47.1%, p=0.42). When the sex of the proband was

included in the multivariable model, neither paternally transmitted disease nor the interaction

term was associated with clinical or genetic variables in all PsA and PsC probands combined.

However, in the same model, male sex of the proband was associated with higher HLA-B*38

carriage (OR=2.6, 95% CI 1.0-6.6, p=0.03) and a higher prevalence of nail lesions (OR=2.2,

95% CI 1.2-4.1, p=0.01). Furthermore, the significant association between male sex and HLA-

B*38 carriage was evident in PsA probands (OR=3.1, 95% CI 1.1-8.5, p=0.03) but not PsC

probands (OR=0.9, 95% CI 0.1-15.0, p=0.94) (Table 5.5).

111

Table 5.5. Significant results from multivariable logistic regression models examining the association between paternally-transmitted

disease and clinical and genetic variables, adjusted for sex of the proband.

Parent Sex (Male) Proband Sex (Male) Parent Sex*Proband Sex

Variable OR P Value 95% CI OR P Value 95% CI OR P Value 95% CI

Nail disease 0.94 0.83 0.53 1.67 2.24 0.01 1.22 4.11 1.14 0.76 0.51 2.56

HLA-B*38 0.72 0.57 0.23 2.22 2.58 0.05 1.02 6.56 1.29 0.71 0.34 4.82

HLA-B*38

(PsA probands

only) 0.36 0.55 0.15 2.02 3.09 0.03 1.13 8.51 1.09 0.91 0.24 5.02

HLA-B*38

(PsC probands

only) 1.79 0.64 0.15 20.91 0.89 0.94 0.05 15.04 3.58 0.44 0.14 93.3

112

5.4 Discussion

We replicated previous findings of a paternal transmission bias in psoriatic disease in a combined

cohort of probands with and without inflammatory arthritis. The paternal transmission bias was

evident in both subsets of PsA and PsC probands, although it was not statistically significant in

the smaller sample of PsC probands. Moreover, we found a significantly greater number of PsA

probands reporting fathers affected with PsC as opposed to PsA (paternal PsC—proband PsA

pairs) compared to PsA probands reporting mothers affected with PsC as opposed to PsA

(maternal PsC—proband PsA pairs). If PsA is considered a more severe form of psoriatic

disease than PsC alone, this finding suggests that there is a greater chance of an increase in

disease severity when psoriatic disease is transmitted by an affected male compared to an

affected female. This complements the previous report of a greater reduction in age of onset

between generations if psoriasis is transmitted by an affected male compared to an affected

female [30], and further supports the phenomenon of genetic anticipation during male

transmission of psoriatic disease.

The previous study in a subset of our PsA patients noted a trend towards less clinical and

radiologic damage, a higher frequency of skin lesions prior to arthritis, higher erythrocyte

sedimentation rate (ESR), and lower incidence of rheumatoid factor in patients with paternally

transmitted disease [225]. Apart from a weak association with fewer damaged joints in the

Toronto PsA patients, these associations with paternally transmitted disease could not be

replicated. Instead, we found that disease expression was more strongly associated with the sex

of the proband. Male probands had a higher prevalence of nail lesions, which is consistent with

other published studies [226, 227]. Interestingly, we also found that male probands, specifically

male PsA probands, had higher carriage of the PsA risk allele HLA-B*38. Previous studies have

demonstrated higher carriage of HLA-C*0602 [228], HLA-B*27 [229, 230], and an HLA

haplotype that includes TNFA, TAP1, and HLA-DRB1 [231] in males with psoriatic disease, but

to our knowledge an association between PsA males and HLA-B*38 has not been previously

described.

113

Paternally transmitted disease was associated with genetic variables only in probands from

Newfoundland. This was likely because probands from Newfoundland stem from a more

genetically homogeneous founder population than the admixed Toronto probands [232]. In

Newfoundland PsA probands, paternally transmitted disease was associated with a higher

carriage of PsA risk allele HLA-B*08, and lower carriage of psoriasis risk allele MICA-129Met.

It should be noted that numerous clinical and genetic variables were analyzed, so some reported

associations might have reached significance by chance. Further studies are necessary to validate

these results in different cohorts, and investigate whether paternally transmitted disease is

associated with other other susceptibility loci within and outside of the major histocompatibility

complex.

The paternal transmission bias alludes to the possible involvement of sex chromosome-linked

effects, unstable repeat expansions, or genomic imprinting in the aetiopathogenesis of psoriatic

disease. Thus far, no psoriatic disease risk loci have been identified on the sex chromosomes.

Twenty years ago, unstable repetitive DNA sequences were hypothesized to expand during

premeiotic cell divisions in the male germ line and lead to psoriasis [233], however none have

been demonstrated to date. Genomic imprinting, or the differential expression of genes

depending on parental sex, is mediated by epigenetic marks such as modifications of DNA and

histones. Aberrant imprinting is associated with cancer, growth defects, and neurodevelopmental

disorders, and has been putatively implicated in psoriasis due to the identification of a strong

genetic association with the CARD15 (NOD2) locus when conditioned on paternal inheritance

[234]. More recently, MICA, IRIF1, PSORS1C3, TNFSF4, and three intergenic regions on

chromosome 8 were found to be hypermethylated in PsA patients with paternally-transmitted

disease compared to patients with maternally-transmitted disease, while PSORS1C1 was found to

be hypomethylated [235].

The present study is not population-based and may suffer from various ascertainment biases. For

example, the sex of the affected parent may have influenced the likelihood of a proband entering

our cohort and being included in the study. Men have been found to suffer from more severe and

extensive psoriasis than women [236], which might result in a greater recognition of paternal

114

skin disease among children of PsC males and a greater likelihood that they would participate in

a study. Similarly, women with PsA report more severe limitations in function and worse quality

of life than men [39], which could result in higher recognition of a woman’s joint disease among

her family members, and a greater likelihood that children of PsA females would participate.

Furthermore, the accuracy of the self-reported family history used in this study depends on the

proband’s ability to discriminate between affected and unaffected relatives. However, this should

not result in paternal or maternal transmission biases because we have previously shown that

probands can discriminate between affected status (either PsC or PsA) versus unaffected status

with high accuracy (91%) [237], and in this study, we did not selectively ascertain sex-specific

parent-child pairs. Nonetheless, future studies should aim to confirm parental diagnoses,

although the feasibility of doing so is limited in cohort studies, and nearly impossible in

population-based studies.

In summary, we have provided further epidemiological evidence of a parent-of-origin effect in

psoriatic disease. Our findings suggest that psoriatic disease may be more penetrant and more

likely to show genetic anticipation (i.e. increase in severity in the next generation) when

transmitted by an affected male compared to an affected female. Further studies are needed to

delineate the contributions of genetic and epigenetic mechanisms to the parent-of-origin effect in

psoriatic disease.

115

Germ Line DNA Methylation Profiling in Psoriatic Disease

Remy A. Pollock, MSc(A), Darren D. O’Rielly, PhD, Art Petronis, MD, PhD, Vinod Chandran,

MD, PhD, Proton Rahman, MD, MSc, Dafna D. Gladman, MD, FRCPC

6.1 Introduction

Psoriasis is a common inflammatory skin disease associated with significant morbidity,

mortality, and poor quality of life that affects approximately 1-3% of Caucasians [3, 40].

Approximately 30% of psoriasis patients develop a severe form known as psoriatic arthritis

(PsA), an inflammatory arthritis characterized by peripheral polyarthritis, axial arthritis, skin and

nail disease, dactylitis, and enthesitis. While the precise aetiology of psoriasis and PsA is not

fully known, it is clear that they both have a strong genetic component as evidenced by high

recurrence risk ratios among first-degree relatives of psoriasis and PsA patients [7, 48], and

higher disease concordance among monozygotic (62-70%) than dizygotic twins (21-23%) [8-10,

49]. Decades of research into genetic risk factors have identified numerous susceptibility loci for

psoriasis and PsA, including HLA-C*06 and HLA-B*27, respectively, as well as several other

independent associations within the major histocompatibility complex (MHC) and low frequency

variants scattered throughout the genome that contribute modestly to disease risk [211].

However, a combination of known susceptibility variants into a genetic risk score estimated that

10 of the strongest common risk variants account for only 11.6% of the genetic variance in

psoriasis [238], illustrating that a large amount of the heritable risk of psoriasis remains

unexplained. Delineation of the remaining heritable risk factors associated with psoriatic disease,

particularly PsA, is integral to the development of biomarkers that can expedite diagnosis and

treatment and improve patient outcomes.

116

An intriguing but overlooked aspect of the inheritance of psoriasis and PsA is the observation of

a parent of origin effect, which refers to the differential risk or pathogenicity of disease that is

dependent on the sex of the affected parent. A paternal transmission effect has been described in

several independent studies of large psoriasis and PsA cohorts from the Faroe Islands, Scotland,

and Canada [29, 30, 225]. These studies have demonstrated a subtle, albeit significantly greater

prevalence of psoriasis among the offspring of psoriatic fathers compared to psoriatic mothers,

and a significantly greater tendency for psoriasis and PsA probands to report an affected father

compared to an affected mother. Paternal transmission is accompanied by a significant reduction

in age of psoriasis onset, and a tendency to manifest as the more severe PsA phenotype in

subsequent generations. These findings cannot be explained by a skewed gender distribution of

psoriasis and PsA, which affect men and women equally [39], or sex chromosome linkage, as no

known susceptibility loci map to the X or Y chromosomes.

Two main mechanisms have been proposed to mediate the parent of origin phenomenon in

psoriasis and PsA. The first mechanism was a trinucleotide repeat expansion mechanism in

which a dynamic repeat polymorphism within a psoriasis susceptibility gene could exceed a

stable threshold more frequently through male germ line transmission than female germ line

transmission due to the mitotic divisions that occur during spermatogenesis in adult males but

not adult females [30]. Such a mechanism is observed in diseases such as Huntington’s Disease,

spinocerebellar ataxia and myotonic dystrophy. No dynamic repeat polymorphisms have been

associated with psoriasis to date. The second mechanism was genomic imprinting, which is

mediated by differential DNA methylation marks placed on paternal and maternal genomes that

are transmitted to the next generation and maintained in adult somatic tissues to ensure parent of

origin specific gene expression. Paternal and maternal imprinting defects due to genetic

mutations or epimutations are associated with childhood-onset neurodevelopmental disorders

such as Beckwith-Widemann, Russell-Silver, Prader-Willi, and Angelman syndromes. In

addition to genomic imprinting, there are also documented cases in mice and humans of

transgenerational inheritance of natural and pharmacologically induced epigenetic marks at non-

imprinted loci, indicating that it is possible for epigenetic marks to survive the extensive

reprogramming events that occur between generations [239] [134, 137, 139, 145, 240, 241].

Differential sensitivities of the male and female germ lines to stochastic or environmentally-

117

induced epigenetic changes at non-imprinted loci, and their differential abilities to correct such

changes was noted in the mouse [134, 137], providing a third possible mechanism to explain the

parent-of-origin effect in psoriatic disease.

As a first step towards addressing the question of whether heritable epimutations modify the risk

of psoriatic disease, we performed genome-wide DNA methylation profiling of sperm cells of

male psoriasis, PsA, and unaffected control subjects to identify germ line variations associated

with skin and joint manifestations of psoriatic disease.

6.2 Methods

6.2.1 Study Subjects and Sperm Cell Isolation

Male psoriasis and PsA patients were recruited from the University of Toronto Psoriatic Disease

Program’s psoriasis and PsA cohorts, respectively. All psoriasis patients were diagnosed by a

dermatologist and examined by a rheumatologist to verify the absence of PsA. All PsA patients

were diagnosed by a rheumatologist and satisfied the CASPAR criteria [75]. Unaffected male

controls with no family history of psoriasis or PsA were also recruited from the general

population. All participants provided written informed consent and the study was conducted with

approval from the University Health Network Research Ethics Board. Participants provided

semen samples that were processed within 2-3 hours. The motile fraction of mature spermatozoa

was isolated from semen samples by two layer density gradient centrifugation using ISolate®

reagent (Irvine Scientific, Santa Ana, CA, USA) according to the manufacturer’s instructions.

Isolated sperm cells were then frozen and biobanked for batch genomic DNA (gDNA)

extraction.

118

6.2.2 Genomic DNA Extraction and Bisulfite Conversion

gDNA was extracted from sperm cells using a modified phenol-chloroform extraction protocol.

Contaminating somatic cells were first lysed using a solution of 0.5% Triton X-100 and 0.1%

SDS. Remaining sperm cells were washed twice in PBS, and lysed with 400ul of 100mM Tris-Cl

(pH 8), 10mM EDTA, 500nM NaCl, 1% SDS, and 2% B-mercaptoethanol and 100ul of

Proteinase K (20mg/ml) at 55°C and 900rpm. An additional 50ul of Proteinase K was added

after 2 hours, and again after 18 hours. After 20 hours of incubation, 20μl of RNase A/T1 and

10μl RNAse H were added and the samples were incubated at 37°C for 30 minutes. Samples

were then mixed with an equal amount of phenol-chloroform-isoamylalcohol (PCA, 25:24:1),

transferred to phase-lock tubes, and mixed for 10 minutes. Supernatants were separated by

centrifugation, mixed again with equal amounts of PCA, and transferred to new phase-lock

tubes. Supernatants were mixed with 24:1 chloroform-isoamylalcohol, mixed briefly, and

separated by centrifugation. DNA was precipitated from the resulting supernatants using 100%

isopropanol, pelleted, washed with 70% ethanol, and re-suspended in Buffer EB (Qiagen,

Mississauga, ON, Canada). gDNA quality and quantity were assessed by NanoDrop

spectrophotometry.

Bisulfite treatment of gDNA was performed using the EZ DNA Methylation™ Kit (Zymo

Research, Irvine, CA, USA) according to the manufacturer’s instructions for Illumina Infinium®

Methylation Assays. Following bisulfite conversion, a quality control step was performed as

described by Zeller et al. to assess conversion efficiency [242]. Methylation-specific PCR was

performed using forward primer 5’ GGAAGGTAGTTGAGGTTGTG 3’ and reverse primer 5’

CCCAAACTCAAAACTCTAACCTAAC 3’ that are specific to a CpG devoid region of the

calponin gene, and which produce a 333bp amplicon from fully bisulfite converted samples. A

second set of primers were designed to detect the wild-type (unconverted) sequence, namely

forward primer 5’ GGAAGGCAGCTGAGGTTGTG 3’ and reverse primer 5’

CCCAAGCTCAGGGCTCTGGCCTGGC 3’. Fully bisulfite converted and unconverted

commercial DNA was used as positive and negative controls for both sets of primers. Reaction

conditions were 1X PCR Buffer, 0.2 mM dNTPs, 2.0 mM MgCl2 (wild type sequence) or 3.0

mM MgCl2 (bisulfite converted sequence), 500 nM forward and reverse primers, and 1.5 U

119

Platinum Taq Polymerase. PCR conditions were: enzymatic activation at 95°C for 5 minutes,

followed by 35 cycles of denaturation at 95°C for 30 seconds, annealing at 72°C (wild type

sequence) or 63°C (bisulfite converted sequence) for 30 seconds, and extension at 72°C for 30

seconds, followed by a single final extension at 72°C for 10 minutes. PCR products were

separated on a 2% agarose gel made with 0.5X TAE and run at 220V for 35 minutes, and

visualized under UV light by ethidium bromide staining.

6.2.3 DNA Methylation Analysis

Bisulfite treated DNA samples were interrogated on Infinium HumanMethylation 450k v1

BeadChips (Illumina, San Diego, CA, USA) according to the manufacturer’s protocol. This chip

interrogates individual CpG sites covering 99% of RefSeq genes and 96% of known CpG islands

in the human genome [148]. Samples were de-identified and randomized to the arrays. Four

samples were chosen randomly and were hybridized twice to serve as technical replicates.

Briefly, 8μl of each sample was used for whole genome amplification followed by

fragmentation, and 15μl of precipitated DNA was hybridized to five arrays at 48°C for 18 hours.

Arrays were washed, and single based extension was performed as per the Illumina protocol.

Arrays were then scanned on the iScan system (Illumina, San Diego, CA, USA). Fluorescence

intensities were quantified and quality control was performed in GenomeStudio Version 2011.1

(Illumina, San Diego, CA, USA) using the HumanMethylation450_15017482_v.1.2 annotation

file. Data were normalized against controls and background subtracted.

6.2.4 Bioinformatics and Statistical Analyses

Bioinformatics and statistical analyses were performed in R/Bioconductor. Data were imported

into the lumi package and colour balance adjustment and quantile normalization were performed

on M-values, which are homoscedastic across the entire methylation range [149, 152, 153].

Principal component analysis and hierarchical clustering was performed to assess the presence of

chip effects, the similarity of technical replicates, and to identify outliers. A total of 485,577

probes representing the same number of unique CpG sites were initially assessed on the arrays.

120

Filtering was performed to remove probes not present in 100% of samples based on detection

call, probes that cross-hybridize to multiple genomic locations, probes containing single

nucleotide polymorphisms (SNPs) at the CpG site or single base extension site with >5% minor

allele frequency (MAF), probes containing 2 or more SNPs anywhere within the probe with >5%

MAF, and the least variable 25% of probes based on interquartile range (IQR) [151, 243].

Differential methylation analysis was performed for the remaining probes using M-values in

methyAnalysis [150]. Data were smoothed using a window size of 250bp and group-wise

methylation differences were compared between psoriasis vs. controls, PsA vs. controls, and PsA

vs. psoriasis) by Student’s t-test. P values were adjusted for multiple testing using the false

discovery rate (FDR). For reporting purposes, M-values were then converted to standard Beta

values, which are interpreted as a percent methylation.

Biological annotation enrichment in each list of differentially methylated CpG sites was

investigated using the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt) [244, 245].

Enrichment was calculated relative to all genes analyzed on the Illumina 450k

HumanMethylation arrays and p values were adjusted for multiple testing using the FDR. Two-

dimensional hierarchical clustering of subject samples and differentially methylated CpG sites

was performed using Cluster 3.0 and visualized using Java TreeView.

Demographic and clinical characteristics of the study subjects were compared between groups of

subjects by ANOVA, Student’s t-test, and Pearson’s Chi-squared test. Multiple logistic

regression was used to test the association of methylation levels of CpG sites in HLA-B and

HCG26 with PsA after adjustment for the presence of PsA risk alleles HLA-B*27, B*08, B*38,

B*57, and C*06. HLA-B and -C genotyping data was available for 49/54 subjects from a

laboratory database. Differences in methylation levels between patients taking NSAIDs,

DMARDs, and biologics was assessed by Mann-Whitney U Test. All analyses were performed

in SPSS.

121

6.2.5 SNP Typing

The rs2385226 SNP in the TRIB1 locus was genotyped in an additional 430 psoriasis patients,

430 PsA patients, and 455 unaffected controls using a Taqman SNP Genotyping Assay (Life

Technologies) on an ABI 7900 real-time PCR with SDS 2.2.2 software. Allele frequencies were

compared by Pearson’s chi-squared test and genotype frequencies were compared by logistic

regression assuming an additive effect.

6.3 Results

6.3.1 Sperm Methylation Analysis Summary

In total, 56 subjects (24 psoriasis patients, 13 PsA patients, and 19 unaffected controls) provided

semen samples from which high quality gDNA was isolated. Bisulfite conversion was successful

in 100% of samples as assessed by methylation-specific PCR (Appendix 5). All 56 samples and

4 technical replicates were interrogated on Infinium HumanMethylation 450k arrays.

Hierarchical clustering of the array data prior to processing showed that three out of four

technical replicates clustered tightly together, and identified 2 samples (from 1 psoriasis subject

and 1 unaffected control) that did not group with the remaining samples (Figure 6.1). Outliers

and replicates were omitted from further analyses. There was no obvious clustering by chip,

suggesting that technical variation due to chip differences did not significantly affect methylation

measurements. Methylation at 485,577 CpG sites was assessed in sperm cells of 23 psoriasis

patients, 13 PsA patients, and 18 unaffected controls. Details of the demographic and clinical

characteristics of these subjects are shown in Table 6.1. On average, psoriasis and PsA patients

were older than controls but this was not statistically significant. PsA patients had an earlier age

of psoriasis onset and as a result had a longer disease duration than psoriasis patients. PsA

patients had a significantly higher usage of medications such as NSAIDs, DMARDs, and

biologics. To increase power, probe filtering was performed in the remaining samples to reduce

the number of statistical tests. After filtering, 331,258 CpG sites were retained and carried

forward for statistical analysis (Figure 6.2).

122

Figure 6.1 Identification of outliers by hierarchical clustering of pre-processed array data.

123

Table 6.1 Demographic and clinical characteristics of the study subjects.

Psoriasis

n=23

# (%) or Mean

(SD)

PsA

n=13

# (%) or Mean

(SD)

Controls

n=18

# (%) or Mean

(SD)

P Value

Males 100% 100% 100% n/a

Age (y) 50.5 (14.4) 52.3 (14.0) 43.8 (12.1) 0.18

Age of Psoriasis 29.9 (13.0) 20.9 (9.9) n/a 0.04

Age of PsA n/a 32.9 (8.9) n/a n/a

Psoriasis

Duration (y)

20.6 (15.1) 31.4 (13.1) n/a 0.04

PsA Duration (y) n/a 19.4 (14.2) n/a n/a

PASI* 2.7 (0-23.8) 1.6 (0-6.6) n/a 0.13

Tender Joints n/a 1.3 (2.5) n/a n/a

Swollen Joints n/a 0.3 (0.9) n/a n/a

NSAIDs 1 (4%) 8 (62%) n/a <0.001

DMARDs 1 (4%) 7 (54%) n/a 0.001

Biologics 4 (17%) 7 (54%) n/a 0.02

*Psoriasis Area and Severity Index; values indicate median PASI score (range); p value from a

Mann-Whitney U test.

124

Figure 6.2 Summary of probe filtering steps beginning with 485,577 probes.

Filter by Detection Call484,137 probes remaining

Filter by cross-reactivity 453,226 probes remaining

Filter by SNPs at CpG or SBE site446,473 probes remaining

Filter by SNPs in probe sequence441,678 probes remaining

Filter by IQR 331,258 probes remaining

125

6.3.2 The Sperm Methylome in Psoriasis and PsA Patients

The 331,258 CpG sites assessed showed a bimodal distribution of methylation within each sperm

sample analyzed, with the majority of sites showing either high methylation levels (beta

[β]>85%) or low methylation levels (β<15%). None of the CpG sites were significantly

differentially methylated between either group of patients and controls after correction for

multiple testing (FDR<0.05). Using an unadjusted p-value threshold of <0.05, 54 CpG sites in 45

unique genes were differentially methylated between psoriasis patients and controls, 94 sites in

80 genes between PsA patients and controls, and 81 sites in 68 genes between PsA and psoriasis

patients (full lists of differentially methylated genes can be found in Appendices 7-9). Of the

differentially methylated CpG sites between psoriasis patients vs. controls, the majority of sites

(67%) were hypermethylated compared to hypomethylated (33%). In contrast, in PsA patients

vs. controls and PsA vs. psoriasis patients, the number of hyper and hypomethylated sites were

roughly equal (55% vs. 45% and 51% vs. 49%, respectively). Hypomethylated sites were slightly

skewed towards having larger mean differences in percent methylation (beta differences [Δβ])

between groups than hypermethylated sites in psoriasis and PsA compared to controls (Figure

6.3).

Next, we investigated the distribution of significantly differentially methylated CpGs in psoriasis

and PsA patients among the genomic annotations provided by the Illumina HumanMethylation

450k bead chip. CpG sites are annotated in two ways: first, by location relative to the nearest

gene, and second, by location relative to a CpG island (Figure 6.4A) [148]. The latter set of

annotations was further broken down into promoter associated and non-promoter associated CpG

islands. Compared to all CpG sites analyzed, differentially methylated CpG sites in psoriasis

patients vs. controls were enriched in the open sea and intergenic regions, but depleted in CpG

islands, CpG island North shores, 1500 bp upstream of transcriptional start sites within gene

promoters (TSS1500) and in 5’ untranslated regions (UTRs). Differentially methylated CpG sites

in PsA patients vs. controls were similarly enriched in the open sea and intergenic regions, as

well as in 3’ UTRs, but depleted in CpG islands and TSS1500. Differentially methylated CpG

sites in PsA vs. psoriasis patients were enriched in intergenic regions, 3’ UTRs, CpG island

North and South shelves, but depleted in CpG islands, TSS1500, and TSS200 (Figure 6.4B).

126

Only a small percentage of the significant CpG sites found within CpG islands were in promoter-

associated CpG islands (9% of significant sites in psoriasis vs. controls, 0% of significant sites in

PsA vs. controls, and 7% of significant sites in PsA vs. psoriasis). These percentages were

significantly lower than the expected percentage of promoter-associated CpG islands among all

CpG islands tested on the array (29%, p<0.001 for all comparisons).

The significantly higher usage of NSAIDs, DMARDs, and biologics within the PsA patients

compared to the psoriasis patients raised the possibility that these drugs could affect the

methylation status of CpG sites in the germ line and confound the analysis of differential

methylation between PsA and psoriasis patients and controls. The effect of medication usage on

germ line methylation status was investigated by comparing median methylation levels of the

differentially methylated CpGs identified in previous analyses (PsA vs. controls and PsA vs.

psoriasis) between PsA patients taking medications and those not taking medications. After

correction for multiple testing, no CpG sites were found to be significantly differentially

methylated in patients taking medications, and thus medication usage was not considered a major

confounding factor in this study.

127

Figure 6.3 Summary of differentially hyper- and hypomethylated CpG sites in sperm cells.

Psoriasis vs. Controls

58 CpG Sites

PsA vs. Controls

104 CpG Sites

67%

33%

% of CpG Sites

HYPER

HYPO

0

5

10

15

20

0-4% 5-9% 10-14% 15-19% 20-24% 25-29% 30-34% 35-40% 40%+

# o

f C

pG

Sit

es

Δβ

55%45%

% of CpG Sites

HYPER

HYPO

0

5

10

15

20

0-4% 5-9% 10-14% 15-19% 20-24% 25-29% 30-34% 35-40% 40%+

# C

pG

Sit

es

Δβ

128

PsA vs. Psoriasis

86 CpG Sites

0

5

10

15

20

0-4% 5-9% 10-14% 15-19% 20-24% 25-29% 30-34% 35-40% 40%+

# C

pG

Sit

es

Δβ

51%49%

% of CpG Sites

HYPER

HYPO

129

Figure 6.4 Differentially methylated CpG sites in sperm cells by genomic location relative to nearby genes and CpG islands. A

Annotation of CpG sites based on location relative to the nearest gene (top) and CpG island (bottom). B Distribution of differentially

methylated CpG sites in each comparison relative to all analyzed sites. ** p<0.001, *p<0.05 (Chi-square test).

A

130

B

0

20

40

60

80

100

Open Sea N Shelf N Shore Island S Shore S Shelf

% o

f S

ites

All Analyzed Sites

Psoriasis vs. Controls

0

20

40

60

80

100

Intergenic TSS 1500 TSS 200 5' UTR 1st Exon Body 3' UTR

% o

f S

ites

All Analyzed Sites

Psoriasis vs. Controls

0

20

40

60

80

100

Intergenic TSS 1500 TSS 200 5' UTR 1st Exon Body 3' UTR

% o

f S

ites

All Analyzed SitesPsA vs. Controls

0

20

40

60

80

100

Open Sea N Shelf N Shore Island S Shore S Shelf

% o

f S

ites

All Analyzed Sites

PsA vs. Controls

0

20

40

60

80

100

Intergenic TSS 1500 TSS 200 5' UTR 1st Exon Body 3' UTR

% o

f S

ites

All Analyzed Sites

PsA vs Psoriasis

0

20

40

60

80

100

Open Sea N Shelf N Shore Island S Shore S Shelf

% o

f S

ites

All Analyzed Sites

PsA vs Psoriasis

** **

**

* * *

*

**

*

**

** ** **

*

131

6.3.3 Biological functional enrichment analysis and hierarchical clustering

To understand the biological functions of the differentially methylated genes identified in each

group-wise comparison of sperm cells, we performed enrichment analysis using WebGestalt

[244] to identify overrepresented biological annotations including, but not limited to: gene

ontologies, KEGG pathways, transcription factor and miRNA targets, protein-protein

interactions, and chromosomal positions (cytobands). Genes involved in phosphatidylinositol

signalling (DGHK and INPP5A) and targets of MIR-182 were significantly enriched in the

differentially methylated genes in psoriasis patients compared to controls. Cytobands 10p,

13q14, 13q34, and 15q22 were also overrepresented within this gene list. Two differentially

methylated genes COL4A1 and SLC6A3 were annotated to the limb dystonia phenotype, which

was found to be enriched in PsA patients compared to controls. Differentially methylated genes

in PsA compared to psoriasis patients were enriched at cytobands 5p15, 11p15, and 20q13, as

well as a protein interaction network involving the genes RBMS1 and PPIF, and another

involving the genes FAT1 and TPPP (Table 6.2).

Differentially methylated CpG sites from the three group-wise comparisons were combined to

perform a two-dimensional unsupervised hierarchical clustering of samples (Figure 6.5).

Methylation levels at the significant CpG sites in sperm clustered the subjects into three distinct

groups corresponding to disease status. Cluster 1 corresponded to the control phenotype and

consisted of the majority of controls (17/18) with 2/23 psoriasis patients, Cluster 2 corresponded

to the PsA phenotype and consisted of all 13/13 PsA patients, 1/18 controls and 2/23 psoriasis

patients, and Cluster 3 corresponded to the psoriasis phenotype and contained the majority of

psoriasis patients (19/23) (Figure 6.5).

132

Table 6.2 Biological functional enrichment analysis of all genes found to be differentially methylated sperm cells.

Gene List Enrichment

Analysis

Annotation

Category

Observed

# Genes

Expected

# Genes

Fold

Enrichment

P Value Adjusted

P Value

Observed Genes

Psoriasis

vs.

Controls

KEGG

Pathway

Phosphatidyl-

inositol

signalling

system

2 0.19 10.58 0.02 0.03 DGKH, INPP5A

miRNA

Target

mir-182 3 0.09 34.92 8.57x10-5 2.0x10-3 RNF6, PRMT8, FAM107B

Cytoband 13q34 2 0.08 25.46 2.80x10-3 0.0196 RASA3, COL4A1

10p 3 0.36 8.43 5.40x10-3 2.52x10-2 ST8SIA6, ADARB2, FAM107B

13q14 2 0.18 11.01 1.42x10-2 3.98x10-2 DGKH, SPERT

15q22 2 0.17 11.81 1.25x10-2 3.98x10-2 ANXA2, MGC15885

PsA vs.

Controls

Phenotype Limb dystonia 2 0.03 61.60 4.0x10-4 3.72x10-2 COL4A1, SLC6A3

PsA vs.

Psoriasis

Cytoband 11p15 5 1 4.99 3.30x10-3 1.65x10-2 C11orf40, PTDSS2, INSC,

OR52M1, COPB1

5p15 3 0.24 12.53 1.80x10-3 1.65x10-2 IRX4, IRX1, TPPP

20q13 4 0.48 5.90 4.80x10-3 1.92x10-2 CDH22, GATA5, ZBTB46,

HAR1A

Protein

Interaction

Hsapiens_

Module_236

2 0.08 23.98 3.10x10-3 2.89x10-2 RBMS1, PPIF

Hsapiens_

Module_415

2 0.09 22.93 3.40x10-3 2.89x10-2 FAT1, TPPP

133

Figure 6.5 Two-dimensional hierarchical clustering of all differentially methylated CpG sites

identified in sperm.

Cluster 1 Cluster 3 Cluster 2

134

6.3.4 Top differentially methylated genes in the context of psoriatic disease pathogenesis

The top 5 genes that were hyper- and hypomethylated with Δβ >20% between groups in the three

group-wise comparisons is shown in Table 6.3. The full lists of differentially methylated CpG

sites, regardless of Δβ, were also manually annotated using genetic or functional evidence from

the literature to identify additional significant genes that are relevant to psoriatic disease,

inflammation or immune dysregulation. The top hypermethylated CpG site in sperm of psoriasis

patients compared to controls was within the body of the keratin 82 locus (KRT82, Δβ=0.26,

p=5.16x10-3). Other relevant hypermethylated CpG sites in sperm cells of psoriasis patients

include those within a CpG island shelf of interferon regulatory factor 6 (IRF6, Δβ=0.22,

p=2.0x10-3), sites with the CpG island shore of the long non-coding RNA TINCR (Δβ=0.14,

p=0.03), and sites within the CpG island of the tumour suppressor gene CSMD1 (Δβ=0.13,

p=1.0x10-3). Furthermore, the NLR containing pyrin domain protein of unknown function,

NLRP13, was found to be hypermethylated (Δβ=0.09, p=7.6x10-3).

In sperm cells of PsA patients compared to controls, one of the top significantly hypomethylated

genes in sperm cells was within the 3’UTR of HLA-B (Δβ=-0.24, p=0.03) on chromosome

6p21.3. Additionally, one CpG site within an intron of the MHC Class II pseudogene HLA-DPB2

was found to be hypermethylated (Δβ=0.14, p=0.02). Similar to psoriasis patients, CSMD1 was

hypermethylated (Δβ=0.15, p=8.8x10-4), as well as the CpG island shore of the nearby non-

coding antisense RNA ERICH1-AS1 (Δβ=0.10, p=0.01) (Table 6.3).

Because of the strong association of PsA with HLA-B risk alleles, we examined the association

with HLA-B 3’ UTR probe (cg27083089) in greater detail, and discovered that the interrogated

CpG site contains an A/G polymorphism (rs2428496) in the G position of the CpG site. The G

polymorphism comprises an intact CpG site, while the A polymorphism results in the loss of the

CpG site. The A polymorphism is present in several alleles of HLA-B, including B*08, B*27,

B*38, B*39, and B*57. Genotypes for rs2428496 were assigned to the subjects using HLA-B

typing which was available on 49/54 subjects, and sequence information from the IMGT/HLA

135

database. Subjects were 63% AA, 33% AG, and 4% GG. The AA genotype was increased in PsA

patients compared to controls, however this did not reach statistical significance (Odds ratio

[OR] 5.3, 95% confidence interval [CI] 0.97-29.4, p=0.055).

In sperm cells of PsA patients compared to psoriasis patients, three CpG sites within the

promoter of a non-protein coding RNA HCG26 was found to be significantly hypomethylated

(Δβ=-0.22, p=4.0x10-3). Six unique CpG sites within the 3’UTR of the tubulin polymerization

promoting protein (TPPP) gene were also found to be hypermethylated (top Δβ=0.25, p=1.4x10-

4) in sperm cells of PsA patients compared to psoriasis patients, as well as a CpG site within the

intron of the myomesin 2 (MYOM2) gene (Δβ=0.29, p=0.01) (Table 6.3). Lastly, one CpG site

within a CpG island shore of FOXD2 was found to be hypermethylated (Δβ=0.16, p=0.02).

Due to the extensive linkage disequilibrium that extends across the MHC, it was of interest

whether low CpG methylation at HCG26 in both PsA compared to psoriasis patients and controls

is independent of HLA-B and HLA-C alleles known to be associated with PsA. A recent meta-

analysis demonstrated that HLA-B alleles B*08, B*27, B*38, B*39, B*57, and C*06 are

associated with PsA compared to psoriasis or unaffected controls [246]. Multivariable logistic

regression was performed modeling the association between methylation levels with PsA versus

psoriasis patients or controls, and adjusting for carriage of the alleles noted above. Adjustment

for B*39 and B*57 were not performed as B*39 was not present in any patient, and B*57 was not

present in PsA patients. Additionally, B*38 was not present in controls so could not be adjusted

for in the PsA vs. controls comparison. Low methylation levels at all three CpG sites within the

HCG26 promoter remained independently associated with PsA compared to psoriasis patients

after adjustment, while only one CpG site remained independently associated with PsA

compared to healthy controls after adjustment (Table 6.4). Methylation levels of the three CpG

sites within HCG26 in each group and individual are shown in Figure 6.6.

136

Table 6.3 Top hyper and hypomethylated genes from each of the groupwise comparisons and genes most relevant to psoriatic disease.

Comparison Hypermethylated Hypomethylated

Gene CpG Site(s) Max. Δβ P Value Gene CpG Site(s) Max. Δβ P Value

Psoriasis vs.

Controls

KRT82 1 0.26 5.16x10-3 NMD3 1 -0.27 1.93x10-3

L1TD1 1 0.22 1.99x10-3 SNTG1 1 -0.23 0.02

GPR123 1 0.21 0.02 LRRTM4 1 -0.22 1.77x19-3

IRX1 1 0.21 0.02 COL4A1 1 -0.22 0.02

ZNRF4 5 0.20 0.03 DHX37 1 -0.22 0.01

IRF6 1 0.16 0.03

TINCR 1 0.14 0.03

CSMD1 1 0.13 1.0x10-3

NLRP13 1 0.09 7.60x10-3

PsA vs.

Controls

FLJ37201 1 0.26 0.02 PACSIN2 1 -0.30 1.84x10-3

ITGB2-AS1 1 0.26 4.07x10-4 SYT8 2 -0.27 0.02

MSRA 1 0.25 0.01 BAZ2B 1 -0.26 9.47x10-3

NRBP2 2 0.24 0.01 HLA-B 1 -0.23 0.03

OR5H15 1 0.24 0.02 NMD3 1 -0.23 0.02

TPPP 2 0.21 0.03 HCG26 3 -0.16 0.01

CSMD1 1 0.15 8.8x10-4 PTDSS2 6 -0.20 0.02

HLA-DPB2 1 0.14 0.02

ERICH1-AS1 1 0.10 0.01

PsA vs.

Psoriasis

EBF1 1 0.32 9.55x10-4 IRX1 1 -0.33 4.01x10-4

TPPP 6 0.29 1.92x10-4 OR52M1 1 -0.32 6.68x10-3

MYOM2 2 0.29 0.01 RBMS1 1 -0.29 0.02

SEMA6A 1 0.28 0.02 FAM167A 1 -0.27 6.10x10-3

PPIF 1 0.27 3.21x10-3 ATP11A 2 -0.27 6.13x10-3

FOXD2 0.16 0.02 HCG26 3 -0.22 4.02x10-3

PTDSS2 2 -0.16 0.03

137

Table 6.4 Association of HCG26 methylation in sperm with PsA compared to psoriasis patients

and controls after adjustment for HLA-B and HLA-C.

Comparison CpG Site Adjusted Association with PsA*

Odds Ratio 95% CI P Value

PsA vs. Psoriasis HCG26 CpG 1 (Promoter) 0.50 0.27-0.91 0.02

HCG26 CpG 2 (Body) 0.55 0.30-1.00 0.05

HCG26 CpG 3 (Body) 0.38 0.17-0.88 0.02

PsA vs. Controls HCG26 CpG 1 (Promoter) 0.49 0.24-1.02 0.06

HCG26 CpG 2 (Body) 0.25 0.06-1.09 0.07

HCG26 CpG 3 (Body) 0.33 0.11-0.96 0.04

*Multivariable logistic regression using HCG26 methylation and B*08, B*27, B*38, and C*06 as

covariates for PsA vs. psoriasis as the outcome, or HCG26 methylation and B*08, B*27, and

C*06 as covariates for PsA vs. controls as the outcome.

138

Figure 6.6. Group-wise (A) and individual (B) differences in methylation levels of the three

CpG sites within HCG26 associated with PsA compared to psoriasis and controls (*p<0.05,

**p<0.001).

A

B

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

HCG26 CpG 1Promoter

HCG26 CpG 2Body

HCG26 CpG 3Body

Meth

yla

tio

n (

β)

PsA

Psoriasis

Controls

** *

** ** * *

139

6.3.5 Association of psoriasis and PsA with SNP probe rs2385226

The Illumina HumanMethylation 450k array contains 65 rs probes for randomly-selected SNPs

which can be used for quality control and sample identification. The largest Δβ on the array in

both psoriasis and PsA patients compared to controls was the rs probe for the SNP rs2385226,

located in an intergenic region 240kb downstream of the TRIB1 locus on chromosome 8q24.13.

Because this region has not been associated with psoriatic disease in previous genome-wide

association studies, we examined the association of rs2385226 alleles with psoriasis and PsA in a

larger extended sample of 430 psoriasis patients, 430 PsA patients, and 455 unaffected controls.

The minor T allele was associated with psoriasis patients compared to controls (p=0.01) but not

PsA patients compared to controls (p=0.51) (Table 6.5A), however the difference in T allele

frequency between psoriasis and PsA patients did not reach statistical significance (p=0.08). The

TT genotype was significantly associated with psoriasis patients compared to controls (OR=1.3,

95% CI 1.1-1.5, p=0.01), but not PsA patients compared to controls, and did not differ

significantly in frequency between PsA and psoriasis patients (Table 6.5B). The rs2385226 SNP

may therefore be a novel genetic variant that appears to be associated specifically with pure

psoriasis without arthritis but not PsA.

140

Table 6.5. Association of rs2385226 alleles and genotypes with an extended sample of psoriatic

disease patients.

A

Alleles P Value

(vs. Controls)

P Value

(vs. Psoriasis) C T

Psoriasis (n=430) 47.7% (410) 52.3% (450) 0.01 n/a

PsA (n=430) 52.0% (447) 48.0% (413) 0.50 0.08

Controls (n=455) 53.6% (488) 46.4% (422) n/a n/a

B

Genotypes Association vs. Controls Association vs. Psoriasis

CC CT TT OR 95% CI P Value OR 95% CI P Value

Psoriasis

(n=430)

23.0%

(99)

49.3%

(212)

27.7%

(119)

1.27 1.05-1.53 0.01 n/a n/a n/a

PsA

(n=430)

28.4%

(122)

47.2%

(203)

24.4%

(105)

1.07 0.89-1.28 0.49 0.85 0.70-1.02 0.08

Controls

(n=455)

28.8%

(131)

49.7%

(226)

21.5%

(98)

n/a n/a n/a n/a n/a n/a

141

6.4 Discussion

In this study, we performed DNA methylation profiling of sperm cells from psoriasis patients,

PsA patients and unaffected control subjects to identify germ line methylation variations

associated with psoriasis and PsA. Consistent with a previous methylation study of sperm cells

of healthy males, CpG methylation levels in psoriasis, PsA, and control samples showed a

bimodal distribution of either very high or very low methylation levels [247]. Overall, germ line

differences between patients and controls were small, with no CpG sites remaining significant

after FDR correction. Although considerable epigenetic variation has been noted in previous

studies of human sperm cells [248], it was also noted that variations are subtle, or are present in

very low frequencies of cells (<1%) [241, 248]. Variations present in sperm cells might therefore

have small effect sizes that require large numbers of samples to achieve the power to detect

differences that are significant after multiple testing correction. Using a more lenient p value

threshold of <0.05, several CpG sites were differentially methylated between psoriasis and PsA

patients compared to controls, and between PsA and psoriasis patients, however, methylation

differences were subtle and averaged less than 20%. Despite the small differences at individual

CpG sites, collectively, they are sufficient to distinguish psoriasis patients, PsA patients, and

controls, and thus demonstrate the presence of unique germ line epigenetic profiles associated

with both psoriasis and PsA.

A previous study of human sperm cells found that the largest degree of epigenetic variation

occurs at functionally important promoter-associated CpG islands [248], which are classically

defined as regions of the genome that are >200bp in length, contain a 50% or greater GC content,

and a ratio of >0.6 for the observed to expected number of CpG dinucleotides [249]. In contrast,

we found that the majority of variation in psoriasis and PsA compared to controls, and in PsA

compared to psoriasis patients, occurred in open sea, intergenic regions, and 3’UTRs within gene

bodies, which are typically areas of low CpG density. Promoter regions (TSS1500 and TSS200),

CpG islands, and promoter-associated CpG islands in particular were generally well conserved.

Promoter-associated CpG islands were traditionally thought to be primarily responsible for

regulating gene expression, however recent evidence suggests that lower density CpGs may play

important roles in regulating distal genes through enhancers [250]. Interestingly,

142

transgenerationally heritable epimutations induced by environmental exposures such as

vinclozolin, bisphenol A, hydrocarbons, pesticides, dioxin, and DDT in rodent sperm cells tend

to map to lower density CpG regions such as these [250]. Therefore, there is a potential for

pathogenicity even among the differentially methylated variations identified in this study that are

not located in CpG islands or promoters.

Psoriasis is an immune-mediated hyperproliferative disorder of the skin mediated by both the

innate and adaptive immune systems, in which a subset of CD4+ T cells called Th17 cells play

an integral role in perpetuating and amplifying skin inflammation [31]. Consistent with what is

known about the pathogenesis of psoriasis, we found differential methylation between psoriasis

patients and controls in genes such as KRT82 (hypermethylated), which encodes a type II keratin

protein that heterodimerizes with type I keratins to form hair and nails, TINCR

(hypermethylated), a long non-coding RNA that is highly expressed during epidermal

differentiation and regulates genes involved in skin barrier formation [251], IRF6

(hypermethylated), a transcription factor that regulates epithelial cell proliferation [252, 253],

and NLRP13 (hypermethylated), which encodes a protein that is highly homologous to other

NLR superfamily members that function in pathogen-associated molecular pattern (PAMP) or

danger/damage-associated molecular pattern (DAMP) recognition and stimulate the formation of

multiprotein inflammasome complexes [254]. We also found an enrichment of genes involved in

phosphatidylinositol signalling (DGHK and INPP5A, hypo- and hypermethylated, respectively),

a pathway that is linked to Akt/mTOR signalling that is being increasingly recognized for its

importance in promoting uncontrolled proliferation of keratinocytes and synovial fibroblasts

upon activation by growth factors and Th17 cytokines such as IL-17 and IL-22 in psoriasis [255,

256].

Like psoriasis, PsA is an inflammatory disorder mediated by both the innate and adaptive

immune systems in which Th17 cells, NK cells, and monocytes play important roles in

inflammation and joint destruction. In PsA vs. psoriasis patients we found hypermethylation of

FOXD2, a forkhead/winged helix transcription factor that is highly expressed in T cells and

monocytes, and may play a role in modulating T cell activation [257, 258]. In PsA patients vs.

143

controls, we found what appeared to be hypomethylation of the 3’UTR of HLA-B, a major

histocompatibility complex (MHC) Class I locus involved in antigen presentation that is the

strongest known risk locus for PsA identified to date. However, the measurement of methylation

at this site was confounded by the presence of an A/G polymorphism that results in a loss of the

CpG site in the A allele. Thus, methylation levels measured at this site merely reflect the

underlying genotype. The polymorphism at this site is a transition mutation that might have

arisen in an ancestral HLA-B allele through spontaneous deamination of methylated CpG sites to

TpG/CpA, which occurs 10x more frequently than other point mutations [259]. While the AA

genotype was increased in PsA patients compared to controls, this analysis was underpowered

and did not reach significance. Nonetheless, it is interesting to speculate that the loss of this CpG

site in the A polymorphism, which is present in PsA risk alleles B*08, B*27, B*38, B*39, and

B*57, might represent the loss of a critical CpG site, and may contribute to the pathogenicity of

these alleles by increasing their expression.

Genetic studies aimed at identifying PsA risk loci in the MHC that are independent of HLA-B

have suggested that the adjacent region encompassing MHC Class III loci MICA and MICB

contain potential risk loci specific to joint disease [17, 20, 260-264]. However, other studies have

failed to replicate these associations [17, 265]. In PsA vs. psoriasis patients, we identified

significant hypomethylation of one CpG site 67bp upstream of the TSS, and two sites within the

body of HCG26, a locus that lies between MICA and MICB. The same three CpG sites were also

hypomethylated in PsA patients compared to controls. Hypomethylation at all three sites was

significantly associated with PsA compared to psoriasis patients independently of HLA-B*08,

B*27, and B*38, and C*06, and at one CpG site with PsA compared to controls, suggesting that

loss of HCG26 methylation is a novel PsA risk factor in the MHC. Further studies are needed to

determine if HCG26 methylation is linked to PsA-associated variants near MICA and MICB.

HCG26 encodes a long non-coding RNA 1180bp in length of unknown function. Although the

mean differences in methylation of all three sites between PsA and psoriasis patients is relatively

subtle in sperm cells (Figure 6.6), it could represent a ‘pre-epimutation’ that can become

increasingly hypomethylated over time due to stochastic events or harmful environmental

exposures, leading to PsA later in life. In this sense it will be necessary in follow-up studies to

144

determine whether HCG26 is hypomethylated in somatic tissues of PsA patients relative to

psoriasis patients.

Aside from the epigenetic associations discovered in this study, a novel genetic association was

found with the intergenic SNP rs2385226, which is located in a gene poor region on 8q24.13. In

an extended sample of 430 psoriasis patients, 430 PsA patients, and 455 unaffected controls, the

minor T allele and TT genotype was significantly associated with psoriasis patients compared to

controls, but not PsA patients compared to controls, suggesting that they are risk factors for pure

psoriasis without arthritis but not PsA. The low odds ratios for the T allele and TT genotype

indicate that this polymorphism has a small effect on psoriasis risk. The nearest gene to this SNP

is TRIB1, a pseudokinase that is highly expressed in T regulatory (Treg) cells and interacts in the

nucleus with FOXP3, an important transcription factor in Treg development and functioning

[266].

The identification of DNA methylation variants associated with psoriatic disease in sperm cells

suggests a potential for inheritance, but to substantiate such claims, it must be demonstrated that

the germ line variants identified herein remain stable for one or more generations, and are

independent of cis-acting genetic mutations. It will therefore be helpful to know whether these

germ line variants are present in somatic tissues derived from the three different germ layers

(ectoderm, mesoderm, and endoderm) of these same patients, as this would support inheritance

from the previous generation. Similarly, it would be helpful to demonstrate the presence of these

variants in somatic tissues of the offspring of these patients. Establishing the independence of

these epigenetic marks from cis-acting genetic mutations is also important, as epigenetic variants

resembling germ line epimutations have subsequently been found to be dependent on upstream

mutations [132]. Finally, it will also be necessary to demonstrate the functional consequences of

the identified epigenetic variants on transcription, which will help to elucidate their contribution

to disease pathogenesis.

145

In summary, this study provides preliminary evidence of epigenetic variations in the germ line

that are associated with psoriasis and PsA. These variations are generally subtle and are enriched

in open sea and intergenic regions, but also occur near or within several genes that function in

inflammatory and immune system processes and thus have potential pathogenic relevance to

psoriasis and PsA. Hypomethylation in the HCG26 locus is associated specifically with PsA

compared to psoriasis patients and controls independently of HLA-B risk alleles. Further

investigation of DNA methylation in the somatic tissues of these patients and their offspring, as

well as genetic and transcriptional investigations are necessary to provide persuasive evidence of

the heritability of these germ line epigenetic variations and their role in the etiology and parent-

of-origin effect in psoriatic disease.

146

General Discussion

The etiopathogenic mechanisms involved in psoriasis and PsA have not been fully characterized.

In particular, there is paucity of information about the link between skin and joint disease, the

roles of specific innate and adaptive immune cell types, and the contribution of epigenetic

factors. The present work aimed to fill these knowledge gaps by employing genomic-scale

experimental techniques and epidemiological analyses to compare subjects derived from two

well-characterized cohorts of psoriasis and PsA patients. Findings from these studies have

possible implications for the development of biomarkers of PsA in patients with psoriasis, which

remains a major unmet need within the clinical landscape of psoriatic disease.

In Chapter 3, whole blood gene expression differences between psoriasis and PsA patients were

investigated for the first time. Previous gene expression microarray studies in psoriasis and PsA

have profiled circulating PBMCs and whole blood as surrogates of disease target tissues. This

strategy is logical as whole blood is relatively simpler to obtain than skin biopsies or synovium,

and pathways identified in the blood of PsA patients have been shown to mirror those found in

the inflamed synovium [267]. A caveat of whole blood studies, however, is that differential gene

expression cannot necessarily be attributed directly to gene deregulation, because it might reflect

different cellular compositions in PsA and psoriasis patients instead of, or in addition to, gene

deregulation. As this was a proof-of-concept study aimed at demonstrating differential

expression between psoriasis and PsA patients, it was reasoned that gene expression signatures

of cellular differences would still be informative, as there is little information in the literature on

the differences in the composition of circulating cells between psoriasis and PsA patients.

Furthermore, for the most interesting validated candidate biomarkers, differential expression was

analyzed in purified pathogenic cell subsets (T cells, NK cells, and monocytes) from a small

number of patients. In future studies, additional experimental ‘deconvolution’ can be performed

in more samples, or bioinformatics approaches can be used to deconvolute gene expression

147

signatures from mixed cell populations in order to gain further insight into gene deregulation at

the molecular level [268].

Microarray analyses identified several differentially expressed genes in PsA patients compared

to psoriasis patients and controls, but there were no significant genes that differentiated psoriasis

patients and controls. Many of the same genes that were significant in PsA vs. psoriasis patients

showed smaller fold changes in the same direction in psoriasis patients vs. controls, indicating

that gene expression changes in psoriasis patients are subtle, and are exacerbated in the PsA

phenotype. These findings support the suggestion that psoriasis and its extra-cutaneous

manifestations such as PsA are not discrete diseases, but are part of a continuous psoriatic

phenotype that can encompass the skin, joints, and gut at the same or different times [269, 270].

The idea of PsA as a subset of psoriasis is further supported by genetic studies showing that both

psoriasis and PsA are associated with HLA-C*06 and HLA-B*57, while PsA shows additional

associations with HLA-B*27, B*38, and B*39 [95, 96], as well as the shared clinical features of

both diseases that are exacerbated in PsA, such as subclinical enthesitis and synovitis in psoriasis

patients [271, 272], which may predict PsA [94], more severe skin disease in PsA [93, 210], and

the higher prevalence of nail lesions in PsA patients (~85%) than psoriasis patients (~50%) [273,

274].

As reviewed in earlier chapters, innate immunity is thought to contribute to inflammation and

joint destruction in PsA through the actions of cells such as NK cells, monocytes, neutrophils,

and macrophages. In accordance with what is known about the pathogenesis of PsA, several

genes with roles in innate immunity, specifically TLR signaling, were among the top

differentially expressed genes in PsA vs. psoriasis and were differentially expressed in follow-up

qPCR array experiments. One of the most strongly over-expressed genes was LY96, which

associates with TLR4 and confers responsiveness to bacterial lipopolysaccharide (LPS). TLRs

are an essential component of the innate immune system and are expressed primarily on

macrophages and dendritic cells (DCs), where they function in recognizing PAMPs such as LPS,

as well as endogenous DAMPs, and trigger intracellular signaling pathways that ultimately

148

activate expression of inflammatory cytokines such as TNFα, type 1 interferons and various

chemokines [275]. TLRs also induce upregulation of costimulatory molecules on DCs, which

present antigen to cells of the adaptive immune system such as T cells, and furthermore, have

been shown to promote Th17 expansion [276].

Although several genes encoding signaling proteins downstream of LY96 and TLR4 were

differentially expressed in PsA vs. psoriasis patients, particularly those involved in MyD88-

dependent signaling through NF-κB, most of these genes were downregulated in the discovery

cohort of PsA patients. There are several possible explanations for this observation. It could

reflect the fact that several patients in the discovery cohort were receiving some form of anti-

inflammatory, anti-rheumatic, or biologic therapy. However, in this study, medications were not

found to profoundly affect gene expression levels, and all patients had active disease at the time

the RNA sample was taken. Alternatively, it could reflect a decrease in certain circulating innate

immune cells in PsA compared to psoriasis patients, such as dendritic cells, as they are recruited

from the bloodstream to sites of inflammation. Lower numbers of plasmacystoid dendritic cells

(pDCs) have been described in peripheral blood of PsA patients compared to controls, while high

numbers of pDCs have been found in the synovial fluid [277]. A third possibility is that it

reflects an increase in cells in which the TLR pathway is downregulated, such as CD163+ M2

monocyte/macrophages, which are involved in tissue remodeling and repair [278, 279]. These

M2 monocytes can display hyporesponsiveness to repeated TLR stimulation as a mechanism for

limiting damage caused by continuous inflammation. This hyporesponsiveness is mediated by

inhibition of adaptors, signaling molecules, and NF-κB subunits involved in MyD88-dependent

TLR4 signaling [280], and may be accompanied by increased expression of LY96 while having

no effect on the expression of TLR4 itself [281]. Such CD163+ cells have been found in

increased quantities in the synovium of PsA patients compared to rheumatoid arthritis patients

[282], and have been detected in the circulation of patients with diabetes and artherosclerosis

[283, 284].

149

Perhaps as important as shedding light on its pathogenesis, the comparison of whole blood gene

expression patterns between PsA and psoriasis patients provided the opportunity to identify

biomarkers of PsA, and validate them in an independent set of PsA and psoriasis patients.

Thirteen out of the 18 genes tested were significant in the validation cohort, however only 4 of

the genes showed the same fold change directionality as in the discovery cohort. Although the

reasons for these discrepant fold changes is not clear, clinical differences between PsA patients

used in the discovery and validation cohorts likely contributed, as several genes showed

moderate correlations with the differing clinical variables. This result highlights the necessity for

a high degree of clinical homogeneity in laboratory studies of PsA, particularly with regard to

attributes such as disease duration and severity. Nonetheless, genes that were significant in the

validation experiment should not be excluded from future analyses on the basis of the present

results, as they could play important roles in the pathogenesis or progression of PsA. Indeed, the

candidate gene EZR, found to be down-regulated in the microarray study but up-regulated in the

validation sample, was recently found to be up-regulated in another microarray study of synovial

tissue and PBMCs of PsA patients and has been shown to be involved in the proliferation of

fibroblast-like synoviocytes in RA [267].

The four genes that showed concordant fold changes between the discovery and validation

cohorts (NOTCH2NL, HAT1, SETD2, and CXCL10) might be fundamental to the disease process

as they were differentially expressed relative to psoriasis patients regardless of the clinical

characteristics of the PsA patients. NOTCH2NL was the best performing biomarker individually,

achieving an AUC of 0.71, while combining the genes improved the AUC to 0.79. These results

suggest that gene expression might not be sufficient as a biomarker of PsA, and integration of

gene expression data with other data such as genetic, demographic, and clinical variables will

likely be necessary to improve the discriminatory ability of gene expression signatures.

Measurement of these biomarkers at the soluble protein level might also be helpful to increasing

the sensitivity and specificity, and determining their amenability to clinical laboratory settings.

Chapter 4 was the first study to measure a soluble protein in the serum of incident PsA cases

(psoriasis ‘converters’) prior to the development of PsA, and compare to baseline serum samples

150

of psoriasis patients who did not develop PsA. CXCL10 was chosen for this initial analysis

because of its putative role in several inflammatory disorders, previous evidence of elevation in

the serum of psoriasis and PsA patients [285], and because a validated commercial kit was

available. Currently, there are no commercial kits available for SETD2 or HAT1, which are

nuclear and cytoplasmic proteins, respectively, that are unlikely to be secreted. Lastly, although

there is no commercial kit for NOTCH2NL, we have found preliminary evidence that it is

secreted and detectable in human serum by indirect sandwich ELISA assay developed in our

laboratory (unpublished data).

CXCL10 is a 98 amino acid, 10kDa protein that is a member of the C-X-C motif subfamily of

chemokines and functions as a ligand for C-X-C motif receptor 3 (CXCR3). CXCL10 is secreted

by activated CD4+, CD8+, NK, and NK-T cells upon stimulation with IFNγ, and is therefore

involved in Th1-type responses, but can also be secreted by a diverse range of cells including

neutrophils, monocytes, fibroblasts, and keratinocytes [285]. CXCL10 can serve as a co-

stimulator of IFNγ secretion by activated CD4+ T cells, which are recruited to sites of CXCL10

secretion because they express the CXCR3 receptor, leading to a positive feedback loop that

amplifies the Th1 immune response and IFNγ-mediated inflammation [286, 287].

Serum CXCL10 was significantly elevated in psoriasis converters at baseline relative to psoriasis

non-converters, and this elevation was independent of age, sex, psoriasis duration, and duration

of follow-up. CXCL10 is similarly elevated in the serum of patients with T1D, autoimmune

thyroiditis, RA, SLE, and multiple sclerosis (MS). CXCL10 is also highly expressed in islet cells

of T1D patients, thyroid tissue of Hashimoto’s thyroiditis patients, brain tissue of MS patients,

synovial fluid and synovial fibroblasts of RA patients, and skin and renal tissues of SLE patients

[285]. Similarly, we found that CXCL10 mRNA expression levels were strikingly elevated in the

synovial fluid of PsA patients compared to their blood. These findings suggest that elevated

serum levels in these disorders compared to controls may be reflective of high localized

CXCL10 production during tissue-specific inflammation. Local CXCL10 production may serve

to recruit CXCR3-expressing cells from the circulation, as evidenced by the high numbers of

infiltrating (Th1) CD4+ T cells (90%) expressing CXCR3 in the synovium and synovial fluid of

151

RA patients [288], and by high CXCR3 expression by CD4+ and CD8+ T cells in skin and renal

biopsies of SLE patients [289, 290]. While typically considered a Th1-associated chemokine,

there is also evidence that IL-17 can synergize with IFNγ to stimulate CXCL10 production in the

human tumour environment [291], and that CXCR3+ Th17 cells are recruited to high levels of

CXCL10 in the liver in immune-mediated liver disease [292].

Based on this information, it is possible that CXCL10 plays a role in both the initiation and

amplification of PsA. In a highly simplified model of tissue-specific disease initiation, factors

such as microtrauma, biomechanical strain or infections could trigger the release of DAMPs or

PAMPs, which are recognized by TLRs on DCs and lead to T cell activation and migration to the

joint, followed by production of pro-inflammatory cytokines such as TNFα, IFNα, and IFN-γ.

IFN-γ and TNFα could stimulate the secretion of CXCL10 from local synovial fibroblasts, which

would promote recruitment of CXCR3+ Th1 or Th17 cells into the joint, and create a positive

feedback loop that amplifies inflammation. Increasing CXCL10 production in the inflamed joint

may result in increased serum CXCL10 due to ‘leakage’ from the joint into the peripheral

circulation, or due to increased production of CXCL10 by circulating Th1 or Th17 cells

themselves, in psoriasis converters prior to the onset of PsA. Th17 cells are known to be

significantly increased in the peripheral circulation of PsA patients [293] and therefore make

interesting candidates as the source of soluble CXCL10. Consistent with this hypothesis,

measurement of CXCL10 mRNA expression in blood leukocyte subsets of PsA patients found

that its expression was highest in T cells, followed by monocytes. Further studies are clearly

needed to shed light on the cellular source of CXCL10 in the blood of psoriasis converters and

PsA patients.

An interesting finding was the significant decrease in serum CXCL10 levels in a subset of

psoriasis converters after PsA diagnosis. This observation is not inconsistent with the microarray

findings of increased whole blood CXCL10 mRNA expression in PsA relative to psoriasis

patients, because although levels decreased after PsA diagnosis, they remained higher than in

psoriasis non-converters. But, as previously noted, this observation should be interpreted

cautiously, as CXCL10 could only be measured at the 2nd time point (after PsA diagnosis) in half

152

of the converters and is potentially biased, because the converters in whom it was measured were

significantly older, had a longer mean psoriasis duration, and had very high baseline CXCL10

levels. Additionally, the majority of these converters (18/23) had begun treatment with NSAIDs,

DMARDs, and even biologic drugs when the second sample was taken, raising the possibility

that the decrease of CXCL10 is due to medications. However, it must be noted that CXCL10

levels also dropped in 4 of the 5 converters who did not start on drugs before the 2nd sample. A

longitudinal study in children with T1D also found that CXCL10 levels decreased at follow-up

relative to baseline values [216]. Similarly, in PsA patients, serum CXCL10 levels have been

found to be inversely related to disease duration, indicating that CXCL10 indeed decreases

during the progression of disease [198]. More research is needed to determine if the observed

decrease in serum CXCL10 in this subset of patients reflects pathogenic changes that occur

during the transition from the initiation/amplification phases to the effecter phase of PsA, during

which CXCL10 may be more involved in localized disease. CXCL10 has been shown to increase

the expression of RANKL, the ligand required for the differentiation of monocytes into

osteoclast precursor cells (OCPs) and osteoclasts, by CD4+ T cells from healthy donors [214],

and has been shown to induce osteoclastogenesis in a collagen-induced arthritis mouse model of

RA [294].

As a biomarker, in the small sample of psoriasis converters tested in the study, CXCL10 appears

to be independent of several recently-described clinical and demographic predictors of PsA in

psoriasis patients, such as PASI score, presence of nail or scalp lesions, education level, obesity,

and family history of PsA [210]. Although the association of CXCL10 with converter status was

highly significant, the specificity of CXCL10 as a biomarker appears to be low, at least in this

sample of psoriasis patients, due to considerable overlap of the distributions of CXCL10 levels

between converters and non-converters. The overlap may be due to the apparent dynamic nature

of CXCL10 expression. In a subset of converters tested in this study, multiple serum samples

were taken between the initial (baseline) sample and the time of PsA diagnosis, and CXCL10

showed a decreasing trend as patients approached PsA diagnosis (Appendix 3). This suggests

that in psoriasis patients destined to develop PsA, soluble CXCL10 levels peak at some time

point prior to PsA onset, but decrease thereafter. Therefore, the lower CXCL10 values observed

153

in some converters may be due to the fact that the baseline sample was taken when they were

temporally closer to PsA onset.

Furthermore, although the psoriasis non-converters did not develop PsA during the duration of

the study, it is possible that some of them may one day develop PsA. Therefore, the higher

CXCL10 values observed in some non-converters could be due to the fact that they are destined

to develop PsA in the future. This potential drawback was minimized by matching converters

and non-converters by duration of psoriasis, which ensured that both groups had an equal

opportunity to develop PsA before entering the study, and by adjusting for duration of follow-up

in multivariable logistic regression analyses, which was defined for converters as the time

between study entry and PsA onset, and for non-converters as the time between study entry and

the most recent clinic visit. This adjustment, however, was not applied to the raw CXCL10

values, which could explain some of the overlap of CXCL10 distributions between converters

and non-converters. For CXCL10 to achieve adequate sensitivity, specificity, and AUC for use

as a biomarker, the temporal changes in its expression in psoriasis patients leading up to PsA

must be better understood so that reliable cutoffs for clinical diagnosis can be established.

In Chapter 5 of this thesis, the increased risk or pathogenicity of psoriasis and PsA during male

compared to female transmission was further explored in two well phenotyped cohorts of

psoriasis and PsA patients. Previous studies have demonstrated a parent-of-origin effect in

psoriasis or PsA patients, but have not directly compared the effect in both groups of patients

from the same ethnic background and geographic area. This study demonstrated that the parent-

of-origin effect is evident in both groups of patients, although the excess of affected fathers

within the psoriasis patients did not reach statistical significance possibly due to the small sample

size compared to previous studies. The study also uncovered a subtlety in the parent-of-origin

effect with regard to the relationship between psoriasis and PsA, which could not have been

detected in either group alone. The finding that the proportion of paternal PsC—proband PsA

pairs was significantly larger than the proportion of maternal PsC—proband PsA pairs (and

conversely, the proportion of maternal PsA—proband PsA pairs was significantly larger than the

proportion of paternal PsA—proband PsA pairs) indicates that the affected fathers of PsA

154

patients tend to have more PsC and less PsA than the affected mothers. If PsA is viewed as a

more severe form of PsC, then this finding could be interpreted as a worsening of the psoriatic

phenotype during passage through the male line. This study thus provided additional evidence of

genetic anticipation associated with the parent-of-origin effect in psoriatic disease.

Although the parent-of-origin effect was replicated in this study, previous associations with

clinical variables such as higher frequency of skin lesions prior to arthritis, higher ESR, and

lower incidence of rheumatoid factor, did not hold up in this larger sample of patients [225]. This

may have been due to the fact that the associations initially reported were weak, and the smaller

sample of PsA patients used in the previous study from our centre was a subsample of the

present population, and might not have been a good representation of PsA patients overall.

Furthermore, the previous study did not examine associations of genetic risk loci in the MHC

with paternally-transmitted disease, as it was performed before high resolution genotyping was

available on these patients. The present study identified carriage of two risk loci within the

MHC, HLA-B*08 and MICA-129Met, as significantly increased and decreased, respectively,

among Newfoundland patients with paternally-transmitted disease. HLA-B*08 is significantly

associated with PsA compared to psoriasis patients regardless of parent-of-origin, with an odds

ratio of 1.61 (p=0.009) in the Toronto cohort of patients [96]. MICA-129Met, on the other hand,

is associated with both PsC and PsA patients with an odds ratio of 1.8 (p<0.001) in the Toronto

cohort [19]. The association with HLA-B*08 appears to be even stronger in patients with

paternally-transmitted disease (OR=3.2, p=0.04), while the association of paternally-transmitted

disease with MICA-129Met appears to reverse and become protective (OR=0.37, p=0.03). These

findings suggest that ignoring the differential effects of maternally and paternally inherited

alleles on psoriatic disease risk in conventional case-control genetic studies can lead to an

underestimation of effect sizes or identification of different associations, which may contribute

to the inability of GWAS to fully explain the heritability of complex diseases [295].

Parent-of-origin effects are ubiquitous across the auto-immune and inflammatory conditions.

Maternal effects have been described in multiple sclerosis [296], type 2 diabetes [297], juvenile

idiopathic arthritis [298], and ankylosing spondylitis [299], while paternal effects have been

155

observed in inflammatory dermatological diseases such as vitiligo [300], and psoriatic disease

[30, 224, 225]. Several possible non-Mendelian genetic mechanisms could explain parent of

origin effects, including sex chromosome linkage, sex-specific bias in the transmission of

unstable trinucleotide repeat polymorphisms, genomic imprinting, and transgenerational

epigenetic inheritance. Maternal origin effects could additionally be mediated by the presence of

risk variants on the mitochondrial genome, which is inherited exclusively from mothers,

transgenerational RNA-mediated effects contributed by the oocyte, and in utero effects on fetal

growth [295]. In utero effects refer to gene-environment interactions shared between the mother

and gestating fetus. Shared environmental exposures can potentially alter the epigenetic status of

both the gestating fetus (F1) and its germline (F2), and are thus intergenerational effects. If F3

individuals inherit the epigenetic mark, it can be called a transgenerational effect [239]. The

latter three mechanisms are not thought to be involved in paternal origin effects, as paternal

mitochondria are ubiquinated and destroyed upon fertilization [301], sperm cells contribute little

RNA to the zygote [302], and fathers and offspring do not share a common (internal)

environment during gestation.

To date, there have been no psoriasis or PsA risk loci identified on the sex chromosomes.

Unstable trinucleotide repeat polymorphism expansions have not yet been studied in the context

of the parent-of-origin effect in psoriasis or PsA, however, a trinucleotide repeat polymorphism

in the transmembrane region of the MICA gene, located in the MHC Class III and adjacent to

HLA-B, has been associated with psoriasis and PsA. Five different alleles of the GCT

polymorphism have been identified in MICA, named A4, A5, A6, and A9 according to the

number of repeats, as well as A5.1, which contains an additional nucleotide insertion (GGCT).

MICA-A5.1 has been associated with psoriasis in a Korean population [303], while A4 and A9

have been associated with PsA compared to controls and psoriasis patients independently of

HLA-B [304] and HLA-C*06 [261-263]. However, in our cohort of psoriasis and PsA patients,

although A4 containing alleles (MICA*001, *0701, *018) and A9 containing alleles (*0201,

*017) were associated with PsA, they did not confer additional risk beyond the HLA-B alleles

with which they are in linkage disequilibrium [17]. Furthermore, A4 and A9 alleles contain the

MICA-129Met polymorphism, but MICA-129Met carriage was found to occur in lower frequency

in patients with paternally-transmitted disease. MICA is an interesting candidate gene for

156

psoriasis and PsA due to the association of numerous polymorphisms in and around MICA with

PsA, and its functional role as a stress-induced protein that serves as a ligand for the activating

NK and γ�T cell receptor NKG2D [305]. The strong evidence of association of MICA

polymorphisms with skin and joint manifetations of psoriatic disease, and the lower frequency of

MICA-129Met in patients with paternally-transmitted disease, make MICA an interesting

candidate gene whose role in the parent-of-origin effect merits further investigation.

The contribution of transgenerational epigenetic inheritance to the heritability and possibly

parent-of-origin effects in autoimmune and inflammatory disorders, and in human disease for

that matter, is vastly understudied. The sixth chapter presented in this thesis takes the preliminary

steps towards addressing this knowledge gap by examining the association of germ line

epigenetic variants with psoriatic disease. Several differentially methylated CpG sites were

identified across the genome. At this stage, this study is not sufficient to prove that they are

inherited but does suggest a potential for vertical transmission via the male germ line. Further

work is needed to demonstrate that the epigenetic mark is stably transmitted between

generations.

In addition to transmission between generations, differential methylation in male and female

gametes would be expected of an epigenetic variant associated with a parent-of-origin effect.

Profiling of oocytes was not performed in this study because their acquisition is prohibitively

invasive. A separate study investigating the parent-of-origin effect compared whole blood

methylation differences between PsA probands with paternally (n=24) and maternally (n=24)

transmitted psoriatic disease [162]. Genome-wide methylation profiling using Illumina

HumanMethylation450k arrays identified 87 significantly differentially methylated CpG sites.

The top three CpG sites were hypermethylated within an intronic CpG island and shore in the

DLGAP2 locus (max. Δβ=0.21, p=9.9x10-7). A fourth significant hypermethylated CpG site in

the intronic CpG island shore of DLGAP2 was also identified (Δβ=0.11, p=0.005). Although

there was no overlap of specific probes or loci between this dataset and the present sperm

dataset, 9 CpG sites in several genes from both datasets mapped to a 2.1 Mb region in 8p23.

157

These included DLGAP2, as well as top genes in sperm cells MYOM2, CSMD1, and ERICH1-

AS1. DLGAP2 is a membrane-associated guanylate kinase that plays a role in synapse

organization, and is maternally imprinted in a tissue-specific manner, being expressed solely

from the paternal allele in testis, but expressed from both parental alleles in the brain [306, 307].

MYOM2 was hypermethylated in sperm cells of PsA compared to psoriasis patients and contains

a fibronectin type III domain typically found in type I cytokine receptors, as well as a binding

motif for master inflammatory regulator NF-κB within its promoter. The tumour suppressor gene

CSMD1 was hypermethylated in sperm cells of both psoriasis and PsA patients compared to

controls. Polymorphisms in CSMD1 have been linked to psoriasis in a genome-wide association

study of Chinese patients [21], and have been shown to interact with smoking to increase

psoriasis risk [308]. Finally, a CpG island shore of the non-coding antisense RNA of unknown

function, ERICH1-AS1, was hypermethylated in sperm cells of PsA patients compared to

controls. This region within 8p23 is therefore an interesting region for follow-up study given the

overlap and enrichment of significant CpG sites in both blood and sperm cells.

It is interesting that the MHC is consistently associated with psoriatic disease in genetic studies,

and now also in epigenetic studies of both sperm and whole blood [162]. The MHC is an

extremely gene-dense region that is more polymorphic and has been associated with more

diseases than any other region in the human genome. As such, it was the chosen to be the first

region profiled in the pilot study of the Human Epigenome Project [309]. This pilot study

demonstrated tissue-specific methylation profiles, substantial inter-individual variation, and an

inverse correlation between methylation status of the upstream regions of genes and their

expression. Although the majority of genes showed a bimodal methylation distribution of either

very low (<30%) or very high (>70%) methylation, 14 regions showed heterogeneous

methylation levels ranging from 30-70%, which could be indicative of reciprocal methylation of

the two parentally-derived alleles. Six of these regions contained polymorphisms and were tested

for allele-specific methylation, but none were found to display this characteristic. To date, there

have been no reports of heritable epimutations or imprinted regions within the human MHC,

although several other regions show tissue-specific methylation heterogeneity within the MHC,

which does not rule out the possibility of tissue-specific imprinting [309].

158

One CpG site in HCG26 found to be hypomethylated in sperm cells of PsA patients compared to

both psoriasis patients and controls was located 67 bp upstream of the TSS, and an additional

two sites were found within the body. The location of these CpG sites suggests a functional role

in regulating the expression of the cognate transcript, implying that hypomethylation might result

in increased expression. HCG26 was not found to be differentially expressed in the whole blood

gene expression study, however, according to BioGPS [310], HCG26 is expressed highly in

CD4+ and CD8+ T cells. Although there is no published information regarding its function, a

variant within HCG26 was recently associated with ulcerative colitis in North Indians [311].

Furthermore, HCG26 overlaps in the sense direction with an intron of an alternatively spliced

transcript of the adjacent HCP5 (P5-1) locus, suggesting a role for HCG26 in the stabilization or

regulation of splicing of HCP5 transcripts [312]. HCP5 is a human endogenous retrovirus whose

single-stranded mRNA is expressed in several human lymphoid tissues such as B cells, activated

lymphocytes, NK cells, and spleen, consistent with involvement in immunity [313]. This single-

stranded mRNA is complementary to the retroviral pol mRNA, and was hypothesized to function

as an antisense transcript that regulates retroviral replication and disease [314]. In a previous

study the rs2395029 polymorphism in HCP5 was associated with psoriasis and PsA and had the

highest OR of any SNP tested [20]. Thus, HCG26, through its putative function in regulating

HCP5 expression, may play a role in immune processes relevant to psoriatic disease.

7.1 Limitations

The following section presents a critical appraisal of the work discussed herein, including a

reiteration of limitations mentioned in previous sections.

Study #1

• In addition to the fact that gene expression differences in whole blood may simply reflect cell

frequency differences between patient groups and controls, a second potential disadvantage of

whole blood gene expression studies is the lower sensitivity to detect subtle expression

changes originating from rare cell types. Gene expression changes occurring in low

159

frequency, yet pathogenically important cells in PsA patients may have been ‘drowned out’

by more common cell types in which the same genes are unchanged.

• Whole blood RNA collection method is known to affect RNA stability and gene expression

measurements. Unfortunately, RNA samples used for the discovery phase were collected

exclusively in PAXgene tubes, while samples used for replication were collected exclusively

in Tempus tubes. A recent study found that these two collection methods differ in their ability

to stabilize some RNA transcripts, and as a result, certain genes appear to be uniquely

expressed depending on the type of tube used [315]. This could partially account for why

some genes were significantly differentially expressed in the discovery but not the replication

cohort.

• PsA patients chosen for discovery and replication testing were not perfectly matched,

differing significantly in terms of duration of PsA and disease severity. Given the evidence

that PsA can be a dynamic disease characterized by periods of remissions and flares, as well

as a progressive disease, it could be speculated that particular cell types in the blood are

important at different stages of its pathogenesis and clinical course. This would likely be

reflected in changes in the whole blood gene expression signature of PsA over time. Having

discovery and replication cohorts with significantly different disease durations and severity

could account for why many genes upregulated in the discovery cohort were downregulated in

the replication cohort, and vice versa.

• There was little overlap of genes identified as differentially expressed between PsA and

controls in the present study and a previous study [110], as only 37 out of 495 (7.5%)

common DEGs were found. These differences may be due to the different microarray

platforms used, which draws into question the validity of each microarray platform. A recent

study showed that compared to Affymetrix one-channel arrays, Agilent two-colour arrays

showed much lower concordance with RNASeq data, and furthermore, some genes showed

fold changes in the opposite direction on Agilent arrays compared to RNASeq [316].

Discordant results from the replication cohort may be partially explained by inaccurate gene

expression measurements in the discovery cohort using the Agilent arrays.

160

• In this study, medications were not found to affect the expression of a significant proportion

of genes, and thus patients taking medications were not excluded from the analysis. This is

consistent with a previous study in which only 55 and 188 were differentially expressed in

PsA patients taking MTX or anti-TNF agent, respectively [112]. However, the few genes

affected by medications functioned in keratinocyte development, apoptosis, cell proliferation,

T cell functioning, cytokines, antigen presentation, osteoclasts, and neutrophils—processes

that may play important roles in PsA pathogenesis. Thus, medications may have affected the

expression of a small number of functionally important genes, and cannot be ruled out as a

potential confounding factor in this study.

Study #2

• The inference brought forward from the previous study was that differential gene expression

would ‘translate’ into differential protein expression in serum. This assumption is often not

true for several reasons: different regulatory mechanisms at the RNA and protein levels,

different half-lives, turnover times, and stabilities of RNA and proteins. Importantly, even if

mRNA and protein levels correlate well within the cell, they may not correlate to soluble

levels if the protein is not secreted. Furthermore, different techniques used to measure RNA

and protein fall subject to different sources of measurement error that may further impact the

ability to correlate RNA and protein expression.

• As discussed in Chapter 4, a post-PsA diagnosis sample was available on only half of the

psoriasis patients who converted to PsA. This high attrition rate produced a subgroup of

patients who were significantly older and had significantly higher baseline CXCL10 levels

than patients who did not provide a post-PsA diagnosis sample. As this subgroup cannot be

considered representative of the entire converter population, the comparison of pre versus

post-PsA CXCL10 levels is biased. Furthermore, the majority of these converters began

medications prior to providing the post-diagnosis sample, which may have contributed to the

decrease in CXCL10 expression.

• The usefulness of soluble CXCL10 as a clinical biomarker remains questionable until more is

known about the dynamic changes in CXCL10 expression in psoriasis patients who convert

161

and who do not convert to PsA over time. Furthermore, the stability of this chemokine after

blood samples are drawn and at various storage conditions and transportation methods must

be determined.

• Although all psoriasis patients classified as non-converters did not develop PsA during the

duration of the study, a proportion of these patients will develop PsA eventually. This right

censoring of the data was not taken into account in the logistic regression model used in this

preliminary analysis. In future studies, a survival analysis might be more suitable.

Study #3

• A limitation of the study stems from the fact that parental disease status is based on patient

self-report and was not confirmed by a dermatologist or rheumatologist. Unfortunately, it is

likely too logistically and monetarily difficult to confirm diagnoses in the sample size

required to achieve the statistical power to demonstrate a significant parent-of-origin effect in

psoriatic disease.

• This study did not analyze differences in age of psoriasis or PsA onset of affected parents and

probands. Had this data been available, it would have enabled a deeper analysis of genetic

anticipation in psoriatic disease and might have helped to provide additional support for a

reduced age of onset during paternal transmission.

• The possibility that the observation of excessive paternal transmission of psoriasis, and

excessive maternal PsA is an artefact of ascertainment and/or reporting bias cannot be ruled

out. These biases may have stemmed from the fact that men may have more severe and

extensive psoriasis than women, while women experience more severe limitations when

affected with PsA. This may have resulted in increased recognition of psoriasis among

children of psoriatic fathers, and increased recognition of PsA among children of PsA

mothers, and an increased likelihood of their participation in a research study and/or their

reporting of parental history.

• This was the fourth study to provide evidence of a parent-of-origin effect in psoriatic disease,

and like the previous studies, the excess of paternally transmitted disease was statistically

162

significant, albeit subtle. This may suggest that the mechanism underlying the parent-of-origin

effect and its contribution to psoriatic disease risk are similarly subtle.

Study #4

• The previous study replicating a parent-of-origin effect in psoriatic disease provided the

rationale for examining the presence of heritable epigenetic marks associated with psoriasis

and PsA. However, it is possible that other genetic mechanisms such as unstable trinucleotide

repeat expansions may be operating independently or in association with heritable epigenetic

marks to mediate the parent-of-origin effect in psoriatic disease.

• No CpG sites were significantly differentially methylated after correction for multiple testing,

so a less stringent unadjusted p value was used, making it very possible that some hits are

false positives. Given that the methylation differences between groups were subtle, and the

number of subjects tested was small relative to the number of statistical comparisons made,

the study was likely underpowered. It is also possible that the results were ‘overcorrected’ in

that smoothing was performed, yet multiple testing correction was still performed at the CpG-

level and not the DMR-level. Larger, predefined units (e.g. CpG islands, promoters, gene

bodies, UTRs) could have been used to reduce the number of statistical tests performed.

Alternatively, taking a candidate gene approach may have helped to avoid the requirement for

multiple testing correction altogether.

• The two waves of epigenomic reprogramming between generations are thought to ensure that

no aberrant epigenetic marks accumulated over a lifetime are transmitted to future

generations. For this reason, as well as the fact that the germ line is not a disease target tissue,

we would not expect to find numerous epigenetic differences between patients and controls.

The differences that were identified likely include false positive results, as discussed above, as

well as artefacts of the technique, similar to the SNP-containing probe within the HLA-B

3’UTR. It is therefore crucial that these results be both validated and replicated.

163

7.2 Conclusions

The first study on gene expression differences in psoriasis patients with and without

inflammatory arthritis represented the first direct comparison of whole blood transcriptomes of

psoriasis and PsA patients. This work demonstrated that gene expression patterns can distinguish

psoriasis and PsA patients, and by doing so identified candidate biomarkers of PsA, which were

then validated using alternative techniques and replicated in an independent cohort of patients.

This work also strengthened the evidence for innate immune dysregulation in the pathogenesis of

PsA through the identification of several genes belonging to the TLR signaling pathway as

differentially expressed between psoriasis and PsA patients.

The second study assessed the ability of one of the replicated gene expression biomarkers,

CXCL10, to serve as a protein biomarker that could predict which psoriasis patients are destined

to develop PsA. This work was the first to compare baseline levels of a soluble chemokine in

longitudinally-followed psoriasis patients who later developed PsA, to levels in psoriasis patients

who did not develop PsA. This study demonstrated that soluble protein levels of CXCL10 can

potentially be used to predict PsA onset, and provided rationale for further investigation of the

role of CXCL10 in PsA pathogenesis.

The third study provided further evidence of a parent-of-origin effect in psoriatic disease,

demonstrating that it was evident in both psoriasis and PsA patients from the same large, well-

phenotyped cohort, and extended the evidence of genetic anticipation during male transmission

by demonstrating a tendency for an increase in disease severity from psoriasis to PsA during

male transmissions. Overall, this study provided additional evidence that non-Mendelian genetic

or epigenetic mechanisms may play a role in psoriatic disease.

The fourth and final study was the first to examine germ line methylation variations in any

human rheumatological disorder. This study demonstrated the presence of DNA methylation

164

variants in sperm cells of patients with psoriasis and PsA, suggesting a potential for the vertical

transmission of epigenetic marks that influence the risk of psoriatic disease and possibly mediate

the parent-of-origin effect.

7.3 Future Directions

The studies presented herein have provided the basis for future work by generating numerous

novel questions regarding specific aspects of the pathogenesis of psoriatic disease and the

possibility of developing transcriptomic, protein, and epigenetic biomarkers of PsA.

Gene expression studies provided the proof-of-concept of gene expression differences between

psoriasis and PsA patients, however whether these are true differences in gene expression

regulation or simply reflect differences in cellular composition remains unknown. This study

identified several putative biomarkers of PsA, most notably NOTCH2NL and CXCL10, which

must be replicated in other populations. However, before this is done, it would be useful to

computationally deconvolute the whole blood data and determine which cell types make large

contributions to the observed expression differences. The relative abundance of these cell types

and the expression of specific genes, such the putative biomarkers as genes involved in TLR

signalling, in purified cell populations can be compared between PsA and psoriasis patients at a

single time point in a case-control fashion. Furthermore, an important observation was the

correlation of several genes with disease duration, which might have contributed to the

discordant fold changes in the discovery versus validation cohorts. This finding suggests that

gene expression is dynamic during the course of psoriasis and PsA. It would therefore be

interesting to examine the change in cell types, as well as the expression of specific genes in a

small group of patients in a longitudinal or time-course fashion, starting ideally from a time prior

to PsA onset to several years beyond PsA onset. This analysis would be complicated by the

effects of medications shortly after PsA diagnosis, but might also provide valuable information

regarding medication effects on gene expression trends, affording the opportunity to identify

pharmacogenomic markers that may help to monitor drug response.

165

The prospective cohort of psoriasis patients who converted to PsA used for the measurement of

CXCL10 represented nearly all incident cases of PsA among the psoriasis patients followed

since 2006. Therefore, it is not possible to assess CXCL10 in additional baseline samples from

individuals who were confirmed to develop PsA. CXCL10 could be measured in baseline

samples from the remaining psoriasis patients within the cohort and from other cohorts, and its

predictive ability for PsA can be assessed retrospectively at some future time using a more

sophisticated statistical model such as a Cox Proportional Hazard model with time-dependent

variables, as performed in a recent study of clinical and demographic predictors of PsA in

patients with psoriasis [210]. Measurement of CXCL10 at baseline in a larger independent set of

patients will also be necessary to accurately assess its performance as a biomarker in terms of

AUC, sensitivity, and specificity. For the time being, studies can focus on seeking confirmatory

evidence in larger sample sizes that CXCL10 and its receptor CXCR3 are differentially

expressed at the mRNA and protein level in the circulation and synovial fluid of PsA patients

compared to patients with rheumatoid arthritis, osteoarthritis, or gout. Preliminary evidence from

our laboratory suggests that CXCL10, CXCR3, and IL-17A expression is higher in the blood of

PsA patients compared to OA and gout patients, while expression of IFNγ and TNFα, the major

inducers of CXCL10 secretion, are higher in PsA patients compared to OA patients. Preliminary

evidence also suggests that CXCL10, CXCR3, and IL-17A are expressed at similar levels in PsA

and RA patients, suggesting a shared inflammatory mechanism between the two disorders.

Next, the cells expressing CXCL10 and CXCR3 could be determined by continuing to isolate

leukocyte subpopulations from PBMCs and synovial fluid mononuclear cells (SFMCs) and

measuring CXCL10 and CXCR3 gene expression. Once candidate cell type(s) are identified,

they can be immunophenotyped in greater detail by flow cytometry. The ability of PsA sera and

synovial fluid to induce CXCL10 expression could also be tested by treating the cells from

healthy donors with sera and/or synovial fluid from patients with PsA, psoriasis, RA, OA, gout,

and healthy controls and measuring the proliferation of the appropriate CXCL10-secreting cells.

The effects of various inhibitors of the CXCL10-CXCR3 pathway could also be tested in vitro.

Finally, the question of whether CXCL10 levels decrease as psoriasis patients develop PsA could

be examined by following a subset of psoriasis patients at high risk of developing PsA

166

longitudinally, and regularly and frequently collecting serum samples for measurement of

CXCL10.

The final study presented in this thesis is a preliminary study that requires several follow-up

experiments. These experiments should begin with a technical validation of the results in the

same samples using the gold standard technique of bisulfite conversion followed by

pyrosequencing. For this validation, both hypo and hypermethylated CpG sites representing a

wide range of beta differences should be tested. Once the accuracy of the arrays is confirmed,

CpGs within interesting candidate genes can be measured in whole blood, buccal cells, purified

leukocyte subpopulations, psoriatic skin, synovium, and any other accessible tissues from the

same patients by pyrosequencing. If a particular region is differentially methylated in several

tissues concurrently, it suggests that it might have been inherited, and should be assessed in the

somatic tissues and germ line of parents or offspring of the probands. At the same time, gene

expression and proteomic studies of cognate transcripts can be performed, as well as sequencing

of the flanking regions to identify cis-acting genetic effects. To determine if the methylation

status of the region is associated with the parent-of-origin effect, it must be demonstrated

through the use of the transmission disequilibium or similar tests, that excessive sharing of a

similar epigenetic status occurs in affected fathers and their affected offspring, but not affected

mothers and their affected offspring. Finally, the presence of histone modifications such as

methylation of H3K4 and H3K36, which are associated with transcriptional activation, and

H3K9 and H3K27, which are associated with transcriptional repression, at sites of differential

methylation would be helpful in determining the effects on chromatin structure and gene

expression. Eventually, if particular epigenetic marks are found to be consistently present in PsA

patients, their performance as biomarkers alone, or in combination with other molecular, clinical,

and demographic variables can be tesed in larger numbers of patients across different clinical

presentations and cohorts.

167

Appendix

Appendix 1. PCR primers used to measure validated gene expression biomarkers.

Gene Direction Sequence (5’->3’)

NOTCH2NL Forward CTGCCTTCCAGAAACAGTGAGA

Reverse CAAAAGCAAAAGCACAAGCACA

HAT1 Forward TACAGCGGAAGATCCATCCAA

Reverse CTGTTGTGCCTCTATCGCCA

SETD2 Forward ATCGAGAGAGGACGCGCTATT

Reverse AGGTACGCCTTGAGTATGTCTT

CXCL10 Forward GTGGCATTCAAGGAGTACCTC

Reverse TGATGGCCTTCGATTCTGGATT

168

Appendix 2. Histograms depicting the distribution of CXCL10 serum concentrations. CXCL10

expression was not normally distributed in PsC converters at baseline (A, p<0.0001), post-

conversion to PsA (B, p=0.01) and in non-converters (C, p=0.03, Kolmogorov–Smirnov test).

A

B

C

169

Appendix 3. Scatter dot plot of paired CXCL10 serum expression from 16 PsC patients at

baseline, follow-up and after the development of PsA. A significant reduction in CXCL10

expression was found after PsA onset (median 491.4, IQR 287.8-589.4 pg/ml) compared to

baseline (median 890.7, IQR 459.2-1202 pg/ml, p<0.01) and follow-up levels (median 562.6,

IQR 424.4-955.7 pg/ml, p<0.05, Friedman test with Dunn’s multiple comparison test).

170

Appendix 4. Psoriasis and psoriatic arthritis family history questionnaire.

171

Appendix 5. Methylation-specific PCR assessing bisulfite conversion efficiency. BS, 333bp amplicons generated from primers specific to

bisulfite converted sequence of calponin-1; WT, 333bp amplicons generated from primers specific to unconverted (wild-type) sequence of

calponin-1. BS-POS, fully bisulfite converted positive control DNA; WT-NEG, fully unconverted (wild-type) negative control DNA.

Samples are identified by lab accession number.

172

Appendix 6. Full list of differentially methylated genes in psoriasis patients vs. controls

(p<0.05).

GeneSymbol EntrezID CHROMOSOME start end NumOfProbes min_p.value

ADARB2 105 chr10 1814066 1814151 3 0.001

SPERT 220082 chr13 46291973 46291973 1 0.001

CSMD1 64478 chr8 2820857 2820857 1 0.001

ST8SIA6 338596 chr10 17347047 17347160 2 0.001

RNF6 6049 chr13 26761337 26761337 1 0.001

LRRC74A 145497 chr14 77333987 77333987 1 0.001

LRRTM4 80059 chr2 77235218 77235218 1 0.002

L1TD1 54596 chr1 62657689 62657689 1 0.002

KCNK2 3776 chr1 215259771 215259771 1 0.004

MGC15885 197003 chr15 62899159 62899159 1 0.004

KRT82 3888 chr12 52798363 52798363 1 0.005

FAM107B 83641 chr10 14620934 14620934 1 0.005

NFIC 4782 chr19 3373819 3373819 1 0.007

NLRP13 126204 chr19 56443824 56443824 1 0.008

DHX37 57647 chr12 125450666 125450666 1 0.011

INPP5A 3632 chr10 134556992 134556992 1 0.012

ASAP1 50807 chr8 131265658 131265658 1 0.013

IRX1 79192 chr5 3959743 3959743 1 0.015

RBM47 54502 chr4 40428028 40428121 3 0.015

PRMT8 56341 chr12 3590738 3590738 1 0.019

ABHD8 79575 chr19 17409380 17409380 1 0.020

COL4A1 1282 chr13 110915134 110915134 1 0.020

GPR123 84435 chr10 134876495 134876495 1 0.021

CYP4F11 57834 chr19 16045054 16045054 1 0.022

TMEM26 219623 chr10 63240299 63240299 1 0.024

TMEM18 129787 chr2 496713 496855 2 0.026

RASA3 22821 chr13 114808107 114808107 1 0.028

ANXA2 302 chr15 60644157 60644157 1 0.030

AKR1C2 1646 chr10 5047487 5047487 1 0.032

DFNA5 1687 chr7 24742552 24742552 1 0.033

ZNRF4 148066 chr19 5507274 5507540 5 0.033

TINCR 257000 chr19 5507540 5507540 1 0.033

IRF6 3664 chr1 209982407 209982407 1 0.035

MUS81 80198 chr11 65631880 65631880 1 0.036

ADM 133 chr11 10373718 10373718 1 0.037

CLDN4 1364 chr7 73245178 73245178 1 0.039

SMOC2 64094 chr6 168963358 168963731 3 0.039

SRSF9 8683 chr12 120903935 120903935 1 0.040

173

DGKH 160851 chr13 42704154 42704154 1 0.040

SLC35F1 222553 chr6 118158769 118158769 1 0.042

GDAP2 54834 chr1 118427435 118427435 1 0.043

CDH8 1006 chr16 63406440 63406440 1 0.044

BATF 10538 chr14 76015669 76015669 1 0.044

LINC01094 100505702 chr4 79627477 79627477 1 0.047

MBD5 55777 chr2 149310951 149310951 1 0.048

AACSP1 729522 chr5 178208610 178208610 1 0.049

174

Appendix 7. Full list of differentially methylated genes in PsA patients vs. controls (p<0.05).

GeneSymbol EntrezID CHROMOSOME start end NumOfProbes min_p.value

ITGB2-AS1 100505746 chr21 46349496 46349497 1 0.000

RREB1 6239 chr6 7232389 7232390 1 0.001

CSMD1 64478 chr8 2820857 2820858 1 0.001

NLRP13 126204 chr19 56443824 56443825 1 0.002

PACSIN2 11252 chr22 43343608 43343609 1 0.002

WWC2 80014 chr4 184060895 184060896 1 0.002

UBE2E1 7324 chr3 23782847 23782848 1 0.002

GALNT9 50614 chr12 132970851 132971019 3 0.003

PTPRS 5802 chr19 5223299 5223343 2 0.003

OR1D4 653166 chr17 3135358 3135359 1 0.004

PRKAG2 51422 chr7 151542804 151542805 1 0.005

FRK 2444 chr6 116262856 116262857 1 0.005

RBFOX1 54715 chr16 6692245 6692246 1 0.005

HAR1A 768096 chr20 61751933 61751934 1 0.005

PTDSS2 81490 chr11 472782 474509 6 0.005

ERAL1 26284 chr17 27184533 27184534 1 0.008

MXI1 4601 chr10 111989324 111989325 1 0.008

CDH6 1004 chr5 31106255 31106256 1 0.009

BAZ2B 29994 chr2 160463692 160463693 1 0.009

FSIP2 401024 chr2 186988953 186988954 1 0.010

SGK223 157285 chr8 8185703 8185742 2 0.010

LINC01060 401164 chr4 189552622 189552623 1 0.010

LCP1 3936 chr13 46719445 46719446 1 0.011

RDH16 8608 chr12 57345407 57345408 1 0.012

ZNF573 126231 chr19 38229377 38229378 1 0.012

NRBP2 340371 chr8 144917532 144917758 2 0.012

SLC35C1 55343 chr11 45822831 45822832 1 0.012

ERICH1-AS1 619343 chr8 735312 735313 1 0.012

MSRA 4482 chr8 10049871 10049872 1 0.013

MAGI2 9863 chr7 77740624 77740625 1 0.014

HCG26 352961 chr6 31438939 31439083 3 0.014

CELF6 60677 chr15 72567956 72567957 1 0.014

SLC35F1 222553 chr6 118158769 118158770 1 0.014

TPPP 11076 chr5 662907 663787 3 0.015

SECISBP2L 9728 chr15 49342629 49342630 1 0.017

BIN1 274 chr2 127841945 127841946 1 0.018

COL4A1 1282 chr13 110918441 110918683 4 0.018

NDFIP1 80762 chr5 141485167 141485168 1 0.018

OR5H15 403274 chr3 97887864 97887865 1 0.018

HLA-DPB2 3116 chr6 33094069 33094306 3 0.019

175

SYT8 90019 chr11 1858572 1858605 2 0.019

OR4E2 26686 chr14 22279816 22279816 3 0.019

FLJ37201 283011 chr10 91453851 91453852 1 0.020

AACS 65985 chr12 125538377 125538378 1 0.020

TEX37 200523 chr2 88837585 88837586 1 0.021

NTNG2 84628 chr9 135114066 135114067 1 0.021

KIAA0232 9778 chr4 6890915 6890977 2 0.021

KIAA0513 9764 chr16 85124401 85124402 1 0.024

GPR63 81491 chr6 97247867 97247868 1 0.024

MOB3A 126308 chr19 2078176 2078177 1 0.025

RBMXL3 139804 chrX 114426686 114426759 2 0.025

NAT8 9027 chr2 73869666 73869667 1 0.025

PPP1R21 129285 chr2 48647546 48647547 1 0.026

HLA-B 3106 chr6 31322121 31322122 1 0.026

SKP2 6502 chr5 36157329 36157330 1 0.029

TRIM24 8805 chr7 138229989 138229990 1 0.031

MIR5702 100847053 chr2 227526367 227526368 1 0.032

DGCR6L 85359 chr22 20284604 20284605 1 0.032

TXNRD1 7296 chr12 104676774 104676775 1 0.033

SLC6A3 6531 chr5 1420305 1420306 1 0.035

FAM114A2 10827 chr5 153372524 153372525 1 0.036

KCNC1 3746 chr11 17793350 17793351 1 0.038

BRINP1 1620 chr9 121929811 121929812 1 0.038

CNTNAP2 26047 chr7 148032668 148032669 1 0.039

MMADHC 27249 chr2 150845309 150845310 1 0.039

ETV1 2115 chr7 13837775 13837776 1 0.040

C6orf58 352999 chr6 127898305 127898306 1 0.042

ARHGAP22 58504 chr10 49765381 49765382 1 0.042

ITPR1 3708 chr3 4630986 4630987 1 0.043

RTN4RL1 146760 chr17 1835482 1835483 1 0.045

ESPNP 284729 chr1 17053886 17053887 1 0.045

IRS1 3667 chr2 227560785 227560786 1 0.045

TNS1 7145 chr2 218829609 218829610 1 0.045

MIR3180-3 100422836 chr16 16404591 16404592 1 0.046

MICAL3 57553 chr22 18479382 18479383 1 0.048

LOC154449 154449 chr6 170531180 170531367 4 0.048

PLEKHG3 26030 chr14 65175225 65175226 1 0.049

176

Appendix 8. Full list of differentially methylated genes in PsA patients vs. psoriasis patients

(p<0.05).

GeneSymbol EntrezID CHROMOSOME start end NumOfProbes min_p.value

TPPP 11076 chr5 662284 663895 6 0.000

IRX1 79192 chr5 3959743 3959743 1 0.000

PPP1R21 129285 chr2 48647546 48647546 1 0.001

C11orf40 143501 chr11 4597246 4597246 1 0.001

EBF1 1879 chr5 158086454 158086454 1 0.001

CTNNA2 1496 chr2 80281335 80281335 1 0.002

MYOM2 9172 chr8 2029571 2029571 2 0.003

PPIF 10105 chr10 81114059 81114059 1 0.003

ABHD8 79575 chr19 17409380 17409380 1 0.004

HCG26 352961 chr6 31438939 31439083 3 0.004

DGCR6L 85359 chr22 20284604 20284604 1 0.006

FAM167A 83648 chr8 11327014 11327014 1 0.006

SECISBP2L 9728 chr15 49342629 49342629 1 0.006

ATP11A 23250 chr13 113539522 113539759 2 0.006

CELF6 60677 chr15 72567956 72567956 1 0.006

OR52M1 119772 chr11 4565489 4565489 1 0.007

PAPD7 11044 chr5 6775909 6775922 2 0.007

AGPAT4 56895 chr6 161622097 161622097 1 0.007

HAR1A 768096 chr20 61751933 61751933 1 0.007

SPERT 220082 chr13 46291973 46291973 1 0.009

ZBTB46 140685 chr20 62387416 62387416 1 0.010

C11orf53 341032 chr11 111148753 111148753 1 0.010

NAMPT 10135 chr7 105969911 105969911 1 0.011

MIR3180-3 100422836 chr16 16404591 16404591 1 0.012

FAT1 2195 chr4 187751549 187751549 1 0.012

SLC35C1 55343 chr11 45822831 45822831 1 0.012

CDH22 64405 chr20 44943725 44943725 1 0.013

MXRA8 54587 chr1 1286917 1286917 1 0.014

DYNC2H1 79659 chr11 103480630 103480630 1 0.014

RBMS1 5937 chr2 161209326 161209326 1 0.016

C6orf195 154386 chr6 2615341 2615341 1 0.016

ANKRD18DP 348840 chr3 197826510 197826510 1 0.017

NDFIP1 80762 chr5 141538333 141538333 1 0.018

ADAM3A 1587 chr8 39380341 39380341 1 0.018

MIR4786 100616417 chr2 240872433 240872433 1 0.019

ADAMTS13 11093 chr9 136297879 136297879 1 0.019

FOXD2 2306 chr1 47974278 47974278 1 0.020

SEMA6A 57556 chr5 116075820 116075820 1 0.020

COPB1 1315 chr11 14495049 14495049 1 0.020

177

MOB3A 126308 chr19 2078176 2078176 1 0.021

PWWP2B 170394 chr10 134218408 134218408 1 0.021

HAS1 3036 chr19 52228400 52228400 1 0.021

SLC39A8 64116 chr4 103172826 103172826 1 0.022

IRX4 50805 chr5 2006984 2007611 4 0.024

LRRTM4 80059 chr2 77235218 77235218 1 0.024

INSC 387755 chr11 15438255 15438255 1 0.026

B4GALT6 9331 chr18 29205358 29205358 1 0.026

LOC100652824 100652824 chr2 203032110 203032110 1 0.027

PTDSS2 81490 chr11 472782 474509 6 0.028

TCP10 6953 chr6 167786059 167786059 1 0.030

VILL 50853 chr3 38033516 38033516 1 0.033

GATA5 140628 chr20 61047376 61047376 1 0.034

AKR1C2 1646 chr10 5047487 5047487 1 0.034

RPTOR 57521 chr17 78809403 78809403 1 0.035

LOC440117 440117 chr12 127359914 127359914 1 0.036

SKP2 6502 chr5 36157329 36157329 1 0.038

FAM8A1 51439 chr6 17600994 17600994 1 0.038

MICAL3 57553 chr22 18479382 18479382 1 0.039

LINC01257 116437 chr12 131645153 131645153 1 0.041

OR5H15 403274 chr3 97887864 97887864 1 0.041

ERAL1 26284 chr17 27184533 27184533 1 0.041

ATCAY 85300 chr19 3910932 3910932 1 0.041

DSE 29940 chr6 116753994 116753994 1 0.043

LOC728323 728323 chr2 242948396 242948396 1 0.044

LINC00977 728724 chr8 129985596 129985596 1 0.045

CTDP1 9150 chr18 77378261 77378261 1 0.045

ZNF568 374900 chr19 37466940 37466940 1 0.049

CCDC88C 440193 chr14 91880061 91880061 1 0.049

178

References

1. Benedek TG. Psoriasis and psoriatic arthropathy, historical aspects: part I. J Clin

Rheumatol. 2013 Jun; 19(4):193-198.

2. Nestle FO, Kaplan DH, Barker J. Psoriasis. N Engl J Med. 2009 Jul 30; 361(5):496-509.

3. Chandran V, Raychaudhuri SP. Geoepidemiology and environmental factors of psoriasis

and psoriatic arthritis. J Autoimmun. 2010 May; 34(3):J314-321.

4. Gudjonsson JE, Elder JT. Psoriasis: epidemiology. Clin Dermatol. 2007 Nov-Dec;

25(6):535-546.

5. Naldi L, Gambini D. The clinical spectrum of psoriasis. Clin Dermatol. 2007 Nov-Dec;

25(6):510-518.

6. Langley RG, Krueger GG, Griffiths CE. Psoriasis: epidemiology, clinical features, and

quality of life. Ann Rheum Dis. 2005 Mar; 64 Suppl 2:ii18-23; discussion ii24-15.

7. Rahman P, Elder JT. Genetic epidemiology of psoriasis and psoriatic arthritis. Ann

Rheum Dis. 2005 Mar; 64 Suppl 2:ii37-39; discussion ii40-31.

8. Bowcock AM, Krueger JG. Getting under the skin: the immunogenetics of psoriasis. Nat

Rev Immunol. 2005 Sep; 5(9):699-711.

9. Lonnberg AS, Skov L, Skytthe A, Kyvik KO, Pedersen OB, Thomsen SF. Heritability of

psoriasis in a large twin sample. Br J Dermatol. 2013 Aug; 169(2):412-416.

10. Duffy DL, Spelman LS, Martin NG. Psoriasis in Australian twins. J Am Acad Dermatol.

1993 Sep; 29(3):428-434.

11. Fan X, Yang S, Huang W, Wang ZM, Sun LD, Liang YH, et al. Fine mapping of the

psoriasis susceptibility locus PSORS1 supports HLA-C as the susceptibility gene in the Han

Chinese population. PLoS Genet. 2008 Mar; 4(3):e1000038.

12. Nair RP, Stuart PE, Nistor I, Hiremagalore R, Chia NV, Jenisch S, et al. Sequence and

haplotype analysis supports HLA-C as the psoriasis susceptibility 1 gene. Am J Hum Genet.

2006 May; 78(5):827-851.

13. Enerback C, Nilsson S, Enlund F, Inerot A, Samuelsson L, Wahlstrom J, et al. Stronger

association with HLA-Cw6 than with corneodesmosin (S-gene) polymorphisms in Swedish

psoriasis patients. Arch Dermatol Res. 2000 Nov; 292(11):525-530.

14. Allen MH, Ameen H, Veal C, Evans J, Ramrakha-Jones VS, Marsland AM, et al. The

major psoriasis susceptibility locus PSORS1 is not a risk factor for late-onset psoriasis. J Invest

Dermatol. 2005 Jan; 124(1):103-106.

179

15. Chandran V. Genetics of psoriasis and psoriatic arthritis. Indian J Dermatol. 2010 Apr-

Jun; 55(2):151-156.

16. Nair RP, Duffin KC, Helms C, Ding J, Stuart PE, Goldgar D, et al. Genome-wide scan

reveals association of psoriasis with IL-23 and NF-kappaB pathways. Nat Genet. 2009 Feb;

41(2):199-204.

17. Pollock R, Chandran V, Barrett J, Eder L, Pellett F, Yao C, et al. Differential major

histocompatibility complex class I chain-related A allele associations with skin and joint

manifestations of psoriatic disease. Tissue Antigens. 2011 Jun; 77(6):554-561.

18. Knight J, Spain SL, Capon F, Hayday A, Nestle FO, Clop A, et al. Conditional analysis

identifies three novel major histocompatibility complex loci associated with psoriasis. Hum Mol

Genet. 2012 Dec 1; 21(23):5185-5192.

19. Pollock RA, Chandran V, Pellett FJ, Thavaneswaran A, Eder L, Barrett J, et al. The

functional MICA-129 polymorphism is associated with skin but not joint manifestations of

psoriatic disease independently of HLA-B and HLA-C. Tissue Antigens. 2013 Apr 24.

20. Liu Y, Helms C, Liao W, Zaba LC, Duan S, Gardner J, et al. A genome-wide association

study of psoriasis and psoriatic arthritis identifies new disease loci. PLoS Genet. 2008 Mar;

4(3):e1000041.

21. Sun LD, Cheng H, Wang ZX, Zhang AP, Wang PG, Xu JH, et al. Association analyses

identify six new psoriasis susceptibility loci in the Chinese population. Nat Genet. 2010 Nov;

42(11):1005-1009.

22. Ellinghaus E, Ellinghaus D, Stuart PE, Nair RP, Debrus S, Raelson JV, et al. Genome-

wide association study identifies a psoriasis susceptibility locus at TRAF3IP2. Nat Genet. 2010

Nov; 42(11):991-995.

23. Genetic Analysis of Psoriasis C, the Wellcome Trust Case Control C, Strange A, Capon

F, Spencer CC, Knight J, et al. A genome-wide association study identifies new psoriasis

susceptibility loci and an interaction between HLA-C and ERAP1. Nat Genet. 2010 Nov;

42(11):985-990.

24. Stuart PE, Nair RP, Ellinghaus E, Ding J, Tejasvi T, Gudjonsson JE, et al. Genome-wide

association analysis identifies three psoriasis susceptibility loci. Nat Genet. 2010 Nov;

42(11):1000-1004.

25. Tsoi LC, Spain SL, Knight J, Ellinghaus E, Stuart PE, Capon F, et al. Identification of 15

new psoriasis susceptibility loci highlights the role of innate immunity. Nat Genet. 2012 Dec;

44(12):1341-1348.

26. Tsoi LC, Spain SL, Ellinghaus E, Stuart PE, Capon F, Knight J, et al. Enhanced meta-

analysis and replication studies identify five new psoriasis susceptibility loci. Nat Commun.

2015; 6:7001.

180

27. Mahil SK, Capon F, Barker JN. Genetics of psoriasis. Dermatol Clin. 2015 Jan; 33(1):1-

11.

28. Grjibovski AM, Olsen AO, Magnus P, Harris JR. Psoriasis in Norwegian twins:

contribution of genetic and environmental effects. J Eur Acad Dermatol Venereol. 2007 Nov;

21(10):1337-1343.

29. Traupe H, Van Gurp PJM, Happle R, Boezeman J, Van de Kerkhof PCM. Psoriasis

vulgaris, fetal growth, and genomic imprinting. American Journal of Medical Genetics. 1992;

42(5):649-654.

30. Burden AD, Javed S, Bailey M, Hodgins M, Connor M, Tillman D. Genetics of psoriasis:

paternal inheritance and a locus on chromosome 6p. J Invest Dermatol. 1998 Jun; 110(6):958-

960.

31. Lories RJ, de Vlam K. Is psoriatic arthritis a result of abnormalities in acquired or innate

immunity? Curr Rheumatol Rep. 2012 Aug; 14(4):375-382.

32. Papoutsaki M, Costanzo A. Treatment of psoriasis and psoriatic arthritis. BioDrugs. 2013

Jan; 27 Suppl 1:3-12.

33. Eder L, Gladman DD. Psoriatic arthritis: phenotypic variance and nosology. Curr

Rheumatol Rep. 2013 Mar; 15(3):316.

34. Gladman DD. Clinical, radiological, and functional assessment in psoriatic arthritis: is it

different from other inflammatory joint diseases? Ann Rheum Dis. 2006 Nov; 65 Suppl 3:iii22-

24.

35. Christophers E, Barker JN, Griffiths CE, Dauden E, Milligan G, Molta C, et al. The risk

of psoriatic arthritis remains constant following initial diagnosis of psoriasis among patients seen

in European dermatology clinics. J Eur Acad Dermatol Venereol. 2010 May; 24(5):548-554.

36. Moll JM, Wright V. Psoriatic arthritis. Semin Arthritis Rheum. 1973; 3(1):55-78.

37. Dhir V, Aggarwal A. Psoriatic arthritis: a critical review. Clin Rev Allergy Immunol.

2013 Apr; 44(2):141-148.

38. Sankowski AJ, Lebkowska UM, Cwikla J, Walecka I, Walecki J. Psoriatic arthritis. Pol J

Radiol. 2013 Jan; 78(1):7-17.

39. Eder L, Thavaneswaran A, Chandran V, Gladman DD. Gender difference in disease

expression, radiographic damage and disability among patients with psoriatic arthritis. Ann

Rheum Dis. 2013 Apr; 72(4):578-582.

40. Gladman DD, Antoni C, Mease P, Clegg DO, Nash P. Psoriatic arthritis: epidemiology,

clinical features, course, and outcome. Ann Rheum Dis. 2005 Mar; 64 Suppl 2:ii14-17.

41. McGonagle D. Enthesitis: an autoinflammatory lesion linking nail and joint involvement

in psoriatic disease. J Eur Acad Dermatol Venereol. 2009 Sep; 23 Suppl 1:9-13.

181

42. Gladman DD, Hing EN, Schentag CT, Cook RJ. Remission in psoriatic arthritis. J

Rheumatol. 2001 May; 28(5):1045-1048.

43. Cantini F, Niccoli L, Nannini C, Cassara E, Pasquetti P, Olivieri I, et al. Criteria,

frequency, and duration of clinical remission in psoriatic arthritis patients with peripheral

involvement requiring second-line drugs. J Rheumatol Suppl. 2009 Aug; 83:78-80.

44. Gladman DD, Stafford-Brady F, Chang CH, Lewandowski K, Russell ML. Longitudinal

study of clinical and radiological progression in psoriatic arthritis. J Rheumatol. 1990 Jun;

17(6):809-812.

45. Wong K, Gladman DD, Husted J, Long JA, Farewell VT. Mortality studies in psoriatic

arthritis: results from a single outpatient clinic. I. Causes and risk of death. Arthritis Rheum.

1997 Oct; 40(10):1868-1872.

46. Gladman DD, Farewell VT, Wong K, Husted J. Mortality studies in psoriatic arthritis:

results from a single outpatient center. II. Prognostic indicators for death. Arthritis Rheum. 1998

Jun; 41(6):1103-1110.

47. Husted JA, Thavaneswaran A, Chandran V, Eder L, Rosen CF, Cook RJ, et al.

Cardiovascular and other comorbidities in patients with psoriatic arthritis: a comparison with

patients with psoriasis. Arthritis Care Res (Hoboken). 2011 Dec; 63(12):1729-1735.

48. Chandran V, Schentag CT, Brockbank JE, Pellett FJ, Shanmugarajah S, Toloza SM, et al.

Familial aggregation of psoriatic arthritis. Ann Rheum Dis. 2009 May; 68(5):664-667.

49. Pedersen OB, Svendsen AJ, Ejstrup L, Skytthe A, Junker P. On the heritability of

psoriatic arthritis. Disease concordance among monozygotic and dizygotic twins. Ann Rheum

Dis. 2008 Oct; 67(10):1417-1421.

50. Eder L, Chandran V, Pellett F, Shanmugarajah S, Rosen CF, Bull SB, et al. Differential

human leucocyte allele association between psoriasis and psoriatic arthritis: a family-based

association study. Ann Rheum Dis. 2012 Aug; 71(8):1361-1365.

51. Pollock RA PF, Ayearst R, Rahman P, Gladman DD, Chandran V. Extended Haplotypes

Between Human Leukocyte Antigen - C and Tumour Necrosis Factor A Gene Loci Reveal

Psoriatic Arthritis Susceptibility Hotspots American College of Rheumatology Scientific

Meeting; 2011 November 5-9, 2011; Chicago, IL; 2011.

52. Nelson GW, Martin MP, Gladman D, Wade J, Trowsdale J, Carrington M. Cutting edge:

heterozygote advantage in autoimmune disease: hierarchy of protection/susceptibility conferred

by HLA and killer Ig-like receptor combinations in psoriatic arthritis. J Immunol. 2004 Oct 1;

173(7):4273-4276.

53. Martin MP, Nelson G, Lee JH, Pellett F, Gao X, Wade J, et al. Cutting edge:

susceptibility to psoriatic arthritis: influence of activating killer Ig-like receptor genes in the

absence of specific HLA-C alleles. J Immunol. 2002 Sep 15; 169(6):2818-2822.

182

54. Chandran V, Bull SB, Pellett FJ, Ayearst R, Pollock RA, Gladman DD. Killer-cell

immunoglobulin-like receptor gene polymorphisms and susceptibility to psoriatic arthritis.

Rheumatology (Oxford). 2014 Feb; 53(2):233-239.

55. Eder L, Law T, Chandran V, Shanmugarajah S, Shen H, Rosen CF, et al. Association

between environmental factors and onset of psoriatic arthritis in patients with psoriasis. Arthritis

Care Res (Hoboken). 2011 Aug; 63(8):1091-1097.

56. Pattison E, Harrison BJ, Griffiths CE, Silman AJ, Bruce IN. Environmental risk factors

for the development of psoriatic arthritis: results from a case-control study. Ann Rheum Dis.

2008 May; 67(5):672-676.

57. Tey HL, Ee HL, Tan AS, Theng TS, Wong SN, Khoo SW. Risk factors associated with

having psoriatic arthritis in patients with cutaneous psoriasis. J Dermatol. 2010 May; 37(5):426-

430.

58. Rahman P, Gladman DD, Schentag CT, Petronis A. Excessive paternal transmission in

psoriatic arthritis. Arthritis & Rheumatism. 1999 Jun; 42(6):1228-1231.

59. Karason A, Gudjonsson JE, Upmanyu R, Antonsdottir AA, Hauksson VB, Runasdottir

EH, et al. A susceptibility gene for psoriatic arthritis maps to chromosome 16q: evidence for

imprinting. American Journal of Human Genetics. 2003 Jan; 72(1):125-131.

60. McGonagle D, Ash Z, Dickie L, McDermott M, Aydin SZ. The early phase of psoriatic

arthritis. Ann Rheum Dis. 2011 Mar; 70 Suppl 1:i71-76.

61. McGonagle D, Benjamin M, Tan AL. The pathogenesis of psoriatic arthritis and

associated nail disease: not autoimmune after all? Curr Opin Rheumatol. 2009 Jul; 21(4):340-

347.

62. Ritchlin C, Haas-Smith SA, Hicks D, Cappuccio J, Osterland CK, Looney RJ. Patterns of

cytokine production in psoriatic synovium. J Rheumatol. 1998 Aug; 25(8):1544-1552.

63. Espinoza LR, Aguilar JL, Espinoza CG, Cuellar ML, Scopelitis E, Silveira LH.

Fibroblast function in psoriatic arthritis. I. Alteration of cell kinetics and growth factor responses.

J Rheumatol. 1994 Aug; 21(8):1502-1506.

64. Espinoza LR, Espinoza CG, Cuellar ML, Scopelitis E, Silveira LH, Grotendorst GR.

Fibroblast function in psoriatic arthritis. II. Increased expression of beta platelet derived growth

factor receptors and increased production of growth factor and cytokines. J Rheumatol. 1994

Aug; 21(8):1507-1511.

65. Cameron AL, Kirby B, Fei W, Griffiths CE. Natural killer and natural killer-T cells in

psoriasis. Arch Dermatol Res. 2002 Nov; 294(8):363-369.

66. Baeten D, Kruithof E, De Rycke L, Boots AM, Mielants H, Veys EM, et al. Infiltration of

the synovial membrane with macrophage subsets and polymorphonuclear cells reflects global

disease activity in spondyloarthropathy. Arthritis Res Ther. 2005; 7(2):R359-369.

183

67. Ritchlin CT, Haas-Smith SA, Li P, Hicks DG, Schwarz EM. Mechanisms of TNF-alpha-

and RANKL-mediated osteoclastogenesis and bone resorption in psoriatic arthritis. J Clin Invest.

2003 Mar; 111(6):821-831.

68. Gossec L, Smolen JS, Gaujoux-Viala C, Ash Z, Marzo-Ortega H, van der Heijde D, et al.

European League Against Rheumatism recommendations for the management of psoriatic

arthritis with pharmacological therapies. Ann Rheum Dis. 2012 Jan; 71(1):4-12.

69. Ritchlin CT, Kavanaugh A, Gladman DD, Mease PJ, Helliwell P, Boehncke WH, et al.

Treatment recommendations for psoriatic arthritis. Ann Rheum Dis. 2009 Sep; 68(9):1387-1394.

70. Haroon M, Gallagher P, FitzGerald O. Diagnostic delay of more than 6 months

contributes to poor radiographic and functional outcome in psoriatic arthritis. Ann Rheum Dis.

2015 Jun; 74(6):1045-1050.

71. Gladman DD, Thavaneswaran A, Chandran V, Cook RJ. Do patients with psoriatic

arthritis who present early fare better than those presenting later in the disease? Ann Rheum Dis.

2011 Dec; 70(12):2152-2154.

72. Helliwell P, Coates L, Chandran V, Gladman D, de Wit M, FitzGerald O, et al.

Qualifying unmet needs and improving standards of care in psoriatic arthritis. Arthritis Care Res

(Hoboken). 2014 Dec; 66(12):1759-1766.

73. Perlis RH. Translating biomarkers to clinical practice. Mol Psychiatry. 2011 Nov;

16(11):1076-1087.

74. Salter H, Holland R. Biomarkers: refining diagnosis and expediting drug development -

reality, aspiration and the role of open innovation. J Intern Med. 2014 Sep; 276(3):215-228.

75. Taylor W, Gladman D, Helliwell P, Marchesoni A, Mease P, Mielants H, et al.

Classification criteria for psoriatic arthritis: development of new criteria from a large

international study. Arthritis Rheum. 2006 Aug; 54(8):2665-2673.

76. Chandran V, Schentag CT, Gladman DD. Sensitivity and specificity of the CASPAR

criteria for psoriatic arthritis in a family medicine clinic setting. J Rheumatol. 2008 Oct;

35(10):2069-2070; author reply 2070.

77. Radtke MA, Reich K, Blome C, Rustenbach S, Augustin M. Prevalence and clinical

features of psoriatic arthritis and joint complaints in 2009 patients with psoriasis: results of a

German national survey. J Eur Acad Dermatol Venereol. 2009 Jun; 23(6):683-691.

78. Dominguez P, Gladman DD, Helliwell P, Mease PJ, Husni ME, Qureshi AA.

Development of screening tools to identify psoriatic arthritis. Curr Rheumatol Rep. 2010 Aug;

12(4):295-299.

79. Alenius GM, Stenberg B, Stenlund H, Lundblad M, Dahlqvist SR. Inflammatory joint

manifestations are prevalent in psoriasis: prevalence study of joint and axial involvement in

psoriatic patients, and evaluation of a psoriatic and arthritic questionnaire. J Rheumatol. 2002

Dec; 29(12):2577-2582.

184

80. Qureshi AA, Dominguez P, Duffin KC, Gladman DD, Helliwell P, Mease PJ, et al.

Psoriatic arthritis screening tools. J Rheumatol. 2008 Jul; 35(7):1423-1425.

81. Husni ME, Meyer KH, Cohen DS, Mody E, Qureshi AA. The PASE questionnaire: pilot-

testing a psoriatic arthritis screening and evaluation tool. J Am Acad Dermatol. 2007 Oct;

57(4):581-587.

82. Gladman DD, Schentag CT, Tom BD, Chandran V, Brockbank J, Rosen C, et al.

Development and initial validation of a screening questionnaire for psoriatic arthritis: the

Toronto Psoriatic Arthritis Screen (ToPAS). Ann Rheum Dis. 2009 Apr; 68(4):497-501.

83. Ibrahim GH, Buch MH, Lawson C, Waxman R, Helliwell PS. Evaluation of an existing

screening tool for psoriatic arthritis in people with psoriasis and the development of a new

instrument: the Psoriasis Epidemiology Screening Tool (PEST) questionnaire. Clin Exp

Rheumatol. 2009 May-Jun; 27(3):469-474.

84. Coates LC, Aslam T, Al Balushi F, Burden AD, Burden-Teh E, Caperon AR, et al.

Comparison of three screening tools to detect psoriatic arthritis in patients with psoriasis

(CONTEST study). Br J Dermatol. 2013 Apr; 168(4):802-807.

85. Biomarkers Definitions Working G. Biomarkers and surrogate endpoints: preferred

definitions and conceptual framework. Clin Pharmacol Ther. 2001 Mar; 69(3):89-95.

86. Chandran V, Gladman DD. Update on biomarkers in psoriatic arthritis. Curr Rheumatol

Rep. 2010 Aug; 12(4):288-294.

87. Gibson DS, Rooney ME, Finnegan S, Qiu J, Thompson DC, Labaer J, et al. Biomarkers

in rheumatology, now and in the future. Rheumatology (Oxford). 2012 Mar; 51(3):423-433.

88. Chandran V, Cook RJ, Edwin J, Shen H, Pellett FJ, Shanmugarajah S, et al. Soluble

biomarkers differentiate patients with psoriatic arthritis from those with psoriasis without

arthritis. Rheumatology (Oxford). 2010 Jul; 49(7):1399-1405.

89. Bogliolo L, Crepaldi G, Caporali R. Biomarkers and prognostic stratification in psoriatic

arthritis. Reumatismo. 2012; 64(2):88-98.

90. Wilson FC, Icen M, Crowson CS, McEvoy MT, Gabriel SE, Kremers HM. Incidence and

clinical predictors of psoriatic arthritis in patients with psoriasis: a population-based study.

Arthritis Rheum. 2009 Feb 15; 61(2):233-239.

91. Langenbruch A, Radtke MA, Krensel M, Jacobi A, Reich K, Augustin M. Nail

involvement as a predictor of concomitant psoriatic arthritis in patients with psoriasis. Br J

Dermatol. 2014 Nov; 171(5):1123-1128.

92. Thumboo J, Uramoto K, Shbeeb MI, O'Fallon WM, Crowson CS, Gibson LE, et al. Risk

factors for the development of psoriatic arthritis: a population based nested case control study. J

Rheumatol. 2002 Apr; 29(4):757-762.

185

93. Haroon M, Kirby B, FitzGerald O. High prevalence of psoriatic arthritis in patients with

severe psoriasis with suboptimal performance of screening questionnaires. Ann Rheum Dis.

2013 May; 72(5):736-740.

94. Tinazzi I, McGonagle D, Biasi D, Confente S, Caimmi C, Girolomoni G, et al.

Preliminary evidence that subclinical enthesopathy may predict psoriatic arthritis in patients with

psoriasis. J Rheumatol. 2011 Dec; 38(12):2691-2692.

95. Winchester R, Minevich G, Steshenko V, Kirby B, Kane D, Greenberg DA, et al. HLA

associations reveal genetic heterogeneity in psoriatic arthritis and in the psoriasis phenotype.

Arthritis Rheum. 2012 Apr; 64(4):1134-1144.

96. Eder L, Chandran V, Pellet F, Shanmugarajah S, Rosen CF, Bull SB, et al. Human

leucocyte antigen risk alleles for psoriatic arthritis among patients with psoriasis. Ann Rheum

Dis. 2012 Jan; 71(1):50-55.

97. Ya-Hui Chiu EMS, Dafna Gladman, Sharon Moorehead, Michelle Smith, Rick Barrett

and Christopher T. Ritchlin. The Transition From Psoriasis (Ps) to Psoriatic Arthritis (PsA) Is

Associated with Elevated Circulating Osteoclast Precursors (OCP) and Increased Expression of

DC-STAMP. Arthritis Rheum. 2012; 64 Suppl 10:2616.

98. Yahui Grace Chiu SS, Dafna D. Gladman, Ben Panepento, Sharon Moorehead, Lihi Eder,

Vinod Chandran, Rick Barrett and Christopher T. Ritchlin. Expression of Dendritic Cell-Specific

Transmembrane Protein (DC-STAMP) and Osteoclast Precursor (OCP) Frequency in Psoriasis

(PsC) Patients Who Develop Psoriatic Arthritis (PsA). Arthritis Rheum. 2011; 63.

99. Biesecker LG. Hypothesis-generating research and predictive medicine. Genome Res.

2013 Jul; 23(7):1051-1053.

100. Makawita S, Diamandis EP. The bottleneck in the cancer biomarker pipeline and protein

quantification through mass spectrometry-based approaches: current strategies for candidate

verification. Clin Chem. 2010 Feb; 56(2):212-222.

101. Pertea M. The human transcriptome: an unfinished story. Genes (Basel). 2012 Sep;

3(3):344-360.

102. Wang X, Jia S, Geoffrey R, Alemzadeh R, Ghosh S, Hessner MJ. Identification of a

molecular signature in human type 1 diabetes mellitus using serum and functional genomics. J

Immunol. 2008 Feb 1; 180(3):1929-1937.

103. Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, et al.

Interferon-inducible gene expression signature in peripheral blood cells of patients with severe

lupus. Proc Natl Acad Sci U S A. 2003 Mar 4; 100(5):2610-2615.

104. Reynier F, Pachot A, Paye M, Xu Q, Turrel-Davin F, Petit F, et al. Specific gene

expression signature associated with development of autoimmune type-I diabetes using whole-

blood microarray analysis. Genes Immun. 2010 Apr; 11(3):269-278.

186

105. Emamian ES, Leon JM, Lessard CJ, Grandits M, Baechler EC, Gaffney PM, et al.

Peripheral blood gene expression profiling in Sjogren's syndrome. Genes Immun. 2009 Jun;

10(4):285-296.

106. Achiron A, Gurevich M, Friedman N, Kaminski N, Mandel M. Blood transcriptional

signatures of multiple sclerosis: unique gene expression of disease activity. Ann Neurol. 2004

Mar; 55(3):410-417.

107. Bomprezzi R, Ringner M, Kim S, Bittner ML, Khan J, Chen Y, et al. Gene expression

profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to

disease. Hum Mol Genet. 2003 Sep 1; 12(17):2191-2199.

108. Geiss GK, Bumgarner RE, Birditt B, Dahl T, Dowidar N, Dunaway DL, et al. Direct

multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol. 2008

Mar; 26(3):317-325.

109. Gu J, Marker-Hermann E, Baeten D, Tsai WC, Gladman D, Xiong M, et al. A 588-gene

microarray analysis of the peripheral blood mononuclear cells of spondyloarthropathy patients.

Rheumatology (Oxford). 2002 Jul; 41(7):759-766.

110. Batliwalla FM, Li W, Ritchlin CT, Xiao X, Brenner M, Laragione T, et al. Microarray

analyses of peripheral blood cells identifies unique gene expression signature in psoriatic

arthritis. Mol Med. 2005 Jan-Dec; 11(1-12):21-29.

111. Stoeckman AK, Baechler EC, Ortmann WA, Behrens TW, Michet CJ, Peterson EJ. A

distinct inflammatory gene expression profile in patients with psoriatic arthritis. Genes Immun.

2006 Oct; 7(7):583-591.

112. Cuchacovich R, Perez-Alamino R, Zea AH, Espinoza LR. Distinct genetic profile in

peripheral blood mononuclear cells of psoriatic arthritis patients treated with methotrexate and

TNF-inhibitors. Clin Rheumatol. 2014 Dec; 33(12):1815-1821.

113. Grcevic D, Jajic Z, Kovacic N, Lukic IK, Velagic V, Grubisic F, et al. Peripheral blood

expression profiles of bone morphogenetic proteins, tumor necrosis factor-superfamily

molecules, and transcription factor Runx2 could be used as markers of the form of arthritis,

disease activity, and therapeutic responsiveness. J Rheumatol. 2010 Feb; 37(2):246-256.

114. Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, et al. A draft map

of the human proteome. Nature. 2014 May 29; 509(7502):575-581.

115. 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):1.

116. Ademowo OS, Hernandez B, Collins E, Rooney C, Fearon U, van Kuijk AW, et al.

Discovery and confirmation of a protein biomarker panel with potential to predict response to

biological therapy in psoriatic arthritis. Ann Rheum Dis. 2014 Sep 3.

187

117. Alenius GM, Eriksson C, Rantapaa Dahlqvist S. Interleukin-6 and soluble interleukin-2

receptor alpha-markers of inflammation in patients with psoriatic arthritis? Clin Exp Rheumatol.

2009 Jan-Feb; 27(1):120-123.

118. Hwang YJ, Jung HJ, Kim MJ, Roh NK, Jung JW, Lee YW, et al. Serum levels of LL-37

and inflammatory cytokines in plaque and guttate psoriasis. Mediators Inflamm. 2014;

2014:268257.

119. Szodoray P, Alex P, Chappell-Woodward CM, Madland TM, Knowlton N, Dozmorov I,

et al. Circulating cytokines in Norwegian patients with psoriatic arthritis determined by a

multiplex cytokine array system. Rheumatology (Oxford). 2007 Mar; 46(3):417-425.

120. Ballestar E. Epigenetic alterations in autoimmune rheumatic diseases. Nat Rev

Rheumatol. 2011 May; 7(5):263-271.

121. Dupont C, Armant DR, Brenner CA. Epigenetics: definition, mechanisms and clinical

perspective. Semin Reprod Med. 2009 Sep; 27(5):351-357.

122. Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nat Rev

Genet. 2007 Apr; 8(4):253-262.

123. Cuddapah S, Barski A, Zhao K. Epigenomics of T cell activation, differentiation, and

memory. Curr Opin Immunol. 2010 Jun; 22(3):341-347.

124. Nile CJ, Read RC, Akil M, Duff GW, Wilson AG. Methylation status of a single CpG

site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis.

Arthritis Rheum. 2008 Sep; 58(9):2686-2693.

125. Ali M, Veale DJ, Reece RJ, Quinn M, Henshaw K, Zanders ED, et al. Overexpression of

transcripts containing LINE-1 in the synovia of patients with rheumatoid arthritis. Ann Rheum

Dis. 2003 Jul; 62(7):663-666.

126. Takami N, Osawa K, Miura Y, Komai K, Taniguchi M, Shiraishi M, et al.

Hypermethylated promoter region of DR3, the death receptor 3 gene, in rheumatoid arthritis

synovial cells. Arthritis Rheum. 2006 Mar; 54(3):779-787.

127. Daxinger L, Whitelaw E. Understanding transgenerational epigenetic inheritance via the

gametes in mammals. Nat Rev Genet. 2012 Mar; 13(3):153-162.

128. Kota SK, Feil R. Epigenetic transitions in germ cell development and meiosis. Dev Cell.

2010 Nov 16; 19(5):675-686.

129. Hackett JA, Sengupta R, Zylicz JJ, Murakami K, Lee C, Down TA, et al. Germline DNA

demethylation dynamics and imprint erasure through 5-hydroxymethylcytosine. Science. 2013

Jan 25; 339(6118):448-452.

130. Ciccone DN, Su H, Hevi S, Gay F, Lei H, Bajko J, et al. KDM1B is a histone H3K4

demethylase required to establish maternal genomic imprints. Nature. 2009 Sep 17;

461(7262):415-418.

188

131. Butler MG. Genomic imprinting disorders in humans: a mini-review. J Assist Reprod

Genet. 2009 Sep-Oct; 26(9-10):477-486.

132. Kong A, Steinthorsdottir V, Masson G, Thorleifsson G, Sulem P, Besenbacher S, et al.

Parental origin of sequence variants associated with complex diseases. Nature. 2009 Dec 17;

462(7275):868-874.

133. Wallace C, Smyth DJ, Maisuria-Armer M, Walker NM, Todd JA, Clayton DG. The

imprinted DLK1-MEG3 gene region on chromosome 14q32.2 alters susceptibility to type 1

diabetes. Nat Genet. 2010 Jan; 42(1):68-71.

134. Morgan HD, Sutherland HG, Martin DI, Whitelaw E. Epigenetic inheritance at the agouti

locus in the mouse. Nat Genet. 1999 Nov; 23(3):314-318.

135. Cooney CA, Dave AA, Wolff GL. Maternal methyl supplements in mice affect

epigenetic variation and DNA methylation of offspring. J Nutr. 2002 Aug; 132(8 Suppl):2393S-

2400S.

136. Dolinoy DC, Huang D, Jirtle RL. Maternal nutrient supplementation counteracts

bisphenol A-induced DNA hypomethylation in early development. Proc Natl Acad Sci U S A.

2007 Aug 7; 104(32):13056-13061.

137. Rakyan VK, Chong S, Champ ME, Cuthbert PC, Morgan HD, Luu KV, et al.

Transgenerational inheritance of epigenetic states at the murine Axin(Fu) allele occurs after

maternal and paternal transmission. Proc Natl Acad Sci U S A. 2003 Mar 4; 100(5):2538-2543.

138. Lambrot R, Xu C, Saint-Phar S, Chountalos G, Cohen T, Paquet M, et al. Low paternal

dietary folate alters the mouse sperm epigenome and is associated with negative pregnancy

outcomes. Nat Commun. 2013; 4:2889.

139. Guerrero-Bosagna C, Settles M, Lucker B, Skinner MK. Epigenetic transgenerational

actions of vinclozolin on promoter regions of the sperm epigenome. PLoS One. 2010; 5(9).

140. Manikkam M, Tracey R, Guerrero-Bosagna C, Skinner MK. Dioxin (TCDD) induces

epigenetic transgenerational inheritance of adult onset disease and sperm epimutations. PLoS

One. 2012; 7(9):e46249.

141. Manikkam M, Haque MM, Guerrero-Bosagna C, Nilsson EE, Skinner MK. Pesticide

methoxychlor promotes the epigenetic transgenerational inheritance of adult-onset disease

through the female germline. PLoS One. 2014; 9(7):e102091.

142. Chan TL, Yuen ST, Kong CK, Chan YW, Chan AS, Ng WF, et al. Heritable germline

epimutation of MSH2 in a family with hereditary nonpolyposis colorectal cancer. Nat Genet.

2006 Oct; 38(10):1178-1183.

143. Ligtenberg MJ, Kuiper RP, Chan TL, Goossens M, Hebeda KM, Voorendt M, et al.

Heritable somatic methylation and inactivation of MSH2 in families with Lynch syndrome due

to deletion of the 3' exons of TACSTD1. Nat Genet. 2009 Jan; 41(1):112-117.

189

144. Hitchins M, Williams R, Cheong K, Halani N, Lin VA, Packham D, et al. MLH1

germline epimutations as a factor in hereditary nonpolyposis colorectal cancer. Gastroenterology.

2005 Nov; 129(5):1392-1399.

145. Hitchins MP, Wong JJ, Suthers G, Suter CM, Martin DI, Hawkins NJ, et al. Inheritance

of a cancer-associated MLH1 germ-line epimutation. N Engl J Med. 2007 Feb 15; 356(7):697-

705.

146. Kaminsky ZA, Tang T, Wang SC, Ptak C, Oh GH, Wong AH, et al. DNA methylation

profiles in monozygotic and dizygotic twins. Nat Genet. 2009 Feb; 41(2):240-245.

147. Kaminsky Z, Tochigi M, Jia P, Pal M, Mill J, Kwan A, et al. A multi-tissue analysis

identifies HLA complex group 9 gene methylation differences in bipolar disorder. Mol

Psychiatry. 2012 Jul; 17(7):728-740.

148. Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, et al. High density DNA

methylation array with single CpG site resolution. Genomics. 2011 Oct; 98(4):288-295.

149. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray.

Bioinformatics. 2008 Jul 1; 24(13):1547-1548.

150. Du P BR. methyAnalysis: DNA methylation data analysis and visualization. R package

version 1100. 2014.

151. Gentleman R CV, Huber W and Hahne F. genefilter: genefilter: methods for filtering

genes from high-throughput experiments. R package version 1500.

152. Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value

and M-value methods for quantifying methylation levels by microarray analysis. BMC

Bioinformatics. 2010; 11:587.

153. Pan Du GF, Spencer Huang, Warren A. Kibbe, Simon Lin. Analyze Illumina Infinium

methylation microarray data. R package version 2201. 2014.

154. Kim YI, Logan JW, Mason JB, Roubenoff R. DNA hypomethylation in inflammatory

arthritis: reversal with methotrexate. Journal of Laboratory & Clinical Medicine. 1996 Aug;

128(2):165-172.

155. Zhang P, Su Y, Chen H, Zhao M, Lu Q. Abnormal DNA methylation in peripheral blood

mononuclear cells and skin lesions from patients with psoriasis vulgaris. Journal of Investigative

Dermatology. 2010 April; 130:S21.

156. Roberson EDO, Liu Y, Ryan C, Joyce CE, Duan S, Cao L, et al. A subset of methylated

CpG sites differentiate psoriatic from normal skin. Journal of Investigative Dermatology. 2012

March; 132(3 PART 1):583-592.

157. Zhang P, Zhao M, Liang G, Yin G, Huang D, Su F, et al. Whole-genome DNA

methylation in skin lesions from patients with psoriasis vulgaris. Journal of Autoimmunity. 2013

March; 41:17-24.

190

158. Hou R, Yin G, An P, Wang C, Liu R, Yang Y, et al. DNA methylation of dermal MSCs

in psoriasis: Identification of epigenetically dysregulated genes. Journal of Dermatological

Science. 2013 November; 72(2):103-109.

159. Gervin K, Vigeland MD, Mattingsdal M, Hammero M, Nygard H, Olsen AO, et al. DNA

methylation and gene expression changes in monozygotic twins discordant for psoriasis:

Identification of epigenetically dysregulated genes. PLoS Genetics. 2012 January; 8(1).

160. Han J, Park SG, Bae JB, Choi J, Lyu JM, Park SH, et al. The characteristics of genome-

wide DNA methylation in naive CD4+ T cells of patients with psoriasis or atopic dermatitis.

Biochemical and Biophysical Research Communications. 2012 25 May; 422(1):157-163.

161. Park GT, Han J, Park SG, Kim S, Kim TY. DNA methylation analysis of CD4+ T cells in

patients with psoriasis. Archives of Dermatological Research. 2014 April; 306(3):259-268.

162. O'Rielly DD, Pollock R, Zhang Y, Al-Ghanim N, Yazdani R, Hamilton S, et al.

Epigenetic studies in maternally versus paternally transmitted psoriatic disease. Annals of the

Rheumatic Diseases. 2014 June; 73.

163. Ruchusatsawat K, Wongpiyabovorn J, Shuangshoti S, Hirankarn N, Mutirangura A.

SHP-1 promoter 2 methylation in normal epithelial tissues and demethylation in psoriasis.

Journal of Molecular Medicine. 2006 Feb; 84(2):175-182.

164. Zhang K, Zhang R, Li X, Yin G, Niu X. Promoter methylation status of p15 and p21

genes in HPP-CFCs of bone marrow of patients with psoriasis. European Journal of

Dermatology. 2009 Mar-Apr; 19(2):141-146.

165. Zhang K, Zhang R, Li X, Yin G, Niu X, Hou R. The mRNA expression and promoter

methylation status of the p16 gene in colony-forming cells with high proliferative potential in

patients with psoriasis. Clinical and Experimental Dermatology. 2007 November; 32(6):702-708.

166. Chen M, Chen ZQ, Cui PG, Yao X, Li YM, Li AS, et al. The methylation pattern of

p16INK4a gene promoter in psoriatic epidermis and its clinical significance. British Journal of

Dermatology. 2008 May; 158(5):987-993.

167. Zhang P, Su Y, Zhao M, Huang W, Lu Q. Abnormal histone modifications in PBMCs

from patients with psoriasis vulgaris. European Journal of Dermatology. 2011; 21(4):552-557.

168. Clop A, Bertoni A, Spain SL, Simpson MA, Pullabhatla V, Tonda R, et al. An in-depth

characterization of the major psoriasis susceptibility locus identifies candidate susceptibility

alleles within an HLA-C enhancer element. PLoS ONE [Electronic Resource]. 2013;

8(8):e71690.

169. Tovar-Castillo LE, Cancino-Diaz JC, Garcia-Vazquez F, Cancino-Gomez FG, Leon-

Dorantes G, Blancas-Gonzalez F, et al. Under-expression of VHL and over-expression of

HDAC-1, HIF-1alpha, LL-37, and IAP-2 in affected skin biopsies of patients with psoriasis.

International Journal of Dermatology. 2007 March; 46(3):239-246.

191

170. Ekman AK, Enerback C. Lack of preclinical support for the efficacy of HDAC inhibitors

in the treatment of psoriasis. Br J Dermatol. 2015 Jul 6.

171. Bovenschen HJ, Van De Kerkhof PC, Van Erp PE, Woestenenk R, Joosten I, Koenen

HJPM. Foxp3 regulatory T cells of psoriasis patients easily differentiate into IL-17A-producing

cells and are found in lesional skin. Journal of Investigative Dermatology. 2011 September;

131(9):1853-1860.

172. Hammitzsch A, De Wit J, Ridley A, Al-Mossawi MH, Bowness P. Comparison of in

vitro effects of kinase and epigenetic inhibitors on TH17 responses in inflammatory arthritis.

Annals of the Rheumatic Diseases. 2014 June; 73.

173. Hammitzsch A, Tallant C, Fedorov O, O'Mahony A, Brennan PE, Hay DA, et al. CBP30,

a selective CBP/p300 bromodomain inhibitor, suppresses human Th17 responses. Proc Natl

Acad Sci U S A. 2015 Aug 25; 112(34):10768-10773.

174. Orecchia A, Scarponi C, Felice F, Cesarini E, Avitabile S, Mai A, et al. Sirtinol treatment

reduces inflammation in human dermal microvascular endothelial cells. PLoS ONE. 2011 12

Sep; 6(9).

175. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for

common human diseases. Nat Rev Genet. 2011 Aug; 12(8):529-541.

176. Michels KB, Binder AM, Dedeurwaerder S, Epstein CB, Greally JM, Gut I, et al.

Recommendations for the design and analysis of epigenome-wide association studies. Nat

Methods. 2013 Oct; 10(10):949-955.

177. Gladman D. Psoriatic arthritis. In: Harris ED SC, Ruddy S, Firestein GS, Sergent JS, ed.

Kelley’s Textbook of Rheumatology: Philadelphia: W.B. Saunders Co.; 2005:1155-1164.

178. O'Rielly DD, Rahman P. Genetics of susceptibility and treatment response in psoriatic

arthritis. Nat Rev Rheumatol. 2011 Dec; 7(12):718-732.

179. Smyth GK. Linear models and empirical bayes methods for assessing differential

expression in microarray experiments. Stat Appl Genet Mol Biol. 2004; 3:Article3.

180. Benjamini Y HY. Controlling the false discovery rate: a practical and powerful approach

to multiple testing. J R Stat Soc B. 1995; 57:289-300.

181. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large

gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4(1):44-57.

182. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward

the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009 Jan; 37(1):1-

13.

183. Stamova BS, Apperson M, Walker WL, Tian Y, Xu H, Adamczy P, et al. Identification

and validation of suitable endogenous reference genes for gene expression studies in human

peripheral blood. BMC Med Genomics. 2009 /; 2:49-49.

192

184. Tang F, Sally B, Ciszewski C, Abadie V, Curran SA, Groh V, et al. Interleukin 15 primes

natural killer cells to kill via NKG2D and cPLA2 and this pathway is active in psoriatic arthritis.

PLoS One. 2013; 8(9):e76292.

185. Appel H, Wu P, Scheer R, Kedor C, Sawitzki B, Thiel A, et al. Synovial and peripheral

blood CD4+FoxP3+ T cells in spondyloarthritis. J Rheumatol. 2011 Nov; 38(11):2445-2451.

186. Menon B, Gullick NJ, Walter GJ, Rajasekhar M, Garrood T, Evans HG, et al.

Interleukin-17+CD8+ T cells are enriched in the joints of patients with psoriatic arthritis and

correlate with disease activity and joint damage progression. Arthritis Rheumatol. 2014 May;

66(5):1272-1281.

187. Benham H, Norris P, Goodall J, Wechalekar MD, FitzGerald O, Szentpetery A, et al.

Th17 and Th22 cells in psoriatic arthritis and psoriasis. Arthritis Res Ther. 2013; 15(5):R136.

188. Vandooren B, Noordenbos T, Ambarus C, Krausz S, Cantaert T, Yeremenko N, et al.

Absence of a classically activated macrophage cytokine signature in peripheral spondylarthritis,

including psoriatic arthritis. Arthritis Rheum. 2009 Apr; 60(4):966-975.

189. Lee S-K, Jeon E-K, Kim Y-J, Seo S-H, Kim C-D, Lim J-S, et al. A global gene

expression analysis of the peripheral blood mononuclear cells reveals the gene expression

signature in psoriasis. Ann Dermatol. 2009 08/; 21(3):237-242.

190. Coda AB, Icen M, Smith JR, Sinha AA. Global transcriptional analysis of psoriatic skin

and blood confirms known disease-associated pathways and highlights novel genomic "hot

spots" for differentially expressed genes. Genomics. 2012 Jul; 100(1):18-26.

191. Shimazu R, Akashi S, Ogata H, Nagai Y, Fukudome K, Miyake K, et al. MD-2, a

molecule that confers lipopolysaccharide responsiveness on Toll-like receptor 4. J Exp Med.

1999 Jun 7; 189(11):1777-1782.

192. Lawrence T, Natoli G. Transcriptional regulation of macrophage polarization: enabling

diversity with identity. Nat Rev Immunol. 2011 Nov; 11(11):750-761.

193. Natoli G. Control of NF-kappaB-dependent transcriptional responses by chromatin

organization. Cold Spring Harb Perspect Biol. 2009 Oct; 1(4):a000224.

194. Nielsen T, Wallden B, Schaper C, Ferree S, Liu S, Gao D, et al. Analytical validation of

the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter

Analysis System using formalin-fixed paraffin-embedded breast tumor specimens. BMC Cancer.

2014; 14:177.

195. Duan Z, Li FQ, Wechsler J, Meade-White K, Williams K, Benson KF, et al. A novel

notch protein, N2N, targeted by neutrophil elastase and implicated in hereditary neutropenia.

Mol Cell Biol. 2004 Jan; 24(1):58-70.

196. Fukushima H, Nakao A, Okamoto F, Shin M, Kajiya H, Sakano S, et al. The association

of Notch2 and NF-kappaB accelerates RANKL-induced osteoclastogenesis. Mol Cell Biol. 2008

Oct; 28(20):6402-6412.

193

197. Proost P, Struyf S, Loos T, Gouwy M, Schutyser E, Conings R, et al. Coexpression and

interaction of CXCL10 and CD26 in mesenchymal cells by synergising inflammatory cytokines:

CXCL8 and CXCL10 are discriminative markers for autoimmune arthropathies. Arthritis Res

Ther. 2006; 8(4):R107.

198. Antonelli A, Fallahi P, Delle Sedie A, Ferrari SM, Maccheroni M, Bombardieri S, et al.

High values of alpha (CXCL10) and beta (CCL2) circulating chemokines in patients with

psoriatic arthritis, in presence or absence of autoimmune thyroiditis. Autoimmunity. 2008 Nov;

41(7):537-542.

199. Wright V MJ. Psoriatic Arthritis. In: Wright V MJ, ed. Seronegative polyarthritis.

Amsterdam: North Holland Publishing Co. ; 1976:169-223.

200. Reich K, Kruger K, Mossner R, Augustin M. Epidemiology and clinical pattern of

psoriatic arthritis in Germany: a prospective interdisciplinary epidemiological study of 1511

patients with plaque-type psoriasis. Br J Dermatol. 2009 May; 160(5):1040-1047.

201. Mease PJ, Gladman DD, Helliwell P, Khraishi MM, Fuiman J, Bananis E, et al.

Comparative performance of psoriatic arthritis screening tools in patients with psoriasis in

European/North American dermatology clinics. J Am Acad Dermatol. 2014 Oct; 71(4):649-655.

202. Taylor WJ. Impact of psoriatic arthritis on the patient: through the lens of the WHO

International Classification of Functioning, Health, and Disability. Curr Rheumatol Rep. 2012

Aug; 14(4):369-374.

203. Mease PJ, Gladman DD, Papp KA, Khraishi MM, Thaci D, Behrens F, et al. Prevalence

of rheumatologist-diagnosed psoriatic arthritis in patients with psoriasis in European/North

American dermatology clinics. J Am Acad Dermatol. 2013 Nov; 69(5):729-735.

204. Queiro-Silva R, Torre-Alonso JC, Tinture-Eguren T, Lopez-Lagunas I. A polyarticular

onset predicts erosive and deforming disease in psoriatic arthritis. Ann Rheum Dis. 2003 Jan;

62(1):68-70.

205. Bond SJ, Farewell VT, Schentag CT, Gladman DD. Predictors for radiological damage in

psoriatic arthritis: results from a single centre. Ann Rheum Dis. 2007 Mar; 66(3):370-376.

206. Pollock RA, Abji F, Liang K, Chandran V, Pellett FJ, Virtanen C, et al. Gene expression

differences between psoriasis patients with and without inflammatory arthritis. J Invest

Dermatol. 2015 Feb; 135(2):620-623.

207. Toyoda Y, Tabata S, Kishi J, Kuramoto T, Mitsuhashi A, Saijo A, et al. Thymidine

phosphorylase regulates the expression of CXCL10 in rheumatoid arthritis fibroblast-like

synoviocytes. Arthritis Rheumatol. 2014 Mar; 66(3):560-568.

208. Taub DD, Lloyd AR, Conlon K, Wang JM, Ortaldo JR, Harada A, et al. Recombinant

human interferon-inducible protein 10 is a chemoattractant for human monocytes and T

lymphocytes and promotes T cell adhesion to endothelial cells. J Exp Med. 1993 Jun 1;

177(6):1809-1814.

194

209. Belperio JA, Keane MP, Arenberg DA, Addison CL, Ehlert JE, Burdick MD, et al. CXC

chemokines in angiogenesis. J Leukoc Biol. 2000 Jul; 68(1):1-8.

210. Eder L HA, Shen H, Rosen C, Chandran V, Cook RJ, Gladman DD. The incidence and

risk factors for PsA in patients with psoriasis - a prospective cohort study. Arthritis Rheumatol.

2014; 66(S10: S21 (Abstract)).

211. Eder L, Chandran V, Gladman DD. What have we learned about genetic susceptibility in

psoriasis and psoriatic arthritis? Curr Opin Rheumatol. 2015 Jan; 27(1):91-98.

212. Rottman JB, Smith TL, Ganley KG, Kikuchi T, Krueger JG. Potential role of the

chemokine receptors CXCR3, CCR4, and the integrin alphaEbeta7 in the pathogenesis of

psoriasis vulgaris. Lab Invest. 2001 Mar; 81(3):335-347.

213. van den Borne P, Quax PH, Hoefer IE, Pasterkamp G. The multifaceted functions of

CXCL10 in cardiovascular disease. Biomed Res Int. 2014; 2014:893106.

214. Kwak HB, Ha H, Kim HN, Lee JH, Kim HS, Lee S, et al. Reciprocal cross-talk between

RANKL and interferon-gamma-inducible protein 10 is responsible for bone-erosive

experimental arthritis. Arthritis Rheum. 2008 May; 58(5):1332-1342.

215. Lee EY, Lee ZH, Song YW. CXCL10 and autoimmune diseases. Autoimmun Rev. 2009

Mar; 8(5):379-383.

216. Antonelli A, Fallahi P, Ferrari SM, Pupilli C, d'Annunzio G, Lorini R, et al. Serum Th1

(CXCL10) and Th2 (CCL2) chemokine levels in children with newly diagnosed Type 1 diabetes:

a longitudinal study. Diabet Med. 2008 Nov; 25(11):1349-1353.

217. Magee KE, Kelsey CE, Kurzinski KL, Ho J, Mlakar LR, Feghali-Bostwick CA, et al.

Interferon-gamma inducible protein-10 as a potential biomarker in localized scleroderma.

Arthritis Res Ther. 2013; 15(6):R188.

218. Ekman AK, Sigurdardottir G, Carlstrom M, Kartul N, Jenmalm MC, Enerback C.

Systemically elevated Th1-, Th2- and Th17-associated chemokines in psoriasis vulgaris before

and after ultraviolet B treatment. Acta Derm Venereol. 2013 Sep 4; 93(5):527-531.

219. Yellin M, Paliienko I, Balanescu A, Ter-Vartanian S, Tseluyko V, Xu LA, et al. A phase

II, randomized, double-blind, placebo-controlled study evaluating the efficacy and safety of

MDX-1100, a fully human anti-CXCL10 monoclonal antibody, in combination with

methotrexate in patients with rheumatoid arthritis. Arthritis Rheum. 2012 Jun; 64(6):1730-1739.

220. Ichikawa T, Kageyama Y, Kobayashi H, Kato N, Tsujimura K, Koide Y. Etanercept

treatment reduces the serum levels of interleukin-15 and interferon-gamma inducible protein-10

in patients with rheumatoid arthritis. Rheumatol Int. 2010 Apr; 30(6):725-730.

221. Ingle PV PD. C-Reactive Protein in Various Disease Condition - An Overview. Asian

Journal of Pharmaceutical and Clinical Research. 2011; 4(1):9-13.

195

222. Devito A. Interferon gamma -induced chemokines in psoriatic arthritis. Clin Ter. 2014

Nov-Dec; 165(6):e442-446.

223. Gladman D. Psoriatic arthritis. In: Harris ED SC RS, Firestein GS, Sergent JS, , ed.

Kelley’s Textbook of Rheumatology. Philadelphia: W.B. Saunders Co.; 2005:1155-1164.

224. Traupe H, van Gurp PJ, Happle R, Boezeman J, van de Kerkhof PC. Psoriasis vulgaris,

fetal growth, and genomic imprinting. Am J Med Genet. 1992 Mar 1; 42(5):649-654.

225. Rahman P, Gladman DD, Schentag CT, Petronis A. Excessive paternal transmission in

psoriatic arthritis. Arthritis Rheum. 1999 Jun; 42(6):1228-1231.

226. Augustin M, Reich K, Blome C, Schafer I, Laass A, Radtke MA. Nail psoriasis in

Germany: epidemiology and burden of disease. Br J Dermatol. 2010 Sep; 163(3):580-585.

227. Puri N MB. Nail changes in psoriasis--a profile. Journal of Pakistan Association of

Dermatologists 2011; 21:165-169.

228. Mallon E, Bunce M, Wojnarowska F, Welsh K. HLA-CW*0602 is a susceptibility factor

in type I psoriasis, and evidence Ala-73 is increased in male type I psoriatics. J Invest Dermatol.

1997 Aug; 109(2):183-186.

229. Bonfiglioli R, Conde RA, Sampaio-Barros PD, Louzada-Junior P, Donadi EA, Bertolo

MB. Frequency of HLA-B27 alleles in Brazilian patients with psoriatic arthritis. Clin Rheumatol.

2008 Jun; 27(6):709-712.

230. Queiro R, Sarasqueta C, Torre JC, Tinture T, Lopez-Lagunas I. Comparative analysis of

psoriatic spondyloarthropathy between men and women. Rheumatol Int. 2001 Oct; 21(2):66-68.

231. Vasku A, Bienertova-Vasku J, Izakovicova-Holla L, Pavkova Goldbergova M,

Kozacikova Z, Splichal Z, et al. Polymorphisms in HLA-related genes and psoriasis heredity in

patients with psoriasis. Int J Dermatol. 2013 Aug; 52(8):960-965.

232. Rahman P, Jones A, Curtis J, Bartlett S, Peddle L, Fernandez BA, et al. The

Newfoundland population: a unique resource for genetic investigation of complex diseases. Hum

Mol Genet. 2003 Oct 15; 12 Spec No 2:R167-172.

233. Zheng CJ, Thomson G, Pen YN. Allelic instability in mitosis can explain "genome

imprinting" and other genetic phenomena in psoriasis. Am J Med Genet. 1994 Jun 1; 51(2):163-

164.

234. Karason A, Gudjonsson JE, Upmanyu R, Antonsdottir AA, Hauksson VB, Runasdottir

EH, et al. A susceptibility gene for psoriatic arthritis maps to chromosome 16q: evidence for

imprinting. Am J Hum Genet. 2003 Jan; 72(1):125-131.

235. O'Rielly DD PR, Zhang Y, Al-Ghanim N, Yazdani R, Hamilton S, Chandran V, Gladman

D, Rahman P. Epigenetic studies in maternally versus paternally transmitted psoriatic disease

Ann Rheum Dis. 2014; 73.

196

236. Hagg D, Eriksson M, Sundstrom A, Schmitt-Egenolf M. The higher proportion of men

with psoriasis treated with biologics may be explained by more severe disease in men. PLoS

One. 2013; 8(5):e63619.

237. Rahman P, Beaton M, Schentag CT, Gladman DD. Accuracy of self-reported family

history in psoriatic arthritis. J Rheumatol. 2000 Mar; 27(3):824-825.

238. Chen H, Poon A, Yeung C, Helms C, Pons J, Bowcock AM, et al. A genetic risk score

combining ten psoriasis risk loci improves disease prediction. PLoS One. 2011; 6(4):e19454.

239. Heard E, Martienssen RA. Transgenerational epigenetic inheritance: myths and

mechanisms. Cell. 2014 Mar 27; 157(1):95-109.

240. Miyakura Y, Sugano K, Akasu T, Yoshida T, Maekawa M, Saitoh S, et al. Extensive but

hemiallelic methylation of the hMLH1 promoter region in early-onset sporadic colon cancers

with microsatellite instability. Clin Gastroenterol Hepatol. 2004 Feb; 2(2):147-156.

241. Suter CM, Martin DI, Ward RL. Germline epimutation of MLH1 in individuals with

multiple cancers. Nat Genet. 2004 May; 36(5):497-501.

242. Zeller C MN, Patel N, Dai W, Willheim-Benartzi C, Brown R. DNA Methylation

Profiling Using Infinium Methylation Assay. Bio-protocol. 2013; 3(10).

243. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al.

Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium

HumanMethylation450 microarray. Epigenetics. 2013 Feb; 8(2):203-209.

244. Zhang B, Kirov S, Snoddy J. WebGestalt: an integrated system for exploring gene sets in

various biological contexts. Nucleic Acids Res. 2005 Jul 1; 33(Web Server issue):W741-748.

245. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit

(WebGestalt): update 2013. Nucleic Acids Res. 2013 Jul; 41(Web Server issue):W77-83.

246. Eder L AF, Rosen C, Haroon M, Chandran V, Queiro R, Bull S, Rahman P, Winchester

R, FitzGerald O, Gladman DD. HLA class I genes as susceptibility markers of psoriatic arthritis

in patients with psoriasis – a meta-analysis. Presented at the GRAPPA 2015 Annual Meeting,

July 7-8, Stockholm, Sweden. 2015.

247. Krausz C, Sandoval J, Sayols S, Chianese C, Giachini C, Heyn H, et al. Novel insights

into DNA methylation features in spermatozoa: stability and peculiarities. PLoS One. 2012;

7(10):e44479.

248. Flanagan JM, Popendikyte V, Pozdniakovaite N, Sobolev M, Assadzadeh A, Schumacher

A, et al. Intra- and interindividual epigenetic variation in human germ cells. Am J Hum Genet.

2006 07/; 79(1):67-84.

249. Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987

Jul 20; 196(2):261-282.

197

250. Skinner MK, Guerrero-Bosagna C. Role of CpG deserts in the epigenetic

transgenerational inheritance of differential DNA methylation regions. BMC Genomics. 2014;

15:692.

251. Kretz M, Siprashvili Z, Chu C, Webster DE, Zehnder A, Qu K, et al. Control of somatic

tissue differentiation by the long non-coding RNA TINCR. Nature. 2013 Jan 10; 493(7431):231-

235.

252. Richardson RJ, Dixon J, Malhotra S, Hardman MJ, Knowles L, Boot-Handford RP, et al.

Irf6 is a key determinant of the keratinocyte proliferation-differentiation switch. Nat Genet. 2006

Nov; 38(11):1329-1334.

253. Biggs LC, Rhea L, Schutte BC, Dunnwald M. Interferon regulatory factor 6 is necessary,

but not sufficient, for keratinocyte differentiation. J Invest Dermatol. 2012 Jan; 132(1):50-58.

254. MacDonald JA, Wijekoon CP, Liao KC, Muruve DA. Biochemical and structural aspects

of the ATP-binding domain in inflammasome-forming human NLRP proteins. IUBMB Life.

2013 Oct; 65(10):851-862.

255. Raychaudhuri SK, Raychaudhuri SP. mTOR Signaling Cascade in Psoriatic Disease:

Double Kinase mTOR Inhibitor a Novel Therapeutic Target. Indian J Dermatol. 2014 Jan;

59(1):67-70.

256. Huang T, Lin X, Meng X, Lin M. Phosphoinositide-3 kinase/protein kinase-

B/mammalian target of rapamycin pathway in psoriasis pathogenesis. A potential therapeutic

target? Acta Derm Venereol. 2014 Jul; 94(4):371-379.

257. Jonsson H, Peng SL. Forkhead transcription factors in immunology. Cell Mol Life Sci.

2005 Feb; 62(4):397-409.

258. Johansson CC, Dahle MK, Blomqvist SR, Gronning LM, Aandahl EM, Enerback S, et al.

A winged helix forkhead (FOXD2) tunes sensitivity to cAMP in T lymphocytes through

regulation of cAMP-dependent protein kinase RIalpha. J Biol Chem. 2003 May 9;

278(19):17573-17579.

259. Holliday R, Grigg GW. DNA methylation and mutation. Mutat Res. 1993 Jan; 285(1):61-

67.

260. Mameli A, Cauli A, Taccari E, Scarpa R, Punzi L, Lapadula G, et al. Association of

MICA alleles with psoriatic arthritis and its clinical forms. A multicenter Italian study. Clin Exp

Rheumatol. 2008 Jul-Aug; 26(4):649-652.

261. Gonzalez S, Martinez-Borra J, Lopez-Vazquez A, Garcia-Fernandez S, Torre-Alonso JC,

Lopez-Larrea C. MICA rather than MICB, TNFA, or HLA-DRB1 is associated with

susceptibility to psoriatic arthritis. J Rheumatol. 2002 May; 29(5):973-978.

262. Gonzalez S, Brautbar C, Martinez-Borra J, Lopez-Vazquez A, Segal R, Blanco-Gelaz

MA, et al. Polymorphism in MICA rather than HLA-B/C genes is associated with psoriatic

arthritis in the Jewish population. Hum Immunol. 2001 Jun; 62(6):632-638.

198

263. Gonzalez S, Martinez-Borra J, Torre-Alonso JC, Gonzalez-Roces S, Sanchez del Rio J,

Rodriguez Perez A, et al. The MICA-A9 triplet repeat polymorphism in the transmembrane

region confers additional susceptibility to the development of psoriatic arthritis and is

independent of the association of Cw*0602 in psoriasis. Arthritis Rheum. 1999 May;

42(5):1010-1016.

264. Song GG, Kim JH, Lee YH. Associations between the major histocompatibility complex

class I chain-related gene A transmembrane (MICA-TM) polymorphism and susceptibility to

psoriasis and psoriatic arthritis: a meta-analysis. Rheumatol Int. 2014 Jan; 34(1):117-123.

265. Okada Y, Han B, Tsoi LC, Stuart PE, Ellinghaus E, Tejasvi T, et al. Fine mapping major

histocompatibility complex associations in psoriasis and its clinical subtypes. Am J Hum Genet.

2014 Aug 7; 95(2):162-172.

266. Dugast E, Kiss-Toth E, Docherty L, Danger R, Chesneau M, Pichard V, et al.

Identification of tribbles-1 as a novel binding partner of Foxp3 in regulatory T cells. J Biol

Chem. 2013 Apr 5; 288(14):10051-10060.

267. Dolcino M, Ottria A, Barbieri A, Patuzzo G, Tinazzi E, Argentino G, et al. Gene

Expression Profiling in Peripheral Blood Cells and Synovial Membranes of Patients with

Psoriatic Arthritis. PLoS One. 2015; 10(6):e0128262.

268. Zhao Y, Simon R. Gene expression deconvolution in clinical samples. Genome Med.

2010; 2(12):93.

269. Scarpa R, Ayala F, Caporaso N, Olivieri I. Psoriasis, psoriatic arthritis, or psoriatic

disease? J Rheumatol. 2006 Feb; 33(2):210-212.

270. Garcia Ruiz D NLdAM, Lupi da Rosa Santos O. Psoriatic arthritis: a clinical entity

distinct from psoriasis? Rev Bras Reumatol. 2012; 52(4).

271. Gisondi P, Tinazzi I, El-Dalati G, Gallo M, Biasi D, Barbara LM, et al. Lower limb

enthesopathy in patients with psoriasis without clinical signs of arthropathy: a hospital-based

case-control study. Ann Rheum Dis. 2008 Jan; 67(1):26-30.

272. Naredo E, Moller I, de Miguel E, Batlle-Gualda E, Acebes C, Brito E, et al. High

prevalence of ultrasonographic synovitis and enthesopathy in patients with psoriasis without

psoriatic arthritis: a prospective case-control study. Rheumatology (Oxford). 2011 Oct;

50(10):1838-1848.

273. Brazzelli V, Carugno A, Alborghetti A, Grasso V, Cananzi R, Fornara L, et al.

Prevalence, severity and clinical features of psoriasis in fingernails and toenails in adult patients:

Italian experience. J Eur Acad Dermatol Venereol. 2012 Nov; 26(11):1354-1359.

274. Armesto S, Esteve A, Coto-Segura P, Drake M, Galache C, Martinez-Borra J, et al. [Nail

psoriasis in individuals with psoriasis vulgaris: a study of 661 patients]. Actas Dermosifiliogr.

2011 Jun; 102(5):365-372.

275. Kawai T, Akira S. TLR signaling. Cell Death Differ. 2006 May; 13(5):816-825.

199

276. Davila E, Kolls J. A "Toll" for Th17 cell expansion. J Leukoc Biol. 2010 Jul; 88(1):5-7.

277. Jongbloed SL, Lebre MC, Fraser AR, Gracie JA, Sturrock RD, Tak PP, et al.

Enumeration and phenotypical analysis of distinct dendritic cell subsets in psoriatic arthritis and

rheumatoid arthritis. Arthritis Res Ther. 2006; 8(1):R15.

278. Wang N, Liang H, Zen K. Molecular mechanisms that influence the macrophage m1-m2

polarization balance. Front Immunol. 2014; 5:614.

279. Porta C, Rimoldi M, Raes G, Brys L, Ghezzi P, Di Liberto D, et al. Tolerance and M2

(alternative) macrophage polarization are related processes orchestrated by p50 nuclear factor

kappaB. Proc Natl Acad Sci U S A. 2009 Sep 1; 106(35):14978-14983.

280. Biswas SK, Lopez-Collazo E. Endotoxin tolerance: new mechanisms, molecules and

clinical significance. Trends Immunol. 2009 Oct; 30(10):475-487.

281. Fan H, Cook JA. Molecular mechanisms of endotoxin tolerance. J Endotoxin Res. 2004;

10(2):71-84.

282. Baeten D, Demetter P, Cuvelier CA, Kruithof E, Van Damme N, De Vos M, et al.

Macrophages expressing the scavenger receptor CD163: a link between immune alterations of

the gut and synovial inflammation in spondyloarthropathy. J Pathol. 2002 Mar; 196(3):343-350.

283. Satoh N, Shimatsu A, Himeno A, Sasaki Y, Yamakage H, Yamada K, et al. Unbalanced

M1/M2 phenotype of peripheral blood monocytes in obese diabetic patients: effect of

pioglitazone. Diabetes Care. 2010 Jan; 33(1):e7.

284. Bouhlel MA, Derudas B, Rigamonti E, Dievart R, Brozek J, Haulon S, et al.

PPARgamma activation primes human monocytes into alternative M2 macrophages with anti-

inflammatory properties. Cell Metab. 2007 Aug; 6(2):137-143.

285. Antonelli A, Ferrari SM, Giuggioli D, Ferrannini E, Ferri C, Fallahi P. Chemokine (C-X-

C motif) ligand (CXCL)10 in autoimmune diseases. Autoimmun Rev. 2014 Mar; 13(3):272-280.

286. Groom JR, Luster AD. CXCR3 ligands: redundant, collaborative and antagonistic

functions. Immunol Cell Biol. 2011 Feb; 89(2):207-215.

287. Lacotte S, Brun S, Muller S, Dumortier H. CXCR3, inflammation, and autoimmune

diseases. Ann N Y Acad Sci. 2009 Sep; 1173:310-317.

288. Ruth JH, Rottman JB, Katschke KJ, Jr., Qin S, Wu L, LaRosa G, et al. Selective

lymphocyte chemokine receptor expression in the rheumatoid joint. Arthritis Rheum. 2001 Dec;

44(12):2750-2760.

289. Wenzel J, Worenkamper E, Freutel S, Henze S, Haller O, Bieber T, et al. Enhanced type I

interferon signalling promotes Th1-biased inflammation in cutaneous lupus erythematosus. J

Pathol. 2005 Mar; 205(4):435-442.

200

290. Segerer S, Banas B, Wornle M, Schmid H, Cohen CD, Kretzler M, et al. CXCR3 is

involved in tubulointerstitial injury in human glomerulonephritis. Am J Pathol. 2004 Feb;

164(2):635-649.

291. Kryczek I, Banerjee M, Cheng P, Vatan L, Szeliga W, Wei S, et al. Phenotype,

distribution, generation, and functional and clinical relevance of Th17 cells in the human tumor

environments. Blood. 2009 Aug 6; 114(6):1141-1149.

292. Oo YH, Banz V, Kavanagh D, Liaskou E, Withers DR, Humphreys E, et al. CXCR3-

dependent recruitment and CCR6-mediated positioning of Th-17 cells in the inflamed liver. J

Hepatol. 2012 Nov; 57(5):1044-1051.

293. Jandus C, Bioley G, Rivals JP, Dudler J, Speiser D, Romero P. Increased numbers of

circulating polyfunctional Th17 memory cells in patients with seronegative spondylarthritides.

Arthritis Rheum. 2008 Aug; 58(8):2307-2317.

294. Lee EY, Seo M, Juhnn YS, Kim JY, Hong YJ, Lee YJ, et al. Potential role and

mechanism of IFN-gamma inducible protein-10 on receptor activator of nuclear factor kappa-B

ligand (RANKL) expression in rheumatoid arthritis. Arthritis Res Ther. 2011; 13(3):R104.

295. Guilmatre A, Sharp AJ. Parent of origin effects. Clin Genet. 2012 03/; 81(3):201-209.

296. Roth MP, Clayton J, Patois E, Alperovitch A. Gender distributions in parents and

children concordant for multiple sclerosis. Neuroepidemiology. 1994; 13(5):211-215.

297. Karter AJ, Rowell SE, Ackerson LM, Mitchell BD, Ferrara A, Selby JV, et al. Excess

maternal transmission of type 2 diabetes. The Northern California Kaiser Permanente Diabetes

Registry. Diabetes Care. 1999 Jun; 22(6):938-943.

298. Zeft A, Shear ES, Thompson SD, Glass DN, Prahalad S. Familial autoimmunity:

maternal parent-of-origin effect in juvenile idiopathic arthritis. Clin Rheumatol. 2008 Feb;

27(2):241-244.

299. Calin A, Brophy S, Blake D. Impact of sex on inheritance of ankylosing spondylitis: a

cohort study. Lancet. 1999 Nov 13; 354(9191):1687-1690.

300. Surekha T, Ishaq M, Jahan P. Parent-of-origin effect: a hint from vitiligo epidemiology. J

Dermatol. 2011 Sep; 38(9):947-949.

301. Sutovsky P, Moreno RD, Ramalho-Santos J, Dominko T, Simerly C, Schatten G.

Ubiquitin tag for sperm mitochondria. Nature. 1999 Nov 25; 402(6760):371-372.

302. Amanai M, Brahmajosyula M, Perry AC. A restricted role for sperm-borne microRNAs

in mammalian fertilization. Biol Reprod. 2006 Dec; 75(6):877-884.

303. Choi HB, Han H, Youn JI, Kim TY, Kim TG. MICA 5.1 allele is a susceptibility marker

for psoriasis in the Korean population. Tissue Antigens. 2000 Dec; 56(6):548-550.

201

304. Grubic Z, Peric P, Eeeuk-Jelicic E, Zunec R, Stingl K, Curkovic B, et al. The MICA-A4

triplet repeats polymorphism in the transmembrane region confers additional risk for

development of psoriatic arthritis in the Croatian population. Eur J Immunogenet. 2004 Apr;

31(2):93-98.

305. Steinle A, Li P, Morris DL, Groh V, Lanier LL, Strong RK, et al. Interactions of human

NKG2D with its ligands MICA, MICB, and homologs of the mouse RAE-1 protein family.

Immunogenetics. 2001 May-Jun; 53(4):279-287.

306. Ranta S, Zhang Y, Ross B, Takkunen E, Hirvasniemi A, de la Chapelle A, et al.

Positional cloning and characterisation of the human DLGAP2 gene and its exclusion in

progressive epilepsy with mental retardation. Eur J Hum Genet. 2000 May; 8(5):381-384.

307. Luedi PP, Dietrich FS, Weidman JR, Bosko JM, Jirtle RL, Hartemink AJ. Computational

and experimental identification of novel human imprinted genes. Genome Res. 2007 Dec;

17(12):1723-1730.

308. Yin XY, Cheng H, Wang WJ, Wang WJ, Fu HY, Liu LH, et al. TNIP1/ANXA6 and

CSMD1 variants interacting with cigarette smoking, alcohol intake affect risk of psoriasis. J

Dermatol Sci. 2013 May; 70(2):94-98.

309. Rakyan VK, Hildmann T, Novik KL, Lewin J, Tost J, Cox AV, et al. DNA methylation

profiling of the human major histocompatibility complex: a pilot study for the human epigenome

project. PLoS Biol. 2004 Dec; 2(12):e405.

310. Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, et al. BioGPS: an extensible

and customizable portal for querying and organizing gene annotation resources. Genome Biol.

2009; 10(11):R130.

311. Juyal G, Negi S, Sood A, Gupta A, Prasad P, Senapati S, et al. Genome-wide association

scan in north Indians reveals three novel HLA-independent risk loci for ulcerative colitis. Gut.

2015 Apr; 64(4):571-579.

312. Nakaya HI, Amaral PP, Louro R, Lopes A, Fachel AA, Moreira YB, et al. Genome

mapping and expression analyses of human intronic noncoding RNAs reveal tissue-specific

patterns and enrichment in genes related to regulation of transcription. Genome Biol. 2007;

8(3):R43.

313. Vernet C, Ribouchon MT, Chimini G, Jouanolle AM, Sidibe I, Pontarotti P. A novel

coding sequence belonging to a new multicopy gene family mapping within the human MHC

class I region. Immunogenetics. 1993; 38(1):47-53.

314. Kulski JK, Dawkins RL. The P5 multicopy gene family in the MHC is related in

sequence to human endogenous retroviruses HERV-L and HERV-16. Immunogenetics. 1999

May; 49(5):404-412.

315. Asare AL, Kolchinsky SA, Gao Z, Wang R, Raddassi K, Bourcier K, et al. Differential

gene expression profiles are dependent upon method of peripheral blood collection and RNA

isolation. BMC Genomics. 2008; 9:474.

202

316. Guo Y, Sheng Q, Li J, Ye F, Samuels DC, Shyr Y. Large scale comparison of gene

expression levels by microarrays and RNAseq using TCGA data. PLoS One. 2013; 8(8):e71462.