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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
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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
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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.
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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.
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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.
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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
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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
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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
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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
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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.
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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.
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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.
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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.
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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:
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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.
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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
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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
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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
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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.
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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:
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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
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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].
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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.
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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)
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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.
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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].
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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,
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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
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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.
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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.
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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
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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).
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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).
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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).
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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.
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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
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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).
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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).
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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
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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,
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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].
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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.
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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
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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
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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.
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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.
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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.
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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
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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].
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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
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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
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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
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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
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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
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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
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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
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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.
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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].
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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
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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.
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• 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
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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
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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
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