I MERCER, HEATHER MILLIKEN, DECEMBER 2013 CELL BIOLOGY the DISTRIBUTION of SINGLE NUCLEOTIDE POLYMORPHISMS in PYODERMA GANGRENO

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

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

Heather Milliken Mercer

December 2013

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

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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

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

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 speck- like 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

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

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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

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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

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

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

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

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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

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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)

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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) (Date)

II. 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) (Date)

III. 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.

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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.

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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.

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 dual- functioning 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

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 Pathologies often confused with PG Vaso-occlusive (venous) disease hepatic veno-occlusive disease, retinal veno- occlusive disease Systemic vasculitis Wegener’s granulomatosis, livedoid vasculitis, polyarteritis nodosa, etc. Infections subcutaneous mycoses, tuberculosis, syphilis, ecthyma gangrenosum Malignancy lymphomas, leukemia Tissue Trauma insect bites, factitious panniculitis Other neutrophilic dermatoses atypical Sweet’s syndrome, Behcet’s disease. Drug Reaction 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).

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.

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).

Compiled Case Studies of Associated Systemic Diseases with Pyoderma Gangrenosum

6% Crohn's

12% Ulcerative Colitis

12% Arthritis 53% 6% 11% 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 “para- immune”. 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.

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.

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 Ulceration with rapidly evolving purulent wound Pustular Discrete pustules usually associated with IBD Bullous Superficial bullae with development of ulcerations Vegetative 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.

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 aCharacteristic margin expansion of 1 to 2 cm per day, or a 50% increase in ulcer size within 1 month. bPain is usually out of proportion to the size of the ulceration. cTypically preceded by a papule, pustule, or bulla. dUsually necessitates skin biopsy and other investigations to rule out causes eUlcer development at sites of minor cutaneous trauma. fInflammatory bowel disease, arthritis, IgA gammopathy, or underlying malignancy. gGenerally 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)

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.

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

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,

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).

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

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.)

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

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), IL-

10, 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).

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

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

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

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.

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 ASC- designates 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) (Goldbach-

Mansky 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

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)

Of the variations among inflammasome complexes, the NALP3 inflammasome demonstrates a strong potential for relationship to the phenotype associated with Pyoderma Gangrenosum. Cryopyrin- associated 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).

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,

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%, Hardy-

Weinberg (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

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 1-

4 (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

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

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. List 2 All PG Genes vs. Immunity/Inflammation (I) All PG genes vs. Apoptosis (A) AB Genes vs. Immunity/Inflammation (I) AB Genes vs. Apoptosis (A) AABB Genes vs. Immunity/Inflammation (I) AABB Genes vs. Apoptosis (A) Apoptosis (A) vs. Immunity/Inflammation(I) A/I vs. Common to PG Genes and I A/I vs. Common to PG Genes and A AABB vs. A/I AB vs. A/I AABB vs. A + I (not shared) AB vs. A + I (not shared) All PG Genes vs. A/I All PG Genes vs. 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 vs. Comparison 2 # Genes in Genome vs.# Immunity/Inflammation vs. # PG Genes vs. # PG/Immune/Inflammation Genes Genes # Genes in Genome vs. # Apoptosis Genes vs. # PG Genes vs. # PG/Apoptosis Genes # Apoptosis Genes vs. # AABB Genes vs. # Immune/Inflammation Genes vs. # AABB Genes # Apoptosis Genes vs. # AB Genes vs. # Immune/Inflammation Genes vs. # AB Genes

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 vs. Comparison 2 # A/I vs. # AABB shared with A/I vs. # A + I vs. # AABB shared with A + I # A/I vs. # AB shared with A/I vs. # A + I vs. # AB shared with A + I # A/I vs. # All PG shared with A/I vs. # 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

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.

PG SNPs

Homozygous State Heterozygous State

AA BB AB 29,997 unique SNPs 28,927 unique SNPs 6,073 unique SNPs

27 in 7,693 unique 36 in 7,529 unique 2 in 2,837 unique Inter-genic RefSeq gene Inter-genic RefSeq gene IDs Inter-genic RefSeq gene IDs Regions IDs Regions Regions

Known Predicted Known Predicted Known Predicted 6,665 1,028 6,480 1,049 2,371 466

NM NR XM XR NM NR XM XR NM NR XM XR 6,340 325 693 335 6,169 311 707 342 2,261 110 308 158

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)

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 Fig. 15: Numbers of SNPs per presentation (homozygous or heterozygous) state (homozygous or heterozygous)

Fig. 16: Number of SNPs found in each chromosome

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.

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 Gene Functional Gene Functional Gene Functional Classification Classification Classification 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

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.

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

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

100 54.6%

80 53 60 40 20

NumberofClusters 0 Apoptosis/Immune Total Number Clusters Related Clusters

Fig. 18: The PG Data Set contains many dual-functioning apoptosis and immunity related gene clusters.

Apoptosis/Immunity Related Genes in Clusters wtih ES > 2 3500

3000

2500 82.5% 2000 1500 3082 2541

1000 NumberofGenes 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

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.

Immunity Apoptosis AB Genes AB Genes 113 Genes Genes 2,767 Genes 2,767 148 1,084 Genes 1,778 Fig. 21: The AB Gene List shares 113 genes with the Affymetrix Immune/Inflammation Fig. 22: The AB Gene List shares 148 genes with Gene List. the NCBI Apoptosis Gene List.

Immunity Apoptosis All PG Genes All PG Genes 429 Genes 597 Genes 9,831 9,831 Genes 1,084 Genes 1,778

Fig. 25: The PG Gene List shares 429 genes with the Fig. 26: The PG Gene List shares 597 genes with Affymetrix Immune/Inflammation Gene List. the NCBI Apoptosis Gene List.

AABB Genes 104 A/I Genes AB Genes A/I Genes 9,263 Genes 216 2,767 27 216 Genes

Fig. 27: The AABB Gene List shares 104 genes with those shared by Fig. 28: The AB Gene List shares 27 genes with those shared by the NCBI Apoptosis Gene List the Affymetrix Immune/Inflammation the NCBI Apoptosis Gene List the Affymetrix Gene List. Immune/Inflammation Gene List.

All PG Genes A/I Genes 9,831 111 216 Genes

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 A + I Genes 751 9,263 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.

AB Genes 207 A +I Genes All PG Genes A + I Genes 2,767 Genes 2,430 9,831 804 2,430 Genes Fig. 31: The AB Gene List shares 207 genes with those NOT shared by the NCBI Apoptosis Gene List the Affymetrix Fig. 32: The PG Gene List shares 804 genes with those NOT Immune/Inflammation Gene List. shared by the NCBI Apoptosis Gene List the Affymetrix Immune/Inflammation Gene List.

Common to Common to A/I Genes A/I Genes 111 All PG Genes 111 All PG Genes 216 Genes and Apoptosis 216 Genes and Immunity 597 429

Fig. 33: The genes shared by NCBI Apoptosis and Affymetrix Fig. 34: The genes shared by NCBI Apoptosis and Affymetrix Immune/Inflammation Gene Lists have 111 genes Immune/Inflammation Gene Lists have 111 genes in in common with PG Genes and the NCBI common with PG Genes and the NCBI Apoptosis List. Apoptosis List.

To determine whether PG patients share an excess of PG SNPs in Immunity/Inflammation- related 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.

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 19,599 Number of Immunity Genes 1,084 Shared PG Genes 9,831 Number of PG/Immunity Genes 429

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 21,000 19,500 Chi2 = 16.334

18,000 16,500 (1° freedom) 15,000 13,500 12,000 10,500 9,000 7,500 6,000

NumberofGenes 4,500 3,000 1,500 0 Number of Number of Shared PG Number of genes in Immunity Genes PG/Immunity genome Genes 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)

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 19,599 Shared PG Genes 9,831 Number of Apoptosis Genes 1,778 Number of PG/Apoptosis Genes 597

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 p=<.0001

18,000 2 16,500 Chi = 67.794 15,000 (1° freedom) 13,500 12,000 10,500 9,000 7,500 6,000

NumberofGenes 4,500 3,000 1,500 0 Number of Shared PG Number of Number of genes in Genes Apop Genes PG/Apop genome 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

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.

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 p = <.0001 6,000 Chi2 = 145.436 4,500 (1° freedom) 3,000

NumberofGenes 1,500 0 Number of Number of Number of Number of Apoptosis AABB genes Immunity AABB genes Related Genes 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 1,778 Number of AABB genes 9,263 Number of Immunity Genes 1,084 Number of AABB genes 9,263

The fraction of genes shared between the AB Data Set, Apoptosis Genes and Immunity/Inflammation Genes (p values from Chi-square with Yates ‘correction)

p=<.0001 3,000 Chi2 = 111.197 2,500 2,000 (1° freedom) 1,500 1,000 500 Number of Genes of Number 0 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

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

Fractions of SNPs shared in the homozygous (AABB) state with Immunity and Apoptotic Genes (p values from Fisher’s Exact Test) 2430 2600 2400 p=.0008

2200 2000 1800 1600 1400 1200 1000 757 800

NumberofGenes 600 216 400 104 200 0

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.

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 216 Number of AB genes shared with AI Genes 27 Number of Apoptosis & Immunity Genes Not shared (A+I) 2430 Number of AB genes shared with A+I 207

Fractions of SNPs shared in the heterozygous (AB) state with Immunity and Apoptotic Genes (p values from Fisher’s Exact Test)

2430 2600 2400 2200 2000 1800 p= 0.1178 1600 1400 1200 1000 800 600 216 207 NumberofGenes 400 27 200 0

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.

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 216 Number of PG genes shared with AI Genes 111 Number of Apoptosis & Immunity Genes Not shared (A+I) 2430 Number of PG genes shared with A+I 804

Fractions of SNPs shared in homozygous (AABB) or heterozygous (AB) state with Immunity and Apoptotic Genes (p values from Fisher’s Exact Test) 2430 2600 2400 p= 0.0005 2200 2000 1800 1600 1400 1200 804 1000 800 NumberofGenes 600 216 400 111 200 0

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.

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.

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

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.

Table 17: SNPs from Primary Candidates causing missense nonsense in addition to other gene relationships

Official Gene ID Relationship Description C18orf42 - chromosome 18 open reading frame 42 missense uncharacterized protein 3’ UTR product C4orf21 - chromosome 4 open reading frame 21 missense uncharacterized protein CDS product Exon C9orf139 - chromosome 9 open reading missense uncharacterized protein frame 139 product CCDC66 - coiled-coil domain containing 66 missense not well described exon 3’ UTR CDC6 - cell division cycle 6 homolog (S. cerevisiae) missense regulation of DNA replication Exon 3’ UTR CLUAP1 - clusterin associated protein 1 missense gene dysfunction related to colonic neoplasms EPHX2 - epoxide hydrolase 2, cytoplasmic missense gene dysfunction related to CDS familial hypercholesterolemia Exon Intron FLT4 - fms-related tyrosine kinase 4 missense receptors for VEGF C and D, Exon mutation associated with Hereditary Lymphedema Type 1A IL4R - interleukin 4 receptor missense Variations have been Synon associated with atopy (manifests as allergic rhinitis, asthma, eczema) KHDC1 - KH homology domain containing 1 missense integral to membrane Exon NMI - N-myc (and STAT) interactor missense interacts with oncogenes, high expression in myeloid leukemias SELPLG - solute carrier family 6 (proline IMINO missense tethers myeloid and T- transporter), member 20 lymphocytes to activated platelets or endothelia expressing P-selectin SLC6A20 - solute carrier family 6 (proline IMINO missense membrane transporter transporter), member 20 Intron UNC93A unc-93 homolog A (C. elegans) missense integral to membrane, Exon associated with Ovarian Neoplasms C6orf70- chromosome 6 open reading frame 70 nonsense transmembrane protein

Table 18: Primary Candidates – SNP located in Exons, CDS, 5’ UTRs, or 3’ UTRs in addition to other gene relationships

Official Gene ID Description Relationship AKR1C2 aldo-ketoreductase family 1, Exon member C2 Intron BCL2L14 BCL2-like 14 (apoptosis Exon facilitator) Intron Upstream C6orf108 chromosome 6 open reading cds frame 108 exon synon C6orf147 chromosome 6 open reading exon frame 147 C9orf16 chromosome 9 open reading exon frame 16 intron CAND1 cullin-associated and downstream neddylation-dissociated 1- exon transcriptional regulator 3’ UTR CCDC69 coiled-coil domain containing 69 exon intron CD209 CD209 molecule – exon transmembrane receptor of 3’ UTR dendritic cells and macrophages FAXC failed axon connections homolog downstream (Drosophila) exon synon intron 3’ UTR FLJ45256 uncharacterized LOC400511 exon FP588 uncharacterized LOC92973 exon upstream HDAC9 histone deacetylase 9 exon 3’ UTR LINC00032 long intergenic non-protein exon coding RNA 32 LINC00242 long intergenic non-protein exon coding RNA 242 LINC00475 long intergenic non-protein exon coding RNA 475 intron LINC00476 long intergenic non-protein exon coding RNA 476 intron LINC00598 long intergenic non-protein exon coding RNA 598 intron LOC100128909 uncharacterized exon LOC100507498 uncharacterized exon

Table 18 Continued: Primary Candidates – SNP located in Exons, 5’ UTRs, or 3’ UTRs in addition to other gene relationships

Official Gene ID Description Relationship LOC644662 Uncharacterized exon intron LOC644838 uncharacterized exon LOC645206 uncharacterized exon LOC647979 uncharacterized exon RBM39 RNA binding motif protein 39 – exon transcriptional activator intron SEC63 SEC63 homolog (S. cerevisiae) ER exon transporter intron SLC6A18 solute carrier family 6, member exon 18 synon SWI5 SWI5 recombination repair exon homolog (yeast)- required for intron double strand break repair TMEM245 transmembrane protein 245 exon intron TRAPPC9 trafficking protein particle exon complex 9 – NFĸ-B signaling intron UG0898H09 uncharacterized exon C6orf106 uncharacterized 3’ UTR C6orf163 uncharacterized 3’ UTR CCDC170 no functional information 3’ UTR available CCDC90A regulates mitochondrial calcium 3’ UTR intake CD80 B lymphocyte activation antigen 3’ UTR CLIC6 chloride intracellular channels 3’ UTR ERCC6 DNA binding protein- excision 3’ UTR repair EXOC6B exocytosis 3’ UTR IFI35 interferon-induced protein 35 3’ UTR PIWIL4 spermatogenesis, represses 3’ UTR transposable elements POU6F2 transcriptional regulation, tumor 3’ UTR suppressor PPM1A negative regulator of cell stress 3’ UTR response SLC6A19 leucine membrane transporter 3’ UTR SLC6A5 sodium/chloride transport 3’ UTR SLC9A3R2 sodium/hydrogen exchange in 3’ UTR colon

Table 18 Continued: Primary Candidates – SNP located in Exons, 5’ UTRs, or 3’ UTRs in addition to other gene relationships

Official Gene ID Description Relationship SMARCD2 actin dependent regulator of 3’ UTR chromatin

SYK spleen tyrosine kinase 3’ UTR

TAF8 RNA polymerase II, TATA box 3’ UTR binding protein (TBP)-associated factor TIMMDC1 translocase of inner 3’ UTR mitochondrial membrane TTC9 not well characterized 3’ UTR VAC14 Vac14 homolog (S. cerevisiae), 5’ UTR activator of PtdIns ZAP70 protein tyrosine kinase, plays a 5’ UTR role in T cell activation IL15RA cytokine receptor, enhances CDS expression of apoptosis inhibitor BCL2

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, therapy- SEPT9 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

Table 20: 106 Genes found in common between Secondary Candidates and A/I Gene List

APAF1 CMA1 IL19 PPP2R2B API5 CTLA4 IL21 PRKCZ AVEN CUL5 IL21R PTGES BAG3 CXCL13 IL22 PTGS2 BCL2L11 DPP4 IL4 RGS3

BCL2L13 DUSP16 IL7 SELPLG BCL2L14 DUSP6 INHBA SOX4 BID EGFR INPP5D SPP1 BIRC8 EPHX2 ITGA9 STAT3 C6 ESR2 ITGB2 STAT5B

C9 ETS1 JAK2 STK17A CASP10 FAF1 MAP3K5 SYK CASP2 FGF1 MAPK10 TIRAP CCL11 FYN MAPK13 TNFRSF11B CCL21 GADD45G MAPK14 TNFRSF1B CCR3 GSTO1 MDM2 TNFRSF21 CCR8 GSTP1 MMP2 TNFRSF9 CD2 GZMB MYB TNFSF10 CD34 HDAC4 NFKB1 TNFSF13B CD4 HMOX1 NMI TNFSF14 CD44 HRK NOS1 TP53BP2 CD48 IFI16 NR3C1 TRAF5 CD53 IFIT2 NR4A2 TRAF6 CD6 IFNG NUMA1 UCHL5

CD7 IFNGR2 PECAM1 VDR CLU IGBP1 PIK3R1 IL15RA PPM1A ZAP70

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,

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 Affymetrix Immune Array Human and Inflammation Apoptosis Gene List 90 1,084

*only those SNPs of interest are shown

Master Apoptosis/ All PG SNPs + Genes Immune/ Inflammation Data Set Gene List 18,477 SNPs 64,997 2,586

13 5’ UTR -init

25 CDS 2 245 Nonsense Exons 84 7 Missense Splice site Fig. 43: A “Master” List of Apoptosis/Immunity/Inflammatory Genes was compiled and compared with the PG Gene List.

Table 21: “Master” genes in which PG SNPs are found in exonic regions

Table 22: “Master” SNPs that cause missense or nonsense

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.

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/RAB6- Genome-wide association with bone mass and geometry in the Framingham Heart Study, interacting/CAST family member 2 EVC2 Ellis van Creveld Ellis-van Creveld syndrome, syndrome 2 TAPBP TAP binding Bare lymphocyte syndrome, type I, protein (tapasin) C12orf43 chromosome 12 Population-based genome-wide association studies reveal six loci influencing plasma levels of liver open reading enzymes, frame 43 EPHX2 epoxide hydrolase Hypercholesterolemia, familial, due to LDLR defect, modifier of, 2, cytoplasmic FLT4 fms-related Hemangioma, capillary infantile, somatic, Lymphedema, hereditary I, tyrosine kinase 4 G6PC2 glucose-6- A Polymorphism Within the G6PC2 Gene is Associated with Fasting Plasma Glucose Levels, Fasting phosphatase, plasma glucose level QTL 1,Genome-wide association analysis of metabolic traits in a birth cohort catalytic, 2 from a founder population, Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels, HHIP hedgehog Genome-wide association analysis identifies 20 loci that influence adult height, Identification of ten interacting protein loci associated with height highlights new biological pathways in human growth, Many sequence variants affecting diversity of adult human height, IL23R interleukin 23 A genome-wide association study identifies IL23R as an inflammatory bowel disease gene,Crohn receptor 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, IL4R interleukin 4 AIDS, slow progression to, Atopy, susceptibility to, receptor MC3R melanocortin 3 Obesity, severe, susceptibility to, Obesity, severe, susceptibility to, BMIQ9,Obesity, susceptibility to, receptor BMIQ9,Obesity/hyperinsulinism, susceptibility to, PPARG peroxisome A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants, proliferator- Carotid intimal medial thickness 1,Diabetes mellitus, insulin-resistant, with acanthosis nigricans and activated receptor hypertension, Diabetes, type 2,Genome-wide association analysis identifies loci for type 2 diabetes gamma 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, SELL selectin L IgA nephropathy susceptibility to, SPINK5 serine peptidase Atopy,Netherton syndrome, inhibitor, Kazal type 5 TECTA tectorin alpha Deafness, autosomal dominant 12,Deafness, autosomal dominant 8,Deafness, autosomal dominant 8/12,Deafness, autosomal recessive 21, TAP2 transporter 2, ATP- Bare lymphocyte syndrome, type I, due to TAP2 deficiency, Wegener-like granulomatosis, binding cassette, sub-family B (MDR/TAP)

TRAPPC1 IL7 IL4 IL22 IL4R IL21R IL15RA IL23R TNFRSF11B TRAPPC9 TNFRSF1B TRAP1 NLRP4 CD2 TNFRSF21 NLRP9 CD34 TNFSF10 NLRP3 CD4 NLRP7 CD44 CD48 CD53 CD6 BID CD7 PLCG2 IKBKE* CD80 CD209 CYP26B CASP10 CDH12 CASP2 1 FLT4 CASP7 BCL211 KHDC1 BCL213 NMI BCL214 SELPLG SELL SELP LY75 LY75-CD302 PTDSS1

NFĸB1 Key

Missense

Intron PARP4 PPM1A 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,

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

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.

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.

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.

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

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

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

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).

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 (death- inducing 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).

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.

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

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.

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

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 IL-

2/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),

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 inflammatory- related 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

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 Gene Description PG SNP relationship to gene IL15RA interleukin 15 receptor, alpha CDS, intron IL23R interleukin 23 receptor CDS, intron, missense, UTR3, downstream IL33 interleukin 33 upstream, intron IL4R interleukin 4 receptor intron, missense, synon

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 Gene SNP ID Relationship to Gene AA IL15RA SNP_A-8403129 intron, CDS

Table 30: The IL23R PG SNPs

PG List Gene SNP ID Relationship to Gene BB IL23R SNP_A-1857631 intron BB IL23R SNP_A-1914207 intron BB IL23R SNP_A-1916487 intron BB IL23R SNP_A-1946676 missense, UTR-3, CDS, intron BB IL23R SNP_A-2131581 intron BB IL23R SNP_A-2084144 intron BB IL23R SNP_A-8371374 intron BB IL23R SNP_A-8624598 intron AA IL23R SNP_A-2131744 intron

Table 31: The IL4R PG SNPs

PG List Gene SNP ID Relationship to Gene AB IL4R SNP_A-8364560 intron AB IL4R SNP_A-8322768 intron AB IL4R SNP_A-8393504 intron AB IL4R SNP_A-8443169 intron BB IL4R SNP_A-2138211 intron BB IL4R SNP_A-2231835 intron BB IL4R SNP_A-2309558 intron

BB IL4R SNP_A-8698067 intron AA IL4R SNP_A-8310173 intron AA IL4R SNP_A-8448573 missense, synon AA IL4R SNP_A-8473405 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.

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 1,4,5- trisphosphate (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 IL-

1β, 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.,

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,

2+ PLCGγ2 hydrolyzes PIP2 to generate DAG and IP3. Downstream DAG targets act to liberate Ca 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

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 PLCG2 SNP_A-1793103 intron BB PLCG2 SNP_A-1959951 intron BB PLCG2 SNP_A-2142638 missense BB PLCG2 SNP_A-8291405 intron BB PLCG2 SNP_A-8312046 intron BB PLCG2 SNP_A-8404658 intron BB PLCG2 SNP_A-8637868 intron AA PLCG2 SNP_A-1802064 intron AA PLCG2 SNP_A-2004923 intron AA PLCG2 SNP_A-8294826 intron AA PLCG2 SNP_A-8296123 intron AA PLCG2 SNP_A-8356844 intron AA PLCG2 SNP_A-8454500 intron AA PLCG2 SNP_A-8486115 intron AA PLCG2 SNP_A-8486609 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,

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.

Table 33: PG SNPs associated with TAP (transporter 2, ATP-binding cassette) binding proteins

AA TAP2 transporter 2, ATP-binding cassette, sub-family B SNP_A-8652719 missense, (MDR/TAP) exon AB TAPBP TAP binding protein (tapasin) 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.

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).

Table 34: PG SNPs with Cytochrome P450 gene associations Gene Gene Description SNP ID PG List CYP26B1 cytochrome P450, family 26, subfamily B, polypeptide 1 SNP_A-2006472 BB SNP_A-4231963 BB SNP_A-2313026 BB SNP_A-4253592 BB SNP_A-8486534 BB SNP_A-8601186 BB SNP_A-8690036 BB SNP_A-1810490 AA SNP_A-2186413 AA SNP_A-2280660 AA SNP_A-2293950 AA SNP_A-4205639 AA SNP_A-8474562 AA SNP_A-8425940 AA SNP_A-8657793 AA SNP_A-8689957 AA SNP_A-8693552 AA CYP4F2 cytochrome P450, family 4, subfamily F, polypeptide 2 SNP_A-4259822 BB SNP_A-8539055 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

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.

Table 35: PG SNPs with SELL and SELPLG (Primary Candidate/Master genes) gene associations

PG SNP List Gene Name SNP ID Gene Description SNP location BB SELL SNP_A-1905707 selectin L intron BB SELL SNP_A-2081388 selectin L intron BB SELL SNP_A-2203047 selectin L upstream (SELP downstream (SELL) BB SELL SNP_A-8685447 selectin L intron AA SELL SNP_A-2261141 selectin L missense BB SELPLG SNP_A-8437819 selectin P ligand downstream (SELPLG) AA SELPLG SNP_A-2188713 selectin P ligand missense

Table 36: PG SNPs with SELP (not found in Primary Candidate/Master genes) gene

PG SNP List Gene Name SNP ID Gene Description SNP location BB SELP SNP_A-2268447 selectin P ligand upstream (SELP) downstream (SELL) BB SELP SNP_A-8614156 selectin P ligand intron AA SELP SNP_A-8596185 selectin P ligand 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;

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).

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

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 Relationship to Gene Gene Gene Description SNP_A-4280126 UTR-3 NLRP4 NLR family, pyrin domain containing 4 SNP_A-1801723 UTR-3 NLRP9 NLR family, pyrin domain containing 9 SNP_A-8603308 exon NLRP3 NLR family, pyrin domain containing 3 SNP_A-2297859 utr5-init NLRP7 NLR family, pyrin domain containing 7

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.

Table 39: PG SNPs located in PTDSS1 genic regions PG List SNP ID Relationship to Gene Gene Gene Description AA SNP_A-1907476 intron PTDSS1 phosphatidylserine synthase 1 AB SNP_A-8341715 intron PTDSS1 phosphatidylserine synthase 1 AA 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

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(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.

Table 40: PG SNPs with associations to CSF1R genes SNP ID Relationship to Gene Gene Gene Description SNP_A-8491654 intron CSF1R colony stimulating factor 1 receptor SNP_A-8434093 upstream, intron CSF1 colony stimulating factor 1 (macrophage) SNP_A-1848362 upstream CSF1 colony stimulating factor 1 (macrophage) SNP_A-8375219 upstream CSF2RB colony stimulating factor 2 receptor, beta, low- affinity (granulocyte-macrophage) SNP_A-1882364 upstream CSF1 colony stimulating factor 1 (macrophage) SNP_A-4272174 upstream CSF1 colony stimulating factor 1 (macrophage) SNP_A-2000292 intron CSF1 colony stimulating factor 1 (macrophage) SNP_A-8604982 upstream CSF1 colony stimulating factor 1 (macrophage) SNP_A-8304338 intron CSF2RB colony stimulating factor 2 receptor, beta, low- affinity (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

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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

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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.

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