MERCER, HEATHER MILLIKEN, DECEMBER 2013
CELL BIOLOGY
THE DISTRIBUTION OF SINGLE NUCLEOTIDE POLYMORPHISMS IN
PYODERMA GANGRENOSUM: BIOMARKER DISCOVERY (124 p.)
Director of Thesis: Helen Piontkivska
i
THE DISTRIBUTION OF SINGLE NUCLEOTIDE POLYMORPHISMS IN PYODERMA GANGRENOSUM:
BIOMARKER DISCOVERY
A thesis submitted
To Kent State University in partial
Fulfillment of the requirements for the
Degree of Master of Science
by
Heather Milliken Mercer
December 2013
ii
Thesis written by
Heather Milliken Mercer
B.S., Kent State University, 1999
M.A., Kent State University, 2005
Approved by
___________________________________, Advisor
___________________________________, Chair, Department of Biological Sciences
___________________________________, Dean, College of Arts and Sciences
iii
TABLE OF CONTENTS
LIST OF FIGURES……vi
LIST OF TABLES……x
LIST OF ABBREVIATIONS……xiii
ACKNOWLEDGMENTS…………xvi
CHAPTER 1: PYODERMA GANGRENOSUM, INFLAMMATION, AND APOPTOSIS…………p. 1
Hypothesis……p. 2
PG Etiology……p. 2
Multiple Pathways……p. 3
Incidence of PG……p. 4
Genetic Links to PG……p. 5
PG Clinical Variations……p. 8
Treatment of PG……p. 12
Inflammation, Apoptosis, and PG……p. 14
Errors in Apoptosis and Disease……p. 25
CHAPTER 2: METHODS…………p. 27
CHAPTER 3: RESULTS…………p. 33
CHAPTER 4: DISCUSSION…………p. 68
Significance of SNP location……p. 68
Significance of SNP state: Homozygous or Heterozygous……p. 70
PAPA Syndrome and PG SNPs in 15q24.3……p. 71
SNP_A-1874315, SNP_A-2188317, and SNP_A-2295701……p. 72
LINGO……p. 73
LOC645752……p. 74
SNP_A-1796928……p. 75
PG SNP Gene Associations, Primary Candidates, Secondary Candidates, and Master SNPs……p. 75
CASP7……p. 76
BCL2……p. 80
IL4, IL23R, IL33, IL15RA and the Interleukins……p. 82
IL15RA……p. 82
IL23R……p. 82
IL33……p. 83
IL4R……p. 84
PLCG2……p. 86
iv
TABLE OF CONTENTS CONTINUED…
Open Reading Frames……p. 89
The Tap Genes……p. 90
Cytochrome p450……p. 92
Selectin Genes……p. 93
Solute Carriers……p. 95
NLR’s……p. 96
“Eat me” Signals……p. 100
Colony Stimulating Factor Genes……p. 100
Limitations to this Work……p.101
Future Directions……p. 103
REFERENCES……p. 104
v
LIST OF FIGURES
Chapter 1: Pyoderma Gangrenosum, Inflammation, and Apoptosis
Fig. 1: Co-morbidities described in 15 adults ranging from age 16-81 in association with PG (Huish et
al., 2001; Rozen et al., 2001; Brunsting et al., 1930)......p. 6
Fig. 2: Co-morbidities described in children in association with PG (Data from 47 patients from Graham
et al. (1994). Children’s ages ranged from 3.5-18……p. 6
Fig. 3: Ulcerative Pyoderma Gangrenosum (Bhat, 2012)......p. 9
Fig. 4: Pustular PG (Bhat, 2012)......p. 10
Fig. 5: Bullous PG (Bhat,2012)......p. 11
Fig. 6: Vegetative PG (Bhat, 2012)......p. 11
Fig. 7: The differentiation of immunity cells (Marieb, 2000)......p. 14
Fig. 8: An Overview of the Immune Response (Marieb, 2000)......p. 15
Fig. 9: The NALP3 Inflammasome (Mariathason & Monack, 2007). (ASC-apoptosis-associated specklike protein containing a caspase-recruitment domain)......p. 22
Fig. 10: Multiple pathways leading to inflammasome assembly (Haneklaus et al., 2013)......p. 23
Chapter 2: Methods
Fig. 11:
Custom High Stringency Settings used in Gene Functional Classification Analysis......p. 29
Fig. 12: The NCBI Gene list is compared to the Affymetrix Immune/Inflammation Gene List......p. 29
Chapter 3: Results
Fig. 13: Polymorphisms found in common among 6 PG patients are broken down and annotated with
RefSeq IDs......p. 34
Fig. 14: Numbers of SNPs per presentation state (homozygous or heterozygous)......p. 35
Fig. 15: Numbers of SNPs per presentation state (homozygous or heterozygous)......p. 35
Fig. 16: Number of SNPs found in each chromosome......p. 35
vi
LIST OF FIGURES CONTINUED…
Chapter 3: Results Continued…
Fig. 17: The PG SNP Data Set was analyzed via DAVID’s Gene Functional Classification Tool……p. 37
Fig. 18: The PG Data Set contains many dual-functioning apoptosis and immunity related gene
clusters…….p. 40
Fig. 19: Number of genes within Gene Functional Classification Clusters with ES>2 related to
Apoptosis/Immunity/Inflammation…….p. 41
Fig. 20: The NCBI Gene list is compared to the Affymetrix Immune/Inflammation Gene List......p. 41
Fig. 21: The AB Gene List shares 113 genes with the Affymetrix Immune/Inflammation
Gene List…….p. 42
Fig. 22: The AB Gene List shares 148 genes with the NCBI Apoptosis Gene List……p. 42
Fig. 23: The AABB Gene List shares 563 genes with the NCBI Apoptosis Gene List……p. 42
Fig. 24: The AABB Gene List shares 396 genes with the Affymetrix Immune/Inflammation
Gene List……p. 42
Fig. 25: The PG Gene List shares 429 genes with the Affymetrix Immune/Inflammation Gene List……p. 43
Fig. 26: The PG Gene List shares 597 genes with the NCBI Apoptosis Gene List……p. 43
Fig. 27: The AABB Gene List shares 104 genes with those shared by the NCBI Apoptosis Gene List the
Affymetrix Immune/Inflammation Gene List……p. 43
Fig. 28: The AB Gene List shares 27 genes with those shared by the NCBI Apoptosis Gene List the
Affymetrix Immune/Inflammation Gene List......p. 43
Fig. 29: The PG Gene List shares 111 genes with those shared by the NCBI Apoptosis Gene List the
Affymetrix Immune/Inflammation Gene List……p. 43
Fig. 30: The AABB Gene List shares 751 genes with those NOT shared by the NCBI Apoptosis Gene List
the Affymetrix Immune/Inflammation Gene List……p. 43
Fig. 31: The AB Gene List shares 207 genes with those NOT shared by the NCBI Apoptosis Gene List the
Affymetrix Immune/Inflammation Gene List……p. 44
vii
LIST OF FIGURES CONTINUED…
Chapter 3: Results Continued…
Fig. 32: The PG Gene List shares 804 genes with those NOT shared by the NCBI Apoptosis Gene List the
Affymetrix Immune/Inflammation Gene List……p. 44
Fig. 33: The genes shared by NCBI Apoptosis and Affymetrix Immune/Inflammation Gene Lists have 111
genes in common with PG Genes and the NCBI Apoptosis List……p. 44
Fig. 34: The genes shared by NCBI Apoptosis and Affymetrix Immune/Inflammation Gene Lists have 111
genes in common with PG Genes and the NCBI Apoptosis List……p. 44
Fig. 35: A comparison of the genes shared between the PG Data Set and Immunity/Inflammation
Gene List and the expected number of Immunity/Inflammation genes within the human
genome……p. 45
Fig. 36: The fraction of genes shared between the PG Data Set and Apoptosis Gene List is compared with
the expected number of Apoptosis genes within the human genome……p. 46
Fig. 37: The fraction of genes shared between the AABB Data Set, Apoptosis Genes and
Immunity/Inflammation Genes is significantly different across comparison groups……p. 47
Fig. 38: The fraction of genes shared between the AB Data Set, Apoptosis Genes and
Immunity/Inflammation Genes is significantly different across comparison groups……p. 48
Fig. 39: The genes found within the AABB Data Set show greater similarity to those shared by
Apoptosis and Immunity Gene Lists than those that are not shared by the two lists......p. 49
Fig. 40: There is no significant difference between the numbers of genes shared by the AB Gene List
and the AI Genes and those that are not shared……p. 50
Fig. 41: The genes found within the AABB Data Set show greater similarity to those shared by
Apoptosis and Immunity Gene Lists than those that are not shared by the two lists…..p. 51
Fig. 42: PG SNPs found in common between the NCBI Apoptosis and Affymetrix Immune/Inflammation
Gene Lists were further analyzed for functional relationships......p. 53
Fig. 43: A “Master” List of Apoptosis/Immunity/Inflammatory Genes was compiled and compared
with the PG Gene List……p. 62
Fig. 44: PG SNPs that may cause alterations in apoptotic and inflammatory gene function possibly
contributing to the PG phenotype (not a complete list)......p. 67
viii
LIST OF FIGURES
Chapter 4: Discussion
Fig. 45: The Caspase Cascade (Sigma Aldrich, March 2013)……..p. 78
Fig. 46: The PLCG2 signaling pathway…………….p. 87
ix
LIST OF TABLES
Chapter 1: Pyoderma Gangrenosum, Inflammation, and Apoptosis
Table 1: List of diseases which are often misdiagnosed as PG (Adapted from Bhat, 2012)......pg. 3
Table 2: Clinical Variations in PG diagnosis (Bhat, 2012)......pg. 8
Table 3: Proposed diagnostic criteria of Classic, Ulcerative Pyoderma Gangrenosum (PG). Diagnosis
requires both major criteria and at least two minor criteria (Su et al., 2004)......pg. 9
Table 4: A survey of disease states associated with defects in engulfment-related genes (Adapted
from Elliot & Ravichandran, 2010)......pg. 18
Table 5: A list of disorders associated with apoptotic dysfunction
(Compiled by Favaloro et al., 2012)……pg. 25
Chapter 2: Methods
Table 6: Lists compared utilizing MIT’s “Compare 2 Lists” online software......p. 30
Table 7: Chi2 statistical analyses of List Comparisons......p. 30
Table 8: Fisher Exact Tests performed on List Comparisons……p. 31
Chapter 3: Results
Table 9: A comparison of the genes shared between the PG Data Set and Immunity/Inflammation
Gene List and the expected number of Immunity/Inflammation genes
in the human genome……p. 45
Table 10: The number of genes shared between the PG Data Set and Apoptosis Gene List is compared
with the expected number of Apoptosis genes within the human genome......p. 46
Table 11: A comparison of Apoptosis and Immunity Genes found within the AABB
Gene List......p. 47
Table 12: A comparison of Apoptosis and Immunity Genes found within the AB Gene List......p. 48
Table 13: The number of shared AI genes found within the AABB Data Set is compared to the number
of A+I genes and A+I genes shared homozygously among PG patients......p. 49
x
LIST OF TABLES CONTINUED…
Chapter 3: Results Continued…
Table 14: The number of shared AI genes found within the AB Data Set is compared to the number
of A+I genes and A+I genes shared homozygously among PG patients......p. 50
Table 15: The number of shared AI genes found within the PG Data Set is compared to the number
of A+I genes and A+I genes shared homozygously among PG patients......p. 51
Table 16: After analysis by Affymetrix Genome-Wide SNP 6.0, the SNPs located in each genomic region
were examined for functional relationships......p. 53
Table 17: SNPs from Primary Candidates causing missense nonsense in addition to other gene
relationships......p. 55
Table 18: Primary Candidates – SNP located in Exons, CDS, 5’ UTRs, or 3’ UTRs in addition to other
gene relationships......p. 56-58
Table 19: Online Mendelian Inheritance in Man (OMIM) Disease records for
Primary Candidate genes......p. 59
Table 20: 106 Genes found in common between Secondary Candidates and A/I
Gene List……p. 60
Table 21: “Master” genes in which PG SNPs are found in exonic regions……….p. 63
Table 22: “Master” SNPs that cause missense or nonsense......p. 64
Table 23: “Master” SNPs found in splice-site, CDS, or 5’ UTR-initiator regions......p. 65
Table 24: OMIM Disease Associations related to any SNP found in the Master List or Primary
Candidate list that causes missense or nonsense......p. 66
Chapter 4: Discussion
Table 25: SNPs located within genes of the caspase family…….p. 79
Table 26: Multiple SNPs are found in the PG Data Set that are located in regions associated
with members of the BCL family of genes……..p. 81
xi
LIST OF TABLES CONTINUED…
Chapter 4 Discussion Continued…
Table 27: The Interleukin genes in which SNPs are coded as CDS, missense, UTR3, or
Missensense......p. 84
Table 28: The IL33 PG SNPs……p. 85
Table 29: IL15RA PG SNP……p. 85
Table 30: The IL23R PG SNPs……p. 85
Table 31: The IL4R PG SNPs……p. 86
Table 32: PG SNPs with PLCG primary gene associations……p. 89
Table 33: PG SNPs associated with TAP (transporter 2, ATP-binding cassette) binding proteins......p. 90
Table 34: PG SNPs with Cytochrome P450 gene associations……p. 93
Table 35: PG SNPs with SELL and SELPLG (Primary Candidate/Master genes) gene associations……p. 95
Table 36: PG SNPs with SELP (not found in Primary Candidate/Master genes) gene……p. 95
Table 37: NLR transcripts possibly affected by PG SNPs……p. 98
Table 38: Members of the NLR family in which PG SNPs are found within introns, upstream, or
downstream regions…….p. 99
Table 39: PG SNPs located in PTDSS1 genic regions……p. 100
Table 40: PG SNPs with associations to CSF1R genes……p. 101
xii
LIST OF ABBREVIATIONS
PG…….Pyoderma Gangrenosum
SNPs……Single Nucleotide Polymorphism
ENCODE……Encycolpedia of DNA elements
IL……Interleukin
PAPA….pyogenic sterile arthritis, pyoderma gangrenosum, and acne
IBD……Irritable Bowel Syndrome
RA……Rheumatoid Arthritis
UC……Ulcerative Colitis
MHC……Major Histocompatibility Complex
GMCSF……Granulocyte Macrophage Colony Stimulating Factor
GCSF……Granulocyte Colony Stimulating Factor
ROS……Reactive Oxygen Species
TLR……Toll-like Receptor
NLR……Nod-like Receptor
PAMP……Pathogen Associated Molecular Pattern
SLE……Systemic Lupus Erythematosus
MOMP……Mitochondrial Outer Membrane Permeabilization
PIDD……p53-induced Death Domain Protein
AIF……Apoptosis Inducing Factor
LMP……Lysosomal Membrane Permeabilization
ASC…… Apoptosis-associated speck-like protein containing CARD
CARD……Caspase Recruitment Domain
CASR……Calcium Sensing Receptor
FCAS……Familial Cold Auto-inflammatory Syndrome
MWS……Muckle Wells Syndrome
NOMID……Neonatal Onset Multi-system Inflammatory Disorder
NBD……Nucleotide Binding Domain
CNV……Copy Number Variant
MAF……Minor Allele Frequency
HW……Hardy-Weinberg
DAVID…… Database for Annotation, Visualization and Integrated Discovery
AA, BB, or AABB……Homozygous PG SNPs
AB……Heterozygous PG SNPs
NM……RefSeq ID prefix representing known mRNA
NR…… RefSeq ID prefix representing known RNA
XM……RefSeq ID prefix representing predicted mRNA
XR…… RefSeq ID prefix representing predicted
ES……Enrichment Score
NCBI……National Center for Biotechnology Information
xiii
LIST OF ABBREVIATIONS CONTINUED…
A……Apoptosis Gene List
I……Immune/Inflammatory Gene List
A+I……Genes not shared by A and I
A/I……Dual functioning Apoptosis/Immunity Genes
CDS…..Coding Sequence
UTR……Untranslated Region
DISC….Death Inducing Signaling Complexes
AICD….Activation Induced Cell Death
CASPR…… Classification Criteria for Psoriatic Arthritis
MS……Multiple Sclerosis
OMIM……Online Mendelian Inheritance in Man
NK……Natural Killer (cells)
xiv
THESIS PREPARATION APPROVAL FORM
Title of Thesis: THE DISTRIBUTION OF SINGLE NUCLEOTIDE POLYMORPHISMS IN
PYODERMA GANGRENOSUM: BIOMARKER DISCOVERY
I.
To be completed by the student:
I certify that this thesis meets the preparation guidelines as presented in the Style
Guide and Instructions for Typing Theses and Dissertations.
_______________________________________
(Signature of the Student)
II.
__________________
(Date)
To be completed by the thesis advisor:
A. I certify the thesis is not in violation of the United States copyright laws.
______________________________________
(Signature of Advisor)
_________________
(Date)
B. This thesis is suitable for submission
_____________________________________
(Signature of Advisor)
III.
_________________
(Date)
To be completed by the Director of the School or Chair of the Department:
I certify, to the best of my knowledge, that the required procedures have been
followed and preparation criteria have been met for this thesis.
_______________________________________
(Signature of the Director/Chair)
xv
____________________
(Date)
ACKNOWLEDGEMENTS
This work would not be possible without Dr. Jaroslaw Maciejewski (Cleveland Clinic
Foundation) who kindly shared LGL SNP data that has been used in making SNP genotype calls
and Dr. E. Mostow (NEOUCOM) who collected the saliva samples from the six PG patients.
I would like to thank my thesis advisor Dr. Helen Piontkivska and my thesis committee including
Dr. Eric Mintz, Dr. Gail Fraizer, and Dr. Judy Fulton for their support and guidance. I would also
like to thank my family for their unwavering support.
xvi
Chapter 1: Pyoderma Gangrenosum, Inflammation, and Apoptosis
Pyoderma gangrenosum (PG) is a rare, inflammatory disease marked by reactive, neutrophilic
dermatosis which commonly manifests as a progressive necrolytic skin ulcer with an irregular,
undermined border (Su et al., 2004). While PG is non-infectious, it is painful, difficult to diagnose and
perplexing to treat. Often a diagnosis of exclusion, a conclusion of PG is based on history of an
underlying disease, typical clinical presentation, histopathology, and exclusion of other diseases or
conditions that would lead to a similar appearance (Bhat, 2012). The course of this disease can be mild
or malignant, chronic or relapsing with remarkable morbidity. Under normal conditions, inflammation is
demonstrative of the body’s natural healing process; however the ulcerative lesions associated with PG
are the result of the inflammatory process itself.
PG ulcers often begin as a minor skin injury or insect bite and progress rapidly into the
maelstrom of inflammation that defines PG. Pathergy, or the development of lesions at sites of skin
damage, occurs in approximately 25% of PG patients (Su et al., 2004). In addition to trauma, various
drug therapies utilizing propylthiouracil, pegfilgastrim (granulocyte stimulating factor), gefinib
(epidermal growth factor receptor inhibitor), and isotretinoin have been known to produce a massive
immune response in some PG patients (Wollina, 2007; Tinoco et al., 2008).
First identified by Brocq in 1916 as “phagedenimse geometrique” (Bhat et al., 2011), PG was
further described by Brunsting et al. (1930) through their work at the Mayo Clinic in the mid 1900’s.
Brunsting and his colleagues concluded that PG was the manifestation of infection that originated in a
distant location such as the bowel in ulcerative colitis (Ruocco et al., 2009). While PG was first fully
characterized in 1930, its complete etiology is still not fully understood. It is believed that neutrophilic
dysfunction is a major factor in PG’s abnormal stimulus response that under normal conditions would
instigate the natural inflammatory process and subsequent healing.
1
2
Hypothesis
PG is a troubling disorder to both patients and the doctors attempting to treat it as evidenced by
the discussion throughout this chapter. The need for quicker diagnosis and effective treatment is
evident. While no obvious gene associations have been identified in conjunction with PG, there is still
sufficient evidence to suggest genetic links.
The results outlined in the forthcoming pages are based on a whole-genome genotyping study
of single nucleotide polymorphisms (SNPs) identified to be in common among a sample of six
northeastern Ohio PG patients. A SNP is a genetic variation in one nucleotide that is found in at least 1%
of the human population. According to the U.S. Department of Energy Office of Science (2008), SNPs
occur every 100 to 300 bases along the 3-billion-base human genome with the majority of them
involving the replacement of cytosine (C) with thymine (T).
It is my hypothesis that because of the prominent role of inflammation and apoptosis genes
played in PG development, there will be an over-enrichment of identified SNPs shared by the
aforementioned PG patients in (a) immune/inflammatory and (b) apoptosis related genes. These dualfunctioning genes in addition to various related genes, represent a cluster of genetic variants that
require further investigation in the search for PG-related genetic biomarkers.
PG Etiology
PG has four distinctive clinical and histological variants that are discussed in further detail later
in this chapter. The specific clinical features of the lesion may provide clues to any associated diseases.
The four different types of PG include ulcerative, bullous, pustular, and vegetative (Bhat, 2012). They
vary by affected body area, duration, aggressiveness, and associated disorders.
PG is not a disorder than can be treated and expected to disappear. Some patients of PG are
victims of recurrence suggesting a possible underlying genetic link to the disease. If an infectious
pathogen were the trigger of the proliferative immune response demonstrative of PG, one would expect
3
that the protocols used to treat those pathogens would successfully treat PG; however, this is often not
the case. In fact, PG is considered to be chronic for many patients marked by frequent recurrence and
delayed healing of lesions despite treatment. Familial relationships have also been observed in PG
epidemiology in addition to unique occurrences (Shands et al., 1987).
To add further complication, PG is often mistaken for other disease systems. PG has often been
confused with dermatological disorders such as Sweet Syndrome, ecthyma gangrenosum, mycobacterial
infections, deep mycoses, purpura fulminans, halogenodermas, erythema nodosum, and nodular
vasculities (Ryan, 1992; Venkateswaran et al., 1994; Hazen, 1992) in addition to those in other
categories such as those shown in Table 1. As misdiagnoses can result in treatment delays, these errors
can in turn, delay a patient’s ability to calm the overwhelming effects of the PG immune response.
Table 1: List of diseases which are often misdiagnosed as PG (Adapted from Bhat, 2012)
Disease Category
Vaso-occlusive (venous) disease
Systemic vasculitis
Infections
Malignancy
Tissue Trauma
Other neutrophilic dermatoses
Drug Reaction
Pathologies often confused with PG
hepatic veno-occlusive disease, retinal venoocclusive disease
Wegener’s granulomatosis, livedoid
vasculitis, polyarteritis nodosa, etc.
subcutaneous mycoses, tuberculosis, syphilis,
ecthyma gangrenosum
lymphomas, leukemia
insect bites, factitious panniculitis
atypical Sweet’s syndrome,
Behcet’s disease.
pustular drug reaction, halogenoderma
Multiple Pathways
Immunologically, dysfunctional cell-mediated immune response has been observed in PG
patients (Ruocco et al., 2009). Furthermore, the deposition of immunoglobulins within dermal blood
vessels has been observed in conjunction with PG although it is not known if these findings are
consistent among PG patients or if these are rare, isolated events (Powell et al., 1983).
4
Neutrophil dysfunction is believed to be the general cause of PG although the specifics have yet
to be established. In a study by Su et al., evidence of abnormal neutrophil trafficking and aberrant
integrin oscillations was revealed (Su et al., 2004). Alternatively, gene members of the interleukin family
have been shown to be affected in PG patients. Interleukin-8 (IL-8) is generally overexpressed in PG
ulcers while Interleukin-16 (IL-16), which is chemotactic to neutrophils, is shown to be up-regulated in
an associated disorder called PAPA Syndrome (consists of pyogenic sterile arthritis, Pyoderma
Gangrenosum, and acne) (Bhat, 2012).
Neutrophilic infiltrates and abscess formation at extracutaneous sites have been repeatedly
reported in patients with PG primarily in the lung however; multi-organ involvement is possible
indicating that PG is a systemic disorder characterized by neutrophilic prevalence in the skin. Pathways
to protect the epidermis from neutrophil infiltration seem to be insufficient in PG resulting in tissue
necrosis. PG was initially believed to be the result of bacterial infection in the immunocompromised
host (Wollina, 2007). Since inflammatory bowel disease is the most common underlying disorder, it has
been hypothesized that cross-reacting antigens in the bowel and the skin could be responsible for
secondary cutaneous manifestation. As there appears to be a variety of pathways that lead to the PG
phenotype, it is clear that PG’s cause is no simple matter.
Incidence of PG
The incidence of PG is estimated to be between 3-10 patients per million population per year
(Bhat, 2012); however because of misdiagnosis and other complications in identifying PG, the incidence
may be much higher. Epidemiology data has shown that females are slightly more likely than males to
develop PG with a peak incidence in both groups between the ages of 20 and 50 (Graham et al., 1994;
Von den Driesch et al., 1997). Approximately 4% of PG patients are children in the general population.
5
Genetic Links to PG
As mentioned previously, other underlying inflammatory and sometimes malignant disorders
are often observed in conjunction with PG. In fact, approximately 50% of PG patients also experience
complicit disorder such as inflammatory bowel disease (IBD), Hepatitis C, diabetes, rheumatoid arthritis
(RA), or leukemia-like conditions (Prystowsky et al., 1989) (See also Figures 1 and 2). Rheumatoid
arthritis and inflammatory bowel disease (either as Crohn’s Disease or ulcerative colitis) are most often
seen in conjunction with PG in adults, however in children, ulcerative colitis (UC) is more common
(Hartley & Rabinowitz, 1997). Ulcerative colitis is found in 10-15% of PG cases, while the associated
disorder Crohn’s Regional Enteritis is found in a slightly smaller percentage of patients (Bernstein et al.,
2001; Hartley & Rabinowitz, 1997). Alternatively, only 3% of Crohn’s or UC patients develop PG (Ozdil et
al., 2003) suggesting that when complicit with PG, these diseases serve as symptoms of a larger, more
systemic disorder. Rose et al. (2003) reports that two thirds of PG patients also suffer from associated
diseases including those mentioned above in addition to monoclonal gammopathy and malignancy.
Other diseases that commonly present along with PG include spondylitis, leukemia, lymphoma, and
myelodysplastic syndrome (Bennett et al., 2000). As mentioned previously, there is also evidence in
the literature to support genetic indicators in many of the diseases mentioned pointing to a greater
connection between PG, associated disorders, and the human genome (Lee et al., 2012; Barrett et al.,
2008; Franke et al., 2010; Todd et al., 2007; Stahl et al., 2010; McGovern et al., 2010; Silverburg et al,
2009; Anderson et al., 2011; Chung et al., 2012).
6
Compiled Case Studies of Associated Systemic
Diseases with Pyoderma Gangrenosum
6%
Crohn's
12%
Ulcerative Colitis
12%
53%
6%
11%
Arthritis
Blood Disease
Hepatitis
Diabetes
Fig. 1: Co-morbidities described in 15 adults ranging in age from 16 to 81 in association with PG
(Huish et al., 2001; Rosen et al., 2001; Brunsting et al., 1930)
Fig. 2: Co-morbidities described in children in association with PG (Data from 47 patients from
Graham et al. (1994). Children’s ages ranged from 3.5-18.
Incidentally, when PG occurs in conjunction with IBD, arthritis, etc. it is characterized as “paraimmune”. PG presentation with malignancy is paraneoplastic, while hematologic PG manifests
alongside leukemia or polycythemia (Bhat, 2012). Although rare, PG episodes resulting from drug
exposure are characterized as drug induced and still some cases are idiopathic.
7
Although underlying disorders are sometimes present in PG patients, there are those cases in
which no complicit disease is present. Other researchers have reported cases in which no systemic
disease was present, but developed as PG progressed (Khandpur et al., 2001; Shands et al., 1987, Alberts
et al., 2002). Additionally, PG also manifests as a stand-alone disorder in as many as 30% of cases (Su et
al., 2004).
Multiple individuals within the same family who are afflicted with PG have been reported
(Shands et al., 1987; Khandpur et al., 2001; Alberts et al., 2002); however familial relationships are not
evident in every PG diagnosis. Both Goncalves et al. (2002) and Bundino et al. (1984) have reported
cases in which familial associations were unknown or unreported. In some cases, family members of PG
patients have been alternatively diagnosed with disorders associated with PG in the absence of PG itself
(Lindor et al., 1997; Wise et al., 2004).
Recent findings lend additional credence to the hypothesis of the existence of genetic indicators
in PG. PAPA syndrome (= pyogenic sterile arthritis, pyoderma gangrenosum, and acne) (OMIM ID
#604410) is a rare autosomal dominant disorder that is classified as an auto-inflammatory disease.
Mutations in PSTPIP1, otherwise known as CD2BP1 (GenBank Accession # XM 044569), are associated
with this disorder. PSTPIP1, found in cytoband 15q24.3, codes for proline/serine/threonine
phosphatase-interacting protein 1, a cytoskeleton associated adaptor protein expressed commonly in
hematopoietic cells. PSTPIP1 also modulates T cell activation, cytoskeleton organization (Yang &
Reinherz, 2006), and interleukin-1β (IL-1β) release (Shohem et al., 2003). Particularly, mutations in
A230T and E250Q proteins have been identified in seven individuals from the same family (Lindor et al.,
1997; Cortis et al., 2004; Dierselhuis et al., 2005; Stichweh et al., 2005; Tallon et al., 2006; Renn et al.,
2007; Schellevis et al., 2011) and also in other sporadic cases (Brenner et al., 2009; Tofteland & Shaver,
2010). These mutations affect the CDC15-like domain of the CD2 binding protein and are discussed in
further detail in later chapters of this document.
8
PG Clinical Variations
Notably, there is no such thing as a singular PG phenotype. There are four main manifestations
of PG including ulcerative, pustular, vegetative, and bullous types (Table 2). There are also other atypical
variants described (Bhat, 2012) such as periostomal and genital types.
Table 2: Clinical Variations in PG diagnosis (Bhat, 2012)
Clinical Variants
Typical Findings
Ulcerative
Pustular
Bullous
Vegetative
Ulceration with rapidly evolving purulent wound
Discrete pustules usually associated with IBD
Superficial bullae with development of ulcerations
Erosions and superficial ulcers
Ulcerative PG is considered to be the classical form and is the most common manifestation of
PG within affected populations (Figure 3). Its characteristic lesion is described as necrotic and
mucopurulent with an edematous, violaceous, serpiginously expanding, undermined border (Bhat et al.,
2011; Ruocco et al., 2009). While the lesions can be found in any area, they are most commonly found
on the lower limbs and trunk (Wollina, 2007). Ulcerative PG episodes can vary in their aggression. In
some instances, the onset is rapid and explosive resulting in severe necrosis while in other cases; the
ulcerations progress gradually and can spontaneously regress (Ruocco et al., 2009). Diagnostic criteria
can be found in Table 3.
9
Table 3: Proposed diagnostic criteria of Classic, Ulcerative Pyoderma Gangrenosum (PG).
Diagnosis requires both major criteria and at least two minor criteria (Su et al., 2004).
Major criteria
1. Rapida progression of a painful,b necrolytic cutaneous ulcerc with an irregular, violaceous,
and undermined border.
2. Other causes of cutaneous ulceration have been excluded.d
Minor criteria
1. History suggestive of pathergye or clinical finding of cribriform scarring
2. Systemic diseases associated with PGf
3. Histopathologic findings (sterile dermal neutrophilia, ± mixed inflammation, ±
lymphocytic vasculitis)
4. Treatment response (rapid response to systemic steroid treatment)g
a
Characteristic margin expansion of 1 to 2 cm per day, or a 50% increase in ulcer size within
1 month.
b
Pain is usually out of proportion to the size of the ulceration.
c
Typically preceded by a papule, pustule, or bulla.
d
Usually necessitates skin biopsy and other investigations to rule out causes
e
Ulcer development at sites of minor cutaneous trauma.
f
Inflammatory bowel disease, arthritis, IgA gammopathy, or underlying malignancy.
g
Generally responds to a dosage of 1 mg/kg to 2 mg/kg per day, with a 50% decrease in size
within 1 month.
Fig. 3: Ulcerative Pyoderma Gangrenosum (Bhat, 2012)
10
Pustular PG is associated with inflammatory bowel disease (IBD) (Figure 4). In these cases, the
pustules often occur on the extremities or the upper trunk (Powell et al., 1996). While this variation is
mostly accompanied by fever, arthralgias, and inflammatory bowel exacerbation, it has been known to
occur in their absence (Bhat et al., 2011). A rare PG symptom also occurs in patients with IBD in which
pustules are isolated to areas surrounding enterostomy or colostomy. It is considered to be a pathergic
phenomenon. Another variety, pyostomatitis vegetans, also coincides with IBD exacerbations with
pustules progressing to erosions in the oral mucus membranes (Paramkusan et al., 2010).
Fig. 4: Pustular PG (Bhat, 2012)
First described by Perry and Winkelmann (1972), bullous PG lesions develop rapidly and are
characterized by central necrosis surrounded by eroded tissue and a ring of erythema (Bhat, 2012)
(Figure 5). The symptomatic rash normally manifests on the face and arms of patients who suffer from
associated myeloproliferative disorders such as leukemia. Patients diagnosed with bullous PG have the
least favorable prognosis of all the PG phenotypes and are most often treated through systemic
immunosuppression.
11
Fig. 5: Bullous PG (Bhat, 2012)
Vegetative PG (Figure 6) is localized and non-aggressive. Originally characterized as malignant
pyoderma, Gibson et al. (1997) more recently classified this manifestation as a variant of Wegener’s
Granulomatosis.
Fig. 6: Vegetative PG (Bhat, 2012)
Two other PG variations are characterized by ulcerations located in the genital regions or
marked neutrophilic infiltration of the internal organs. In the latter category, deemed extracutaneous
neutrophilic disease, the lungs are the most often affected (Ruocco et al., 2009; Callen, 1998).
As reported earlier, PG can manifest in children, but only rarely. PG cases in children only
represent about 4% of all confirmed occurrences (Bhat, 2012). PG in children often accompanies a more
favorable prognosis with associated disorders being largely absent (Bhat, 2012) however Graham et al.,
(1994) reported that associated disorders were similar in incidence to their presentation in adults. In
12
Graham’s study of PG in 45 children, ulcerative colitis was the most commonly associated disease
(26.6%) followed by leukemia (17.7%) (1994). Pathergy as a disease stimulus in children’s cases is also
rare. While lesions are found in specific areas that vary among PG types in adults, PG lesions in children
are more generalized in their location.
Treatment of PG
There is no singular treatment that works for every PG case. Generally speaking, a localized
topical therapy is useful when used alongside systemic therapy, but not as a stand-alone treatment.
Foam or laminate dressings have been successful to control the heavy exudates of PG lesions (Bhat,
2012) in addition to wet, saline compresses in the case of sloughy or purulent lesions (Wollina, 2007).
Skin grafts and other surgical treatments coincide with an increased risk of pathergic response and are
therefore contraindicated. Other topical agents such as tacrolimus, potent corticosteroids, and
cyclosporine have demonstrated success although data to support this is lacking (Miller et al., 2010;
Bhat, 2012).
The most reliable and effective treatment for rapid, aggressive forms of PG is systemic
corticosteroids. Prednisolone or pulse therapy with suprapharmocologic doses of
methylprednisolone/dexamethasone has been used with success in some resistant PG cases while
cyclosporine may also be used for those PG cases in which the effectiveness of steroid treatment is
questionable (Bhat, 2012).
Prednisone is particularly effective as an immunosuppressant, and affects virtually all of the
immune system. It can therefore be used in autoimmune diseases, inflammatory diseases, and the
prevention of organ transplant rejection. Prednisone is a glucocorticoid binding to the cytosolic
glucocorticoid receptor. Glucocorticoids are able to prevent the transcription of many of immune genes,
13
including the interleukin 2 (IL-2) gene. Also, systemic corticosteroid treatment such as prednisone 1
mg/kg bodyweight per day may be helpful in refractory cases.
Cyclosporine is an immunosuppressant drug widely used in post-allogeneic organ transplant to
reduce the activity of the patient's immune system and mitigate the risk of organ rejection. Cyclosporine
is thought to bind to the cytosolic protein cyclophilin (immunophilin) of immunocompetent
lymphocytes, especially T-lymphocytes. This complex of cyclosporine and cyclophilin inhibits calcineurin,
which under normal circumstances is responsible for activating the transcription of the IL-2 gene. It also
inhibits lymphokine production and interleukin release and therefore leads to a reduced function of
effector T-cells (Matis, 1992).
Infliximab 5 mg/kg per week is also used in treatment (primarily for ulcerative PG). Infliximab
has also been approved by the U.S. Food and Drug Administration for the treatment of psoriasis, Crohn's
disease, ankylosing spondylitis, psoriatic arthritis, rheumatoid arthritis, and ulcerative colitis.
Other therapies that have been successful in some PG patients include sodium cromoglycate,
dapsone, minocycline, clofazimine, nicotine, benzyl peroxide, macrophage colony stimulating factor,
interferon-α, immunoglobulins, hyperbaric oxygen therapy, plasma exchange, and surgery (Rose et al.,
2003).
14
Inflammation, Apoptosis, and PG
As discussed previously, PG is marked by the hyperactivity of neutrophils and other
inflammatory players. The inflammatory response is an essential part of animal physiology designed to
inhibit infection and ensure survival. Early in development, hematopoietins called colony-stimulating
factors (CSF’s) induce the differentiation of stem cells into lymphoid and myeloid progenitor cells in
bone marrow tissue (Clark & Kamen, 1987).
Fig. 7: The differentiation of immunity cells (Figure from Marieb, 2000)
The lymphoid lineage further differentiates into B and T cell progenitors, and Natural Killer cells
(NK) (See Figure 7). Plasma cells and memory cells are descendants of the B cell progenitors as are
helper T and cytotoxic C cells of the T cell lineage. Myeloid progenitors further differentiate into
15
granulocytes including neutrophils, basophils, eosinophils, and monocytes. Basophils produce mast cells
while the monocytes will further differentiate into macrophages and dendritic cells.
When the body’s protective measures have been breached, damaged cells release histamine,
leukotrienes, kinins, and prostaglandins which are chemotactic to neutrophils, macrophages, and other
immune responders. These stimulatory chemicals also facilitate the adherence of phagocytes to the
offending pathogens. Specialized phagocytic cells, notably macrophages and dendritic cells, engulf the
offending particles and then “present” pieces of the pathogen’s own proteins on specialized major
histocompatibility class II (MHC II) molecules located on their membrane surfaces. Other immune
responders such as basophils and eosinophils in addition to the adaptive immune system’s B cells, T
cells, and antibodies are also attracted to the area where they each fill a specialized role in the
inflammatory process (See Figure 8). Another immunity player, the neutrophil, also presents itself to
potentially play a pivotal role in the development of PG.
Fig. 8: A schematic overview of the Immune Response (Figure from Marieb, 2000; an imprint from
Addison Wesley Longman, Inc.)
16
When stimulated by paracrine, autocrine, or endocrine signaling, the colony-stimulating factors
that spurred the differentiation of lymphoid and myeloid progenitors in the bone marrow act to
accelerate neutrophil production at a rate of 2.5 billion cells per hour (Weisbart et al., 1989).
Granulocyte-Macrophage Colony Stimulating Factor (GMCSF), Granulocyte Colony Stimulating Factor
(GCSF) and Interleukin-3 (IL-3) are all bone marrow products that have demonstrated the ability to
intensify neutrophil production (Weisbart et al., 1989).
Once this flurry of chemotactic signaling has lured the neutrophils to the area of injury, they are
immobilized there. The neutrophils then commence adherence to pathogens, initiate phagocytosis and
degranulation, and finally emit a burst of oxidative products including NO, O2-, H2O2, and OCl(hypochlorite) delivering the final death blow (Klebanoff & Clark, 1978) to deleterious targets and the
neutrophil itself. This pathogen induced response is independent of traditional death receptors, instead
relying on neutrophil NADPH-oxidase derived reactive oxygen species (ROS) (Zhang et al., 2003). In PG
however, there may be no pathogen that initiated the immune response as PG episodes can be
triggered by multiple stimuli.
Neutrophils are the most abundant and short-lived of the leukocytes, initiating apoptosis within
5.4 days of release from the bone marrow (Pillay et al., 2010). Their clearance from inflammatory sites is
pivotal in resolving an inflammatory episode. Consequently, the cell cycle of neutrophils is a tightly
regulated process. It has been demonstrated that upon bacterial phagocytosis, neutrophils undergo
rapid apoptosis (Luo & Loison, 2008). The self-destruction of neutrophils after their function has been
performed in addition to their removal from the area of infection is critical in inflammatory resolution.
Neutrophils have the ability to identify pathogens through cellular and intracellular Toll-like
receptors (TLR) and Nod like receptors (NLR) (Takeuchi & Akira, 2010). TLRs detect pathogen-associated
molecular patterns (PAMPs) or ‘common elicitors’, which are molecules that are unique to microbes but
17
not multicellular organisms (Akira & Takeda, 2004). When a TLR is engaged, the MAPK and NFĸB
pathways are activated, pro-inflammatory cytokines and anti-microbial peptides are released to
neutralize potential threats, and apoptosis is initiated (Blomgran et al., 2012). Once neutrophils become
apoptotic, macrophages move in to ingest the senescing leukocytes.
At the early stages of apoptosis, dying cells release chemoattractants that function as “find me”
signals. These signals include triphosphate nucleotides (ATP/UTP), lysophosphatidylcholine (lysoPC), IL10, TGFβ, prostaglandins (Voll et al., 1997; Fadok et al., 1998; McDonald et al., 1999; Ogden et al., 2005)
and CX3CL1, a chemokine (Lauber et al., 2003; Truman et al., 2008; Elliot et al., 2009; Munoz et al.,
2010). The senescing cell then suppresses the release of inflammatory cytokines such as those
stimulated by engagement of Toll-like receptors (Voll et al., 1997; Fadok et al., 1998). Once the
apoptotic cell has been located, physical contact between “eat me” signals on the dying cell and
engulfment receptors on the phagocyte is made (Elliot & Ravichandran, 2010). Various eat me signals
have been identified including C1q, thrombospondin (extracellularly), PS and calreticulin (CRT)
(intracellularly) in addition to the “don’t eat me signal” CD47 (Bratton et al., 2011). Phosphatidylserine
(PtdSer) has been identified as a key “eat me” signal (Fadok et al., 1992; Vandivier et al., 2006) and once
bound, initiates the intracellular cascade of signaling events that lead to apoptosis. The recognition
process also induces macrophages to release TGF-b, IL-10, and PGE2 to thwart further inflammation and
inhibit pro-inflammatory cytokines such as TNF-α and IL-8 (Fadok et al., 1998). Intracellularly, PtdSer
engagement leads to activation of GTPase Rac and reorganization of cytoskeleton components that
result in the internalization of cellular corpses (Albert et al., 2000; Gumienny et al., 2001).
When
apoptosis is initiated through Ptd-ser engagement, p38 MAPK regulates IL-10 transcriptionally as well as
TGFβ through translational control as demonstrated in mice (Chung et al., 2007; Xiao et al., 2008).
Furthermore, suppression of TLR-dependent release of IL-6, IL-8, and TNF has also been shown to be
regulated at the level of transcription (Cvetanovic & Ucker, 2004).
18
Many studies have linked PtdSer’s failure to appropriately signal circulating phagocytes of
imminent cell demise is a hallmark of autoimmunity (Mevorach et al., 1998; Asano et al., 2004, Botto et
al., 1998, Scott et al., 2001, Cohen et al., 2002; Hanayama et al., 2004; Lacy-Hulbert et al., 2007,
Rodriguez-Manzanet et al., 2010). In a 2003 study of nuclear antigens released during secondary
necrosis, Taniguchi et al. connected these compounds to systemic lupus erythematosus and rheumatoid
arthritis. In addition to PtdSer recognition and nuclear antigen autoimmune response, further
connections between corpse disposal and pathogenesis have been made in human and mouse studies
as shown in Table 4 (Elliot & Ravichandran, 2010).
Under normal conditions, the genes involved in signaling a cell’s imminent demise to other cells
initiate the release of anti-inflammatory signals and promote resolution of the inflammatory process
(Elliot & Ravichandran, 2010). As shown in Table 4, many of the aforementioned gene products have
been linked to disease when they fail to perform these duties.
Table 4: A survey of disease states associated with defects in engulfment-related genes (Adapted
from Elliot & Ravichandran, 2010)
Find Me Gene
Disease Category
Species
G2A
Auto-Immune
Mouse
CX3CL1
Auto-Immune
Mouse
Eat-me/tickling Gene
MER
Auto-Immune, cancer, neuropathy, atherosclerosis
Human/Mouse
MFG-E8
Auto-immune, atherosclerosis, neuropathy
Mouse
C1Q
Auto-immune, neuropathy
Mouse
avb3/5
Auto-immune, atherosclerosis
Mouse
TIM-4
Auto-immune
Mouse
Engulfment Gene
GULP1
Arthritis
Human
Post-engulfment Gene
Mouse
LXRab
Auto-immune
Mouse
PPAR
Auto-Immune
Mouse
Dnase II
Auto-immune
Mouse
19
As neutrophils age, the levels of the anti-apoptotic protein, MCL-1, decline (Leuenroth et al.,
2000) and furthermore, factors such as GMCSF act to prolong neutrophil life, doing so by upregulation of
MCL-1 (Moulding et al., 1998). According to Kirschnek et al. (2011), neutrophil life span can be extended
by upregulation of inflammatory mediators such as interleukin 3 (IL3) and tumor necrosis factor (TNF) in
addition to GMCSF or pathogen originating lipopolysaccharides. During phagocytosis-induced apoptosis,
the expression of pro-inflammatory cytokines is down-regulated in neutrophils as demonstrated by
Kobayashi et al. (2003). Failure to undergo apoptosis, may therefore increase the levels of proinflammatory cytokines in areas surrounding neutrophil accumulation and propagate the inflammatory
response at inappropriate times.
In mammals, there are a few metabolic pathways that trigger apoptosis. One such pathway
requires the binding of a ligand to a death domain containing adaptor molecule, or death receptor, such
as a member of the tumor necrosis factor receptor (TNFR) superfamily including FAS, TRAIL (tumor
necrosis factor receptor) (Ashkenazi & Dixit, 1998), FADD (Fas associated via death domain), TRADD
(TNFRSF1A associated via death domain),IRAK, and MYD88 or interleukin receptor (IL-1R) (Ozbabacan et
al., 2012). These molecules can initiate the NFĸB signaling pathway to activate anti-apoptotic processes
or form DISCs (death-inducing signaling complexes) initiating a cascade of apoptotic signaling necessary
to release mitochondrial products (Adams, 2003) and complete the act of cell death. DISC transduces
the death signal through the phosphorylation or proteolysis of various other compounds including BID,
BIM, BAD, BMF, NOXA, and PUMA, assorted caspases, and kinases.
Pro-apoptotic proteins such as Bax and Bak are activated through the mechanisms of BH3
proteins. Other anti-apoptotic Bcl-2 proteins, such as Bcl-2, Bcl-XL, Bcl-w, and Mcl-1, most likely exhibit
their effects through direct binding of pro-apoptotic proteins (Kirschnek et al., 2011).Through the
proteolysis of BID, the BCL2 homology-3 (BH3)-only protein, DISC encourages the translocation of the
20
truncated BID to the mitochondria and stimulates MOMP (mitochondrial outer membrane
permeabilization). MOMP results in the release of apoptosis inducing proteins such as cytochrome C
and induction factors SMAC and Diablo that are normally contained within the mitochondria.
Cytochrome C, now liberated from the mitochondria, directs the assembly of the apoptosome of which
caspase 9 and APAF1 are part. Caspase 9, an initiator proenzyme, activates executioner caspases 3, 6
and 7 (Portt et al., 2011). DISC can also propagate the apoptotic message through the activation of
caspases or the kinase RIP.
When the stress trigger is DNA damage, the p53 tumor suppressor activates the BH3-only
proteins PUMA and NOXA through transcription promoting MOMP via BAX and BAK (Vousden & Lane,
2007). In an alternative stress induced pathway, caspase 2 complexes with the p53 induced protein with
a death domain (PIDD) protein and RIP-associated with a death domain (RAIDD) to form what is known
as the piddosome (Kroemer & Martin, 2005). DNA damage can thus activate caspase 2 to direct
additional caspase activation or induce MOMP.
In addition to the aforementioned proteins, MOMP is also responsible for the release of
apoptosis-inducing factor (AIF), Omi, and EndoG which can trigger caspase independent cell death
(Kroemer & Martin, 2005). These factors can translocate to the nucleus where ROS (reactive oxygen
species) production and subsequent DNA damage can trigger cell death. Upon death receptor
stimulation, TRADD is activated by RIP, which stimulates JNK. JNK is involved in the signaling of
lysosomal stress in an association that remains unclear. Lysosomal stress causes lysosomal membrane
permeabilization (LMP), resulting in the release of cathpepsin B and D (Kroemer & Jaatela, 2005). These
cathpepsin proteases can also trigger MOMP or further proteolysis eventually resulting in cell death.
While the intrinsic and extrinsic apoptotic pathways have long been thought to be the main
modes of cell death, two other pathways have recently been described. Pyropoptosis and pyronecrosis
21
also involve nod-like receptor (NLR) proteins (Bergsbaken et al., 2009; Willingham et al., 2007). NALP3 is
a member of the NLR family of proteins. Like the Toll-like Receptors (TLRs) discussed in detail in earlier
sections of this document, Nod-like Receptors (NLRs) also recognize and bind pathogen-associated
molecular patterns (PAMPs) (Akira & Takeda, 2004). In addition to molecules introduced to the body of
microbial origin, some NLRs bind substances released from damaged tissues (Meylan et al., 2006).
Formerly known as cryopyrin, NALP3 joins with ASC (apoptosis-associated speck-like protein containing
a CARD, or caspase-recruitment domain), to form the inflammasome (Agostini et al., 2004)(See Figure
9). Once assembled, the inflammasome activates caspase 1 which in turn, activates interleukin-1β and
interleukin 18, by cleaving them from their inactive precursors (Blomgram et al., 2012; Martinon et al,
2009; Schroder & Tschopp, 2010). Members of the Interleukin family act as inflammatory cytokines
designed to call other immunity cells to the damaged area.
Since the identification of the first inflammasome (Martinon et al., 2002), several others have
been identified. Defined by the NLR protein that they contain (such as the NALP1 inflammasome, the
NALP3 or cryopyrin “CIAS1” inflammasome, and the IPAF inflammasome), some of these complexes can
lead to cellular death dependent upon which type of cell they are created in.
22
Fig. 9: The Function of NALP3 Inflammasome (From: Mariathason & Monack, 2007; reprinted by
permission from Macmillan Publishers Ltd: Nature Reviews Immunology, 7: 31-40, 2007). Here ASCdesignates apoptosis-associated speck- like protein containing a caspase-recruitment domain.
Mutations in the NALP3 gene can cause constitutive production of the inflammasome and
thereby, continuous production of IL-1β (Agostini et al., 2004; Dowds et al., 2004). Supporting this idea
is the decrease in inflammation in patients treated with IL-1β receptor antagonist (IL-Ra) (GoldbachMansky et al., 2009; Neven et al., 2008). Over-expression of NALP3 and Cryopyrin-associated periodic
syndromes (CAPS) associated mutant NLRP3 have been shown to induce cell death in monocytes
[Derouet et al., 2004; Dowds et al., 2003; Fujisawa et al., 2007; Saito et al., 2008). It was previously
reported that a patient overexpressing IL1-β, had two polymorphism in the gene encoding the NALP3
inflammasome, NLRP3 (Q705K) and CARD-8 (C10X) (Gen Bank: NM 001184900) (Verma et al., 2008).
As shown on Fig. 10, there are multiple routes that lead to inflammasome assembly. In addition
to the aforementioned pathways, calcium concentrations have been shown to affect inflammation
through the ability of Calcium-sensing receptor (CASR) to activate the NLRP3 inflammasome through PLC
(phospholipase C) signaling (Haneklaus et al., 2013). Activation of CASR by extracellular Ca2+ results in
23
the inhibition of adenylate cyclase and a reduction in cyclic AMP (cAMP) levels. cAMP subsequently
binds NLRP3 to function as a negative inhibitor (Lee et al., 2012).
Fig. 10: Multiple pathways leading to inflammasome assembly (From: Haneklaus et al., 2013;
reprinted from Current Opinion in Immunology, 2013, 25:40-45, with permission from Elsevier)
24
Of the variations among inflammasome complexes, the NALP3 inflammasome demonstrates a
strong potential for relationship to the phenotype associated with Pyoderma Gangrenosum. Cryopyrinassociated periodic syndromes (CAPS) are auto-immune disorders related to the gene encoding NALP3
including such disorders as Familial Cold Auto-inflammatory Syndrome (FCAS, also known as Familial
Cold Uticaria), Muckle Wells Syndrome (MWS) and Neonatal Onset Multisystem Inflammatory Disease
(NOMID) (Hoffman et al., 2001; McDermott & Aksentijevich, 2002; Mariathasan et al., 2006). Mutations
within the nucleotide-binding domain (NBD) of NALP3 are associated with the aforementioned disorders
and characterized as periodic-fever syndromes incurring elevated immune responses (Ting et al., 2006;
Agostini et al., 2004; Dowds et al., 2004; Yu et al., 2006). As one potential outcome of NALP3 activation
is IL-1β release, its regulation is crucial in maintaining a normal inflammatory response. One of the
most potent pyrogens, IL-1β is believed to be released from the cell due to the formation of the
inflammasome although the details remain unclear (Martinon et al., 2002). IL-1β can be activated and
subsequently released via two known pathways: through the TLR activation and subsequent production
of pro-IL-1β (Mariathasan et al., 2006) or through the P2X7 receptor of which ATP is thought to be the
primary ligand (Hogquist et al., 1991).
25
Errors in Apoptosis and Disease
Evidence to suggest that errors in apoptosis and corpse clearance participate in disease
processes is abundant. Compiled by Favaloro et al. (2012), Table 5 lists various disorders in which
apoptosis, through both up or down regulation, is implicated. The association between apoptosis and
autoimmune disorders has been particularly well documented (Savill et al., 2002; Gaipl et al., 2004;
Erwig and Henson, 2007; Nagata et al., 2010). The importance of this process lies mainly in two areas:
the physical removal of dying cells and the production of anti-inflammatory mediators by phagocytes
which work to mitigate the inflammatory response.
Table 5: A list of disorders associated with apoptotic dysfunction (Compiled by Favaloro et al., 2012)
Cancer
Autoimmune diseases
Cardiovascular disorders Neurological disorders
Breast
Systemic Lupus erythematosus
Ischemia
Alzheimer
Lung
Autoimmune
Heart Failure
Parkinson
lymphoproliferative syndrome
Kidney
Rheumatoid arthritis
Infectious diseases
Huntington
Ovary and
Thyroiditis
Bacterial
Amyotrophic Lateral
uterus
Sclerosis
CNS
Viral
Stroke
Gastro-enteric
trait
Head and Neck
Melanoma
Lymphomas
Leukemia
The physical removal of cellular corpses sequesters the dying cells preventing the release of
toxic, inflammatory compounds into the extracellular environment such as occurs in necrosis (Elliot &
Ravichandran, 2010). When apoptotic cells are not cleared in a timely fashion, the integrity of the
cellular membrane which holds the deleterious compounds at bay becomes compromised. Once
membrane integrity is lost, secondary necrosis can begin. Secondary necrosis is believed to invite
further inflammatory response towards intracellular antigens and corpse DNA (Elliot & Ravichandran,
26
2010). According to Gaipl et al., this process may contribute to the onset of some autoimmune
disorders in humans specifically systemic lupus erythematosus and rheumatoid arthritis (Gaipl et al.,
2004). As evidence of this, Raza et al. (2006) have demonstrated that leukocyte apoptosis is inhibited in
rheumatoid arthritis patients thus propagating the abnormal immune response associated with the
disorder.
As summarized in the previous pages, the initiation and resolution of the inflammatory
response is complex and overlaps many other cellular processes. The inflammatory and apoptotic cell
signaling pathways are intricately linked. The efforts of both systems are coordinated through the
control of various gene products through up and down regulation. It is therefore understandable that
through cross-talk and interactions between complicit pathways, multiple methods exist through which
the normal inflammatory resolution could be thwarted. While a link between neutrophil dysfunction
and PG’s neutrophilic abundance suggests an apoptotic disorder, there may also exist an increased
“calling” of inflammatory players or upregulation of inflammatory products that give rise to the
necrolysis that accompanies the PG phenotype.
Genes functioning in both apoptotic and immunity related pathways may be contributing to the
PG phenotype. The vast majority of PG research so far has focused on the immune system as a target
for PG identification and treatment, but our work hypothesizes that dual-functioning genes may
represent a viable focal point. A diagnosis of PG is sometimes as baffling as determining its treatment.
Genes complicit in PG development may serve as biomarkers leading to faster and more efficient
disease diagnosis. The identification of biomarkers may thus also help to indicate drug targets for better
PG treatments.
Chapter 2: Methods
Saliva samples from six Caucasian PG patients were obtained through the use of Oragene Kits
(DNA Genotek, Inc., 2012) and processed at the Case Western Reserve University’s Genomics Core
Facility. From the samples, a list of SNPs shared by these patients was generated and analyzed utilizing
the Affymetrix Genome-Wide SNP 6.0 Array containing approximately 906,000 SNPs and copy number
variants (CNV’s)and converted into Affymetrix CEL files (Semenets et al., 2010). Genotype calls were
made using Birdseed algorithm and 12-samples SNP set of Large granular lymphocytic leukemia (LGL)
patients (courtesy of Dr. J Maciejewski, Cleveland Clinic Foundation) utilizing the Affymetrix Genotyping
Console to ensure high quality genotyping. Further filtering was performed in an effort to eliminate
poor genotype calls using the following parameters: Minor allele frequency (MAF) > 5%, HardyWeinberg (HW) p-value > 0.001, and SNP call rate > 95% (Miyagawa et al., 2008; Nishida et al., 2008).
This procedure produced results consistent with those recorded in other studies using the same NSP
platform and performance indicative of a larger number of samples (such as 12 and 24) (Nishida et al.,
2008).
The gene sets were then examined with various software programs (Excel, Programmer’s
Notepad) to search for patterns within the data set. DAVID (The Database for Annotation, Visualization
and Integrated Discovery) was utilized to convert gene identifiers and locate functional gene groups
(Gene Functional Classification). Affymetrix NetAffyx was used to analyze SNPs in terms of location,
gene associations, and corresponding transcripts.
The PG Data Set consists of 64,997 SNPs after Affymetrix probes were removed. The data set
was divided into AA, BB, and AB subsets of SNPs consisting of 29,997 SNPs, 28,927 SNPs, and 6,073 SNPs
respectively. “AA” and “BB” are essentially arbitrary designations assigned by Affymetrix indicating that
27
28
a SNP occurs in a homozygous state in that specific indivudal1. It was determined that the PG SNPs are
associated with 17,517 RefSeq gene identifiers (IDs): homozygous SNPs are associated with 14,654
unique gene IDs while heterozygous SNPS are associated with 2,863 unique gene IDs. With duplicates
removed (uniqued), the entire gene ID list for the PG Data Set consisted of 15,544 gene IDs. It should be
noted that the gene identifiers include NM (mRNA), NR (RNA, XM (predicted mRNA), and XR (predicted
RNA) prefixes according to the RefSeq gene identification system. Rarely, a SNP was found to be lacking
a primary gene association. These SNPs were labeled as being located in “intergenic regions” by the
researcher. For ease of purpose, the PG SNP groups will be referred to as AABB (pertaining to
homozygously presenting SNPs) or AB (pertaining to heterozygously presenting SNPs).
The RefSeq IDs coded as “NM” or known mRNA were converted into Official Gene IDs resulting
in 9,831 aliases. The PG Gene List was divided into 3000 ID increments or less and designated as Sets 14 (n= 3000, 3000, 3000, and 831 respectively). Each PG Gene Set was analyzed utilizing DAVID’s Gene
Functional Classification Tool with custom settings (shown in Fig. 11) through which the data was sorted
into gene groups. Gene Groups assigned with an Enrichment Score (ES) > 2 by DAVID (further described
in Results) were further analyzed for their association within apoptotic and immune/inflammation
pathways. A list of genes was allocated from NCBI (National Center for Biotechnology Information) that
are expected to be major players in the apoptotic process. The Human Immune and Inflammation 9K
SNP Panel was also procured from Affymetrix, Inc. (2006). Both lists contained Official Gene identifiers.
The NCBI Apoptosis List contained 1,778 unique gene IDs when it was downloaded from NCBI (February,
2013). The Affymetrix Immune/Inflammation List contained 1,084 unique gene IDs. Each gene group
with an ES>2 as assigned by DAVID was compared with a list of 2,646 genes found either in the NCBI
Apoptosis Gene List or the Affymetrix Immune/Inflammation Gene List.
1
http://www.affymetrix.com/support/help/faqs/dna_ge_arrays/faq_32.jsp
29
Fig. 11:
Custom High Stringency Settings used in Gene Functional Classification Analysis
As mentioned in the previous analysis, an Apoptosis Gene List from NCBI was downloaded in
addition to an Immune/Inflammation Gene List from Affymetrix. These gene lists were compared to
each other in order to create a list of 216 dual-functioning apoptosis and inflammation/immunity genes
as shown in Fig. 12 .
Fig. 12: The NCBI Gene list is compared to the Affymetrix Immune/Inflammation Gene List
The gene identifiers in the primary gene associations of the AABB and AB SNP lists were chosen
if designated as “NM” (mRNA) and uniqued to form lists of RefSeq identifiers. Once compiled, these lists
were submitted to DAVID’s Gene ID Conversion and converted into Official Gene IDs. The lists yielded
from this analysis (AB Genes, AABB Genes, and All PG Genes) were then compared and contrasted with
the NCBI Apoptosis List (A), the Affymetrix Immune/Inflammation List (I), those genes found in common
between A and I (A/I), and those genes not found in common between A and I (A + I). In order to
30
conduct these comparisons, Whitehead Institute of Biomedical Research’s (MIT) “Compare Two Lists”
online software was utilized (http://jura.wi.mit.edu/bioc/tools/compare.php). The lists compared are
compiled in Table 6. Chi2 and/or Fisher Exact Tests were conducted on the data when appropriate. The
results from the comparisons and statistical analyses shown in Tables 6-8 can be found in Figures 21-41
in the Results section.
Table 6: Lists compared utilizing MIT’s “Compare 2 Lists” online software
List 1
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.
All PG Genes
All PG genes
AB Genes
AB Genes
AABB Genes
AABB Genes
Apoptosis (A)
A/I
A/I
AABB
AB
AABB
AB
All PG Genes
All PG Genes
List 2
Immunity/Inflammation (I)
Apoptosis (A)
Immunity/Inflammation (I)
Apoptosis (A)
Immunity/Inflammation (I)
Apoptosis (A)
Immunity/Inflammation(I)
Common to PG Genes and I
Common to PG Genes and A
A/I
A/I
A + I (not shared)
A + I (not shared)
A/I
A + I (not shared)
Chi2 analyses were conducted on some group comparisons from Table 6 as shown in Table 7.
Table 7: Chi2 statistical analyses of List Comparisons
Comparison 1
# Genes in Genome vs.# Immunity/Inflammation
Genes
# Genes in Genome vs. # Apoptosis Genes
# Apoptosis Genes vs. # AABB Genes
vs.
vs.
# Apoptosis Genes vs. # AB Genes
vs.
vs.
vs.
Comparison 2
# PG Genes vs. # PG/Immune/Inflammation
Genes
# PG Genes vs. # PG/Apoptosis Genes
# Immune/Inflammation Genes vs. # AABB
Genes
# Immune/Inflammation Genes vs. # AB
Genes
31
Fisher Exact Tests were conducted on some group comparisons from Table 6 as shown in Table 8.
Table 8: Fisher Exact Tests performed on List Comparisons
Comparison 1
# A/I vs. # AABB shared with A/I
# A/I vs. # AB shared with A/I
# A/I vs. # All PG shared with A/I
vs.
vs.
vs.
vs.
Comparison 2
# A + I vs. # AABB shared with A + I
# A + I vs. # AB shared with A + I
# A + I vs. # All PG shared with A +I
In the comparison of the PG Gene List and the genes shared by the Apoptosis and
Immune/Inflammation Gene Lists, it was revealed that 111 genes were shared. Any SNP found within
the AA, BB, or AB PG Data Set whose primary gene association was listed as one of these 111 genes was
compiled to create a list of 2,889 SNPs. These SNPs were analyzed through Affymetrix NetAffyx
Genotyping Batch Queries to reveal the functional relationships between the SNP and its gene
associations. Possible relationships include exon, intron, missense, nonsense, CDS (Coding DNA
Sequence), 5’ UTR (untranslated region), 3’ UTR, downstream, upstream, or synon (synonymous).
The SNPs designated as being located within an exon, CDS, 5’ UTR, or 3’ UTR in addition to those
causing nonsense and missense were deemed “Primary Candidates”. Those SNPs designated as being
located within intronic, upstream, or downstream regions in addition to those designated as
synonymous were deemed “Secondary Candidates”. These two groups were further analyzed for
possible associations to Pyoderma Gangrenosum. For ease of purpose, the genes or SNPs within these
groups will be referred to as Primary SNPs, Primary genes, Secondary SNPs, or Secondary genes.
A “Master List” was created including the NCBI Apoptosis List, Affymetrix
Immunity/Inflammation List, and an additional list of Apoptosis genes from SABiosciences. Any PG SNP
whose primary gene association was included in the Master List was submitted to Affymetrix NetAffyx
Genotyping for analysis. Due to limitations in Affymetrix genotyping services, the list of 18,477 SNPs
was separated into subsets consisting of 8477, 5000, and 5000 SNPs. Genotype Expression Comparison
32
Sheets were downloaded as tab separated values files. Those SNPs whose relationship was listed as
exon, missense, nonsense, 5’ UTR, 3’UTR, splice-site, CDS, or 5’ UTR-init (initiator) were chosen and
examined for relationships to PG. For ease of purpose, these results will be referred to as the “Master”
List (Master SNPs or Master genes).
Chapter 3: Results
The PG Data Set consists of three Excel Spreadsheets composed of 64,997 SNPs after Affymetrix
probes were removed. The three data sets divided into AA, BB, and AB SNPs were composed of 29,997
SNPs, 28,927 SNPs, and 6,073 SNPs respectively associated with 15, 554 RefSeq gene IDs demonstrating
that multiple SNPs could be associated with a single gene ID. In the AA SNP list, 7,693 RefSeq IDs were
unique and 27 were located in inter-genic regions. Six thousand, six-hundred and sixty five gene IDs
were “known” or annotated by DAVID while 1,028 were related to predicted models. Of the known
annotations, 6,340 were prefixed NM, referring to mRNA and the remaining 325 were identified as NR,
or RNA. Of the AA predicted models, 693 and 335 were designated as XM (predicted mRNA) or XR
(predicted RNA) respectively.
The BB SNP’s were associated with 7,529 unique gene identifiers as primary gene associations
with 36 being located in an inter-genic region. Of the unique gene IDs, 6,480 were known by DAVID and
1,049 were predicted models. The known annotations consisted of 6,169 NM prefixed IDs and 311 NR
prefixes. The predicted models consisted of 707 XM and 342 XR.
The AB SNP list initially composed of 6,073 SNPs contained primary gene associations with 2,837
unique gene IDs. Two gene associations were listed as inter-genic. Annotated gene IDs numbered 2,371
in this data set with 466 designated as predicted. Of the annotated group, 2,261 had NM prefixes and
110 were prefixed NR. Three hundred and eight AB SNPs has XM gene associations while 158 were
associated with XR IDs.
33
34
PG SNPs
Homozygous State
AA
29,997 unique SNPs
27 in
Inter-genic
Regions
7,693 unique
RefSeq gene
IDs
NM
6,340
BB
28,927 unique SNPs
36 in
Inter-genic
Regions
Predicted
1,028
Known
6,665
NR
325
XM
693
Heterozygous State
XR
335
AB
6,073 unique SNPs
7,529 unique
RefSeq gene IDs
Predicted
1,049
Known
6,480
NM
6,169
NR
311
2 in
Inter-genic
Regions
XM
707
XR
342
2,837 unique
RefSeq gene IDs
Known
2,371
NM
2,261
NR
110
Predicted
466
XM
308
XR
158
Fig. 13: Polymorphisms found in common among 6 PG patients are broken down and annotated with
RefSeq IDs (NM = known mRNA, NR = known RNA, XM = predicted mRNA, XR = predicted RNA)
35
Initial analyses of numbers of SNPs per chromosome and SNPs per presentation state
(homozygous or heterozygous) were conducted using Excel. As is shown in Fig. 14, 91% of PG SNPs are
present in a homozygous state while 9% can be found heterozygously. Fig. 15 demonstrates that AA and
BB SNPs represented 46% and 45% of the total PG SNP data set. As demonstrated in Fig. 16, 9% of total
PG SNPs were found in chromosome 22 with the least being found in chromosome 19, X, and Y.
Fig. 14: Numbers of SNPs per presentation state
(homozygous or heterozygous)
Fig. 15: Numbers of SNPs per presentation
state (homozygous or heterozygous)
Fig. 16: Number of SNPs found in each chromosome
36
The RefSeq IDs coded as “NM” or known mRNA were converted into Official Gene IDs by DAVID
resulting in 9,831 aliases. The PG Gene List was divided into 3000 ID increments and designated as Sets
1-4 consisting of 3000, 3000, 3000, and 831 genes respectively. Each set was uploaded into DAVID’s
Gene Functional Classification Tool and analyzed utilizing the Custom High Stringency Settings discussed
in “Methods”. Gene Groups assigned with an Enrichment Score (ES) > 2 by DAVID were further analyzed
for their association within apoptotic and immunity pathways through comparison with a list of 2,646
genes found either in the NCBI Apoptosis Gene List or the Affymetrix Immune/Inflammation Gene List.
Set 1 consisting of 3000 Official Gene IDs was found to contain 2, 613 aliases identified as Homo sapiens
and 2, 596 DAVID IDs. Genes in Set 1 were clustered into 52 gene groups with 22 assigned an ES of 2 or
greater. One thousand, three hundred and eighty seven genes were not clustered. Set 2 consisted of
3000 genes, 2906 of which are known to be Homo sapiens. Two thousand eight hundred and fifty-three
DAVID IDs were clustered into 70 groups, 48 of which had an ES>2. One thousand and sixty-five genes
were not clustered. Set 3 was composed of 3000 genes, 2818 of which are Homo sapiens and 2811 of
which are assigned DAVID IDs. Set 3 genes were grouped into 59 clusters, 12 in which the ES was
greater than two. Consisting of 831 genes, Set 4 contained 801 Homo sapiens genes and 801 DAVID IDs
groups into 24 clusters. One thousand two hundred and ninety-eight genes were not clustered. Fifteen
gene groups had an ES >2 and 471 genes were not clustered.
37
64,997 SNPs
9,831 Official Gene IDs
Set 1 n= 3000
Set 2 n= 3000
Set 3 n= 3000
Set 4 n= 831
2613 H. sapiens
2906 H. sapiens
2818 H. sapiens
801 H. sapiens
2596 DAVID IDs
2853 DAVID IDs
2811 DAVID IDs
801 DAVID IDs
Gene Functional
Classification
Gene Functional
Classification
Gene Functional
Classification
Gene Functional
Classification
52 Clusters
70 Clusters
59 Clusters
22 Clusters
22 = ES > 2
48 = ES > 2
12 = ES > 2
15 = ES > 2
Fig. 17: The PG SNP Data Set was analyzed via DAVID’s Gene Functional Classification Tool
38
The Enrichment Score (ES) is based on EASE scoring, a modified Fisher Exact P-value assessment
as described by DAVID’s Technical Center (1,2Huang et al., 2009). If members of two independent
groups fall into the same category, the Fisher Exact is used to determine if the proportion of those
categorizations differs by group. This analysis is used as a measure of gene-enrichment in groups with
assigned enrichment terms. In DAVID, the following example is used:
A Hypothetical Example:
In human genome background (30,000 gene total), 40 genes are involved in p53 signaling
pathway. A given gene list has found that 3 out of 300 belong to p53 signaling pathway. Then
we ask the question if 3/300 is more than random chance comparing to the human background
of 40/30000.
A 2x2 contingency table is built on above numbers:
User Genes
Genome
In Pathway
3-1
40
Not In Pathway
297
29960
Fisher Exact P-Value = 0.008 (using 3 instead of 3-1). Since P-Value <= 0.01, this user gene list is
specifically associated (enriched) in p53 signaling pathway than random chance.
However, EASE Score is more conservative to examine the situation. EASE Score = 0.06
(using 3-1 instead of 3). Since P-Value > 0.01, this user gene list is specifically associated
(enriched) in p53 signaling pathway no more than random chance. (1,2Huang et al., 2009)
Gene Functional Classification was performed on each data set with custom high stringency
settings. As DAVID’s default settings are Classification Stringency = Medium, Similarity Term Overlap =
4, Similarity Threshold = .35, Initial Group Membership = 4, Final Group Membership = 4, Multiple
Linkage Threshold = .4, the Custom Settings used in this research were chosen for higher exclusivity of
data. The Custom Settings utilized in this research are as follows: Classification Stringency = High,
Similarity Term Overlap = 4, Similarity Threshold = .40, Initial Group Membership = 5, Final Group
Membership = 5, Multiple Linkage Threshold = .5. These setting as seen in DAVID are shown in Fig. 11
of the Methods section of this document.
39
Similarity Term Overlap and Similarity Threshold are both based on Kappa statistics. Kappa
statistics are a statistical measure of agreement between categorical terms. In DAVID, genes are linked
to terms describing their functional nature. The Similarity Term Overlap option is the minimum number
of overlap terms that must be present in order to include the corresponding genes in the cluster. The
higher the value, the more closely related the genes are in terms of function. The Similarity Threshold is
the minimum Kappa value that is determined to be significant. This number can vary between 0 and 1.
A higher value requires more stringency in gene relationships and fewer classification groups and gene
members. The Initial Group Membership option reflects the numbers of genes that must be present in a
group before it is considered for a classification category. This number must be greater than two
although lower membership requirements generate more clusters with lower gene membership. Once
DAVID determines Initial Group Membership, an additional analysis is completed termed the Final
Group Membership. This number must also be greater than two and allows the user to eliminate
smaller functional clusters. Multiple Linkage Threshold ranges from 0-100% with DAVID’s default setting
at 50% or .5. Two functional clusters sharing genes in percentages over that of the Multiple Linkage
Threshold are merged into an exclusive group.
The Gene Functional Clusters resulting from the analysis of the PG annotated gene IDs were
further examined for relationships to immune/inflammatory and apoptotic pathways. Clusters with an
ES less than two were excluded from further analysis. The number of clusters involved in the designated
pathways in addition to the number of genes involved in those clusters was compiled and submitted to
statistical testing.
Each gene group assigned an ES>2 was compared with a list of 2,646 genes found within the
NCBI Apoptosis list or Affymetrix Immune/Inflammation Gene List in order to determine a group’s
relationship to the aforementioned pathways. In order to conduct these comparisons, Whitehead
40
Institute of Biomedical Research’s (MIT) “Compare Two Lists” online software was utilized
(http://jura.wi.mit.edu/bioc/tools/compare.php). In this assessment, 53 out of 97 gene clusters shared
at least one gene with the Apoptosis/Immunity/Inflammation Gene List (Figure 18). Additionally, 2541
out of 3082 were found within those clusters representing a dramatic over-enrichment of genes
involved in apoptosis, immunity, and inflammation within the PG Data Set as shown in Figure 19. Using
the Human Genome Consortium’s (U.S. Department of Energy Office of Science) assessment that
19, 599 protein-coding genes exist within the human genome (2012), it was determined that
approximately 13.5% of those genes are related in some way to the pathways of interest in this study
based on the procured gene lists. This assessment demonstrates that 82.5% of clustered genes (54.6%
of clusters with ES >2) within the PG Data Set are related to those pathways (Figures 18 and 19).
Apoptosis/Immunity Related Gene
Clusters with ES > 2
Number of Clusters
97
54.6%
100
80
53
60
40
20
0
Apoptosis/Immune
Related Clusters
Total Number Clusters
Fig. 18: The PG Data Set contains many dual-functioning apoptosis and immunity related gene
clusters.
41
Apoptosis/Immunity Related Genes
in Clusters wtih ES > 2
3500
Number of Genes
3000
2500
82.5%
2000
1500
1000
2541
3082
500
0
Apoptosis/Immune Related Genes
Total Number Genes
Fig. 19: Number of genes within Gene Functional Classification Clusters with ES>2 related to
Apoptosis/Immunity/Inflammation.
The NCBI Apoptosis Gene List (n=1,778) and the Affymetrix Immune/Inflammation Gene List
(n=1,084) were then compared to each other in order to create a list of dual-functioning apoptosis and
inflammation/immunity genes. It was determined that these two lists share 216 dual-functioning genes
as shown in Figure 20.
Fig. 20: The NCBI Gene list is compared to the Affymetrix Immune/Inflammation Gene List
42
The gene identifiers in the primary gene associations of the AABB and AB SNP lists were chosen
if designated as “NM” (mRNA) and uniqued to form lists of RefSeq identifiers. Once compiled, these lists
were submitted to DAVID’s Gene ID Conversion and converted into Official Gene IDs. The lists yielded
from this analysis (AB Genes, AABB Genes, and All PG Genes) were then compared and contrasted with
the NCBI Apoptosis List (A), the Affymetrix Immune/Inflammation List (I), those genes found in common
between A and I (A/I), and those genes not found in common between A and I (A + I). In order to
conduct these comparisons, Whitehead Institute of Biomedical Research’s (MIT) “Compare Two Lists”
online software was utilized (http://jura.wi.mit.edu/bioc/tools/compare.php). The lists compared are
compiled in Tables 6-8 of the Methods Section. The results of those comparisons are shown in Fig.(s) 21
– 34.
AB Genes
2,767
113
Genes
Immunity
Genes
1,084
Fig. 21: The AB Gene List shares 113 genes with
the Affymetrix Immune/Inflammation
Gene List.
AB Genes
2,767
148
Genes
Apoptosis
Genes
1,778
Fig. 22: The AB Gene List shares 148 genes with
the NCBI Apoptosis Gene List.
43
All PG Genes
9,831
429
Genes
Immunity
Genes
All PG Genes
9,831
1,084
Fig. 25: The PG Gene List shares 429 genes with the
Affymetrix Immune/Inflammation Gene List.
AABB Genes
9,263
104
Genes
AB Genes
2,767
216
Fig. 27: The AABB Gene List shares 104 genes with those shared by
the NCBI Apoptosis Gene List the Affymetrix Immune/Inflammation
Gene List.
A/I Genes
27
Genes
216
Fig. 28: The AB Gene List shares 27 genes with those shared by
the NCBI Apoptosis Gene List the Affymetrix
Immune/Inflammation Gene List.
All PG Genes
A/I Genes
111
Genes
216
Fig. 29: The PG Gene List shares 111 genes with those shared by the NCBI
Apoptosis Gene List the Affymetrix Immune/Inflammation Gene List.
AABB Genes
9,263
1,778
Fig. 26: The PG Gene List shares 597 genes with
the NCBI Apoptosis Gene List.
A/I Genes
9,831
597
Genes
Apoptosis
Genes
A + I Genes
751
Genes
2,430
Fig. 30: The AABB Gene List shares 751 genes with those NOT shared by the
NCBI Apoptosis Gene List the Affymetrix Immune/Inflammation Gene List.
44
AB Genes
2,767
207
Genes
A +I Genes
All PG Genes
2,430
9,831
Fig. 31: The AB Gene List shares 207 genes with those NOT
shared by the NCBI Apoptosis Gene List the Affymetrix
Immune/Inflammation Gene List.
A/I Genes
216
111
Genes
Common to
All PG Genes
and Apoptosis
597
Fig. 33: The genes shared by NCBI Apoptosis and Affymetrix
Immune/Inflammation Gene Lists have 111 genes
in common with PG Genes and the NCBI
Apoptosis List.
A + I Genes
804
Genes
2,430
Fig. 32: The PG Gene List shares 804 genes with those NOT
shared by the NCBI Apoptosis Gene List the Affymetrix
Immune/Inflammation Gene List.
A/I Genes
216
111
Genes
Common to
All PG Genes
and Immunity
429
Fig. 34: The genes shared by NCBI Apoptosis and Affymetrix
Immune/Inflammation Gene Lists have 111 genes in
common with PG Genes and the NCBI Apoptosis List.
To determine whether PG patients share an excess of PG SNPs in Immunity/Inflammationrelated genes compared to the expected number of Immunity/Inflammation-related genes in known
protein coding regions of the genome, Chi2 (with Yates’ correction) was conducted. According to the
Human Genome Consortium, there are 19,599 protein coding genes within the human genome (2008).
As mentioned previously, a list of Immunity/Inflammation genes from Affymetrix was obtained including
1,084 genes. The association between these groups was determined to be statistically significant with a
two-tailed p value of less than .0001 (Chi-square= 16.334 with one degree of freedom). The result of
this assessment is shown in Fig. 35 and Table 9.
45
Table 9: A comparison of the genes shared between the PG Data Set and Immunity/Inflammation
Gene List and the expected number of Immunity/Inflammation genes
in the human genome
Number of genes in genome
Number of Immunity Genes
Shared PG Genes
Number of PG/Immunity Genes
19,599
1,084
9,831
429
Number of Genes
The fraction of genes shared between the PG Data Set and
Immunity/Inflammation Gene List is compared with the
exprected number of Immunity/Inflammation genes
within the human genome
(p values from Chi-square with Yates'correction)
p=<.0001
Chi2 = 16.334
(1° freedom)
21,000
19,500
18,000
16,500
15,000
13,500
12,000
10,500
9,000
7,500
6,000
4,500
3,000
1,500
0
Number of
genes in
genome
Number of
Immunity
Genes
Shared PG Number of
Genes
PG/Immunity
Genes
Fig. 35: A comparison of the genes shared between the PG Data Set and Immunity/Inflammation
Gene List and the expected number of Immunity/Inflammation genes within the human
genome
To determine whether PG patients share an excess of SNPs in Apoptosis-related genes
compared to the expected number of Apoptosis-related genes in known protein coding regions of the
genome, Chi2 (with Yates’ correction) was conducted. Once again, the expected number of 19,599
protein coding genes within the human genome (U.S. Department of Energy Office of Science, 2008)
46
was utilized along with the number of Apoptosis-related genes from NCBI (n= 1,778 genes). The
association between these groups was determine to be statistically significant with a two-tailed p value
of less than .0001 (Chi2 = 67.794 with one degree of freedom). The results of this assessment are shown
in Fig. 36 and Table 10.
Table 10: The number of genes shared between the PG Data Set and Apoptosis Gene List is compared
with the expected number of Apoptosis genes within the human genome
Number of genes in genome
Shared PG Genes
Number of Apoptosis Genes
Number of PG/Apoptosis Genes
19,599
9,831
1,778
597
Number of Genes
The fraction of genes shared between the PG Data Set and
Apoptosis Gene List is compared with the expected number
of Apoptosis genes within the human genome
(p values from Chi-square with Yates'correction)
21,000
19,500
18,000
16,500
15,000
13,500
12,000
10,500
9,000
7,500
6,000
4,500
3,000
1,500
0
p=<.0001
Chi2 = 67.794
(1° freedom)
Number of
genes in
genome
Shared PG
Genes
Number of
Apop Genes
Number of
PG/Apop
Genes
Fig. 36: The fraction of genes shared between the PG Data Set and Apoptosis Gene List is compared
with the expected number of Apoptosis genes within the human genome
47
To determine whether PG patients share an excess of SNPs in Apoptosis-related or
Immunity/Inflammation-related genes in either PG Gene Subset (AABB or AB), Chi2 (with Yates’
correction) was conducted utilizing the assessment of 1,778 and 1,084 genes related to Apoptosis and
Immunity/Inflammation respectively. From the PG Data Set, the numbers of AABB and AB genes were
also used with 9,263 and 2,767 genes respectively. The results of these assessments are shown in Fig.
(s) 37 and 38 and Tables 11 and 12.
Number of Genes
The fraction of genes shared between the AABB Data Set,
Apoptosis Genes and Immunity/Inflammation Genes
(p values from Chi-square with Yates ‘correction)
10,500
9,000
7,500
6,000
4,500
3,000
1,500
0
p = <.0001
Chi2 = 145.436
(1° freedom)
Number of
Apoptosis
Related
Genes
Number of
AABB genes
Number of
Immunity
Genes
Number of
AABB genes
Fig. 37: The fraction of genes shared between the AABB Data Set, Apoptosis Genes and
Immunity/Inflammation Genes is significantly different across comparison groups
Table 11: A comparison of Apoptosis and Immunity Genes found within the AABB Gene List
Number of Apoptosis Related Genes
Number of AABB genes
Number of Immunity Genes
Number of AABB genes
1,778
9,263
1,084
9,263
48
Number of Genes
The fraction of genes shared between the AB Data Set,
Apoptosis Genes and Immunity/Inflammation Genes
(p values from Chi-square with Yates ‘correction)
3,000
2,500
2,000
1,500
1,000
500
0
p=<.0001
Chi2 = 111.197
(1° freedom)
Number of Number of Number of Number of
Apoptosis AB genes Immunity AB genes
Related
Genes
Genes
Fig. 38: The fraction of genes shared between the AB Data Set, Apoptosis Genes and
Immunity/Inflammation Genes is significantly different across comparison groups
Table 12: A comparison of Apoptosis and Immunity Genes found within the AB Gene List
Number of Apoptosis Related Genes
1,778
Number of AB genes
2,767
Number of Immunity Genes
1,084
Number of AB genes
2,767
To determine whether PG patients share an excess of SNPs in the Apoptosis AND
Immune/Inflammation-related genes (dual-functioning genes) compared to the SNPs from genes of
other functional categories, we conducted Fisher’s exact tests of 2x2 contingency tables. The test
performed on the AABB PG Gene List showed a statistically significant excess of SNPs from A/I genes
compared to fraction of SNPs from either Apoptosis or Immune/Inflammation genes indicating that such
dual-function genes likely play a significant role in PG-related inflammation while the same test
performed on the AB PG Gene List did not. When the AABB and AB gene lists were combined and
uniqued, the test did demonstrate a significant excess of SNPs from A/I genes compared to the fraction
49
of SNPs from either A or I groups separately suggesting an over-enrichment of dual-functioning
apoptosis and immunity/inflammation related genes within the PG Data Set. The results of these
assessments can be found in Fig. (s) 39-41 and Tables 13-15.
Table 13: The number of shared AI genes found within the AABB Data Set is compared to the number
of A+I genes and A+I genes shared homozygously among PG patients.
Number of Apoptosis/Immunity (AI) Genes
216
Number of AABB genes shared with AI Genes
104
Number of Apoptosis & Immunity Genes Not shared (A+I)
2430
Number of AABB genes shared with A+I
757
Number of Genes
Fractions of SNPs shared in the homozygous (AABB) state with
Immunity and Apoptotic Genes
(p values from Fisher’s Exact Test)
2600
2400
2200
2000
1800
1600
1400
1200
1000
800
600
400
200
0
2430
p=.0008
757
216
104
Fig. 39: The genes found within the AABB Data Set show greater similarity to those shared by
Apoptosis and Immunity Gene Lists than those that are not shared by the two lists.
50
Table 14: The number of shared AI genes found within the AB Data Set is compared to the
number of A+I genes and A+I genes shared homozygously among PG patients.
Number of Apoptosis/Immunity (AI) Genes
Number of AB genes shared with AI Genes
Number of Apoptosis & Immunity Genes Not shared (A+I)
Number of AB genes shared with A+I
216
27
2430
207
Number of Genes
Fractions of SNPs shared in the heterozygous (AB) state with
Immunity and Apoptotic Genes
(p values from Fisher’s Exact Test)
2600
2400
2200
2000
1800
1600
1400
1200
1000
800
600
400
200
0
2430
p= 0.1178
216
27
207
Fig. 40: There is no significant difference between the numbers of genes shared by the AB Gene List
and the AI Genes and those that are not shared.
51
Table 15: The number of shared AI genes found within the PG Data Set is compared to the number
of A+I genes and A+I genes shared homozygously among PG patients.
Number of Apoptosis/Immunity (AI) Genes
Number of PG genes shared with AI Genes
Number of Apoptosis & Immunity Genes Not shared (A+I)
Number of PG genes shared with A+I
216
111
2430
804
Number of Genes
Fractions of SNPs shared in homozygous (AABB) or heterozygous
(AB) state with Immunity and Apoptotic Genes
(p values from Fisher’s Exact Test)
2600
2400
2200
2000
1800
1600
1400
1200
1000
800
600
400
200
0
2430
p= 0.0005
804
216
111
Fig. 41: The genes found within the AABB Data Set show greater similarity to those shared by
Apoptosis and Immunity Gene Lists than those that are not shared by the two lists.
52
In the comparison of the PG Gene List (n= 9,831) and the genes shared by the Apoptosis and
Immune/Inflammation Gene Lists (n=216), it was revealed that 111 genes were shared in common. Any
SNP found within the AA, BB, or AB PG Data Set whose primary gene association was listed as one of
these 111 genes was compiled to create a list of 2,889 SNPs. These SNPs were analyzed through
Affymetrix NetAffyx Genotyping Batch Queries to reveal the functional relationships between the SNP
and its gene associations. In the PG Data Set, only the primary gene association was used to determine
a SNP’s membership to a group. In Affymetrix, all of a particular SNP’s gene associations and
relationships are listed resulting in the Affymetrix List containing 891 genes relating to the 2, 889 SNPs
when only the corresponding SNPs from 111 genes would be expected when using a direct comparison
of lists. The aforementioned gene functional relationships discovered during this analysis are found in
Figure 42 and Table 16.
Of the 2,889 SNPs within 891 genes identified by Affymetrix, four were described with locations
within a coding sequence (CDS), 60 within exons, 1178 in intronic regions, 32 in 3’ UTRs, one in a 5’ UTR,
1871 were located downstream, and 1665 were described as upstream (Fig. 42). Fifteen SNPs resulting
in missense and one resulting in nonsense were also included in this list in addition to eight being
described as synon, or synonymous. Those SNPs described as CDS, exon, missense, nonsense, UTR3,
and UTR5 were included in the Primary Candidate group (n=99 with duplicates removed) as they were
thought to be most likely to result in alterations to protein synthesis and therefore more likely to affect
related cell signaling pathways. Those SNPs described as downstream (1872), intron (1179), synon
(4722), and upstream (1665) were collectively described as Secondary Candidates (n=2853 with
duplicates removed). It should be noted that some SNPs play multiple roles depending upon which
transcript is being processed. These two groups were further analyzed for possible associations to
Pyoderma Gangrenosum.
53
Fig. 42: PG SNPs found in common between the NCBI Apoptosis and Affymetrix
Immune/Inflammation Gene Lists were further analyzed for functional relationships.
Table 16: After analysis by Affymetrix Genome-Wide SNP 6.0, the SNPs located in each genomic
region were examined for functional relationships.
Relationship to gene
Number of SNPs
Upstream
1665
Downstream
1871
Introns
1178
Exons
60
3’ UTR
32
5’ UTR
1
Missense
14
CDS
4
Nonsense
1
54
There were 16 SNPs identified by the Affymetrix database that would cause missense (15) or
nonsense (1) within their transcript. One SNP was found in each gene listed in Table 17 with the
exception of KHDC1, in which two SNPs were listed. Eighty-three SNPs were found in exons, UTR3’,
UTR5’, and CDS regions. One SNP was found in each gene with the exception of the following genes in
which two SNPs were found: BCL214, C6ORF147, FP588, FAXC, and PPMIA. Three SNPs were related to
both EXOC6B and SYK. Lists of the genes and the SNP’s relationships to them can be found in Tables 17
and 18. For ease of purpose, SNPs are not listed twice within these tables even if they have multiple
relationships to the genes. Only those relationships most likely to result in gene dysfunction are listed.
A list of diseases from Online Mendelian Inheritance in Man (OMIM) associated any of the 99 Primary
Candidate genes can be found in Table 19.
The Secondary Candidates list was composed of 2,853 SNPs after duplicates were removed.
SNPs were designated by Affymetrix as introns (1,179), synons (8), and those located in downstream
(1,872), or upstream (1,665) from the gene. As before, individual SNPs can have multiple descriptors.
The genes with which the Secondary Candidate SNPs are associated (n=877) were compared with the
216 genes found in common between the Apoptosis and Immune/Inflammation Gene Lists. This
comparison resulted in a list composed of 106 genes as shown in Table 20.
55
Table 17: SNPs from Primary Candidates causing missense nonsense in addition to other gene
relationships
Official Gene ID
C18orf42 - chromosome 18 open reading frame 42
C4orf21 - chromosome 4 open reading frame 21
C9orf139 - chromosome 9 open reading
frame 139
CCDC66 - coiled-coil domain containing 66
CDC6 - cell division cycle 6 homolog (S. cerevisiae)
CLUAP1 - clusterin associated protein 1
EPHX2 - epoxide hydrolase 2, cytoplasmic
FLT4 - fms-related tyrosine kinase 4
Relationship
missense
3’ UTR
missense
CDS
Exon
missense
missense
exon
3’ UTR
missense
Exon
3’ UTR
missense
missense
CDS
Exon
Intron
missense
Exon
IL4R - interleukin 4 receptor
missense
Synon
KHDC1 - KH homology domain containing 1
missense
Exon
missense
NMI - N-myc (and STAT) interactor
SELPLG - solute carrier family 6 (proline IMINO
transporter), member 20
missense
SLC6A20 - solute carrier family 6 (proline IMINO
transporter), member 20
UNC93A unc-93 homolog A (C. elegans)
missense
Intron
missense
Exon
C6orf70- chromosome 6 open reading frame 70
nonsense
Description
uncharacterized protein
product
uncharacterized protein
product
uncharacterized protein
product
not well described
regulation of DNA replication
gene dysfunction related to
colonic neoplasms
gene dysfunction related to
familial hypercholesterolemia
receptors for VEGF C and D,
mutation associated with
Hereditary Lymphedema Type
1A
Variations have been
associated with atopy
(manifests as allergic rhinitis,
asthma, eczema)
integral to membrane
interacts with oncogenes, high
expression in myeloid
leukemias
tethers myeloid and Tlymphocytes to activated
platelets or endothelia
expressing P-selectin
membrane transporter
integral to membrane,
associated with Ovarian
Neoplasms
transmembrane protein
56
Table 18: Primary Candidates – SNP located in Exons, CDS, 5’ UTRs, or 3’ UTRs in addition to other
gene relationships
Official Gene ID
AKR1C2
BCL2L14
Description
aldo-ketoreductase family 1,
member C2
BCL2-like 14 (apoptosis
facilitator)
C6orf108
chromosome 6 open reading
frame 108
C6orf147
chromosome 6 open reading
frame 147
chromosome 9 open reading
frame 16
cullin-associated and
neddylation-dissociated 1transcriptional regulator
coiled-coil domain containing 69
C9orf16
CAND1
CCDC69
CD209
FAXC
FLJ45256
FP588
HDAC9
LINC00032
LINC00242
LINC00475
LINC00476
LINC00598
LOC100128909
LOC100507498
CD209 molecule –
transmembrane receptor of
dendritic cells and macrophages
failed axon connections homolog
(Drosophila)
uncharacterized LOC400511
uncharacterized LOC92973
histone deacetylase 9
long intergenic non-protein
coding RNA 32
long intergenic non-protein
coding RNA 242
long intergenic non-protein
coding RNA 475
long intergenic non-protein
coding RNA 476
long intergenic non-protein
coding RNA 598
uncharacterized
uncharacterized
Relationship
Exon
Intron
Exon
Intron
Upstream
cds
exon
synon
exon
exon
intron
downstream
exon
3’ UTR
exon
intron
exon
3’ UTR
downstream
exon
synon
intron
3’ UTR
exon
exon
upstream
exon
3’ UTR
exon
exon
exon
intron
exon
intron
exon
intron
exon
exon
57
Table 18 Continued: Primary Candidates – SNP located in Exons, 5’ UTRs, or 3’ UTRs in addition to
other gene relationships
Official Gene ID
LOC644662
LOC644838
LOC645206
LOC647979
RBM39
SEC63
SLC6A18
SWI5
TMEM245
TRAPPC9
UG0898H09
C6orf106
C6orf163
CCDC170
CCDC90A
CD80
CLIC6
ERCC6
EXOC6B
IFI35
PIWIL4
POU6F2
PPM1A
SLC6A19
SLC6A5
SLC9A3R2
Description
Uncharacterized
uncharacterized
uncharacterized
uncharacterized
RNA binding motif protein 39 –
transcriptional activator
SEC63 homolog (S. cerevisiae) ER
transporter
solute carrier family 6, member
18
SWI5 recombination repair
homolog (yeast)- required for
double strand break repair
transmembrane protein 245
trafficking protein particle
complex 9 – NFĸ-B signaling
uncharacterized
uncharacterized
uncharacterized
no functional information
available
regulates mitochondrial calcium
intake
B lymphocyte activation antigen
chloride intracellular channels
DNA binding protein- excision
repair
exocytosis
interferon-induced protein 35
spermatogenesis, represses
transposable elements
transcriptional regulation, tumor
suppressor
negative regulator of cell stress
response
leucine membrane transporter
sodium/chloride transport
sodium/hydrogen exchange in
colon
Relationship
exon
intron
exon
exon
exon
exon
intron
exon
intron
exon
synon
exon
intron
exon
intron
exon
intron
exon
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
3’ UTR
58
Table 18 Continued: Primary Candidates – SNP located in Exons, 5’ UTRs, or 3’ UTRs in addition to
other gene relationships
Official Gene ID
SMARCD2
Description
actin dependent regulator of
chromatin
Relationship
3’ UTR
SYK
spleen tyrosine kinase
3’ UTR
TAF8
RNA polymerase II, TATA box
binding protein (TBP)-associated
factor
translocase of inner
mitochondrial membrane
not well characterized
Vac14 homolog (S. cerevisiae),
activator of PtdIns
protein tyrosine kinase, plays a
role in T cell activation
cytokine receptor, enhances
expression of apoptosis inhibitor
BCL2
3’ UTR
TIMMDC1
TTC9
VAC14
ZAP70
IL15RA
3’ UTR
3’ UTR
5’ UTR
5’ UTR
CDS
59
Table 19: Online Mendelian Inheritance in Man (OMIM) Disease records for Primary Candidate genes
Gene ID
NCBI
OMIM disorder
Record
EPHX2
132811 Hypercholesterolemia, familial, due to LDLR defect, modifier of}
NCBI
FMO3
136132 Trimethylaminuria
NCBI
FLT4
136352 Hemangioma, capillary infantile, somatic: Lymphedema, hereditary I
NCBI
IL4R
147781 AIDS, slow progression to: Atopy, susceptibility to
NCBI
ABCB1
171050 Colchicine resistance: Inflammatory bowel disease
NCBI
ZAP70
176947 Selective T-cell defect
NCBI
AKR1C2
600450 46XY sex reversal 8: Obesity, hyperphagia, and developmental delay
NCBI
LOC100129316
600529 3-methylglutaconic aciduria, type I
NCBI
OPCML
600632 Ovarian cancer, somatic
NCBI
CDC6
602627 Meier-Gorlin syndrome 5
NCBI
LOC100507351/
604061 Amyotrophy, hereditary neuralgic: Leukemia, acute myeloid, therapySEPT9
NCBI
related: Ovarian carcinoma
SLC6A5
604159 Hyperekplexia 3
NCBI
CD209
604672 protection against Dengue fever: susceptibility to HIV type 1 and
NCBI
Mycobacterium tuberculosis
CYP26B1
605207 Craniosynostosis with radiohumeral fusions and other skeletal and
NCBI
craniofacial anomalies
SLC6A20
605616 Hyperglycinuria: Iminoglycinuria, digenic
NCBI
SEC63
608648 Polycystic liver disease
NCBI
SLC6A19
608893 Hartnup disorder: Hyperglycinuria: Iminoglycinuria, digenic
NCBI
POU6F2/YAE1D1
609062 Wilms tumor susceptibility-5
NCBI
FBP1
611570 Fructose-1,6-bidphosphatase deficiency
NCBI
TRAPPC9
611966 Mental retardation, autosomal recessive 13
NCBI
ERCC6
609413 Cerebrooculofacioskeletal syndrome 1: Cockayne syndrome, type B:
NCBI
De Sanctis-Cacchione syndrome: UV-sensitive syndrome 1: Lung
cancer, susceptibility to: Macular degeneration, age-related,
susceptibility to
60
Table 20: 106 Genes found in common between Secondary Candidates and A/I Gene List
APAF1
API5
AVEN
BAG3
BCL2L11
BCL2L13
BCL2L14
BID
BIRC8
C6
C9
CASP10
CASP2
CCL11
CCL21
CCR3
CCR8
CD2
CD34
CD4
CD44
CD48
CD53
CD6
CD7
CLU
IL15RA
CMA1
CTLA4
CUL5
CXCL13
DPP4
DUSP16
DUSP6
EGFR
EPHX2
ESR2
ETS1
FAF1
FGF1
FYN
GADD45G
GSTO1
GSTP1
GZMB
HDAC4
HMOX1
HRK
IFI16
IFIT2
IFNG
IFNGR2
IGBP1
PPM1A
IL19
IL21
IL21R
IL22
IL4
IL7
INHBA
INPP5D
ITGA9
ITGB2
JAK2
MAP3K5
MAPK10
MAPK13
MAPK14
MDM2
MMP2
MYB
NFKB1
NMI
NOS1
NR3C1
NR4A2
NUMA1
PECAM1
PIK3R1
ZAP70
PPP2R2B
PRKCZ
PTGES
PTGS2
RGS3
SELPLG
SOX4
SPP1
STAT3
STAT5B
STK17A
SYK
TIRAP
TNFRSF11B
TNFRSF1B
TNFRSF21
TNFRSF9
TNFSF10
TNFSF13B
TNFSF14
TP53BP2
TRAF5
TRAF6
UCHL5
VDR
61
As mentioned previously and utilized throughout this work, gene lists were obtained from NCBI
(Apoptosis) and Affymetrix (Immunity/Inflammation) in an effort to make comparisons between the PG
Data Set and known functional genes. As the rate at which bioinformatics data is generated is
exceedingly fast, these lists change quite frequently. For example, when the NCBI Apoptosis List was
first downloaded, it contained 1, 778 genes. When this researcher accessed it again a short time later, it
contained almost 2,700 genes. This is obviously problematic as it is virtually impossible to re-analyze
data in a large project such as this one every time new information is added to a database. That being
stated, the initially downloaded Apoptosis gene List from NCBI seemed to be lacking. For example,
while many of the caspases were included, some of them were missing. It was known to this researcher
that additional members of the caspase family existed within the PG Data Set, yet were not recovered
during assessments explained in this document. This seemed ill-conceived as members of the caspase
family are major players in the apoptotic signaling pathway. In an effort to create a more complete
picture of the genes within the PG data set that may give rise to the symptoms of PG, a “Master List”
was created which focuses on all genes thought to function within the pathways of interest, not just
those of dual function.
The Master List was created including the NCBI Apoptosis List, Affymetrix
Immunity/Inflammation List, and an additional list of genes included in Qiagen SABiosciences PCR Array
for Human Apoptosis which contained 90 genes. The Qiagen SABiosciences gene list was chosen
because it contains only genes considered pivotal to human apoptosis and ignores those that are loosely
related. Once compiled and uniqued, the Master List consisted of 2,586 genes. Any PG SNP whose
primary gene association was included in the Master List was submitted to Affymetrix NetAffyx
Genotyping for analysis. This analysis differs from previous analyses in this work because all gene
associations were analyzed, not only those that were found in common between the Apoptosis and
Immune/Inflammation gene lists. Those SNPs whose relationship was listed as exon, missense,
62
nonsense, 5’ UTR, 3’UTR, splice-site, CDS, or 5’ UTR-init (initiator) were chosen and examined for
relationships to PG (Fig. 43). Further breakdowns of PG SNPs found in exonic regions, those that causes
missense or nonsense, and those found in splice-sites, CDS, or 5’ UTR-initiator regions can be found in
Tables 21-23 respectively.
NCBI Apoptosis List
1,778
SABiosciences PCR
Array Human
Apoptosis
Affymetrix Immune
and Inflammation
Gene List
90
1,084
*only those SNPs of interest are shown
Master Apoptosis/
Immune/ Inflammation
Gene List
18,477 SNPs
2,586
All PG SNPs + Genes
Data Set
64,997
13
5’ UTR-init
25 CDS
245
Exons
84
Missense
7
Splice site
2
Nonsense
Fig. 43: A “Master” List of Apoptosis/Immunity/Inflammatory Genes was compiled and compared
with the PG Gene List.
63
Table 21: “Master” genes in which PG SNPs are found in exonic regions
64
Table 22: “Master” SNPs that cause missense or nonsense
65
Table 23: “Master” SNPs found in splice-site, CDS, or 5’ UTR-initiator regions
Any gene located in the Master or Primary Candidate List in which a SNP that causes missense or
nonsense during transcription was uploaded to DAVID for analysis. Once duplicates were removed, this
list was composed of 86 genes, 79 of which were recognized as Homo sapiens. These genes were
analyzed through DAVID for any potential disease associations. These disease associations can be found
in Table 24. Fig. 44 shows genes related to apoptotic signaling in which PG SNPs are associated. It is not
known how the average population would fare from such an analysis.
66
Table 24: OMIM Disease Associations related to any SNP found in the Master List or
Primary Candidate list that causes missense or nonsense.
Gene ID
Gene Name
OMIM_DISEASE
ERC2
ELKS/RAB6interacting/CAST
family member 2
Ellis van Creveld
syndrome 2
TAP binding
protein (tapasin)
chromosome 12
open reading
frame 43
epoxide hydrolase
2, cytoplasmic
fms-related
tyrosine kinase 4
glucose-6phosphatase,
catalytic, 2
Genome-wide association with bone mass and geometry in the Framingham Heart Study,
EVC2
TAPBP
C12orf43
EPHX2
FLT4
G6PC2
HHIP
hedgehog
interacting protein
IL23R
interleukin 23
receptor
IL4R
interleukin 4
receptor
melanocortin 3
receptor
peroxisome
proliferatoractivated receptor
gamma
MC3R
PPARG
SELL
selectin L
SPINK5
serine peptidase
inhibitor, Kazal
type 5
tectorin alpha
TECTA
TAP2
transporter 2, ATPbinding cassette,
sub-family B
(MDR/TAP)
Ellis-van Creveld syndrome,
Bare lymphocyte syndrome, type I,
Population-based genome-wide association studies reveal six loci influencing plasma levels of liver
enzymes,
Hypercholesterolemia, familial, due to LDLR defect, modifier of,
Hemangioma, capillary infantile, somatic, Lymphedema, hereditary I,
A Polymorphism Within the G6PC2 Gene is Associated with Fasting Plasma Glucose Levels, Fasting
plasma glucose level QTL 1,Genome-wide association analysis of metabolic traits in a birth cohort
from a founder population, Variations in the G6PC2/ABCB11 genomic region are associated with
fasting glucose levels,
Genome-wide association analysis identifies 20 loci that influence adult height, Identification of ten
loci associated with height highlights new biological pathways in human growth, Many sequence
variants affecting diversity of adult human height,
A genome-wide association study identifies IL23R as an inflammatory bowel disease gene,Crohn
disease, ileal, protection against, Genome-wide association defines more than 30 distinct
susceptibility loci for Crohn's disease, Genome-wide association study for Crohn's disease in the
Quebec Founder Population identifies multiple validated disease loci, Genome-wide association
study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease
pathogenesis, Genome-wide association study of 14,000 cases of seven common diseases and 3,000
shared controls, Loci on 20q13 and 21q22 are associated with pediatric-onset inflammatory bowel
disease, Novel Crohn disease locus identified by genome-wide association maps to a gene desert on
5p13.1 and modulates expression of PTGER4,Psoriasis, protection against,
AIDS, slow progression to, Atopy, susceptibility to,
Obesity, severe, susceptibility to, Obesity, severe, susceptibility to, BMIQ9,Obesity, susceptibility to,
BMIQ9,Obesity/hyperinsulinism, susceptibility to,
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants,
Carotid intimal medial thickness 1,Diabetes mellitus, insulin-resistant, with acanthosis nigricans and
hypertension, Diabetes, type 2,Genome-wide association analysis identifies loci for type 2 diabetes
and triglyceride levels,Glioblastoma, susceptibility to, Insulin resistance, severe,
digenic,Lipodystrophy, familial partial,Lipodystrophy, familial partial, type 3,Meta-analysis of
genome-wide association data and large-scale replication identifies additional susceptibility loci for
type 2 diabetes, Obesity, resistance to, Obesity, severe, Replication of genome-wide association
signals in UK samples reveals risk loci for type 2 diabetes,
IgA nephropathy susceptibility to,
Atopy,Netherton syndrome,
Deafness, autosomal dominant 12,Deafness, autosomal dominant 8,Deafness, autosomal dominant
8/12,Deafness, autosomal recessive 21,
Bare lymphocyte syndrome, type I, due to TAP2 deficiency, Wegener-like granulomatosis,
67
TNFRSF11B
TNFRSF1B
TNFRSF21
TNFSF10
TRAPPC1
TRAPPC9
TRAP1
BID
IL7 IL4 IL22 IL4R IL21R IL15RA IL23R
CD2
CD34
CD4
CD44
CD48
CD53
CD6
CD7
CD80
CD209
CDH12
FLT4
KHDC1
NMI
SELPLG
SELL
SELP
LY75
LY75-CD302
PTDSS1
NLRP4
NLRP9
NLRP3
NLRP7
PLCG2
IKBKE*
CYP26B
1
BCL211
BCL213
BCL214
CASP10
CASP2
CASP7
NFĸB1
Key
Missense
Intron
PPM1A
PARP4
Exon
3’ UTR
CDS
5’UTRinit
Fig. 44: PG SNPs that may cause alterations in apoptotic and inflammatory gene function
possibly contributing to the PG phenotype (not a complete list). Adapted from
Cellsignaling Technology, 2013. Illustration reproduced courtesy of Cell Signaling Technology, Inc.
(www.cellsignal.com).
Chapter 4: Discussion
Through the literature review conducted for this work, it is evident that the immune system has
been the main focus in PG research. The novelty of this research is the incorporation of apoptosis as a
major target in the effort of solving the PG puzzle. Clearly from the analysis of genes involved in
apoptotic and inflammatory pathways there is a crossover in gene function between the two pathways.
In the future, it may be beneficial to widen the search even further to include cell membrane antigens
and those involved in autophagy, which have been demonstrated to exhibit crossover function with
apoptosis. While the results of the major analyses included in prior chapters have been discussed, this
researcher felt that there were possible areas of significance within the PG Data Set that were not
covered by those examinations or required further discussion. Certainly, with approximately 65,000
rows of data to analyze, it would be impossible to examine each individual SNP’s potential role in the
disease process within a reasonable time frame, however some large groups of SNPs were uncovered
that could be discussed as clusters and also broad genetic phenomena which could be commented
upon. In this chapter, the significance of SNP location and presentation state will be discussed in
addition to some SNPs that either eluded identification as possible PG players by prior examinations or
may require further analysis.
Significance of SNP Location
The sequencing of the human genome was completed in 2003 by the Human Genome
Consortium (U.S. Department of Energy Office of Science) with a focus on protein-coding regions. As
mentioned previously in this document, the information added to the knowledge-base of not only this
project, but countless other bioinformatics efforts is staggering and increases at an accelerated pace.
More recently another huge effort, this one to sequence the non-protein coding regions of the genome,
68
69
has been undertaken in an effort to better understand the complex interactions of these mysterious
regions with the other more well-known areas of the genome.
The list of Primary Candidates compiled in this research consists of those SNPs found in exonic
regions, 5’ and 3’ UTRs, and those that cause missense or nonsense within genes of interest. These SNPs
are thought to be the most obvious variants to analyze as they are located in known coding regions
(exon and CDS), may cause direct alteration of transcription resulting in dysfunctional protein
production, or are located in areas that are increasingly being found to affect gene regulation. Many of
the individual SNPs within the PG Data Set located in these regions or described as thus are discussed in
more detail later in this document.
The 5’ untranslated region of an mRNA begins at the transcription site and ends one nucleotide
before the start codon. Transcriptional regulatory elements known to exist within the 5’ UTR include
translation initiators (2Kozack, 1987), upstream start codons (uAUGs), and upstream open reading
frames (uORFs) (Van derVelden & Thomas, 1999; Mignone et al., 2002). The 3’ untranslated region is the
area immediately following the stop codon on a messenger RNA. There are several regulatory elements
found in the 3’ UTR including the polyadenylation signal and binding sites for other molecules. A
messenger RNA contains a region composed of several hundred adenine residues called the poly-A tail.
The polyadenylation signal marks the cleavage site of the translating peptide and is most commonly
found as an AAUAAA sequence or similar variation (Neilson et al., 2010; Ryan et al., 2008). Stabilizing or
destabilizing proteins such as AU-rich elements (AREs), composed mainly of adenine and uracil
nucleotides, must bind to mRNA’s with these binding sites found within the 3’ UTR region (von Roretz &
Gallouzi, 2008). Additionally, other proteins, such as selenocysteine insertion sequences (SECIS) cause
the formation of a stem-loop structure. This formation signals to the ribosome to translate “UGA” as
selenocysteine instead of a stop codon. In addition to the binding proteins mentioned above, 3’ UTRs
70
can contain binding sites for micro-RNA’s (miRNA) (Ha et al., 2008). Micro-RNA’s generally serve as
post-transcriptional negative regulators of gene expression (Chen et al., 2008). Variations in any of the
above mentioned 3’UTR’s could potentially alter resulting proteins.
The ENCODE (Encyclopedia of DNA Elements) project, focused on those regions of the genome
previously referred to as “junk” DNA, published its initial findings in 2007 (National Human Genome
Research Institute). ENCODE focuses on approximately 30 Mb, or approximately 1% of the human
genome. Findings from ENCODE analyses have revealed that the majority of bases are linked to at least
one primary transcript, the existence of many non-protein coding transcripts produced from areas of the
genome that were previously thought to be transcriptionally inert, and previously unidentified
transcription start sites demonstrating chromatin and sequence specific binding properties required for
the initiation of transcription (Encode Project Consortium, 2007). Additionally, a 2012 study by H. Li et
al., demonstrated that the first intron within a coding region is generally the longest intron, those
introns are enriched with CpG islands, and usually contain higher numbers of TATA, CAAT, and GC boxes
compared to other introns of the same gene. These findings bring the list of “Secondary Candidates”
including PG SNPs located within intronic regions and those located upstream and downstream of
protein-coding regions into a new light, one in which their significance in gene expression is not
immeasurable, but simply yet uncharacterized.
Significance of SNP State: Homozygous or Heterozygous
Very little research has been done on the significance of homozygous vs. heterozygous SNP
representation in the human genome, however it stands to reason that due to what is known of the
deleterious ramifications of loss of heterozygosity, that SNPs found homozygously have a higher
probability of contributing to disease processes. If a SNP is found heterozygously and alters protein
function in any way, there is reason to believe that some of the translation products will be functional.
71
However, a SNP that is found homozygously and alters translation offers little hope that a normal, viable
protein will be constructed.
Assuming that a particular SNP alters a protein product in some way, those found in
heterozygous states may affect function if the protein is known to form dimers, trimers, or participate in
other multimeric complexes due to a change in protein-protein affinity. Any SNP found homozygously
or heterozygously that alters translational products has the potential of altering metabolic function.
In the PG data set, 91% of shared SNPs were found in homozygous states. It is unknown as to
whether this large proportion of SNPs has any clinical significance.
PAPA Syndrome and PG SNPs in 15q24.3
PAPA syndrome (pyogenic sterile arthritis, pyoderma gangrenosum, and acne) (OMIM ID
#604410) is a rare autosomal dominant disorder that is classified as an auto-inflammatory disease.
Mutations in PSTPIP1, otherwise known as CD2BP1 (GenBank Accession XM 044569), are associated
with this disorder. PSTPIP1, found in cytoband 15q24.3, codes for proline/serine/threonine
phosphatase-interacting protein 1, a cytoskeleton associated adaptor protein expressed commonly in
hematopoietic cells. PSTPIP1 also modulates T cell activation, cytoskeleton organization (Yang &
Reinherz, 2006), and interleukin-1β (IL-1β) release (Shohem et al., 2003). Particularly, mutations in
A230T and E250Q proteins have been identified in seven individuals from the same family (Lindor et al.,
1997; Cortis et al., 2004; Dierselhuis et al., 2005; Stichweh et al., 2005; Tallon et al., 2006; Renn et al.,
2007; Schellevis et al., 2011) and also in other sporadic cases (Brenner et al., 2009; Tofteland & Shaver,
2010). These mutations affect the CDC15-like domain of the CD2 binding protein. The aforementioned
mutations are located in the 15q24.3 cytoband in which three SNPs from the PG patient dataset are
found.
72
Wise et al. (2002) theorizes how CD2BP1 mutation might result in the overwhelming immune
response seen in PAPA syndrome. One theory pivots on CD2’s ability to help cells match up with an
Antigen Presenting Cell (APC). If CD2 is mutated and therefore cannot correctly bind with the Major
Histocompatibility Class (MHC), this may result in a reduced clearance of aging neutrophils. Another
hypothesis is that CD2BP1 mutation might somehow increase the signal for proliferation and infiltration
of the initiators of the inflammatory process and alter the apoptotic pathways creating a prolonged
neutrophil presence. Interestingly, CD2BP1 protein binds pyrin, which is indicated in Familial
Mediterranean Fever (FMF) and encoded by the MEFV gene (Shohem et al., 2001). Pyrin is a known
mediator of apoptotic pathways.
Interestingly, four SNPs from the PG SNP dataset are located in the 15q24.3 genomic region in
which the E250Q and A230T mutations are found. Three SNPs are associated with LOC64572 and
LINGO1 genes. One SNP is located slightly farther upstream in the TBC1D2B coding region. While these
SNPs have not been directly shown to be involved in the process of PAPA development, it is possible
that some of these SNPs may have some sort of regulatory influence over other genes in the region.
SNP_A-1874315, SNP_A-2188317, and SNP_A-2295701
Three SNPs found in the q24.3 region of chromosome 15 are linked to two specific genes:
LOC645752 (ENSG0000022502) and LINGO1 (ENSG00000169873). LOC645752 is a golgi autoantigen of
the golgin subfamily and is described as a 6 pseudogene. LINGO1 is a protein coding gene that
expresses a NOGO receptor interacting protein containing a leucine rich repeat and Ig domain.
SNP_A-1874315 (rs11072679 NCBI) is located at physical position 78,125,584. The reverse
strand polymorphism is A/G, thus the forward strand would consist of C/T. This SNP is located 80,975
downstream of the LOC645752 transcript ENST 0000049104 and 165,169 bases downstream of ENST
00000512414. LINGO1 resides 200,875 bases upstream of SNP_A-1874315.
73
SNP_A-2188317 (rs4886931 NCBI) is located at physical position 78,129,207 on chromosome 15.
This is a C/G polymorphism on the forward strand. It is located 77,352 bases downstream of the
ENST00000449104 transcript and 168,792 positions downstream of transcript ENST00000512414 of
LOC64572. SNP_A-2188317 is found 204,489 bases upstream of LINGO1.
In position 78,115,998 on chromosome 15 lies SNP_A-229507 (rs488692 NCBI). It is a C/T
polymorphism on the forward strand. This SNP is located 90,561 bases downstream of LOC64572
transcript ENST00000449104 and 155,583 positions downstream of ENST00000512414. It is located
191,289 bases upstream of LINGO1.
LINGO
LINGO1 is found at the 15q24.3 cytoband and is also known as LERN1, LRRN6A, UNQ201,
FLJ14594, MGC17422, and LOC84894. Aceview reports high expression of LINGO1, 21 distinct introns,
18 mRNAs, 16 alternatively spliced variants, and 2 unspliced forms. There are 12 probable promotors
and 2 non overlapping alternative last exons (Thierry-Mieg & Thierry-Mieg, 2006; Carim-Todd et al.,
2003). It has been proposed that the efficacy of LINGO1 translation may be reduced by the presence of
an un-translated open reading frame that initiates at the AUG upstream of the main open reading frame
(NCBI Aceview, retrieved February 2013).
Functionally, LINGO1 has been described by Mi et al. (2004) as an NGR1 binding partner. The
NGR1/NGFR signaling complex, of which LINGO1 is part, upregulates RhoA activity and in turn,
suppresses axonal regeneration in the adult CNS (Mi et al., 2004). Inoue et al. (2007) found that LINGO1
mRNA levels were significantly increased in the substantia nigra of Parkinson Disease patients suggesting
that LINGO1 suppression may have a neuroprotective effect. In a 2007 study of multiple sclerosis
lesions, it was found that a receptor of the Nogo family of myelin inhibitors (NgR) requires a co-receptor
(TROY) and an adaptor protein LINGO1 (Satoh et al.) in order to participate in cell signaling. While
74
Western blots of MS brains showed an up-regulation of TROY, LINGO1 was surprisingly reduced
although the researchers contend that the sample size was small (seven MS brain samples).
LINGO1 also has functions related to apoptosis. In a study contrasting the gene expression
between follicular cells enclosing developmentally competent bovine oocytes (BCB+) with follicular cells
enclosing incompetent oocytes (BCB-), LINGO1 expression was significantly reduced in the BCB+ cells
(Janowski et al., 2012). As the BCB+ cells demonstrate a higher level of apoptosis in their attempt to
support the developing oocyte, the anti-apoptotic effects of LINGO1 expression can be assumed. In
another study of macrophages and their possible inflammatory involvement in atherosclerotic lesion
formation, LINGO1 was found to be significantly upregulated in classically activated (M1) macrophages
along with key players of the NFĸB pathway (Hirose et al., 2011).
LOC645752
LOC645752 is also located in the 15q24.3 cytoband, covers 12.63 kb, and is only moderately
expressed. The two high quality proteins encoded by LOC645752 are expected to localize the nucleus
and are not associated with any known phenotypes at this time. (Thierry-Meig & Thierry Meig, 2006).
As stated previously, LOC645752 encodes a golgi auto-antigen, a member of the golgin
subfamily localized to the perinuclear region of the cell containing the Golgi complex. While it might be
intuitive that Golgi proteins would be protected from immune surveillance, several Golgi proteins have
been reported to be targets of autoimmune response. Autoantibodies against the Golgi complex were
first identified in a lymphoma patient (Rodriguez et al., 1982) followed by other reports suggesting a role
for anti-Golgi antibodies in other autoimmune disorders such as systemic lupus erythematosus (SLE)
(Fritzler et al., 1984), rheumatoid arthritis (Hong et al., 1992), mixed connective tissue disease (Rossie et
al., 1992), and Wegener’s granulomatosis (Mayet et al., 1991) some of which have been mistaken for
PG. Interestingly, Golgi auto-antigens were not localized to apoptotic blebs during cell death in one
75
study although immunofluorescence analysis showed that the Golgi complex was altered and developed
specific characteristics during apoptotic and necrotic events (Kooy et al., 1994). Furthermore, several
Golgi auto-antigens are cleaved into smaller peptides during apoptosis and necrosis (Casiano et al.,
1998). Nozawa et al. (2004) suggest that the coiled coil motif of the Golgi auto-antigens stemming from
the cytoplasmic face of the Golgi complex may be the target of immune response; however the reason
for this is unknown. Bizarro et al. (1999) suggest that the presence of anti-Golgi complex antibodies may
constitute an early sign of systemic autoimmune disease. The existence of a SNP in this particular gene
known to cause immune system overreaction may suggest that alteration of LOC645752 gene function
or regulation somehow results from the polymorphism.
SNP_A-1796928
SNP_A-1796928 (rs8030999) is found in position 78,311,343 on chromosome 15. An A/G
polymorphism, it resides in an intronic region of the gene TBC1D2B. This gene is described as a member
of the TBC1 domain family, member 2B. While the TBC1D2B gene has 13 transcripts, the following are
listed as in association with this SNP: ENST00000418039, ENST00000409931 (NM_015079),
ENST00000300584(NM_144572), ENST00000420639 (Retired)). TBC1D2B is a GTPase Activator for
Rab. Rab is linked with ATG8 activity which is integral to human autophagy, a process closely related to
apoptosis (Behrends et al., 2010).
PG SNP Gene Associations, Primary Candidates, Secondary Candidates, and Master SNPs
As a frame of reference, groups of SNPs found via various methods throughout this research
were deemed Primary Candidates, Secondary Candidates, and Master genes or SNPs. Recall that the
NCBI Apoptosis Gene List, Affymetrix Immune/Inflammation List, and the PG Data Set shared 111 genes.
Any SNP with a primary gene association listed as one of those 111 genes was submitted to Affymetrix
NetAffyx for analysis. Upon analysis, any SNP causing missense or nonsense along with location within
76
an exon, CDS, 5’UTR, or 3’UTR was coded as a Primary Candidate. Those located in an intron or
upstream/downstream of one of the 111 Primary Candidate Genes in addition to denotation as synon
were included in the Secondary Candidates. Furthermore, a Master List composed of the NCBI
Apoptosis Gene List, Affymetrix Immune/Inflammation Gene List, and SABiosciences Apoptosis Gene List
was compiled. Any SNP with a primary gene association listed in the Master Gene List was denoted as a
Master SNP. The genes and representative SNPs found within these groups warrant further analysis,
however other SNPs may have eluded identification due to the research protocols set forth by this
study. In an effort to provide a thorough analysis of the PG Data Set and identify as many potential
genetic contributors to PG as possible, genes related to members of the Primary and Secondary
Candidates and Master List must be reviewed. Within the remainder of this chapter, genes or gene
groups found within the Primary Candidates, Secondary Candidates, Master List, or PG Data Set that
require further explanation will be discussed.
CASP7
Two SNPs are located in the protein coding region associated with the CASP7 gene on
chromosome 10. The caspase family of proteases plays an essential role in the execution phase of
apoptosis. Initially inactivated proenzymes, the caspases must undergo proteolysis to form two
subunits. These subunits dimerize forming the active caspase enzyme. Caspase 7 is cleaved by caspase
3 and 10 and is activated when stimulated by cell death stimuli. Refseq annotates four transcripts, while
other sources estimate at least 15 spliced variants (NCBI Aceview, March 2013). The gene contains 21
introns, produces 16 different mRNAs through transcription, 15 alternatively spliced variants, and one
unspliced isoform. NCBI’s Aceview lists five alternate promotors, four non-overlapping terminal exons,
and five validated alternative polyadenylation sites (2013).
77
CASP7’s functions have been examined for connection to various diseases including Alzheimer’s
disease, bacterial infections, leukemia, and lymphoproliferative disorders. It is proposed to participate
with apoptotic pathways and in the aging process, heart development, Cytochrome C release, and
response to UV light. Potentially, CASP7 can produce 12 “good” proteins according to NCBI’s Aceview.
As mentioned in various chapters of this document, apoptosis occurs through two alternate
pathways: the extrinsic and the intrinsic. The extrinsic pathway is activated via death receptors such as
TNF-R, FAS, TRAIL-R1 and IL-1R. Upon ligand activation, death domain containing adaptor molecules
such as FADD (Fas associated via death domain), TRADD (TNFRSF1A associated via death domain), TRAIL
(tumor necrosis factor receptor), and MYD88 along with procaspases are recruited to the death domains
of the cell membrane death receptors (Ozbabacan et al., 2012). These molecules form DISCs (deathinducing signaling complexes) initiating a cascade of apoptotic signaling through the activation of the
proenzymes necessary to complete the act of cell death.
The intrinsic apoptotic pathway, initiated by stress signals, causes Cytochrome C release from
the mitochondria. BCL2 proteins Bax, Bad, Bid, and Bak are responsible for increasing the permeability
of the mitochondrial membrane sufficient to allow cytochrome C’s entrance into the cytoplasm. Once
released, Cytochrome C binds to apoptotic protease activating factor (APAF1) to form the apoptosome
which stimulates initiator Caspase 9 to activate the executioner caspases 3, 6, or 7 (Portt et al.,
2011)(Figure 45).
While initiated through different measures, the intrinsic and extrinsic apoptotic pathways
communicate to each other through Caspase 8, which in turn, activates the intrinsic pathway through
the actions of BCL2 family member, BID (Brunelle and Letai, 2009).
78
Fig. 45: The Caspase Cascade. (Sigma Aldrich, March 2013)
In the PG SNP data set (Master List), CASP 7 has two SNPs located in exons. One SNP also lies
within 17 intronic regions of CASP7 according to Affymetrix (March 2013). SNP_A-2202465 is located in
position 115478980 on chromosome 10 in cytoband q25.3. It is a C/T variation and is associated with
transcript ENST00000448834 which is listed as 692 base pairs in length by Ensembl. While Affymetrix
lists this exon as “transcribed locus”, Ensembl denotes it as antisense with no known protein product.
SNP_A-2202465 is also located in another exon of CASP7 and is also a C/T variant, this one
described by Affymetrix as “caspase 7, apoptosis-related peptidase”. The transcript associated with this
SNP is ENST00000468790 and is 607 base pairs in length (Ensembl). No protein product is produced as
this transcript is denoted as a “processed transcript” by Ensembl.
SNP_A-8289641 is located in position 115471561 of 10q25.3. It is an A/G variation and is part of
16 intronic regions and one exon. The exonic region is associated with transcript ENST0000044834, the
same transcript in which CASP7’s other PG SNP is located.
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Twenty SNPs within the PG Data Set are located within genomic regions associated with a
caspase family member. The SNPs found in Table 25 are all located in upstream, downstream, or
intronic regions of the primary gene association, with the exception of those associated with Caspase 7
that have been discussed previously. Of the genes listed in Table 25, only mutations in Caspase 10 have
an OMIM association which have been linked to Auto-immune Lymphoproliferative Syndrome II, Gastric
Cancer, and Non-Hodgkin Lymphoma.
Table 25: SNPs located within genes of the caspase family
PG SNP List
Gene
SNP ID
AB
CASP10
SNP_A-2237318
BB
CASP12
SNP_A-4219984
BB
CASP12
SNP_A-8490502
AA
CASP12
SNP_A-1831391
AA
CASP12
SNP_A-1841247
AA
CASP12
SNP_A-2170770
AA
CASP12
SNP_A-2227355
AA
CASP12
SNP_A-8626899
AB
CASP14
SNP_A-2059216
AA
CASP2
SNP_A-2012961
BB
CASP3
SNP_A-8472395
BB
CASP3
SNP_A-8484862
AA
CASP5
SNP_A-2036712
AA
CASP5
SNP_A-2148279
AA
CASP6
SNP_A-8692589
BB
CASP7
SNP_A-8289641
BB
CASP7
SNP_A-8517124
AA
CASP7
SNP_A-2005532
AA
CASP7
SNP_A-2202465
AA
CASP7
SNP_A-4268254
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BCL2
The BCL2 gene encodes a protein that helps regulate the permeability of the outer
mitochondrial membrane. This regulatory process helps to block the initiation of apoptosis.
Constitutive expression of BCL2 is believed to be a major cause of follicular lymphoma (NCBI Aceview).
The gene contains three GT- AG introns and produces four splice varied mRNA’s. Two alternative
promoters have been identified along with two non-overlappping terminal exons and two alternative
polyadenylation sites. BCL2 has been examined for association with Alzheimer’s Disease, rheumatoid
arthritis, autism, and multiple neoplastic disorders. It has been found to participate in apoptosis, focal
adhesion, and negative regulation of FAS and TNF. Mutations in BCL2 are linked to B cell Lymphoma
(OMIM 151430).
Two PG SNPs are located in the 3’ untranslated region of BCL2. While this region of the genome
is untranslated, some 3’ UTRs are known to include regulatory elements such as polyadenylation signals
and binding elements that govern translation. SNP_A-8300759 is located in position 60793921 of the
q21.33 region of chromosome 18. This polymorphism is a C/T variant and affects three transcripts as
listed by Affymetrix.
SNP_A-8556901 is located in position 60793494 in the q21.33 region of chromosome 18, but is
an A/G variant. Its location is also listed in three 3’ UTR regions of BCL2 and affects the same transcripts
as SNP_A-8300759.
Both transcripts ENST00000398117 and ENST00000333681 translate a protein that is 239
residues in length although their length varies: 7,461 base pairs and 3,209 base pairs respectively. Table
26 lists all of the PG SNPs located in members of the BCL family of genes. All those listed are found in
intronic, upstream, or downstream regions with the exception of those previously discussed.
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Table 26: Multiple SNPs are found in the PG Data Set that are located in regions associated
with members of the BCL family of genes
AB
BCL11A
SNP_A-1963492
AA
BCL11B
SNP_A-2176309
AB
BCL11A
SNP_A-2107543
AA
BCL11B
SNP_A-2192661
AB
BCL11A
SNP_A-8401892
AA
BCL11B
SNP_A-2211768
AB
BCL11A
SNP_A-8486327
AA
BCL11B
SNP_A-2220490
AB
BCL11A
SNP_A-8604506
AA
BCL11B
SNP_A-4236301
AB
BCL11A
SNP_A-8614089
AA
BCL11B
SNP_A-8306520
BB
BCL11A
SNP_A-1806377
AA
BCL11B
SNP_A-8590478
BB
BCL11A
SNP_A-1963490
AA
BCL11B
SNP_A-8648055
BB
BCL11A
SNP_A-2206361
AB
BCL2
SNP_A-8373256
BB
BCL11A
SNP_A-2290977
AB
BCL2
SNP_A-8459936
BB
BCL11A
SNP_A-2298230
BB
BCL2
SNP_A-2105995
BB
BCL11A
SNP_A-8405820
BB
BCL2
SNP_A-8300759
BB
BCL11A
SNP_A-8539762
BB
BCL2
SNP_A-8556901
BB
BCL11A
SNP_A-8552693
BB
BCL2
SNP_A-8589458
BB
BCL11A
SNP_A-8499288
AA
BCL2
SNP_A-8366974
BB
BCL11A
SNP_A-8526989
AA
BCL2
SNP_A-8453135
BB
BCL11A
SNP_A-8575285
AA
BCL2L11
SNP_A-8657905
AA
BCL11A
SNP_A-1963493
BB
BCL2L13
SNP_A-2232330
AA
BCL11A
SNP_A-1963495
BB
BCL2L13
SNP_A-4285381
AA
BCL11A
SNP_A-2128255
AA
BCL2L13
SNP_A-8370980
AA
BCL11A
SNP_A-2299478
AA
BCL2L13
SNP_A-8592524
AA
BCL11A
SNP_A-2303142
BB
BCL2L14
SNP_A-1950377
AA
BCL11A
SNP_A-8355097
BB
BCL2L14
SNP_A-2034823
AA
BCL11A
SNP_A-8321921
BB
BCL2L14
SNP_A-2043795
AA
BCL11A
SNP_A-8521868
BB
BCL2L14
SNP_A-8459194
AA
BCL11A
SNP_A-8694128
AA
BCL2L14
SNP_A-2073949
BB
BCL11B
SNP_A-1847830
AA
BCL2L14
SNP_A-8328445
BB
BCL11B
SNP_A-2003974
AA
BCL2L14
SNP_A-8424479
BB
BCL11B
SNP_A-2051006
AA
BCL2L14
SNP_A-8645009
BB
BCL11B
SNP_A-2294605
AA
BCL2L2
SNP_A-1869993
BB
BCL11B
SNP_A-8362067
BB
BCL8
SNP_A-2118582
BB
BCL11B
SNP_A-8319820
AB
BCL9
SNP_A-8383182
BB
BCL11B
SNP_A-8477903
BB
BCL9
SNP_A-1895400
BB
BCL11B
SNP_A-8488495
BB
BCL9
SNP_A-1965991
BB
BCL11B
SNP_A-8637832
BB
BCL9
SNP_A-2182811
BB
BCL11B
SNP_A-8635053
BB
BCL9
SNP_A-4194580
AA
BCL11B
SNP_A-2043196
BB
BCL9
SNP_A-8448712
AA
BCL11B
SNP_A-2253785
AA
BCL9
SNP_A-1966002
AA
BCL11B
SNP_A-2169833
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IL4, IL23R, IL33, IL15RA and the Interleukins
The Interleukins are a family of cytokines. The majority of interleukins are synthesized by helper
CD4 T lymphocytes but are also produced by monocytes, macrophages, and endothelial cells. Their
major function is to promote the development and differentiation of T and B cells, and other
hematopoietic cells.
IL15RA (ILRα)
Interleukin 15 (IL-15) is integral to the development and optimal functioning of NK and CD8+
memory T cells (Ma et al., 2006). Similar in structure to IL-2, both cytokines are stimulated through IL2/IL-15Rβγc signaling receptors and unique alpha chain subunits (IL-2Ra andIL-15Ra) (Han et al., 2011).
IL-2’s role is to maintain CD4+CD25+ T-regulatory cells and a process known as activation-induced cell
death (AICD) which leads to the elimination of activated T-cells (Han et al., 2011). IL-15 inhibits AICD
and supports a continued immune response (Fehniger et al., 2002).
IL23R
Multiple sources have associated SNP_A-1946676 (rs11209026) and rs753051 with Psoriatic
Arthritis formation (Cargill et al., 2007; Liu et al., 2008; Filer et al., 2008; Rahman et al., 2009;
Huffmeiser et al., 2009), an inflammatory disease defined as have at least three of the following:
current psoriasis (assigned a score of 2; all other features were assigned a
score of 1), a history of psoriasis (unless current psoriasis was present), a family
history of psoriasis (unless current psoriasis was present or there was a
history of psoriasis), dactylitis, juxtaarticular new bone formation, rheumatoid
factor negativity, and nail dystrophy. (p. 2665)
by the Classification Criteria for Psoriatic Arthritis (CASPAR) (Taylor et al., 2006). SNP_A-1946676
(rs11209026) is found within the PG (BB) Data Set while rs753051 is not. SNP_A-1946676 causes an
Arg381Gln non-synonymous substitution and has been linked to Crohn’s Disease (Duerr et al., 2006),
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Psoriasis (Cargill et al., 2007), and Psoriatic Arthritis (Cargill et al., 2007; Filer et al., 2008; Rahman et al.,
2009; Huffmeier et al., 2009) although some studies have demonstrated variations in expression
between ethnic groups (Catanoso et al., 2013).
SNP_A-1946676 is located on Chromosome 1 in position 67,705,958 in cytoband 31.3. It is an
A/G substitution and results in missense in transcripts NM_144701, ENST00000347310 (transcript length
2912, protein length 629), ENST00000441823 (Retired by Ensembl), ENST431791 (Retired by Ensembl),
and ENST00000395227 (transcript length 1962, protein length 374). It is located in a 3’ UTR in
transcripts ENST00000540911 (Retired by Ensembl) and ENST00000540775 (Retired by Ensembl), a CDS
in transcript ENST00000425614 (transcript length 1292, protein length 391) and multiple introns.
IL33
IL-33 is a cytokine whose effects are mediated through the stimulation of receptors IL-1 receptor
like 1 (IL1RL1) or T1/ST2 or co-receptors such as the IL-1 receptor accessory protein (IL1RAcP), both of
which belong to the Toll/IL-1 receptor (TIR) superfamily (Chackerian et al., 2007). IL-33 is known to
induce expression of IL-5 and IL-13 in vitro and upregulation has been correlated with increased
eosinophils and serum immunoglobulins in vivo (Schmitz et al., 2005) in addition to the activation and
maturation of human mast cells (Allakhverdi et al., 2007; Pushparaj et al., 2009). In humans with atopic
dermatitis, IL-33 has been shown to be elevated to ten times the normal amount expressed in
asymptomatic dermal tissue (Pushparaj et al., 2009). Studies of asthma (Hayakawa et al., 2007),
rheumatoid arthritis (Xu et al., 2008), multiple sclerosis (Li et al., 2012) and anaphylaxis (Pushparaj et al.,
2009) have implicated IL-33 as a potential factor in disease progression. IL-33 seems to have variable
expression dependent upon unknown factors as it has shown to be protective against inflammatoryrelated disorders such as atherosclerosis (Miller et al., 2008) and hepatitis (Volarevic et al., 2012) yet
indicates a predisposition to Alzheimer’s (Chapuis et al., 2009), asthma (Gudbjartssen et al., 2009; Bosse
84
et al., 2009; Wu et al., 2010), nasal polyposis (Buysschaert et al., 2010), allergic rhinitis (Castano et al.,
2009), and atopic dermatitis (Shimizu et al., 2005). Multiple studies have documented the upregulation of IL-33 in the colonic mucosa of patients diagnosed with inflammatory bowel disease,
particularly those with ulcerative colitis (Seidelin et al., 2010; Beltran et al., 2010; Pastorelli et al., 2010;
Kobori et al., 2010; Sponheim et al., 2010). In the PG Data Set, IL-33 SNPs are encoded in introns or
upstream of the gene itself suggesting that perhaps these particular SNPs play a role in IL-33 regulation.
IL4R
Cytokine IL-4, like the other members of its family, regulates and activates T cells. It initiates its
activity through the binding of IL4R which is composed of at least two subunits to form a complex with
“novel binding affinity” (p. 2663) that is essential for proper IL4 cell signaling transduction (Zurawski et
al., 1993). The receptors for IL-4 and IL-13 work synergistically signaling through the Jak-Stat pathway.
Kelly-Welch et al. (2003) proposes that polymorphisms near the docking sites of other interacting
molecules may play significant roles in allergy and asthma. Additionally, in a study of IL4R mutations by
Hershey et al. (1997), it was found that an arg576 mutation in IL4R (not found in the PG Data Set) was
associated with atopy.
While many SNPs from the PG DataSet are related to the Interleuking family, only those of
interest are found in Tables 27-31.
Table 27: The Interleukin genes in which SNPs are coded as CDS, missense, UTR3, or missense
Gene Name
IL15RA
IL23R
IL33
IL4R
Gene Description
interleukin 15 receptor, alpha
interleukin 23 receptor
interleukin 33
interleukin 4 receptor
PG SNP relationship to gene
CDS, intron
CDS, intron, missense, UTR3, downstream
upstream, intron
intron, missense, synon
85
Table 28: The IL33 PG SNPs
PG List
Gene
SNP ID
Relationship to Gene
AB
IL33
SNP_A-1903411
downstream
BB
IL33
SNP_A-4226152
intron
BB
IL33
SNP_A-8281683
intron
BB
IL33
SNP_A-8391474
downstream
BB
IL33
SNP_A-8655817
downstream
AA
IL33
SNP_A-2014021
intron
AA
IL33
SNP_A-2247428
downstream
AA
IL33
SNP_A-8539475
downstream
Table 29: IL15RA PG SNP
PG List
AA
Gene
IL15RA
SNP ID
SNP_A-8403129
Relationship to Gene
intron, CDS
Table 30: The IL23R PG SNPs
PG List
BB
BB
BB
BB
BB
BB
BB
BB
AA
Gene
IL23R
IL23R
IL23R
IL23R
IL23R
IL23R
IL23R
IL23R
IL23R
SNP ID
SNP_A-1857631
SNP_A-1914207
SNP_A-1916487
SNP_A-1946676
SNP_A-2131581
SNP_A-2084144
SNP_A-8371374
SNP_A-8624598
SNP_A-2131744
Relationship to Gene
intron
intron
intron
missense, UTR-3, CDS, intron
intron
intron
intron
intron
intron
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Table 31: The IL4R PG SNPs
PG List
AB
AB
AB
AB
BB
BB
BB
BB
AA
AA
AA
Gene
IL4R
IL4R
IL4R
IL4R
IL4R
IL4R
IL4R
IL4R
IL4R
IL4R
IL4R
SNP ID
SNP_A-8364560
SNP_A-8322768
SNP_A-8393504
SNP_A-8443169
SNP_A-2138211
SNP_A-2231835
SNP_A-2309558
SNP_A-8698067
SNP_A-8310173
SNP_A-8448573
SNP_A-8473405
Relationship to Gene
intron
intron
intron
intron
intron
intron
intron
intron
intron
missense, synon
intron
PLCG2
PLCG2 is a member of the phospholipase C family that catalyzes the hydrolysis of phospholipids
to yield diacylglycerols (NCBI Aceview). Deletions in this gene are linked to two OMIM records:
Autoinflammation, antibody deficiency, and immune dysregulation syndrome (614878) and Familial cold
autoinflammatory syndrome 3 (614465). RefSeq annotates one representative, but other sources
indicate as many as 25 spliced variants. NCBI’s Aceview denotes 57 introns, 29 mRNAs, 14 probable
alternative promotors, eleven non-overlapping terminal exons, and eight alternative polyadenylation
sites. PLCG2 is suspected to be involved in leukemia, immune deficiency disorders, multiple intracellular
signaling pathways, and signal transduction within the cell.
SNP_A-2142638 is located in position 81922813 in cytoband q23.3 on chromosome 16 as an A/G
variant. Affymetrix lists this SNP as resulting is two missense transcripts (NM_002661 and
ENST00000359376). ENST00000359376 is 4308 base pairs in length and codes for a protein 1265 amino
acids in length (Ensembl, March 2013). Table 32 shows the SNPs causing missense mutations in protein
sequences in addition to those SNPs located within introns.
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PLCG2 is a phosphoinositide-specific member of the phospholipase C (PI-PLC) family and
functions in the complicated web of cell signaling and immune response. Specifically, PLC is responsible
for converting phosphatidylinositol 4,5-bis-phosphate (PIP2) into diacylglycerol (DAG) and inositol 1,4,5trisphosphate (IP3) causing the release of calcium stores from the endoplasmic reticulum. The release of
calcium can activate the release of reactive oxygen species (ROS) from the mitochondria leading to the
assembly of the NLRPR inflammasome. This complex further initiates pyroptosis or the production of IL1β, a potent cytokine involved in inflammatory recruitment (Haneklaus et al., 2013). The PLCG2 pathway
is shown in Figure 46.
Fig. 46: The PLCG2 signaling pathway (From: Haneklaus et al., 2013; reprinted from Current Opinion
in Immunology, 2013, 25:40-45, with permission from Elsevier).
PLCG2, also known as PLCγ2, is integrated into the complex interactions between immunity
regulators. It is highly expressed and is required for proper functioning of immune cells such as B cells,
NK cells, mast cells, macrophages, and platelets (Hiller & Sundler, 2002; Abdel-Halim et al., 2005; Wang
et al., 2000; Wen et al., 2002). While PLCLγ-1 seems to play a major functional role in T cells (Yu et al.,
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2005), PLCγ2 is more active in B cells (Kurosaki et al., 2000; Marshall et al., 2000). Localized in the
cytoplasm but recruited to the membrane when stimulated by the B cell receptor (BCR) signalosome,
PLCGγ2 hydrolyzes PIP2 to generate DAG and IP3. Downstream DAG targets act to liberate Ca2+ into the
cytoplasm. It is now known that PLCGγ2 can also mediate external Ca2+ entry independent of its
catalytic potential. It is has been suggested that this mediation is accomplished through unknown
protein-protein interactions (Patterson et al., 2002; Putney, 2002; Putney et al., 2001; van Rossum et al.,
2005).
In a 2005 study of murine PLCGγ2, researchers described the effects of a mutation within the
PLCGγ2 genomic region named PLCGγ2Ali5 (Yu et al., 2005). The variant identified is a single nucleotide
polymorphism resulting in a single amino acid substitution of aspartic acid with glycine at position 993
(D993G). The aspartic acid residue is located within the catalytic domain, or so called “ridge”
surrounding the active site opening of PLCγ2 (Ellis et al., 1995). While Ellis et al. (1995) has suggested
that this alteration may have an inhibitory impact on enzyme activity through the prevention of
membrane interaction, Yu and colleagues demonstrated that the mutation may actually cause the
protein to remain for longer periods at the membrane and enhance its activity post-activation (2005).
Mice homozygous for the mutation have deformed footpads, dermatitis resulting in exudite, and chronic
inflammation affecting the bone with results of severe arthritis. The infiltrate collected from regions
experiencing extreme dermatitis included granulocytes, macrophages, lymphocytes, mast cells, and
eosinophils (Yu et al., 2005). Interestingly, mice that were heterozygous for the mutation were not
phenotypically similar to the Ali5 mice although they did show signs of glomerulonephritis. The Ali5
mutation is located at the surface region of the catalytic domain of PLCγ2 and removes its negative
charge. This domain’s responsibility is normally to restrict membrane interaction and thus modulate
PLCγ2 activity through repulsion to the negatively charged inner plasma membrane. The mutation
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therefore may reduce repulsion and stabilize the protein when it is in membrane proximity (Yu et al.,
2005).
Table 32: PG SNPs with PLCG primary gene associations
BB
BB
BB
BB
BB
BB
BB
AA
AA
AA
AA
AA
AA
AA
AA
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
PLCG2
SNP_A-1793103
SNP_A-1959951
SNP_A-2142638
SNP_A-8291405
SNP_A-8312046
SNP_A-8404658
SNP_A-8637868
SNP_A-1802064
SNP_A-2004923
SNP_A-8294826
SNP_A-8296123
SNP_A-8356844
SNP_A-8454500
SNP_A-8486115
SNP_A-8486609
intron
intron
missense
intron
intron
intron
intron
intron
intron
intron
intron
intron
intron
intron
intron
Open Reading Frames
Several PG SNPs were found within opening reading frames in various regions of the genome.
Open reading frames (ORFs) are called thus because they usually begin with a start codon and end with
a stop codon. ORFs do no always indicate the presence of a true gene, but they can. An ORF looks like a
coding sequence but as to whether it is transcribed is sometimes unknown and is likely to vary for each
individual ORF. Gene expression regulatory elements, as some ORF’s may be, function at the
transcription, translation, and protein levels. As mentioned previously, translation is often controlled by
sequences in the 5’ and 3’ UTR’s of coding sequences. In addition, upstream open reading frames
(uORFs) have been identified as regulatory elements for downstream translation targets (Sachs et al.,
2006). It was once believed that uORFs were present in less than 10% of mammalian genes (Kozack,
90
19872), however recent genome-wide association studies have revealed that uORFs exist for
approximately 50% of human transcripts (Iacono et al., 2005; Calvo et al., 2009) and that some of them
cause a reduction in translation (Calvo et al., 2009). Alderete et al. (1999) reported that the expression
of some uORFs were correlated with genetic polymorphisms while others have reported correlation
with cellular stress (Watatani et al., 2008; Raveh-Amit et al., 2009) and disease presentation (Wen et al.,
2009). Although a direct causal relationship has yet to be fully established, one study of uORFs located
upstream of the McKusick-Kaufman Syndrom (MKKS) gene suggested that uORFs were related to a
repression of MKKS expression (Akimoto et al., 2013). Additionally, in a recent study of esophageal
squamous cell carcinoma, Wei et al. demonstrated that a SNP located in open reading frame C20orf54
modifies susceptibility to the disease (2013) suggesting that variations within ORFs can affect gene
expression.
In the PG Data Set, 2,090 SNPs are listed as related to an open reading frame.Certainly, it would
be interesting to examine the genes neighboring these open reading frames in either an upstream or
downstream location, however that process is beyond the scope of this work
The TAP genes
Two SNPs located in the PG data set are associated with TAP (transporter 2, ATP-binding
cassette) binding proteins as shown in Table 33. SNP_A-8652719 and SNP_A-8445779 reside in the
p21.32 region of chromosome 6 resulting in missense of multiple TAP transcripts. SNP_A-8657219 is a
C/T variant found homozygously in the TAP2 gene causing missense in transcripts ENST00000374899,
ENST00000374897, ENST00000464100, and ENST00000452392 with ENST00000556934 listed as
Retired.
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Table 33: PG SNPs associated with TAP (transporter 2, ATP-binding cassette) binding proteins
AA
AB
TAP2
transporter 2, ATP-binding cassette, sub-family B
(MDR/TAP)
TAP binding protein (tapasin)
SNP_A-8652719
missense,
exon
TAPBP
SNP_A-8445779 missense,
intron, exon
SNP_A-8445779 is a C/G variant in the gene encoding TABP (tapasin binding protein) presenting
heterozygously within the PG data set (Table 33). This particular SNP causing missense in transcripts
ENST00000434618, ENST00000475304, ENST00000489157, ENST00000426633, and ENST00000465592
with ENST00000458089 listed as Retired. ENST00000480730 and ENST00000437116 are described as
exonic in location by Affymetrix while Ensembl as retained introns.
TAP is an ATP-binding cassette transporter that translocates peptides from the cytoplasm to
major histocompatibility complexes (MHC) class I molecules in the endoplasmic reticulum. Various
studies have demonstrated that genes related to MHC class I expression are related to antigen
presentation to T-cells (OMIM 170261, 2012). Mutations in TAP2 have been linked to a condition known
as Bare Lymphocyte Syndrome in which a malfunction in the TAP2 gene causes human leukocyte (HLA)
type I antigen deficiency affecting the cytotoxicity of Natural Killer (NK) cells and causing reduced
numbers of alpha-beta T-cells (de la Salle et al., 1994). In the research of de la Salle et al., two of five
children in a Moroccan family were homozygous for a C-to-T change in the TAP2 gene, resulting in an
arg253-to-stop substitution which resulted in the disease (1994). Originally mistaking disease symptoms
for Wegener’s Granulomatosis, Moins-Teisserenc et al. (1999) described five patients with chronic
necrotizing granulomatous lesions, small-vessel vasculitis, and recurrent respiratory-tract infections. In
two of the five patients in Moins-Teisserence et al.’s study, adenosine (A) was deleted at codon 326
causing a frameshift and a premature stop codon (1999). Analysis of cDNA revealed homozygous
presentation for the TAP2 null allele, whereas the symptom-free parents of one patient were
heterozygous.
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Certainly the phenotypes described as Bare Lymphocyte Syndrome are suggestively similar to
symptoms of PG making the TAP genes a viable candidate in PG expression.
Cytochrome p450
The SNPs from the PG Data Set connected with CYP26B1 are located downstream, upstream or
in intronic regions of DYS, the gene encoding dysferlin with the exception of SNP_A-2280660 which
causes missense. Mutations in DYS are associated with Myoshi Muscular Dystrophy (OMIM 254130),
Muscular dystrophy, limb-girdle, type 2B (OMIM 253601), and Myopathy, distal, with anterior tibial
onset (OMIM 606768), however one SNP from the PG Data Set has been linked to atherosclerosis, an
inflammatory disorder of the blood vessels. While OMIM (Johns Hopkins University, March 2013) links
this SNP to Craniosynostosis with radiohumeral fusions and other skeletal and craniofacial anomalies
(OMIM 614416), other research has suggested that CYP26B1 polymorphism SNP_A-2280660 increases
the gene’s ability to catabolize retinoic acid. In atherosclerotic lesions, retinoic acid ameliorates
inflammation and promotes resolution making this SNP a viable candidate for contribution to the
increased inflammatory response present in PG.
SNP_A-2280660 (rs2241057) is located in position 72,361,960 in the p13.2 region of
chromosome 2. In the PG Data Set it is present as an AA SNP. It causes missense in CYP26B1 transcripts
ENST00000001146, ENST00000412253, and ENST00000546307 (Ensembl.org). Table 34 shows all of the
PG SNPs with Cytochrome P450 gene family associations. With the exception of SNP_A-2280660 and
SNP_A-8539055 (located in CYP4F2) which both result in missense. While SNP_A-8539055 has no OMIM
associations, NCBI’s Aceview suggests possible connections to brain ischemia, hypertension, liver
neoplasms, and stroke (retrieved June 2013).
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Table 34: PG SNPs with Cytochrome P450 gene associations
Gene
Gene Description
SNP ID
CYP26B1 cytochrome P450, family 26, subfamily B, polypeptide 1 SNP_A-2006472
SNP_A-4231963
SNP_A-2313026
SNP_A-4253592
SNP_A-8486534
SNP_A-8601186
SNP_A-8690036
SNP_A-1810490
SNP_A-2186413
SNP_A-2280660
SNP_A-2293950
SNP_A-4205639
SNP_A-8474562
SNP_A-8425940
SNP_A-8657793
SNP_A-8689957
SNP_A-8693552
CYP4F2
cytochrome P450, family 4, subfamily F, polypeptide 2
SNP_A-4259822
SNP_A-8539055
PG List
BB
BB
BB
BB
BB
BB
BB
AA
AA
AA
AA
AA
AA
AA
AA
AA
AA
BB
BB
Selectin Genes
The SELL gene is a member of the selectin family that is involved in leukocyte adhesion and
rolling at inflammation sites. Acting as a homing device for leukocytes, its gene product is essential for
the binding of leukocytes to endothelial tissue. Dysfunction within the gene has been related to
Immunoglobin A nephropathy, autoimmune disorders, Diabetes Mellitus, and cardiovascular disease
(NCBI Aceview, retrieved June 2013).
A 1993 study by Mayadas et al. demonstrated that P-selectin deficient mice encountered
abnormal leukocyte behavior including elevated numbers of neutrophils, severely diminished leukocyte
rolling in mesentery venules, and delayed neutrophil recruitment to induced inflammation sites. Soluble
P-selectin levels are elevated in cases of atherosclerosis (Burger and Wagner, 2003) and are indicative of
increased risk for future cardiovascular events (Hillis et al., 2002; Ridker et al., 2001). In mice, it is also
94
known to be involved in bronchoconstriction and inflammation in allergic airway reactivity (Lukacs et al.,
2002).
Forlow et al. (2002) reported that mice lacking functional SELP and SELL (SELE) kept within
pathogen-free barrier conditions encountered high circulating neutrophil counts and developed severe
ulcerative dermatitis, conjunctivitis, and lung pathology and subsequent early death. Hypothesizing that
the disease phenotype was caused by defective lymphocyte functioning, the researchers crossed the
SELP and SELL deficient mice with RAG1 mice which lack mature B and T lymphocytes. The triple
knockout mice had high circulating neutrophil counts, but did not present with the severe disease
symptoms of lung pathology and dermatitis that was encountered with the double knockouts. Based on
these results, Forlow and colleagues concluded that the disease phenotype, but not the elevated
neutrophil counts in SELP and SELL knockouts, were the result of lymphocyte function (2002).
In the PG data set, SNPs are found within SELL, SELP, and SELPLG coding regions although only
SELL and SELPLG are found within the Primary Candidate/Master List. SNPs found within SELP were
included in this discussion due to known gene associations. As shown by Tables 35 and 36, in the PG
Data Set, we find SNPs causing missense in SELL and SELPLG, but not within SELP. SNPs found within
SELP are located upstream, downsteam or within intronic regions of the gene. Through the
aforementioned findings of research conducted on members of this gene family, it is conceivable that
these SNPs may contribute to the PG disease process.
95
Table 35: PG SNPs with SELL and SELPLG (Primary Candidate/Master genes) gene associations
PG SNP List
BB
BB
BB
Gene Name
SELL
SELL
SELL
SNP ID
SNP_A-1905707
SNP_A-2081388
SNP_A-2203047
Gene Description
selectin L
selectin L
selectin L
BB
AA
BB
SELL
SELL
SELPLG
SNP_A-8685447
SNP_A-2261141
SNP_A-8437819
selectin L
selectin L
selectin P ligand
AA
SELPLG
SNP_A-2188713
selectin P ligand
SNP location
intron
intron
upstream (SELP
downstream (SELL)
intron
missense
downstream
(SELPLG)
missense
Table 36: PG SNPs with SELP (not found in Primary Candidate/Master genes) gene
PG SNP List
BB
Gene Name
SELP
SNP ID
SNP_A-2268447
Gene Description
selectin P ligand
BB
AA
SELP
SELP
SNP_A-8614156
SNP_A-8596185
selectin P ligand
selectin P ligand
SNP location
upstream (SELP)
downstream (SELL)
intron
intron
Solute Carriers
Solute carriers are membrane associated molecules whose purpose is to transport materials
across the cell membrane. There are 300 known solute carriers divided into 52 families (Hediger et al.,
2004). Within the Primary Candidate/Master List, there are 21 solute carriers with 136 representative
PG SNPs. Upon analysis of the 136 SNPS via Affymetrix NetAffyx (April 2003), no obvious connections
could be made to PG etiology. Within the entire PG Data Set, there are 1,135 SNPs associated with 176
separate solute carrier genes. While only a cursory analysis was conducted on the entire solute carrier
population found within the PG Data Set, one particular solute carrier gene was discovered with
potential links to auto-inflammatory disease. The SLC11A1 protein, formerly known as NRAMP1 (natural
resistance associated macrophage protein 1), is localized in the endosomal and lysosomal compartment
of quiescent macrophages (Canonne-Hergaux et al., 2002; Govoni et al., 1999; Gruenheid et al., 1997;
96
Searle et al., 1998). Functioning as a divalent cation transporter, SLC11A1 regulates (Atkinson et al.,
1998) and is regulated by (Atkinson et al., 1997), the concentration of intracellular ions, particularly iron.
SLC11A1 exhibits many effects on macrophage activity including the initiation of processes
leading to the generation of nitric oxide(NO), upregulation of MHC class II expression, increased
production of proinflammatory cytokines (notably interleukin [IL]-1b and tumor necrosis factor [TNF]-a),
production of reactive oxygen species (ROS), and upregulation of KC, a member of the IL-8 family, which
attracts neutrophils (Karupiah et al., 2000; Blackwell et al., 1996; Roach et al., 1994; Blackwell et al.,
1994). In a 2008 mouse study, polymorphisms in the SLC11A1 gene, particularly those located in the
promotor region, were shown to exacerbate or protect against the development of auto-immunity
dependent upon the variant (O’Brien et al., 2008). According to NCBI’s Aceview (June 2013), mutations
in SLC11A1 have been linked with tuberculosis, leprosy, rheumatoid arthritis and Crohn’s Disease. In the
PG Data Set, SNP_A-8499530 (rs7608307) is listed as a BB SNP in association with SLC11A1 as its primary
gene association. This SNP is located 4,234 bp upstream of transcript ENST00000468221 and 9,696 bp
downstream of C2orf6.
NLR’s
In earlier portions of this document, the NLR (Nod-like receptor) family of membrane receptors
that initiate the construction of the inflammasome upon engagement were discussed in detail. The
inflammasome, aptly named, upregulates the production of inflammatory cytokines thereby
exacerbating the immune response. Mutations within NLR genes have been linked to multiple
inflammatory diseases.
The NLR’s are divided into three groups dependent upon their N-terminal domain: those
containing a caspase recruitment domain (CARD) and a pyrin domain (NLRP), those that contain only the
CARD domain (NLRC) and those containing a baculovirus inhibitor repeat (BIR).
97
NLRP3 is of significance in Cold Auto-inflammatory Syndrome and Muckle-Wells Syndrome.
NLRP3 encodes the protein cryopyrin which in addition to being integral to the development of the
inflammasome, has also been suggested to function as a signaling protein in the regulation of apoptosis
(Hoffman et al., 2001). Mariathasan et al. (2006) demonstrated that cryopyrin-deficient macrophages
could not activate CASP1 via Toll-like receptors plus ATP. While most of the literature reports the
effects of nullifying the NLRP3 gene, some studies have reported hyperactivation due to genetic
mutation. Meng et al. (2009) found that mice with a mutant NLRP3 gene produced elevated levels of
IL1β upon stimulation in addition to skin inflammation characterized by neutrophil infiltration.
Additionally, Jin et al. (2010) found that a mutation in NLRP1 was connected to Vitiligo-Associated
Multiple Autoimmune Disease Susceptibility 1 (OMIM606579). NLRP7 has been implicated in
hydatidiform mole, a disorder characterized by recurrent spontaneous abortion (OMIM 609661)
(Deveault et al., 2009; Djuric et al., 2006). It is believed that inflammation provides the key to the
reproductive problems characterized by this disorder.
In the PG Data Set, SNP_A-8603308 is found homozygously (BB) within exonic and intronic
regions of the NLRP3 gene. No known protein alterations occur as a result of this A/G variant found in
the q44 region of chromosome 1. The exon is located within transcript ENST00000474792 which is
listed as a processed transcript with no protein product by Ensembl. All other PG SNPs affecting NLRP3
are located upstream or within introns of the gene. SNP_A-2297859 is located in a UTR5-initiatior region
of NLRP7. It is an A/G variant found in the q13.2 region of chromosome 19 and presents itself
homozygously (BB) in the PG Data Set. Perhaps due to updates within the Affymetrix Database, the BB
PG Data Set lists this SNPs primary gene association as NLRP2 while Affymetrix (June 2013) lists it as
NLRP7. Ensembl lists this SNP as affective to transcript ENST00000446217. As mentioned previously in
this chapter, SNPs located within initiator sequences have been known to affect transcription. SNPs
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found within the PG DataSet that are located within NLR family members can be found in Tables 37 and
38.
Table 37: NLR transcripts possibly affected by PG SNPs
SNP ID
SNP_A-4280126
SNP_A-1801723
SNP_A-8603308
SNP_A-2297859
Relationship to Gene
UTR-3
UTR-3
exon
utr5-init
Gene
NLRP4
NLRP9
NLRP3
NLRP7
Gene Description
NLR family, pyrin domain containing 4
NLR family, pyrin domain containing 9
NLR family, pyrin domain containing 3
NLR family, pyrin domain containing 7
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Table 38: Members of the NLR family in which PG SNPs are found within introns,
upstream, or downstream regions
PG List
Gene
SNP ID
BB
NLRC5
SNP_A-2099381
BB
NLRC5
SNP_A-2220604
AA
NLRC5
SNP_A-4249804
AA
NLRC5
SNP_A-8565125
AA
NLRC5
SNP_A-8490007
AA
NLRP1
SNP_A-8424279
BB
NLRP10 SNP_A-8485607
AA
NLRP10 SNP_A-8318524
BB
NLRP11 SNP_A-8313379
AA
NLRP11 SNP_A-8500456
BB
NLRP13 SNP_A-1850840
BB
NLRP13 SNP_A-4207794
BB
NLRP13 SNP_A-4218369
BB
NLRP13 SNP_A-8314620
BB
NLRP13 SNP_A-8653877
AA
NLRP13 SNP_A-4192667
AA
NLRP13 SNP_A-4261560
AA
NLRP14 SNP_A-8336429
BB
NLRP2
SNP_A-2297859
AB
NLRP3
SNP_A-8361252
AB
NLRP3
SNP_A-8508121
BB
NLRP3
SNP_A-4242364
BB
NLRP3
SNP_A-8603308
AA
NLRP3
SNP_A-8684066
BB
NLRP4
SNP_A-8614766
AA
NLRP4
SNP_A-4280126
AA
NLRP5
SNP_A-8433067
BB
NLRP6
SNP_A-1907086
BB
NLRP6
SNP_A-8481284
AA
NLRP6
SNP_A-2033704
AA
NLRP6
SNP_A-2214054
AA
NLRP6
SNP_A-8654515
BB
NLRP8
SNP_A-1911332
BB
NLRP8
SNP_A-2306136
AA
NLRP8
SNP_A-8645405
BB
NLRP9
SNP_A-1780797
BB
NLRP9
SNP_A-1801723
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“Eat me” Signals
Apoptotic cells exhibit messages on their cellular surfaces alerting macrophages that they are
ready for disposal. Phosphatidylserine synthase 1 (PTDSS1) is the main “eat me” signal for senescing
cells and is discussed in detail in earlier portions of this document. Found within the PG Data set both
homozygously and heterozygously, three SNPs are located in intronic regions of the PTDSS1 gene. No
known associations between these particular SNPs and the functional nature of PTDSS1 have been made
as of this writing. SNPs located in the PTDSS1 gene are listed in Table 39.
PG List
AA
AB
AA
Table 39: PG SNPs located in PTDSS1 genic regions
SNP ID
Relationship to Gene
Gene
Gene Description
SNP_A-1907476 intron
PTDSS1 phosphatidylserine synthase 1
SNP_A-8341715 intron
PTDSS1 phosphatidylserine synthase 1
SNP_A-1786597 intron
PTDSS1 phosphatidylserine synthase 1
Colony Stimulating Factor Genes
Macrophage Colony Stimulating Factor (CSF) protein stimulates hematopoietic stem cells to
differentiate into macrophages or other derivative cell types. CSF1, its receptor (CSF1R), and an
associate receptor (CSF2RB) incur polymorphisms within the PG Data Set in introns and regions
upstream from their associate gene. CSF2RB functions as part of a receptor subunit for IL5, CSF, and IL3.
Mutations within this gene have been linked to surfactant metabolic pulmonary dysfunction (OMIM
614370). In the PG Data Set, SNPs are located in upstream and intronic regions of CSF2RB. A secondary
gene association to CSF2RB SNPs is Neutrophil Cytosolic Factor 4 (NCF4) which has been indicated in
Type 3 Granulomatosis Disease (OMIM 613960). SNPs are also located in upstream and intronic regions
of CSF1, a gene that has been associated with Leukoencephalopathy (OMIM 221820) and is downstream
of the Short Stature Homeobox Gene (SHOX) which has been linked to Langer mesomelic dysplasia
101
(OMIM 249700), Leri-Weill dyschondrosteosis (OMIM 127300) and Idiopathic familial short stature
(OMIM 300582). PG SNPs located in CSF family genes are listed in Table 40.
SNP ID
SNP_A-8491654
SNP_A-8434093
SNP_A-1848362
SNP_A-8375219
SNP_A-1882364
SNP_A-4272174
SNP_A-2000292
SNP_A-8604982
SNP_A-8304338
Table 40: PG SNPs with associations to CSF1R genes
Relationship to Gene
Gene
Gene Description
intron
CSF1R
colony stimulating factor 1 receptor
upstream, intron
CSF1
colony stimulating factor 1 (macrophage)
upstream
CSF1
colony stimulating factor 1 (macrophage)
upstream
CSF2RB
colony stimulating factor 2 receptor, beta, lowaffinity (granulocyte-macrophage)
upstream
CSF1
colony stimulating factor 1 (macrophage)
upstream
CSF1
colony stimulating factor 1 (macrophage)
intron
CSF1
colony stimulating factor 1 (macrophage)
upstream
CSF1
colony stimulating factor 1 (macrophage)
intron
CSF2RB
colony stimulating factor 2 receptor, beta, lowaffinity (granulocyte-macrophage)
Limitations to this work
Bioinformatics offers many challenges. Considering that the Human Genome Project was
recently completed in 2003 and the ENCODE Project in 2007 (National Human Genome Research
Institute), it is obvious that this area of research is a fledgling, evolving field. An analysis utilizing
bioinformatics is only as effective as the programs and databases used to filter the large quantities of
data. Including probes, the PG data set consisted of over 65,000 rows of data. The idea that a blank cell
could be unknowingly included within that huge database is conceivable. When transitioning large data
sets between programs, formatting issues can result in lost, duplicated, or altered data. Within a data
set so large, it is sometimes impossible to be aware of these errors.
There are many laboratories all over the world working feverishly to add information to the
knowledge-base of the human genome. While the rapid addition of information is necessary, exciting,
and informative, it can also lead to error. Additions and changes to known annotations can occur so
102
rapidly as to alter one’s findings as soon as a study is completed. In this work, the Apoptosis Gene List
from NCBI downloaded between January and February 2013 consisted of 1,778 genes. By April 2013,
the same list was now composed of almost 2,700 genes. In the short span of three months, the analysis
of the PG data set was already outdated. This rapid advancement of scientific progress makes the
replication of research not impossible, but certainly dynamic.
The functional gene lists obtained from various organizations were used to measure the
proportion of genes within the PG data set, but may not have offered a complete picture. For example,
the NCBI Apoptosis List contained many of the caspases, but not all of them. As the caspase family
performs a pivotal role within the apoptotic pathway, it was inconceivable to this researcher to assess
the PG Data Set with an incomplete list. In fact, the creation of the Master List, composed of the NCBI
Apoptosis List, SABiosciences Apoptosis Array List, and the Affymetrix Human Immune and Inflammation
Array, was initiated by the incomplete nature of the NCBI list.
Another challenge in bioinformatics research is the multitude of gene identifiers used in
annotation. Different laboratories use different annotations resulting in confusion and error in the
conversion of gene IDs. In addition, some annotation methods are more inclusive than others and
include all potential transcripts, both known and predicted. Other annotation methods include only
what is safely known at the time of the analysis. These variations create a great variety of results
depending upon which system is chosen.
Lastly, this research was conducted using DNA collected from saliva samples in six PG patients.
Although PG is extremely rare and finding participants is difficult, this analysis offers little in the way of
statistical reassurance. In addition, no tissue samples were collected or other information about the
patients is known with regards to associated disorders or familial relationships in the development of
the disease. Many genes are often more or less actively expressed dependent upon the tissue that they
103
are found in. While I have indicated the possibility of many genes as PG causing candidates, it is
unknown as to whether these findings will translate into actual disease models. My hope is that this
research has helped to narrow the scope of genetic culprits at play in the development of PG,
broadened the scheme of functional relationships in association with PG to include the apoptotic
pathway, and indicate the need for further studies into the specific genes and SNPs identified
throughout this project.
Future Directions
This document outlines a research study in which the SNPs shared between six Northeastern
Ohio pyoderma gangrenosum (PG) patients were sorted and analyzed in an effort to uncover a pattern
within the similarities of the patients’ genetic codes. Any research into a debilitating disorder such as
PG must be focused on quicker diagnoses and potential treatment targets.
The results of this study identified 111 genes that were shared among patients in the PG Data
Set and that were Apoptotic genes, and Immune/Inflammatory genes. These 111 genes must be
further scutinized in an effort to identify possible biomarkers that may enable scientists to definitively
and effectively diagnose PG. As stated many times throughout this work, PG is often misdiagnosed or
the diagnosis is delayed due to similarities between PG and other disorders. Once biomarkers for PG are
known, it may be beneficial to look at these particular genes in relationship to other PG-like disorders,
especially neutrophilic dermatoses. These 111 genes must also be analyzed as potential drug targets.
This small cluster of genes may harbor a potential target for pharmaceuticals that can prevent and/or
alleviate PG symptoms.
104
References
Abdel-Halim SA, Jansson J, Eriksson P, Sirsjö A, Olofsson Kurosaki PS, Maeda A, Ishiai M, Hashimoto A,
Inabe K, Takata, M. Regulation of the phospholipase C-gamma2 pathway in B cells.
Immunol. Rev. 2005; 176: 19–29.
AceView: a comprehensive cDNA-supported gene and transcripts annotation, Genome Biology 2006,
7 (Suppl 1):S12.
Adams JM. Ways of dying: multiple pathways to apoptosis. Genes and Develoment 2003; 17: 2481–
2495.
Affymetrix Gene Chip Human Immune and Inflammation 9K SNP Kit.
http://www.affymetrix.com/estore/. Accessed November 2012 – June 2013.
Affymetrix Genome-Wide SNP 6.0 Array. http://www.affymetrix.com/estore/. Accessed November
2012 – June 2013.
Agostini L, Martinon F, Burns K, McDermott MF, Hawkins PN, et al.NALP3 forms an IL-1betaprocessing inflammasome with increased activity in Muckle-Wells autoinflammatory disorder.
Immunity 2004; 20: 319–325.
Akimoto C, Sakashita E, Kasashima K, Kuroiwa K, Tominaga K, Hamamoto T, Endo H. Translational
repression of the McKusick–Kaufman syndrome transcript by unique upstream open reading
frames encoding mitochondrial proteins with alternative polyadenylation sites. Biochimica et
Biophysica Acta 1830 (2013); 2728–2738
Akira S, Takeda K. Toll-like receptor signaling. Nat Rev Immunol 2004;4: 499–511.
Albert, ML, Kim JI, Birge RB. alphavbeta5 integrin recruits the CrkII-Dock180-rac1 complex for
phagocytosis of apoptotic cells. Nat. Cell Biol. 2000; 2:899–905. doi:10.1038/35046549
Alberts JH, Sams HH, Miller JL, King LE. Familial ulcerative pyoderma gangrenosum: a report of 2 kindred.
Cutis. 2002;69:427-430.
Alderete JP, S. Jarrahian, A.P. Geballe. Translational effects of mutations and polymorphisms in a
repressive upstream open reading frame of the human cytomegalovirus UL4 gene,
J. Virol.1999 ;73: 8330–8337.
Allakhverdi Z, Smith DE, Comeau MR, Delespesse G. Cutting edge: The ST2 ligand IL-33 potently
activates and drives maturation of human mast cells. J Immunol.2007; 179:2051-4.
Anderson CA, Boucher G, Lees CW, Franke A, D'Amato M, Taylor KD, et al. Meta-analysis
identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed
associations to 47. Nat. Genet. 2011; 43 (3): 246-252.
105
References
Asano K, Miwa M, Miwa K, Hanayama R, Nagase H, Nagata S, Tanaka M. Masking of
phosphatidylserine inhibits apoptotic cell engulfment and induces autoantibody production in
mice. J. Exp. Med. 2004; 200:459–467. doi:10.1084/jem.20040342
Ashkenazi A, Dixit VM. Death receptors: signaling and modulation. Science.1998; 281: 1305–1308.
Atkinson PG, Barton CH. Ectopic expression of Nramp1 in COS-1 cells modulates iron accumulation.
FEBS Lett 1998;425:239e42.
Atkinson PG, Blackwell JM, Barton CH. Nramp1 locus encodes a 65 kDa interferon-gamma-inducible
protein in murine macrophages. Biochem J.1997;325(Pt3):779-786.
Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr RH, Rioux JD. Genome-wide association defines more
than 30 distinct susceptibility loci for Crohn’s disease. Nature Genetics. 2008;40:955–62.
Behrends C, Sowa M, Gygi S, Harper JW. Network organization of the human autophagy system.
Nature. 2010; 466(7302): 68-76. Doi:10.1038/nature09204.
Beltran CJ, Nunez LE, Dıaz-Jimenez D, Farfan N, Candia E, et al. Characterization of the novel ST2/IL33 system in patients with inflammatory bowel disease. Inflamm Bowel Dis. 2010; 16: 1097–
1107.
Bennett ML, Jackson JM, Jorizzo JL, Fleischer AB, White WL, Callen JP. Pyoderma gangrenosum. A
comparision of typical and atypical cases with an emphasis on time to remission. Case review of
86 patients of 2 institutions. Medicine [Baltimore] 2000, 79:37-46.
Bergsbaken T, Fink SL, Cookson BT. Pyroptosis: host cell death and inflammation. Nature reviews
Microbiology 2009; 7: 99–109.
Bernstein CN, Blanchard JF, Rawsthorne P, Yu N. The prevalence of extraintestinal diseases in
inflammatory bowel disease: a population-based study. The American Journal of
Gastroenterology 2001; 96:1116-1122.
Bhat, RM. Pyoderma Gangrenosum: An update. Indian Dermatology Online Journal 2012; 3 (1):7-13.
Bhat RM, Nandakishore B, Sequeira FF, Sukumar D, Kamath GH, Martis J, et al. Pyoderma
Gangrenosum: An Indian perspective. Clinical and Experimental Dermatology 2011;36:242-7.
Bizzaro N, Pasini P, Ghirardello A, Finco B: High anti-golgi autoantibody levels: an early sign of
autoimmune disease? Clin Rheumatol 1999, 18:346-348.
Blackwell JM. Structure and function of the natural-resistance-associated macrophage protein
(Nramp1), a candidate protein for infectious and autoimmune disease susceptibility. Mol Med
Today 1996;2:205-211.
106
References
Blackwell JM, Barton CH, White JK, Roach TI, Shaw MA, Whitehead SH, et al. Genetic regulation of
leishmanial and mycobacterial infections: the Lsh/Ity/Bcg gene story continues. Immunol Lett.
1994;43:99-107.
Blomgran R, Brodin V, Verma D, Bergstro I, Soderkvist P, SjowallC , Eriksson P, Lerm M, Stendahl O,
Sarndahl E. Common Genetic Variations in the NALP3 Inflammasome Are Associated with
Delayed Apoptosis of Human Neutrophils . plosOne 2012.7:3:e31326.
Bosse Y, Lemire M, Poon AH, Daley D, He JQ, et al. Asthma and genes encoding components of the
vitamin D pathway. Respir Res.2009;10:98.
Botto M, Dell’Agnola C, Bygrave AE, Thompson EM, Cook HT, Petry F, Loos M, Pandolfi PP, Walport MJ.
Homozygous C1qdeficiency causes glomerulonephritis associated with multiple apoptotic
bodies. Nat. Genet. 1998; 19:56–59.
Bratton DL, Henson PM. Neutrophil clearance: when the party is over, clean-up begins. Trends in
Immunology. 2011; 32: 350–357.
Brenner M, Ruzicka T, Plewig G, Thomas P, Herzer P. Targeted treatment of pyoderma gangrenosum in
PAPA (pyogenic arthritis, pyoderma gangrenosum and acne) syndrome with the recombinant
human interleukin-1 receptor antagonist anakinra. British Journal of Dermatolgy
2009;161:1199–201.
Brown S, Heinisch I, Ross E, Shaw K, Buckley CD, Savill J. Apoptosis disables CD31-mediated cell
detachment from phagocytes promoting binding and engulfment. Nature 2002; 418:200–203.
Brunelle JK, Letai A. Control of mitochondrial apoptosis by the Bcl-2 family.
J. Cell Sci. 2009; 122: 437–441.
Brunsting LA, Goeckerman WH, O'Leary P. Pyoderma [ecthyma] gangrenosum: Clinical and
experimental observations in five cases occurring in adults. Arch Dermatol Syph.1930;22:655680.
Bundino S, Zina AM. Pyoderma gangrenosum associated with selective hereditary IgA deficiency.
Dermatologica. 1984;168:230-23.
Burger PC, Wagner DD. Platelet P-selectin facilitates atherosclerotic lesion development. Blood
2003;101: 2661-2666.
Buysschaert ID, Grulois V, Eloy P, Jorissen M, Rombaux P, et al. Genetic evidence for a role of IL33 in
nasal polyposis. Allergy.2010; 65: 616–622.
Callen JP. Pyoderma gangrenosum. Lancet .1998;351:581-5.
107
References
Calvo SE, Pagliarini DJ, MoothaVK. Upstream open reading frames cause widespread reduction of
protein expression and are polymorphic among humans, Proc. Natl. Acad. Sci. U.S.A. 2009; 106:
7507–7512.
Canonne-Hergaux F, Calafat J, Richer E, Cellier M, Grinstein S, Borregaard N, et al. Expression and
subcellular localization of NRAMP1 in human neutrophil granules. Blood 2002;100:268e75.
Cargill M, Schrodi SJ, Chang M et al. A large-scale genetic association study confirms IL12B and leads to
the identification of IL23R as psoriasis-risk genes. Am J Hum Genet. 2007;80:273–290.
Carim-Todd L., Escarceller M, Estivill X., Sumoy L. LRRN6A/LERN1 (leucine-rich repeat neuronal protein
1), a novel gene with enriched expression in limbic system and neocortex. Europ. J.
Neurosci. 2003;18: 3167-3182.
Casiano CA, Ochs RL, Tan EM. Distinct cleavage products of nuclear proteins in apoptosis and necrosis
revealed by autoantibody probes. Cell Death Differ.1998;5:183-190.
Castano R, Bosse Y, Endam LM, Desrosiers M. Evidence of association of interleukin-1 receptor- like 1
gene polymorphisms with chronic rhinosinusitis. Am J Rhinol Allergy. 2009; 23: 377–384
Catanoso MG, Boiardi L, Macchioni P, Garagnani P, Sazzini M, De Fanti S, Farnetti E, Casali B,
Chiarolanza I, Nicoli D, Luiselli D, Salvarani C. IL-23A, IL-23R, IL-17A and IL-17R polymorphisms
in different psoriatic arthritis clinical manifestations in the northern Italian population.
Rheumatol Int.2013;33:1165–1176.
Cell Signaling Technology. Cell Signaling: TGFb/SMAd.
http://www.cellsignal.com/pathways/tgf-beta-smad.jsp. Accessed on March 24, 2013.
Chackerian AA, Oldham ER, Murphy EE, Schmitz J, Pflanz S, Kastelein RA. IL-1 receptor accessory protein
and ST2 comprise the IL-33 receptor complex. J Immunol .2007;179:2551–2555.
Chapuis J, Hot D, Hansmannel F, Kerdraon O, Ferreira S, et al. Transcriptomic and genetic studies
identify IL-33 as a candidate gene for Alzheimer’s disease. Mol Psychiatry.2009;14:1004–1016
Chen K, Song F, Calin GA, Wei Q, Hao X, Zhang W. Polymorphisms in microRNA targets: a gold mine
for molecular epidemiology. Carcinogenesis.2008; 29 (7): 1306–11.
Chung EY, Liu J, Homma Y, Zhang Y, Brendolan A, Saggese M, Han J, Silverstein R, Selleri L, Ma X.
Interleukin-10 expression in macrophages during phagocytosis of apoptotic cells is
mediated by homeodomain proteins Pbx1 and Prep-1. Immunity.2007; 27:952–964
Chung SA, Xie G, Roshandel D, Sherva R, Edberg JC, et al. Meta-Analysis of Genetic Polymorphisms in
Granulomatosis With Polyangiitis (Wegener’s) Reveals Shared Susceptibility Loci With
Rheumatoid Arthritis. Arthritis & Rheumatism. 2012; 62:10:3463-3471.
108
References
Clark SC, Kamen R. The human hematopoietic colony-stimulating factors. Science. 1987; 236: 1229-37.
Cohen PL, Caricchio R, Abraham V, Camenisch TD, Jennette JC, Roubey RA, Earp HS, Matsushima G,
Reap EA. Delayed apoptotic cell clearance and lupus-like autoimmunity in mice lacking the
c-mer membrane tyrosine kinase. J. Exp. Med. 2002; 196:135–140.
Cortis E, De Benedetti F, Insalaco A, Cioschi S, Muratori F, D’Urbano LE, et al. Abnormal production of
the tumor necrosis factor α and clinical efficacy of the TNF inhibitor etanercept in a patient with
PAPA syndrome. Journal of Pediatrics 2004;145:851–5.
Cvetanovic M, Ucker D. Innate Immune Discrimination of Apoptotic Cells: Repression of
Proinflammatory Macrophage Transcription Is Coupled Directly to Specific Recognition. Journal
of Immunology. 2004; 172:880-889
DNA Genotek, Inc. (2012). Oragene. http://www.dnagenotek.com/US/index.html.
de la Salle H, Hanau D, Fricker D, Urlacher A, Kelly A, Salamero J, Powis SH, Donato L, Bausinger H,
Laforet M, Jeras M, Spehner D, Bieber T, Falkenrodt A, Cazenave JP, Trowsdale J, Tongio MM
Homozygous human TAP peptide transporter mutation in HLA class I deficiency. Science. 1994;
265: 237-241, 1994. Note: Erratum: Science 266: 1464 only
Derouet M, Thomas L, Cross A, Moots RJ, Edwards SW. Granulocyte macrophage colonystimulating factor signaling and proteasome inhibition delay neutrophil apoptosis by increasing
the stability of Mcl-1. The Journal of Biological Chemistry. 2004; 279: 26915–26921.
Deveault C, Qian JH, Chebaro W, Ao A, Gilbert L, Mehio A, Khan R, Tan SL, Wischmeijer A, Coullin P, Xie
X, Slim R. NLRP7 mutations in women with diploid androgenetic and triploid moles: a proposed
mechanism for mole formation. Hum. Molec. Genet. 18: 888-897, 2009. Note: Erratum: Hum.
Molec. Genet. 2009; 18: 4907.
Dierselhuis MP, Frenkel J, Wulffraat NM, Boelens JJ. Anakinra for flares of pyogenic arthritis in
syndrome. Rheumatology (Oxford) 2005;44:406–8.
PAPA
Djuric U, El-Maarri O, Lamb B, Kuick R, Seoud M, Coullin P, Oldenburg J, Hanash S, Slim R. Familial
molar tissues due to mutations in the inflammatory gene, NALP7, have normal postzygotic
DNA methylation. Hum. Genet. 2006; 120: 390-395
Dowds TA, Masumoto J, Chen FF, Ogura Y, Inohara N, et al. Regulation of cryopyrin/Pypaf1
signaling by pyrin, the familial Mediterranean fever gene product. Biochemical and biophysical
research communications 2003;302:575–580.
Dowds TA, Masumoto J, Zhu L, Inohara N, Nunez G. Cryopyrininduced interleukin 1beta secretion
in monocytic cells: enhanced activity of disease-associated mutants and requirement for ASC.
The Journal of Biological Chemistry 2004;279:21924–21928.
109
References
Duerr RH, Taylor KD, Brant SR et al. A genome-wide association study identifies IL23R as an
inflammatory bowel disease gene. Science 2006; 314:1461–1463 (Epub 2006 Oct 26).
Elliott MR, Chekeni FB, Trampont PC, Lazarowski ER, Kadl A, Walk SF, Park D, Woodson RI,
Ostankovich M, Sharma P, et al.Nucleotides released by apoptotic cells act as a find-me signal
to promote phagocytic clearance. Nature 2009; 461:282–286.
Elliott MR, Ravichandran KS. Clearance of apoptotic cells: implications in health and disease. J. Cell
Biology.2010; 189: 1059–1070.
Ellis NA, Groden J, Ye TZ, Straughen J, Lennon DJ, Ciocci S, Proytcheva M, German J. The Bloom’s
syndrome gene product is homologous to RecQ helicases. Cell.1995;83:655–666.
Ensembl. http://www.ensembl.org/index.html. Accessed January – July 2013.
Erwig LP, Henson PM. Immunological consequences of apoptotic cell phagocytosis. Am. J. Pathol.
2007;171:2–8.
Fadok VA, Voelker DR, Campbell PA, Cohen JJ, Bratton DL, Henson PM. Exposure of
phosphatidylserine on the surface of apoptotic lymphocytes triggers specific recognition and
removal by macrophages. J. Immunol. 1992; 148:2207–2216.
Fadok VA, Bratton DL, Konowal A, Freed PW, Westcott JY, Henson PM. Macrophages that have
ingested apoptotic cells in vitro inhibit proinflammatory cytokine production through
autocrine/paracrine mechanisms involving TGF-beta, PGE2, and PAF. Journal of Clinical
Investigation. 1998; 101: 890–898.
Favaloro B, Allocati N, Graziano V, Di Ilio C, De Laurenzi V. Role of Apoptosis in disease. Aging (Albany
NY). May 31 2012 (online); 4(5): 330–349.
Fehniger TA, Cooper MA, Caligiuri MA . Interleukin-2 and interleukin-15: immunotherapy for cancer.
Cytokine Growth Factor Rev. 2002;13:169–83.
Filer C, Ho P, Smith RL et al. Investigation of association of the IL12B and IL23R genes with
psoriatic arthritis. Arthritis Rheu. 2008;58:3705–3709
Forlow S B, White EJ, Thomas KL, Bagby GJ, Foley PL, Ley K. T cell requirement for development of
chronic ulcerative dermatitis in E- and P-selectin-deficient mice. J. Immun. 2002; 169: 47974804, 2002. Note: Erratum: J. Immun. 170: 643 only, 2003.
Franke A, McGovern DP, Barrett JC, Wang K, Radford-Smith GL, Ahmad T . Genome-wide meta- analysis
increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nature Genetics.
2010;42:1118–25.
110
References
Fritzler MJ, Etherington J, Sokoluk C, Kinsella TD, Valencia DW. Antibodies from patients with
autoimmune disease react with a cytoplasmic antigen in the Golgi apparatus. Journal of
Immunology. 1984;132:2904-2908.
Fujisawa A, Kambe N, Saito M, Nishikomori R, Tanizaki H, et al. Disease-associated mutations in CIAS1
induce cathepsin B-dependent rapid cell death of human THP-1 monocytic cells. Blood. 2007;
109: 2903–2911.
Gaipl US, Franz S, Voll RE, Sheriff A, Kalden JR, Herrmann M. Defects in the disposal of dying cells
lead to autoimmunity. Curr. Rheumatol. Rep. 2004; 6:401–407.
Gibson LE, Daoud MS, Muller SA, Perry HO. Malignant pyodermas revisited. Mayo Clin Proc. 1997; 72:
734–736.
Goldbach-Mansky R, Kastner DL.Autoinflammation: the prominent role of IL-1 in monogenic
autoinflammatory diseases and implications for common illnesses. The Journal of allergy and
clinical immunology. 2009;124: 1141–1149.
Goncalves J, Degardin C, Laurent F, Batard ML, Pellerin P. Familial pyoderma gangrenosum following a
mammoplasty reduction: a case report. Ann Chir Plast Esthet 47:308-310, 2002.
Govoni G, Canonne-Hergaux F, Pfeifer CG, Marcus SL, Mills SD, Hackam DJ, et al. Functional
expression of Nramp1 in vitro in the murine macrophage line RAW264.7. Infect Immun
1999;67:2225-2232.
Graham JA, Hansen KK, Rabinowitz LG, et al.Pyoderma gangrenosum in infants and children.
Pediatric Dermatology. 1994; 11:10-17.
Gruenheid S, Pinner E, Desjardins M, Gros P. Natural resistance to infection with intracellular
pathogens: the Nramp1 protein is recruited to the membrane of the phagosome. J Exp Med
1997;185:717-730.
Gudbjartsson DF, Bjornsdottir US, Halapi E, Helgadottir A, Sulem P, et al. Sequence variants
affecting eosinophil numbers associate with asthma and myocardial infarction. Nat Genet
2009;41:342–347.
Gumienny TL, Brugnera E, Tosello-Trampont AC, Kinchen JM, Haney LB, Nishiwaki K, Walk SF,
Nemergut ME, Macara IG, Francis R, et al. CED-12/ELMO, a novel member of the
CrkII/Dock180/Rac pathway, is required for phagocytosis and cell migration. Cell. 2001;107:27–
41.
Ha M, Pang M, Agarwal V, Chen ZJ. Interspecies regulation of microRNAs and their targets. Biochem
Biophys Acta. 2008; 1779 (11): 735–42.
111
References
Han K, Zhu X , Liu B, Jeng E, Kong L, Yovandich JL, Vyas V, Marcus WD, Chavaillaz P, Romero CA, Rhode
PR, Wong HC. IL-15:IL-15 receptor alpha superagonist complex: High-level co-expression in
recombinant mammalian cells, purification and characterization. Cytokine. 2011;56:804–810.
Haneklaus M, O’Neill L, Coll R. Modulatory mechanisms controlling the NLRP3 inflammasome in
inflammation: recent developments. Current Opinion in Immunology. 2013, 25:40–45.
Hanayama R,Tanaka M, Miyasaka K, Aozasa K, Koike M, Uchiyama Y, Nagata S. Autoimmune disease
and impaired uptake of apoptotic cells in MFG-E8-deficient mice. Science. 2004;304:1147–1150.
Hartley AH, Rabinowitz LG. Pediatric dermatology. Dermatol Clin. 1997;15:111–119.
Hayakawa H, Hayakawa M, Kume A, Tominaga S. Soluble ST2 blocks interleukin-33 signaling in allergic
airway inflammation. J Biol Chem. 2007;282:26369–26380.
Hazen, PG. Pyoderma gangrenosum in infancy (letter). Journal of the American Academy of
Dermatology. 1992;27:275-276.
Hediger MA, Romero MF, Peng JB, Rolfs A, Takanaga H, Bruford EA. The ABCs of solute carriers:
physiological, pathological and therapeutic implications of human membrane transport
proteins: Introduction. Pflugers Arch. 2004; 447(5): 465–8.
Hershey GK, Friedrich MF, Esswein LA, Thomas ML, Chatila TA. The Association of Atopy with a Gain-ofFunction Mutation in the α Subunit of the Interleukin-4 Receptor. New England Journal of
Medicine. 1997;337(24):1720-1725.
Hiller G, and Sundler R. Regulation of phospholipase C-gamma 2 via phosphatidylinositol 3-kinase in
macrophages. Cell Signal. 2002; 14: 169–173.
Hillis GS, Terregino C, Taggart P, Killian A, Zhao N, Dalsey WC, Mangione A. Elevated soluble P-selectin
levels are associated with an increased risk of early adverse effects in patients with
presumed myocardial ischemia. Am. Heart J. 2002;143: 235-241.
Hirose K, Iwabuchi K, Shimada K, Kiyanagi T, Iwahara C, Nakayama H, Daida H. Different responses to
oxidized low-density lipoproteins in human polarized macrophages. Lipids in Health and
Disease. 2011;10(1):p10-18.
Hoffman HM, Mueller JL, Broide DH, Wanderer AA, Kolodner RD. Mutation of a new gene
encoding a putative pyrin-like protein causes familial cold autoinflammatory syndrome and
Muckle-Wells syndrome. Nature Genet. 2001; 29: 301-305.
Hogquist KA, Nett MA, Unanue ER, Chaplin DD. Interleukin 1 is processed and
released during apoptosis. Proc. Natl Acad. Sci. USA. 1991; 88: 8485–8489.
112
References
Hong HS, Morshed SA, Tanaka S, Fujiwara T, Ikehara Y, Nishioka M. Anti-Golgi antibody in
rheumatoid arthritis patients recognizes a novel antigen of 79 kDa (doublet) by western blot.
Scandinavian Journal of Immunology. 1992;36:785-792.
1
Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the
comprehensive functional analysis of large gene lists. Nucleic Acids Research. 2009; 37(1):1-13.
2
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using
DAVID Bioinformatics Resources. Nature Protocols. 2009;4(1):44-57.
Huffmeier U, Lascorz J, Bohm B et al. Genetic variants of the IL-23R pathway: association with
psoriatic arthritis and psoriasis vulgaris, but no specific risk factor for arthritis. J Invest Dermato.
2009; 129:355–358.
Huish SB, de la Paz EM, Ellis PR, Stern PJ. Pyoderma Gangrenosum of the Hand: A Case Series and
Review of the Literature. The Journal of Hand Surgery. July 2001;26(4). 679-685.
Iacono M, Mignone F, Pesole G. uAUG and uORFs in human and rodent 5′
untranslated mRNAs. Gene. 2005; 349:97–105.
Inoue H, Lin L, Lee X, Shao Z, Mendes S, Snodgrass-Belt P, Sweigard H, Engber T, Pepinsky B, Yang L, Beal
MF, Mi S, Isacson O. Inhibition of the leucine-rich repeat protein LINGO-1 enhances survival,
structure, and function of dopaminergic neurons in Parkinson's disease models. Proc. Nat.
Acad. Sci. 2007; 104: 14430-14435.
Janowski D, Salilew-Wondim D, torner H, Tesfaye D, Ghanem N, Tomek W, El-Sayed A, schellander K,
Holker M. Incidence of apoptosis and transcript abundance in bovine follicular cells is
associated with the quality of the enclosed oocyte. Theriogenology. 2012;78:656-669.
Jin Y, Riccardi SL, Gowan K, Fain PR, Spritz RA. Fine-Mapping of Vitiligo Susceptibility Loci on
Chromosomes 7 and 9 and Interactions with NLRP1 (NALP1). Journal of Investigative
Dermatology. 2010;13: 774-783.
Johns Hopkins University. Online Mendelian Inheritance in Man (OMIM). http://www.omim.org/.
Accessed March-June 2013.
Karupiah G, Hunt NH, King NJ, Chaudhri G. NADPH oxidase, Nramp1 and nitric oxide synthase 2 in the
host antimicrobial response. Rev Immunogenet. 2000;2:387e415.
Kelly-Welch, A. E., Hanson, E. M., Boothby, M. R., Keegan, A. D. Interleukin-4 and interleukin-13
signaling connections maps. Science. 2003; 300: 1527-1528.
113
References
Khandpur S, Mehta S, Reddy BSN. Pyoderma gangrenosum in two siblings: a familial predisposition. Ped
Dermatol. 2001;18:308-312.
Kirschnek S, Vier J, Gautam S, Frankenberg T, Rangelova S, Eitz-Ferrer P, Grespi F, Ottina E, Villunger A,
Hacker H and Hacker G. Molecular analysis of neutrophil spontaneous apoptosis reveals
a strong role for the pro-apoptotic BH3-only protein Noxa. Cell Death and Differentiation. 2011;
18:1805–1814.
Kanehisa Laboratories. Kyoto Encyclopedia of Genes and Genomes (KEGG pathway)
http://www.genome.jp/kegg/pathway.html. Accessed May 2013.
Klebanoff SJ, Clark RA. The Neutrophil: Function and Clinical Disorders. Amsterdam: North-Holland
Publishing Co; 1978.
Kobayashi SD, Voyich JM, Braughton KR, DeLeo FR. Down-regulation of proinflammatory capacity
during apoptosis in human polymorphonuclear leukocytes. J Immunol. 2003; 170: 3357–3368.
Kobori A, Yagi Y, Imaeda H, Ban H, Bamba S, et al. Interleukin-33 expression is specifically
enhanced in inflamed mucosa of ulcerative colitis. J Gastroenterol. 2010; 45: 999–1007.
Kooy J, Toh BH, Gleeson PA. Heterogeneity of human anti- Golgi auto-antibodies: reactivity with
components from 35 to 260 kDa. Immunol Cell Biol. 1994;72:123-127.
1
Kozak M. Effects of intercistronic length on the efficiency of reinitiation by eucaryotic ribosomes.
Mol Cell Biol. 1987; 7(10):3438-3445.
2
Kozak M. An analysis of 5′-noncoding sequences from 699 vertebrate messenger
RNAs. Nucleic Acids Res. 1987;15: 8125–8148.
Kroemer G, Jaattela M. Lysosomes and autophagy in cell death control. Nature Rev. Cancer. 2005; 5:
886–897.
Kroemer G, Martin SJ. Caspase-independent cell death. Nature Med. 2005;11: 725–730.
Kurosaki T, Maeda A, Ishiai M, Hashimoto A, Inabe K, and Takata M. Regulation of the phospholipase Cgamma2 pathway in B cells. Immunol. Rev. 2000; 176:19–29.
Lacy-Hulbert A, Smith AM, Tissire H, Barry M, Crowley D, Bronson RT, Roes JT, Savill JS, Hynes RO..
Ulcerative colitis and autoimmunity induced by loss of myeloid alphav integrins. Proc. Natl.
Acad. Sci. USA. 2007; 104:15823–15828.
Lauber K, Bohn E, Kröber SM, Xiao YJ, Blumenthal SG, Lindemann RK, Marini P, Wiedig C,
Zobywalski A, Baksh S, et al. Apoptotic cells induce migration of phagocytes via caspase-3mediated release of a lipid attraction signal. Cell. 2003; 113:717–730.
114
References
Lee GS, Subramanian N, Kim AI, Aksentijevich I, Goldbach- Mansky R, Sacks DB, Germain RN, Kastner DL,
Chae JJ. The calcium-sensing receptor regulates the NLRP3 inflammasome through Ca(2+)
and cAMP. Nature. 2012; 492:123-127.
Leuenroth SJ, Grutkoski PS, Ayala A, Simms HH. The loss of Mcl1 expression in human
polymorphonuclear leukocytes promotes apoptosis. J Leukoc Biol. 2000; 68: 158–166.
Li H, Chen D, Zhang J. Analysis of Intron Sequence Features Associated with Transcriptional
Regulation in Human Genes. PLOS ONE. 2012 ;7:10. e46784
Lindor NM, Arsenault TM, Solomon H, Seidman CE, McEvoy MT. A new autosomal dominant
disorder of pyogenic sterile arthritis, pyoderma gangrenosum and acne: PAPA syndrome. Mayo
Clinic Proceedings. 1997;72:611–5.
Liu Y, Helms C, Liao W et al. A genome-wide association study of psoriasis and psoriatic arthritis
identifies new disease loci. PLoS Genet. 2008; 4:e1000041
Lukacs NW, John A, Berlin A, Bullard DC, Knibbs R, Stoolman LM. E- and P-selectins are essential for the
development of cockroach allergen-induced airway responses. J. Immun. 2002;169: 2120-2125.
Luo HR, Loison F. Constitutive neutrophil apoptosis: mechanisms and regulation. American Journal
of Hematology. 2008;83: 288–295.
Ma A, Koka R, Burkett P. Diverse functions of IL-2, IL-15, and IL-7 in lymphoid homeostasis. Annu Rev
Immunol. 2006;24:657–79.
Mariathasan S, Monack D. Inflammasome adaptors and sensors: intracellular regulators of
infection and inflammation. Nature Reviews Immunology. 2007;7: 31-40.
Mariathasan S, Weiss DS, Newton K, McBride J, O’Rourke K, Roose-Girma M, Lee WP, Weinrauch Y,
Monack DM, Dixit VM. Cryopyrin activates the inflammasome in response to toxins and ATP.
Nature. 2006; 440: 228–232.
Marieb, EN (2000). Human Anatomy & Physiology: Fifth Edition. San Francisco. Benjamin-Cummings
Publishing Co.
Marshall AJ, Niiro H, Yun TJ, Clark EA. Regulation of B-cell activation and differentiation by the
phosphatidylinositol 3-kinase and phospholipase Cgamma pathway. Immunol. Rev. 2000; 176:
30–46.
Martinon F, Burns K, Tschopp J. The inflammasome: a molecular platform triggering activation of
inflammatory caspases and processing of proIL-1β. Mol. Cell. 2002; 10: 417–426.
Martinon F, Mayor A, Tschopp J. The inflammasomes: guardians of the body. Annual review of
immunology. 2009; 27: 229–265.
115
References
Matis WL, Ellis CN, Griffiths CE, Lazarus GS. Treatment of pyoderma gangrenosum with cyclosporine.
Arch Dermatol. 1992 Aug;128(8):1060-4.
Mayadas TN, Johnson RC, Rayburn H, Hynes RO, Wagner DD. Leukocyte rolling and extravasation are
severely compromised in P selectin-deficient mice. Cell. 1993; 74: 541-554.
Mayet WJ, Hermann E, Csernok E, Knuth A, Poralla T, Gross WL, Meyer zum Buschenfelde KH. A human
renal cancer line as a new antigen source for the detection of antibodies to cytoplasmic and
nuclear antigens in sera of patients with Wegener’s granulomatosis. Journal of Immunological
Methods. 1991;143:57-68.
McDermott MF, Aksentijevich I. The autoinflammatory syndromes. Curr. Opin. Allergy
Clin. Immunol. 2002; 2: 511–516.
McDonald PP, Fadok VA, Bratton D, Henson PM. Transcriptional and translational regulation of
inflammatory mediator production by endogenous TGF-beta in macrophages that have ingested
apoptotic cells. J. Immunol. 1999; 163:6164–6172.
McGovern DP, Gardet A, Torkvist L, Goyette P, Essers J, Taylor KD. Genome-wide association
identifies multiple ulcerative colitis susceptibility loci. Nature Genetics. 2010;42:332–7.
Meng G, Zhang F, Fuss I, Kitani A, Strober W. A mutation in the Nlrp3 gene causing inflammasome
hyperactivation potentiates Th17 cell-dominant immune responses. Immunity. 2009; 30: 860874.
Mevorach D, Zhou JL, Song X, Elkon KB. Systemic exposure to irradiated apoptotic cells induces
autoantibody production. J. Exp. Med. 1998; 188:387–392.
Meylan E, Tschopp J, Karin M. Intracellular pattern recognition receptors in the host response.
Nature. 2006; 442, 39–44.
Mi S, Lee X, Shao Z, Thill G, Ji B, Relton J, Levesque M, Allaire N, Perrin S, Sands B, Crowell T, Cate R L,
McCoy J M., Pepinsky R B. LINGO-1 is a component of the Nogo-66 receptor/p75 signaling
complex. Nature Neurosci. 2004;7: 221-228.
Mignone F, Gissi C, Liuni S, Pesole G. Untranslated regions of mRNAs. Genome Biol. 2002;
3(3):REVIEWS0004.
Miller J, Yentzer BA, Clark A, Jorizzo JL, Feldman SR. Pyoderma gangrenosum: A review and update on
new therapies. J. Am Acad Dermatol. 2010;62:646-54.
Miller AM, Xu D, Asquith DL, Denby L, Li Y, et al.IL-33 reduces the development of atherosclerosis. J Exp
Med. 2008;205: 339–346.
116
References
Miyagawa T, Nishida N, Ohashi J, Kimura R, Fujimoto A, Kawashima M, Koike A, Sasaki T, Tanii H, Otowa
T, Momose Y, Nakahara Y, Gotoh J, Okazaki Y, Tsuji S, Tokunaga K. Appropriate data
cleaning methods for genome-wide association study. Journal of Human Genetics. 2008;53:886893.
Moins-Teisserenc H T, Gadola S D, Cells M, Dunbar P R, Exley A, Blake N, Baykal C, Lambert J,
Bigliardi P, Willemsen M, Jones M, Buechner S, Colonna M, Gross W L, Cerundolo V
Association of a syndrome resembling Wegener's granulomatosis with low surface expression of
HLA class-I molecules. Lancet. 1999;354: 1598-1603. Note: Erratum: Lancet. 2000;356: 170 only.
Moulding DA, Quayle JA, Hart CA, Edwards SW.Mcl-1 expression in human neutrophils: regulation
by cytokines and correlation with cell survival. Blood. 1998; 92: 2495–2502.
Muñoz LE, Peter C, Herrmann M, Wesselborg S, Lauber K. Scent of dying cells: the role of
attraction signals in the clearance of apoptotic cells and its immunological consequences.
Autoimmun. Rev. 2010; 9:425–430.
Nagata S, Hanayama R, Kawane K. Autoimmunity and the clearance of dead cells. Cell. 2010;
140:619–630.
National Institute of Allergy and Infectious Diseases (NIAID)(NIH). DAVID Bioinformatics Resources 6.7.
http://david.abcc.ncifcrf.gov/ . Accessed September 2012-July 2013.
National Center for Biotechnology Information. Human Apoptosis Genes. Accessed February, 2013.
National Center for Biotechnology Information. Aceview: BCL2. Accessed March, 2013
National Center for Biotechnology Information. Aceview: CASP7. Accessed March, 2013.
National Center for Biotechnology Information. Aceview: CYPF42. Accessed March 2013.
National Center for Biotechnology Information. Aceview: LINGO1. Accessed March 2013.
National Center for Biotechnology Information. Aceview: PLCG2. Accessed March 2013.
National Center for Biotechnology Information. Aceview: SELL. Accessed March 2013.
National Center for Biotechnology Information. Aceview: SLC11A1. Accessed June 2013.
National Human Genome Research Institute. ENCODE project. http://www.genome.gov/10005107.
Accessed June 2013.
Neilson JR, Sandberg R. Heterogeneity in mammalian RNA 3' end formation. Exp Cell Res. May 2010;
316 (8): 1357–64.
Neven B, Prieur AM, Quartier dit Maire P. Cryopyrinopathies: update on pathogenesis and
treatment. Nature Clinical Practice Rheumatology. 2008;4:481–489.
117
References
Nishida N, Koike A, Tajima A, Ogasawara Y, Ishibashi Y, Uehara Y, Inoue I, Tokunaga K. Evaluating the
performance of Affymetrix SNP Array 6.0 platform with 400 Japanese individuals. BMC
Genomics. 2008;9:431.
Nozawa K, Fritzler M, von Muhlen C, Chan E. Giantin is the major Golgi autoantigen in human anti-Golgi
complex sera. Arthritis Res Ther. 2004;6.
O’Brien B, Archer NS, Simpson AM, Torpy FR, Nassif NT. Association of SLC11A1 promoter
polymorphisms with the incidence of autoimmune and inflammatory diseases: A meta-analysis.
Journal of Autoimmunity. 2008;31:42-51.
Ogden CA, Pound JD,Batth BK, Owens S, Johannessen I, Wood K, Gregory CD. Enhanced apoptotic cell
clearance capacity and B cell survival factor production by IL-10- activated macrophages:
implications for Burkitt’s lymphoma. J. Immunol. 2005;174:3015–3023.
Ozbabacan S, Keskin O, Nussinov R, Gursoy A. Enriching the human apoptosis pathway by
predicting the structures of protein–protein complexes. Journal of Structural Biology. 2012;
179: 338-346.
Ozdil S, Demir K, Boztas G, Danalioglu A, Karaca C, Akyüz F, Aksoy N, Kaymakoglu S, Mungan Z, Besisik F,
Cakaloglu Y, Okten A. Crohn's disease: analysis of 105 patients. Hepatogastroenterology.
2003; 50(Suppl 2):cclxxxvii-ccxci.
Paramkusam G, Meduri V, Gangashetty N. Pyoderma Gangrenosum with oral involvement: Case report
and review of literature. Int J Oral Sci. 2010;2:111-6.
Pastorelli L, Garg RR, Hoang SB, Spina L, Mattioli B, et al. Epithelialderived IL-33 and its receptor ST2 are
dysregulated in ulcerative colitis and in experimental Th1/Th2 driven enteritis. Proc Natl Acad
Sci USA. 2010;107: 8017–8022.
Patterson RL, van Rossum DB, Ford DL, Hurt KJ, Bae SS, Suh PG, Kurosaki T, Snyder SH, Gill DL.
Phospholipase C-gamma is required for agonist-induced Ca2+ entry. Cell. 2002;111: 529–541.
Perry HO, Winkelmann RK. Bullous pyoderma gangrenosum and leukemia. Archives Of Dermatology.
1972;106(6):901-5.
Pillay J, den Braber I, Vrisekoop N, Kwast LM, de Boer RJ, et al. In vivo labeling with H20-H-2 reveals a
human neutrophil lifespan of 5.4 days. Blood. 2010;116: 625–627.
Portt L, Norman G, Clapp C, Greenwood M, Greenwood MT. Antiapoptosis and cell survival: a review.
Biochim. Biophys. Acta. 2011;1813:238–259.
Powell FC, Schroeter AL, Perry HO, Su WP. Direct immunofluorescence in pyoderma gangrenosum. Br J
Dermatol. 1983;108:287-93.
118
References
Powell FC, Su WP, Perry HO. Pyoderma gangrenosum: Classification and management. J Am Acad
Dermatol. 1996;34:395-409.
Powell FC. Pyoderma Gangrenosum and Sarcoidosis. Archives of Dermatology. 1984;120:959-960.
Prystowsky JH, Kahn SN, Lazarus GS. Present status of pyoderma gangrenosum: Review of 21 cases. Arch
Dermatol.1989;125:57-64.
Pushparaj PN, Tay HK, H’ng SC, Pitman N, Xu D, et al. The cytokine interleukin-33 mediates
anaphylactic shock. Proc Natl Acad Sci USA. 2009;106:9773–9778.
Putney, J.W. PLC-gamma: an old player has a new role. Nat.Cell Biol. 2002;4:E280–E281.
Putney JW, Broad LM, Braun FJ, Lievremont JP, Bird GS. Mechanisms of capacitative calcium entry. J.
Cell Sci. 2001;114:2223–2229.
Qiagen Technology. TNF Superfamily Pathway.
http://www.qiagen.com/products/genes%20and%20pathways/Pathway%20Details?pwid=438.
Accessed on March 23, 2013.
Qiagen SaBiosciences. RT2 Profiler PCR Array Human Apoptosis (gene list).
http://www.sabiosciences.com/rt_pcr_product/HTML/PAHS-012A.html. Downloaded on March
23, 2013.
Rahman P, Inman RD, Maksymowych WP, Reeve JP, Peddle L, Gladman DD. Association of
interleukin 23 receptor variants with psoriatic arthritis. J Rheumatol. 2009;36:137–140.
Raveh-Amit H, Maissel A, Poller J, Marom L, Elroy-Stein O, Shapira M, Livneh E. Translational control
of protein kinase Ceta by two upstream open reading frames. Mol. Cell. Biol. 2009;29:6140–
6148.
Raza K, Scheel-Toellner D, Lee CY, Pilling D, Curnow SJ, et al. Synovial fluid leukocyte apoptosis is
inhibited in patients with very early rheumatoid arthritis. Arthritis Res Ther. 2006;8: R120.
Ridker PM, Buring JE, Rifai N. Soluble P-selectin and the risk of future cardiovascular events.
Circulation. 2001;103:491-495
Renn CN, Helmer A, Megahed M. Pyogenic arthritis, pyoderma gangrenosum and acne syndrome
(PAPA syndrome). Hautarzt. 2007;58:383–4.
Roach TI, Chatterjee D, Blackwell JM. Induction of early-response genes KC and JE by mycobacterial
lipoarabinomannans: regulation of KC expression in murine macrophages by Lsh/Ity/Bcg
(candidate Nramp). Infect Immun. 1994;62:1176e84.
119
References
Rodriguez JL, Gelpi C, Thomson TM, Real FJ, Fernandez J. Antigolgi complex autoantibodies in a patient
with Sjögren syndrome and lymphoma. Clin Exp Immunol. 1982;49:579-586.
Rodriguez-Manzanet R, Sanjuan MA, Wu HY, Quintana FJ, Xiao S, Anderson AC, Weiner HL, Green DR,
Kuchroo VK. T and B cell hyperactivity and autoimmunity associated with niche-specific defects
in apoptotic body clearance in TIM-4-deficient mice. Proc. Natl. Acad. Sci. USA. 2010;107:8706–
8711.
Rose GE, Barnes EA, Uddin JM. Pyoderma gangrenosum of the ocular adnexa: A rare condition with
characteristic clinical appearances. Ophthalmology. 2003;110:801-805.
Rossie KM, Piesco NP, Charley MR, Oddis CV, Steen VD, Fratto J, Deng JS. A monoclonal antibody
recognizing golgi apparatus produced using affinity purified material from a patient with
connective tissue disease. Scandinavian Journal of Rheumatology. 1992;21: 109-115.
Rozen SM, Nahabedian MY, Manson PN. Management strategies for pyoderma gangrenosum: case
studies and review of literature. Ann Plast Surg. 2001;47:310-315.
Ruocco E, Sangiuliano S, Gravina AG, Miranda A, Nicoletti G. Pyoderma gangrenosum: An
updated review. Journal of the European Academy of Dermatology and Venereology.
2009;23:1008-17.
Ryan RJ. Cutaneous vasculitis (1992). In Champion RH, Burton JL, Ebling FJG, Rook’s textbook of
dermatology, 5th edition. Oxford:Blackwell Scientific;1893-1962.
Ryan K, Bauer DL. Finishing touches: post-translational modification of protein factors involved in
mammalian pre-mRNA 3' end formation. Int J Biochem Cell Biol.2008;40(11):2384–96.
Sachs MS, Geballe AP, Downstream control of upstream open reading frames. Genes Dev. 2006;20:915–
921.
Saito M, Nishikomori R, Kambe N, Fujisawa A, Tanizaki H, et al. Disease-associated CIAS1
mutations induce monocyte death, revealing low-level mosaicism in mutation-negative
cryopyrin-associated periodic syndrome patients. Blood. 2008;111: 2132–2141.
Satoh J, Tabunoki H, Yamamura T, Arima K, Konno H. TROY and LINGO1 expressions in astrocytes
and macrophages/microglia in multiple sclerosis lesions. Neuropathology and Applied
Neurobiology. 2007;33:99-107.
Savill J, Dransfield I, Gregory C, Haslett C. A blast from the past: clearance of apoptotic cells
regulates immune responses. Nat. Rev. Immunol. 2002;2:965–975.
120
References
Schellevis MA, Stoffels M, Hoppenreijs EP, Bodar E, Simon A, van der Meer JW. Variable expression and
treatment of PAPA syndrome. Annals of the Rheumatic Diseases. 2011;70:1168–70.
Schmitz J, Owyang A, Oldham E, Song Y, Murphy E, McClanahan TK, et al. IL-33, an interleukin-1-like
cytokine that signals via the IL-1 receptor-related protein ST2 and induces T helper type 2associated cytokines. Immunity. 2005;23:479-90.
Schroder K, Tschopp J. The inflammasomes. Cell. 2010;140: 821–832.
Scott RS, McMahon EJ, Pop SM, Reap EA, Caricchio R, Cohen PL, Earp HS, Matsushima GK. Phagocytosis
and clearance of apoptotic cells is mediated by MER. Nature. 2001;411:207–211.
Searle S, Bright NA, Roach TI, Atkinson PG, Barton CH, Meloen RH, et al. Localisation of Nramp1 in
macrophages: modulation with activation and infection. J Cell Sci. 1998;111(Pt 19):2855e66.
Seidelin JB, Bjerrum JT, Coskun M, Widjaya B, Vainer B, Nielsen OH. IL-33 is upregulated in
colonocytes of ulcerative colitis. Immunol Lett. 2010;128: 80–85.
Semenets N , Gvozdjan K , Cottingham T, Fulton J , Mostow E, Piontkivska H. Bioinformatics in wound
healing: PG or not PG? SNPs as genomic biomarkers in pyoderma gangrenosum. (23rd Annual
SAWC/WHS, Symposium on Advanced Wound Care and Wound Healing Society Meeting, April
2010, Orlando, FL). Abstract. 2010.
Shands JW, Flowers FP, Hill HM, Smith JO. Pyoderma gangrenosum in a kindred. Journal of the
American Academy of Dermatology. 1987;16:931–934.
Shimizu M, Matsuda A, Yanagisawa K, Hirota T, Akahoshi M, et al. Functional SNPs in the distal
promoter of the ST2 gene are associated with atopic dermatitis. Hum Mol Genet. 2005;14:
2919–2927.
Shohem NG, Mansfield E, Wood G, Centola M, Kastner DL. The FMF protein, pyrin, specifically interacts
with the SH3 domain of the cytoskeletal protein PSTPIP1. American Society for Human Genetics
Annual Meeting, San Diego, CA; 2001.
Shohem NG, Centola M, Mansfield E, Hull KM, Wood G, Wise CA, et al. Pyrin binds the
PSTPIP1/CD2BP1 protein, defining familial Mediterranean fever and PAPA syndrome as
disorders in the same pathway. Proceedings of the National Academy of the Sciences. 2003;
100:13501–6.
Sigma Aldrich LLC. Pathways Slides and Charts: Caspase Cascade.
http://www.sigmaaldrich.com/life-science/cell-biology/learning-center/pathway-slides-a
nd/caspace-cascade.html. Accessed on March 23, 2013.
121
References
Silverberg MS, Cho JH, Rioux JD, McGovern DP, Wu J, Annese V. Ulcerative colitis-risk loci on
chromosomes 1p36 and 12q15 found by genome-wide association study. Nature Genetics.
2009; 41:216–20.
Sponheim J, Pollheimer J, Olsen T, Balogh J, Hammarstro¨m C, et al. Inflammatory bowel diseaseassociated interleukin-33 is preferentially expressed in ulceration-associated myofibroblasts. Am
J Pathol. 2010;177:2804–2815.
Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, Thomson BP. Genome-wide association study
meta-analysis identifies seven new rheumatoid arthritis risk loci. Nature Genetics. 2010;42; 508–
14.
Stichweh DS, Punaro M, Pascual V. Dramatic improvement of pyoderma gangrenosum with
infliximab in a patient with PAPA syndrome. Pediatric Dermatology. 2005;22:262–5.
Su WP, Davis MD, Weenig RH, Powell FC, Perry HO. Pyoderma gangrenosum: clinicopathologic
correlation and proposed diagnostic criteria. International Journal of Dermatology. 2004;43:790800.
Takeuchi O, Akira S. Pattern recognition receptors and inflammation. Cell. 2010;140: 805–820.
Tallon B, Corkill M. Peculiarities of PAPA syndrome. Rheumatology (Oxford). 2006;45:1140–3.
Taniguchi N, Kawahara K, Yone K, Hashiguchi T, Yamakuchi M, Goto M, Inoue K, Yamada S, Ijiri K,
Matsunaga S, et al. High mobility group box chromosomal protein 1 plays a role in the
pathogenesis of rheumatoid arthritis as a novel cytokine. Arthritis Rheum. 2003;48:971–981.
Taylor W, Gladman D, Helliwell P, Marchesoni A, Mease P, Mielants H. CASPAR Study Group:
classification criteria for Psoriatic Arthritis: new criteria from a large international study. Arthritis
Rheum.2006;54:2665–2673.
Thierry-Mieg D, Thierry-Mieg J. Aceview: a comprehensive cDNA-supported gene and transcripts
annotation. Genome Biology. 2006;7 Suppl 1:S12.1-14. Epub 2006 Aug 7.
Ting JP, Kastner DL, Hoffman HM. CATERPILLERs, pyrin and hereditary immunological
disorders. Nature Rev. Immunol. 2006;6,183–195.
Tinoco MP, Tamler C, Maciel G, Soares D, Avelleria JC, Azulay D, Pyoderma Gangrenosum following
isotretinoin therapy for acne nodulocystic. Int J Dermatol. 2008;47:953-6.
122
References
Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V. Robust associations of four new
chromosome regions from genome-wide analyses of type 1 diabetes. Nature Genetics. 2007;
39:857–64.
Tofteland ND, Shaver TS. Clinical efficacy of etanercept for treatment of PAPA syndrome. Journal
of Clinical Rheumatology. 2010;16:244–5.
Truman LA, Ford CA, Pasikowska M, Pound JD, Wilkinson SJ, Dumitriu IE, Melville L, Melrose, LA, Ogden
CA, Nibbs R, et al. CX3CL1/fractalkine is released from apoptotic lymphocytes to stimulate
macrophage chemotaxis. Blood. 2008;112:5026–5036.
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, Human
Genome Program. Human Genome Project Information.
http://www.ornl.gov/sci/techresources/Human_Genome/faq/genenumber.shtml. Accessed
May 2013.
U.S. Department of Energy Office of Science. Office of Biological and Environmental Research: Human
Genome Program. SNP Fact Sheet.
http://www.ornl.gov/sci/techresources/Human_Genome/faq/snps.shtml. Accessed June 2013.
Van den Driesch P. Pyoderma gangrenosum: a report of 44 cases with follow-up. British Journal of
Dermatology. 1997;137:1000-1005.
Van der Velden AW, Thomas AA. The role of the 5’ untranslated region of an mRNA in translation
regulation during development. Int J Biochem Cell Biol. 1999;31(1):87-106.
Van Rossum DB, Patterson RL, Sharma S, Barrow RK, Kornberg M, Gill DL, Snyder SH. Phospholipase
Cgamma1 controls surface expression of TRPC3 through an intermolecular PH domain. Nature.
2005;434:99–104.
Vandivier RW, Henson PM, Douglas IS. Burying the dead: the impact of failed apoptotic cell removal
(efferocytosis) on chronic inflammatory lung disease. Chest. 2006;129:1673–1682.
doi:10.1378/chest.129.6.1673.
Venkatweswaran S, Garg BR, Reddy BSN, Ratnaker C. Pyoderma gangrenosum in childhood: case
report. Journal of Dermatology. 1994;21:670-673.
Verma D, Lerm M, Blomgran Julinder R, Eriksson P, Soderkvist P, et al. Gene polymorphisms in the
NALP3 inflammasome are associated with interleukin-1 production and severe inflammation:
relation to common inflammatory diseases? Arthritis and Rheumatism. 2008;58: 888–894.
Volarevic V, Mitrovic M, Milovanovic M, Zelen I, Nikolic I, et al. Protective role of IL-33/ST2 axis in
Con A-induced hepatitis. J Hepatol. 2012;56: 26–33.
123
References
Voll RE, Herrmann M, Roth EA, Stach C, Kalden JR, Girkontaite I. Immunosuppressive effects of
apoptotic cells. Nature. 1997;390:350– 351.
von Roretz C, Gallouzi IE. Decoding ARE-mediated decay: is microRNA part of the equation?. J Cell Biol.
April 2008;181(2): 189–94.
Von Den Driesch P. Pyoderma Gangrenosum: A Report of 44 Cases With Follow Up. British Journal of
Dermatology. 1997;137:1000-1005
Vousden KH & Lane DP. p53 in health and disease. Nature Rev. Mol. Cell Biol. 2007; 8: 275–283.
Wang D, Feng J, Wen R, Marine JC, Sangster MY, Parganas E, Hoffmeyer A, Jackson CW, Cleveland JL,
Murray PJ, Ihle JN. Phospholipase Cgamma2 is essential in the functions of B cell and several
Fc receptors. Immunity. 2000;13,25–35.
Watatani Y, Ichikawa K, Nakanishi N, Fujimoto M, Takeda H, Kimura N, Hirose H, Takahashi S,
Takahashi Y. Stress-induced translation of ATF5 mRNA is regulated by the 5′-untranslated region,
J. Biol. Chem. 2008;283:2543–2553.
Wei W, Ji A,Wang J, Wei Z,Lian C, Yang J,Ma L, Ma L,Qin X,Wang LD. Functional single nucleotide
polymorphism in C20orf54 modifies susceptibility to esophageal squamous cell carcinoma.
Diseases of the Esophagus. 2013; 26: 97–103.
Weisbart RH, Gasson J, Golde D. Proceedings of the UCLA American College for Physicians
Conference: Colony-Stimulating Factors and Host Defense. Annals of Internal Medicine.
1989;110:4297-303.
Wen R, Jou ST, Chen Y, Hoffmeyer A, Wang D. Phospholipase C gamma 2 is essential for specific
functions of Fc epsilon R and Fc gamma R. J. Immunol. 2002;169:6743–6752.
Wen Y, Liu Y, Xu Y, Zhao Y, Hua R, Wang K, et al. Loss-of-function mutations of an inhibitory upstream
ORF in the human hairless transcript cause Marie Unna hereditary hypotrichosis. Nat. Genet.
2009;41:228–233.
Willingham SB, Bergstralh DT, O’Connor W, Morrison AC, Taxman DJ, et al. Microbial pathogen- induced
necrotic cell death mediated by the inflammasome components CIAS1/cryopyrin/NLRP3
and ASC. Cell Host & Microbe. 2007;2: 147–159.
Wise CA, Gillum JD, Seidman CE, Lindor NM, Veile R, Bashiardes S, Lovett M. Mutations in CD2BP1
disrupt binding to PTP PEST and are responsible for PAPA syndrome, an autoinflammatory
disorder. Human Molecular Genetics. 2002;11(8):961–969.
Wollina U. Pyoderma grangrenosum: A review. Orphanet J Rare Dis.2007;2:19.
124
References
Wu H, Romieu I, Shi M, Hancock DB, Li H, et al. Evaluation of candidate genes in a genome-wide
association study of childhood asthma in Mexicans. J Allergy Clin Immunol. 2010;125: 321–327.
Xiao YQ, Freire-de-Lima CG, Schiemann SP, Bratton DL, Vandivier RW, Henson PM. Transcriptional and
translational regulation of TGF-beta production in response to apoptotic cells. J. Immunol. 2008;
181:3575–3585.
Xu D, Jiang HR, Kewin P, Li Y, Mu R, et al. IL-33 exacerbates antigeninduced arthritis by activating
mast cells. Proc Natl Acad Sci USA. 2008;105:10913–10918.
Yang H, Reinherz E. CD2BP1 modulates CD2-dependent T cell activation via linkage to protein
tyrosine phosphatase (PTP)- PEST. Journal of Immunology. 2006;176:5898–907.
Yu P, Constien R, Dear N, Katan M, Hanke P, Bunney T, Kunder S, et al. Autoimmunity and
inflammation due to a gain of function mutation in phospholipase Cγ2 that specifically increases
external Ca2+ entry. Immunity. 2005;22:451-465.
Yu J, Wu J, Zhang Z, Datta P, Ibrahimi I, Taniguchi S, Sagara J, Fernandes-Alnemri T, Alnemri ES.
Cryopyrin and pyrin activate caspase-1, but not NF-κB, via ASC oligomerization. Cell Death Differ.
2006;13:236–249.
Zhang B, Hirahashi J, Cullere X, Mayadas TN. Elucidation of molecular events leading to neutrophil
apoptosis following phagocytosis: cross-talk between caspase 8, reactive oxygen species, and
MAPK/ERK activation. Journal of Biological Chemistry. 2003;278:28443–28454.
Zurawski SM, Vega F, Huyghe B, Zurawski G. Receptors for interleukin-13 and interleukin-4 are
complex and share a novel component that functions in signal transduction. EMBO J. 1993;12:
2663-2670.