The Role of Circulating Mitochondrial DNA in Juvenile Idiopathic Arthritis

Lindsay Kristin Cho

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Immunology University of Toronto

Lindsay Kristin Cho

Master of Science

Department of Immunology University of Toronto

2016 Abstract

Previous studies have demonstrated mitochondrial DNA (mtDNA) activates Toll-like receptor-9 signaling, and may activate inflammasomes leading to the release of pro-inflammatory cytokine

IL-1b. Chronic inflammation may lead to cell death and the release of mtDNA into circulation, where elevated levels have been reported in plasma from patients with autoimmune diseases.

Juvenile idiopathic arthritis (JIA) is characterized by inflammatory arthritis of unknown etiology.

Classified into seven subtypes, the systemic JIA (sJIA) and enthesitis related arthritis subtypes are thought to have autoinflammatory components as patients have elevated levels of IL-1β. mtDNA, transfected with lipofectamine, was immunostimulatory; mtDNA stimulated upregulation of pro-

IL-1b gene and protein expression and IL-1b secretion in a dose-dependent manner. Additionally, treatment significantly decreased plasma levels of circulating mtDNA in JIA patients. In sJIA patients, circulatory mtDNA was moderately associated with disease activity and molecules related to inflammasome activation. Altogether, these results provide new insights into the pathogenesis of JIA.

Acknowledgments

My graduate school experience has been a remarkable journey where I have gained knowledge and grown immensely. Without the support of several people, this project would not have been possible, and I would not have been able to achieve my goals. I would like to take this opportunity to thank these people. First and foremost, thank you to Dr. Rae Yeung for being my supervisor, a true mentor, and an inspirational role model. You introduced me to the world of research and scientific inquiry, and I am forever grateful for the continuous encouragement, optimism, and support you have provided me with throughout the years. Thank you for always pushing me to reach towards my true potential. You inspire me with your unwavering kindness and determination, and without your guidance, I would not have been as successful in this journey. As well, thank you to my supervisory committee members Dr. Dana Philpott and Dr. Rob Inman for the assistance they provided at all levels of my research project. I appreciate your enthusiasm, advice, support, and the time you’ve both given to me while listening to my presentations and helping to shape my project. I would also like to thank the past and present members of the Yeung lab: Dr. Trang Duong – thank you for always being available whenever I had any issues; you always made time in your busy day to help me troubleshoot my experiments, answer my questions, and calm me down. Thank you for teaching me and for helping me to make sense of everything that I didn’t understand. You were an amazing resource and a true friend, thanks for everything! Suzanne (Suz) Tam – thanks for all of your support in the lab. You welcomed me into the lab when I was a naïve undergrad, and I will miss coming to your office and listening to your banter with TD. As well, thanks for always sharing your snacks with me! Devina Ramsaroop – thanks for your technical assistance with my experiments. Without your help, I would not have been as efficient in completing my experiments and I appreciate that you always took time out of your busy day to answer all of my clinical questions. Martin Alphonse – thank you for being a mentor in the lab and for your assistance in my training. Your wealth of knowledge and friendship has been invaluable. Lysa Langevin and Alicia Fisch(y) – you girls have been amazing friends! Thanks for being there on the daily, I wouldn’t have make it through grad school without you both. We will forever be Minsoo’s angels ;) Thanks as well to Minsoo Yoon and Zainab Motala (honorary Yeung lab member!). I’m grateful I had the opportunity to become close friends with all four of you. Thanks for making grad school such a fun experience, and I will miss our everyday laughter and jokes. Thanks as well to all of the other members of the lab including Kevin Sorokin, Andrey Mikhaylov, and Simon Eng: you always kept our lunch hours interesting, and I (mostly) enjoyed listening to and participating in your discussions. Finally, thank you to my friends (especially Jonathon Asa and CJ Wu) and my family for your eternal love and encouragement. I am indebted to all of you for your support, without which I would not have been able to successfully complete this part of my journey.

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Table of Contents

Acknowledgments ...... iii

Table of Contents ...... iv

List of Tables ...... vi

List of Figures ...... vii

List of Abbreviations ...... ix

Introduction: ...... 1

1.1 Innate immune response: ...... 1

1.1.1 Pattern recognition receptors and Toll-like receptor signaling: ...... 1

1.1.2 Inflammasome activation: ...... 5

1.1.3 Interleukin-1b and Interleukin-18: ...... 9

1.2 Circulating nucleic acids: ...... 12

1.2.1 Brief overview: ...... 12

1.2.2 Circulating mitochondrial DNA: ...... 14

1.3 Juvenile idiopathic arthritis: ...... 17

1.3.1 Clinical characteristics: ...... 17

1.3.2 Immunological characteristics: ...... 20

Rationale: ...... 25

Hypothesis: ...... 25

Experimental aims: ...... 26

Methods: ...... 27

5.1 Generation of DNA fragments: ...... 27

5.2 Cell culture: ...... 28

5.3 Quantitative real-time reverse-transcriptase PCR: ...... 30

5.4 Western blot analysis: ...... 30

5.5 Enzyme-linked immunosorbent assays: ...... 31 iv

5.6 Patient samples: ...... 32

5.7 Biological sample collection: ...... 32

5.8 Microarray analysis: ...... 33

5.9 Cytokine and chemokine analysis: ...... 33

5.10 Quantification of CNAs: ...... 34

5.11 Statistical analysis: ...... 35

Results ...... 38

6.1 Generation of mtDNA fragments through PCR ...... 38

6.2 PMA stimulation differentiates THP-1 monocytes into macrophages ...... 40

6.3 Stimulation with mtDNA upregulates IL-1b gene and protein expression ...... 41

6.4 Stimulation with mtDNA induces release of IL-1b from macrophages ...... 46

6.5 Patient cohort demographics: ...... 50

6.6 Circulating mtDNA is significantly decreased after treatment in autoinflammatory subtypes...... 52

6.7 Gene expression significantly differs after treatment in systemic JIA patients ...... 56

6.8 Associations between circulating mtDNA and clinical measures of disease activity .60

6.9 Associations between circulating mtDNA and biological activity levels ...... 62

Discussion: ...... 65

7.1 mtDNA stimulation induces production of IL-1b in macrophages ...... 65

7.2 mtDNA is weakly associated with JIA disease activity ...... 69

7.3 Future directions: ...... 73

Conclusion: ...... 76

References: ...... 78

List of Tables

Table 1. Sequences of mtDNA and gDNA fragments synthesized ……………………………..35

Table 2. Sequences of primers and probes ……………………………………………………...35

Table 3. Subset of genes measured by microarray………………………………………………36

Table 4. Cytokines and chemokines measured by multiplex ELISA …………………………...36

Table 5. Characteristics and measures of BBOP patients at enrollment ………………………..50

Table 6. Associations between circulating mtDNA and clinical measures of disease activity.…60

Table 7. Associations between circulating mtDNA and patient cytokine levels ……………...... 61

List of Figures

Figure 1. TLR signaling pathway. Adapted from (2)...... 4

Figure 2. NLRP3 inflammasome activation pathway. Adapted from (11)...... 8

Figure 3. Verification of DNA fragments by gel electrophoresis...... 39

Figure 4. Stimulation with PMA for 24 hours results in robust IL-1b secretion from macrophages...... 40

Figure 5. Stimulation with mtDNA for 6 hours increased IL-1B gene expression...... 42

Figure 6. Stimulation with mtDNA upregulates IL-1B gene expression in a dose-dependent manner...... 43

Figure 7. Pro-IL-1b levels increased after stimulation with mtDNA for 12 hours...... 44

Figure 8. Pro-IL-1b levels increased in a dose-dependent manner after mtDNA stimulation. .... 45

Figure 9. Kinetics of IL-1b secretion from macrophages...... 47

Figure 10. Stimulation with mtDNA induces dose-dependent release of IL-1b from macrophages...... 48

Figure 11. Stimulation with nucleic acids induces release of IL-1b from macrophages...... 49

Figure 12. CNAs are stable at room temperature after extraction from patient plasma...... 53

Figure 13. Circulating levels of mtDNA is significantly decreased after treatment in sJIA and ERA subtypes...... 54

Figure 14. Concentrations of circulating nucleic acids are not significantly different after treatment...... 55

Figure 15. Expression of IL-1 and IL-18 associated genes are significantly different after treatment...... 58

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Figure 16. Inflammasome associated genes are significantly different after treatment...... 59

Figure 17. Circulating mtDNA is associated with pre-treatment gene expression in ERA and sJIA patients...... 64

Figure 18. Proposed model for mtDNA-induced macrophage secretion of IL-1b ...... 77

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List of Abbreviations

16s 16s ribosomal RNA-transfer RNA-Leucine 2-ME 2-mercaptoethanol ACR American college of rheumatology AIM2 Absent in melanoma 2 ANA Antinuclear antibody AP-1 Activator-protein 1 ASC Apoptosis-associated speck-like protein containing a CARD ATP Adenosine triphosphate BBOP Biologically based outcome predictors in JIA study Ca2+ Calcium CAPS Cryopyrin associated periodic syndrome CARD Caspase activation and recruitment domain cDNA Complementary DNA CHAQ Childhood health assessment questionnaire CNA Circulating nucleic acid Cox II Cytochrome Oxidase II CREB cAMP response element-binding protein CRP C-reactive protein DAMP Damage associated molecular pattern DD Death domain DNA Deoxyribonucleic acid EGFR Epidermal growth factor receptor ELISA Enzyme-linked immunosorbent assay ER Endoplasmic reticulum ERA Enthesitis related arthritis ERAD ER-associated degradation ESR Erythrocyte sedimentation rate EULAR European league against rheumatism FasL Fas ligand GAPDH Glyceraldehyde 3-phosphate dehydrogenase gDNA Genomic DNA HLA Human leukocyte antigen

IFN Interferon Ig Immunoglobulin IkB Inhibitor of NFκB IKK IkB-kinase complex IL Interleukin IL-1R Type I IL-1 receptor IL-1RA IL-1 receptor antagonist IL-1RAP IL-1 receptor accessory protein IL-18BP IL-18 binding protein IL-18Ra IL-18 receptor a chain IL-18Rb IL-18 receptor b chain ILAR International league of associations for rheumatology IRAK IL-1R-associated kinase IRE Interferon-stimulated response element IRF Interferon regulatory factor JAQQ Juvenile arthritis quality of life questionnaire JIA Juvenile idiopathic arthritis JNK c-Jun N-terminal kinase K+ Potassium LPS Lipopolysaccharide LRR Leucine-rich repeat MAL MyD88-adaptor-like protein MAPK Mitogen-activated protein kinase MAVS Mitochondrial antiviral signaling protein MAS Macrophage activation syndrome MHC Major histocompatibility complex MIF-1 Migration inhibitor factor-1 MP Microparticle MRI Magnetic resonance imaging mtDNA Mitochondrial DNA MyD88 Myeloid differentiation primary-response protein 88 NACHT Nucleotide-binding and oligomerization domain NEMO NFκB essential modulator

NET Neutrophil extracellular trap NFkB Nuclear factor-kB NLR NOD-like receptor NLRC4 NOD-, LRR- and CARD-containing 4 NLRP1 NOD-, LRR- and pyrin domain-containing 1 NLRP3 NOD-, LRR- and pyrin domain-containing 3, or cryopyrin RA Rheumatoid arthritis RF Rheumatoid factor RNA Ribonucleic acid ROM Range of motion ROS Reactive oxygen species rRNA Ribosomal RNA PBMC Peripheral blood mononuclear cells P2X7R P2X7 receptor PAMP Pathogen associated molecular pattern PGA Physician global assessment PKC Protein kinase C PMA Phorbol 12-myristate 13-acetate PRR Pattern recognition receptor PYHIN pyrin and HIN domain-containing protein qPCR Quantitative polymerase chain reaction sJIA Systemic arthritis SLE Systemic lupus erythematosus SpA Juvenile spondylarthropathy sPLA2-IIa Secretory phospholipase A2 IIA Syk Spleen tyrosine kinase TAB TAK-binding protein TAK Transforming growth factor-b activated kinase TCAG The Centre for Applied Genomics Th T-helper TIR Toll/IL-1 receptor TLR Toll-like Receptor TNF-a Tumor necrosis factor-a

TRAF TNF receptor-associated factor TRAM TRIF-related adaptor molecule TRIF TIR domain-containing adaptor protein inducing IFNβ tRNA Transfer RNA UPR Unfolded protein response VAS Visual analog scale

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Introduction: 1.1 Innate immune response:

1.1.1 Pattern recognition receptors and Toll-like receptor signaling:

The innate immune system uses several mechanisms to defend against the invasion of pathogens. Innate immune cells, such as dendritic cells and macrophages, have extracellular and intracellular germ-line encoded pattern recognition receptors (PRRs) to detect and respond to the presence of diverse pathogen- and damage-associated molecular patterns (PAMPs and DAMPs, respectively). PAMPs are conserved microbial molecules that are not produced by mammalian host cells, such as bacterial cell wall component lipopolysaccharide (LPS). In contrast, DAMPs are molecules released by stressed cells that act as endogenous danger signals to promote inflammation and tissue repair.

One major family of PRRs which recognize PAMPs are Toll-like receptors (TLRs). TLRs are type I integral membrane glycoproteins which contain extracellular leucine-rich repeat (LRR) motifs and conserved cytoplasmic domains, also known as the Toll/IL-1 receptor (TIR) homology domain as they resemble the cytoplasmic regions of interleukin (IL)-1 receptors. Currently, 11 members of the TLR family have been identified in humans; TLRs 1, 2, 4, 5, 6, and 11 are expressed on the plasma membrane while TLRs 3, 7, 8, and 9 are expressed on the endosomal membrane. In general, the extracellular TLRs recognize bacterial components while the intracellular TLRs recognize nucleic acids. For instance, TLR-4 recognizes lipopolysaccharide

(LPS) while TLR-9 recognizes unmethylated CpG DNA sequences (1).

When PRRs are engaged by DAMPs and PAMPs, a variety of signaling pathways are initiated. This includes activation of the nuclear factor-kB (NFkB) signaling pathway which leads

1 2 to the transcription of pro-inflammatory cytokines such as tumor necrosis factor (TNF)-a. In particular, TLR signaling can activate two main signaling pathways, dependent on which adaptor molecule is recruited. The two main signaling pathways are either myeloid differentiation primary- response protein 88 (MyD88)-dependent or MyD88-independent. All TLRs are capable of signaling via the MyD88-dependent pathway except for TLR-3; TLR-4 can signal through both

MyD88-dependent or MyD88-independent pathways. The MyD88-independent pathway uses the

TIR domain-containing adaptor protein inducing IFNβ (TRIF) and TRIF-related adaptor molecule

(TRAM) adaptor molecules (2).

In general, TLR signaling begins with ligand-induced dimerization of receptors which recruits downstream signaling molecules, including adaptor proteins such as MyD88, to the cytoplasmic TIR domain (Figure 1). Both TLR-2 and TLR-4 require an additional adaptor protein,

MyD88-adaptor-like protein (MAL), in order to associate with MyD88. TLR-9 also signals through the MyD88-dependent pathway. TLR association with MyD88 then recruits IL-1R- associated kinase 4 (IRAK4) and IRAK1, where binding of MyD88 to IRAK4 activates IRAK4- mediated phosphorylation of IRAK1 and subsequent IRAK1 autophosphorylation. TNF receptor- associated factor 6 (TRAF6), an E3 ubiquitin ligase, is able to bind phosphorylated IRAK1 and the IRAK1-TRAF6 complex dissociates from the receptor to form a complex with transforming growth factor-b activated kinase (TAK1), TAK1-binding protein (TAB1), and TAB2 or TAB3 at the plasma membrane. Interaction with this complex induces phosphorylation of both TAK1 and

TAB2/3. Subsequently, IRAK1 is degraded and the rest of the complex is translocated to the cytoplasm where TRAF6 is ubiquitinated, inducing the activation of TAK1. Activated TAK1 mediates the phosphorylation of various mitogen-activated protein (MAP) kinases such as c-Jun

N-terminal kinase (JNK) and p38 MAPK. TAK1 can also activate NFκB signaling through

3 phosphorylation of the inhibitor of NFκB (IkB)-kinase (IKK) complex, which consists of IKK-α,

IKK-β and IKK-γ (also known as the NFκB essential modulator, NEMO). The IKK complex is the converging point for the activation of NFkB by various stimuli. Thus, phosphorylation of the

IKK complex leads to phosphorylation of IkB, which regulates NFkB activation through sequestering the transcription factor in the cytoplasm. IkB phosphorylation induces its ubiquitination and degradation by the proteasome, allowing NFkB to translocate to the nucleus and induce expression of its target genes, including pro-inflammatory cytokines and chemokines

(1,3). Stimulation of TLRs can also lead to the activation of the transcription factor interferon regulatory factor (IRF)-5, which binds interferon (IFN)-stimulated response elements (IREs) in the nucleus to induce the expression of Type 1 IFNs, IFNa and IFNb (1).

TLR4 ligand

TLR4 receptor

cytoplasm MyD88 MAL

IRAK4 IRAK1 IRAK4 IRAK1 MyD88 TRAF6 TRAF6 TAB1 TAB2/3

TA K1

TLR9 receptor

TLR9 ligand IKK IKK-! complex endosome IKK-" IKK-β

p38 proteasome JNK degradation I#B MAPK NF#B

AP1 CREB

nucleus Pro-inflammatory NF#B AP1 CREB cytokines

Figure 1. MyD88-dependent TLR signaling pathway. Adapted from (2).

TLR signaling is initiated by ligand-induced dimerization of the TLR receptor. This leads to recruitment of TIR domain containing-adaptor proteins such as MAL and MyD88. Engagement of adaptor molecules stimulates downstream signaling pathways leading to the activation of the TAB1-TAB2/3-TAK1 complex via IRAK4, IRAK1, and TRAF6. Subsequent phosphorylation of the IKK complex results in phosphorylation of IkB resulting in degradation via the proteasome, allowing NFkB to translocate to the nucleus and induce expression of pro-inflammatory cytokines. Activation of TAK1 also results in phosphorylation and activation of MAPKs, JNK and p38, resulting in the activation of downstream transcription factors.

1.1.2 Inflammasome activation:

Inflammasomes are thought to be key regulators of innate, adaptive, and host responses to

PAMPs and DAMPs, and have mainly been characterized in immune cells of the myeloid lineage such as macrophages. Various PAMPs and DAMPs may also be recognized by intracellular PRRs including the NOD-like receptor (NLR) family of receptors. NLR proteins typically contain three domains including an amino-terminal death-fold domain (DD), central nucleotide-binding and oligomerization (NACHT) domain, and carboxy-terminal LRRs. The amino-terminal death-fold domain can be either a pyrin or caspase activation and recruitment (CARD) domain. Unlike other

PRRs, some NLR proteins respond to PAMPs and DAMPs through the assembly of multimeric protein complexes called inflammasomes (4).

Several types of inflammasomes have been described to date. They are comprised of the inflammasome sensor molecule, an adaptor protein, and caspase-1. The best characterized inflammasomes contain either a NLR sensor molecule, including NLRP1 (NOD-, LRR- and pyrin domain-containing 1), NLRP3 (also known as cryopyrin), and NLRC4 (NOD-, LRR- and CARD- containing 4), or a PYHIN (pyrin and HIN domain-containing protein) family molecule such as absent in melanoma 2 (AIM2). While several activation pathways have been described, not all of the ligands which activate inflammasomes are known. The NLRP3 inflammasome is activated by a variety of PAMPs and DAMPs originating from various pathogens; activators of the NLRP3 inflammasome includes crystalline material, peptide aggregates, bacterial toxins, bacterial components such as LPS, and adenosine triphosphate (ATP) (4,5). In addition, it is thought that the C-terminal LRRs on the NLR molecules might be involved in interacting with ligands.

In general, inflammasome activation leads to the recruitment of the adaptor protein apoptosis-associated speck-like protein containing a CARD (ASC), which interacts with NLR

6 proteins via its pyrin domain. This pyrin-mediated interaction triggers ASC to assemble into a large protein speck which consists of multimers of ASC dimers. ASC also contains a CARD domain, which allows it to recruit and interact with pro-caspase-1. Therefore, assembly of the inflammasome complex activates recruitment and self-cleavage of pro-caspase-1 into caspase-1, its active form. Studies have reported that the NLRC4 and NLRP1 inflammasomes can activate caspase-1 directly through their CARD domain, without ASC recruitment, however recruitment of ASC greatly enhances the formation of inflammasome complexes (6). Active caspase-1, an inflammatory cysteine protease, proteolytically cleaves pro-IL-1β and pro-IL-18 cytokines into their mature, active forms, which are released from cells in a non-classical secretion pathway. In addition, inflammasome activation may lead to pyroptosis, a rapid inflammatory form of programmed cell death which requires activation of caspase-1.

Unlike other NLRs, activation of the NLRP3 inflammasome is regulated at both transcription and post-translational levels, where two signals are required for inflammasome activation. In resting myeloid cells, NLRP3 is expressed at low levels (Figure 2). Upregulation of

NLRP3 transcription and translation requires a priming signal, also known as signal 1, through activation of the NFkB pathway through either TLR signaling or cytokine receptors. A second signal is required for assembly of the NLRP3 inflammasome and subsequent maturation and secretion of IL-1β and IL-18 cytokines. Various mechanisms have been reported to act as a second signal to activate NLRP3 inflammasomes, including potassium (K+) efflux, calcium (Ca2+) influx, and generation of mitochondrial reactive oxygen species (ROS) (6). Other mechanisms regulating the NLRP3 inflammasome activation have also been reported. Studies have indicated that kinase signaling regulates inflammasome activation, where spleen tyrosine kinase (Syk) and JNK are crucial kinases upstream of both NLRP3 and AIM2 inflammasomes (7,8). Hara et al.

7 demonstrated that the formation of ASC specks required the activity of either Syk or JNK, where phosphorylation of ASC was critical for activation of ASC speck formation and subsequent caspase-1 activation (9). However, more research is required to determine the mechanism of how

Syk and JNK regulates formation of ASC specks. It has also been reported that spatial arrangement of intracellular organelles is important for NLRP3 inflammasome activation, where activation of the inflammasome triggers the reorganization of organelles so that the inflammasome complex is in close-proximity with the mitochondria. Studies have reported that the mitochondrial antiviral signaling protein (MAVS) adaptor protein is required for recruitment of NLRP3 to the mitochondria (5).

In addition, the NLRP3 inflammasome has an important role in regulation of innate immune inflammatory responses and sensing danger signals as various DAMPs have been reported to activate the NLRP3 inflammasome. For instance, multiple studies have reported that extracellular ATP, a well-known endogenous danger signal released from damaged cells, can act as the second signal to activate the NLRP3 inflammasome via the P2X7 receptor (P2X7R) and

Pannexin-1 receptor (5). However, inflammasomes are also implicated in autoimmune and autoinflammatory diseases; genetic polymorphisms and mutations in inflammasome components have been associated with the susceptibility, activity, and treatment responses of several autoimmune and autoinflammatory diseases. Autoinflammatory diseases are characterized by recurrent episodes of acute inflammation and clinical manifestations include rash, arthritis, and

CNS involvement (10). For instance, mutations in the NLRP3 gene leads to dysregulation of the

NLRP3 inflammasome and causes cryopyrin associated periodic syndrome (CAPS), a prototypic autoinflammatory disease (4).

extracellular space PRR

cytosol microbial PAMPs, endogenous DAMPs (ATP), Signal 2 crystals, Ca2+ influx, inflammasome + activation K efflux, Signal 1 mitochondrial ROS NLRP3 “priming” NF!B Inflammasome activation assembly

ASC

NF!B caspase-1

proteolytic cleavage NLRP3 nucleus

IL-1β pro-IL-1β pro-IL-1β IL-18 pro-IL-18 pro-IL-18

Figure 2. NLRP3 inflammasome activation pathway. Adapted from (11).

Activation of the NLRP3 inflammasome requires two signals. The inflammasome is “primed” through activation of the NFkB pathway. Stimulation of PRRs, such as TLR4 or TLR9, induces the activation of NFkB signaling, leading to the transcription and translation of NLRP3, pro-IL- 1b, and pro-IL-18. A second signal is required for activation of the inflammasome and assembly of the multimeric complex consisting of NLRP3, adaptor protein ASC, and pro-caspase-1. Microbial PAMPs, endogenous DAMPs, crystals, calcium influx, potassium efflux, and mitochondrial ROS have a role in the regulation of NLRP3 inflammasome activation. Assembly of the inflammasome complex cleaves pro-caspase-1 into its bioactive form, caspase-1, allowing it to process pro-IL-1b and pro-IL-18 into their mature, secreted forms.

1.1.3 Interleukin-1b and Interleukin-18:

The IL-1 family of cytokines, including IL-1b and IL-18, are pro-inflammatory cytokines induced in response to PAMPs or DAMPs released by pathogens or damaged cells. The main cytokines in this family are IL-1a and IL-1b, which can both independently signal through the type I IL-1 receptor (IL-1R1). The IL-1R1 is in the same family of receptors as TLRs, and has three extracellular immunoglobulin (Ig)-like domains and conserved cytosolic TIR domains (12).

Therefore, the IL-1R1 signaling pathway is similar to the TLR signaling pathway. Binding of IL-

1b with the IL-1R1 induces a conformational change in the receptor, facilitating its recruitment and dimerization with the IL-1 receptor accessory protein (IL-1RAP), its co-receptor required for signal transduction. While the IL-1 receptor antagonist (IL-1RA) is also able to bind the IL-1R1 with similar specificity and affinity, it does not activate the receptor nor downstream signaling.

Similar to TLR signaling, the IL-1R1 signals through the MyD88-dependent pathway, leading to the activation of NFkB, JNK, and p38 MAPK signaling pathways. Activation of these pathways results in upregulation of multiple IL-1 responsive genes in multiple cell types, including monocytes, macrophages, epithelial cells, endothelial cells, and fibroblasts. Therefore, IL-1R1 signaling results in the production of pro-inflammatory cytokines such as TNF-a, IL-6, IL-8 and

IL-1b, creating in a positive feedback loop which amplifies the IL-1 response (12). A pro- inflammatory cytokine, IL-1b leads to fever and an acute-phase response. IL-1b can also enhance

T cell activation and antigen recognition, and has been reported to induce the development of Th17 cells together with IL-6 and TGF-b. Studies also indicate IL-1b is required for the production of

IL-17 from NKT cells (13).

IL-18 signals through binding the IL-18 receptor a chain (IL-18Ra), which induces a conformational change allowing it to form a heterodimeric complex with the IL-18 receptor b

10 chain (IL-18Rb). While the IL-18 receptor complex is in the same family of receptors as the IL-

1R1, the receptors are specific for their ligands and cannot be substituted for each other.

Interestingly, IL-18 has a much higher affinity for its receptor than IL-1b does for the IL-1R1.

While IL-18 is a pro-inflammatory cytokine, it does not result in fever as IL-18 does not induce production of cyclooxygenase-2 and prostaglandin E2 (13). However, IL-18 induces Th1 responses, together with IL-12 or IL-15, resulting in the production of IFNg from T-cells and NK cells. IL-12 and IL-15 can also increase expression of IL-18Rb in cells. The activity of IL-18 is also regulated by the presence of a high affinity, naturally occurring IL-18 binding protein (IL-

18BP). IL-18BP is a constitutively secreted protein, and is present in the serum of healthy humans at a 20-fold molar excess compared to IL-18 (14). In addition, IL-18BP is able to bind IL-18 at a higher affinity than that of the IL-18R, and a shift in the balance of IL-18 and IL-18BP levels has been implicated in several autoimmune diseases; the highest levels of serum IL-18 are observed in MAS patients (14). Therefore, when measuring levels of bioactive IL_18, it is important to consider the amount of free IL-18 and IL-18BP in the circulation, since IL-18BP may bind IL-18 and prevent it from activating IL-18R signaling.

Like the regulation of the NLRP3 inflammasome, secretion of IL-1b from macrophages has two main checkpoints. While monocytes constitutively express pro-IL-1b, macrophages require NFkB signaling to initiate the transcription and translation of pro-IL-1b. In contrast, pro-

IL-18 is constitutively expressed and its expression increases after macrophage activation.

Although mature IL-18 is exclusively processed through the activation of inflammasomes and caspase-1, other proteases can proteolytically cleave pro-IL-1b into its active, secreted form; these include proteinase-3 in neutrophils, and other proteases secreted by mast cells and neutrophils such as chymase and granzyme A (15).

While the main mechanism of IL-1b production is through inflammasomes, studies have indicated that IL-1b can also be produced through non-inflammasome mediated mechanisms.

While caspase-8 is recognized for its role in apoptosis, evidence has indicated that caspase-8 is required for the activation of NFκB signaling, where loss of caspase-8 results in defective NFκB signaling and failure to induce robust expression of pro-IL-1b (16). However, there is no consensus on the molecular mechanism by which caspase-8 mediates NFκB signaling. Studies have also demonstrated that caspase-8 can directly cleave pro-IL-1b into its mature bioactive form, either with or independent of the inflammasome depending on the stimuli (17). One mechanism of caspase-8-mediated IL-1β cleavage is through the Fas-Fas ligand (FasL) pathway. Evidence indicated Fas signaling resulted in FADD-mediated caspase-8 activation in macrophages and dendritic cells, and subsequent processing and release of IL-1β (18). In addition to the inflammasomes that activate caspase-1, a new non-canonical inflammasome which activates murine caspase-11 leading to the processing of IL-1β has been described; however, the intracellular receptor associated with this inflammasome has not yet been described. In humans, the ortholog of mouse caspase-11 is either caspase-4 or caspase-5 (19).

As a major mediator of inflammation in a variety of tissues, IL-1β is a key pro- inflammatory cytokine which affects multiple cell types. Produced by monocytes, macrophages, and dendritic cells, IL-1β may contribute greatly to autoinflammatory diseases. The effectiveness of treatments with recombinant IL-1RA or antibodies against IL-1b have demonstrated a central role for IL-1b in autoinflammatory diseases.

1.2 Circulating nucleic acids:

1.2.1 Brief overview:

There are several consequences to chronic inflammation, which can lead to tissue damage and cell death. Studies have indicated that cell death can result in the release of cellular contents, including nucleic acids. Nucleic acids found outside of cells in bodily fluids or in circulation are defined as circulating nucleic acids (CNAs). In the blood, CNAs are comprised of multiple types of single-stranded and double-stranded nucleic acids, including both genomic DNA (gDNA) and mitochondrial DNA (mtDNA) of various lengths; both short fragments under 1kb and larger fragments are found in circulation (20). While the origin of CNAs is not fully understood, studies have suggested that small, fragmented CNAs can be passively released during apoptotic or necrotic cell death, or actively released through other mechanisms similar to the release of neutrophil extracellular traps (NETs) (21,22). However, the exact mechanisms of CNAs release or secretion are not well understood.

The amount of CNAs present in circulation is influenced by the clearance, degradation, and other physiological filtering mechanisms present in the circulatory and lymphatic systems.

Most nucleic acids are cleared from circulation by the liver and kidney, where the half-life of nucleic acids varies widely, ranging from 15 minutes to several hours long (20,23). However, some forms of CNAs may survive longer than others as double-stranded DNA injected into mice remained in circulation approximately two times longer than single-stranded DNA (20,23). In general, healthy humans are able to efficiently and rapidly clear CNAs from the circulation, in part due to nuclease activity in the blood. However, studies have detected elevated levels of total CNAs, including both gDNA and mtDNA of various sizes, in the plasma of patients with certain disease conditions, including cancer, trauma, autoimmune diseases, and inflammatory conditions (24).

While elevated plasma levels of CNAs were observed when compared to healthy donors, a wide range of CNAs concentrations have been reported by various studies; an average concentration of

30 ng/mL has been reported in blood collected from healthy donors (20,25-30).

Studies have also suggested the prognostic potential of CNAs as a biomarker of disease activity, as measuring CNAs is a relatively inexpensive and quick way to assess disease activity

(20,28). The detection of CNAs can also provide helpful information including the presence of mutations, methylation state, and DNA integrity. The potential of CNAs as a biomarker has also been suggested in multiple conditions, where studies have examined the correlation between levels of CNAs and disease activity in various conditions including cancers, sepsis, trauma, stroke, rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE) (20,28,31,32). In patients with autoimmune diseases, multiple studies have demonstrated that serum levels of CNAs are significantly higher in SLE patients compared to healthy controls, where the amount of CNAs in circulation correlates with changes in disease activity and severity (26,28). Previous studies have also reported elevated amounts of CNAs in both synovial fluid and serum from RA patients (25).

Thus, there is evidence to suggest the potential role of CNAs as a biomarker of disease activity.

1.2.2 Circulating mitochondrial DNA:

Some studies have suggested CNAs may be involved in immune responses, as mice injected with DNA released by human T-cells were shown to produce antibodies (24). However, the immune response to CNAs is not well understood. One component of CNAs, mtDNA, is interesting due to its similarity to bacterial DNA. According to the endosymbiotic theory, mitochondria are direct descendants of a bacterial endosymbiont that established itself in an early eukaryotic cell (33). Supporting this theory, mitochondria possess many morphological and biochemical features of their bacterial ancestors including a double-membrane, unique cell membrane lipids, a small circular genome containing unmethylated CpG DNA, absence of histones, and the ability to replicate independent of the nucleus (34). Like bacterial DNA, mtDNA typically encodes a limited number of genes in a circular chromosome and has a high abundance of unmethylated CpG regions (33). Located inside mitochondria, each cell may contain hundreds of copies of mtDNA. Human mtDNA is approximately 16.5 kb and encodes for 37 genes, including rRNAs, tRNAs, and proteins essential for the formation of a functional mitochondrion (33). As well, mitochondrial DNA sequences have been integrated into and are present in the nuclear genome as pseudogenes; however, these fragments are not transcribed or translated into functional proteins (35). In humans, elevated levels of circulating mtDNA have been detected in the plasma of patients with various conditions, including autoimmune diseases. In the plasma of SLE patients, elevated levels of circulatory mtDNA were detected, and not gDNA, when compared to healthy controls (36). In addition, increased levels of mtDNA was detected in the synovial fluid and serum of patients with rheumatoid arthritis (37,38). In contrast, mtDNA was not detected in the synovial fluid of healthy controls (38).

In the circulation, mtDNA is thought to exist in multiple forms. One study on the quantification of mtDNA from plasma or serum observed that mtDNA can circulate either freely

15 or associated with particles; in contrast, nuclear DNA predominately circulates in a free form (39).

Further evidence indicates that CNAs, including circulating mtDNA, can bind and form complexes with blood serum proteins such as serum albumin and immunoglobulins, or associate with the surface of erythrocytes or leukocytes (40). In addition, the origins of mtDNA are not well understood. Evidence suggests mtDNA, like other CNAs, are released from cells which die from various forms of cell death including apoptosis and necrosis (34). However, mtDNA may be actively secreted through various mechanisms as well (21). One study demonstrated that activated platelets can release extracellular mitochondria either in extracellular vesicles, known as microparticles (MPs), or as free mitochondria (41). Experiments demonstrated that mtDNA could be liberated from the extracellular mitochondria by serum enzymes such as secretory phospholipase A2 IIA (sPLA2-IIa). Produced by different cell types through stimulation with pro- inflammatory cytokines, sPLA2-IIa is a phospholipid hydrolase enzyme that is present in serum and can digest the mitochondrial membrane, allowing for the release of mtDNA from extracellular mitochondria into the serum (41,42). Furthermore, the internalization mechanisms of mtDNA have not been not well described. It has been suggested that particle-bound CNAs can bind cell-surface

DNA receptors, entering cells via receptor-mediated endocytosis. For instance, CNAs in immune complexes can enter cells via Fc receptors (43). In contrast, free CNAs may enter cells through spontaneous adsorption through the cell membrane (40).

While the immune response to CNAs is not well defined, studies have indicated that mtDNA can act as a DAMP to activate innate immune responses. In neutrophils stimulated with mtDNA, calcium flux, phosphorylation of p38 MAPK, and secretion of IL-8 was observed (44).

Through its similarities to bacterial DNA, increasing evidence has revealed that mtDNA, which has abundant unmethylated CpG islands, may also activate TLR-9 signaling. While TLR-9 is typically located in the ER of resting cells, stimulation with unmethylated CpG-containing DNA

16 activates the translocation of TLR-9 to the endosomal membrane. Data suggests mtDNA can activate p38 MAPKs through TLR-9 signaling in neutrophils, as incubation with chloroquine suppressed p38 MAPK activation; chloroquine inhibits TLR-9 activity through inhibiting endosomal acidification, which is required for TLR-9 signaling (45). Further experiments demonstrated mtDNA can induce an inflammatory response in macrophages, resulting in the secretion of inflammatory cytokines such as TNF-a and IL-6 (46). Stimulation with mtDNA also increased TLR-9 and NFkB mRNA expression and phosphorylation of IKBa and the NFkB p65 subunit, suggesting mtDNA activates NFkB signaling through TLR-9 in macrophages (46).

Activation of immune responses was not observed when neutrophils and macrophages were stimulated with nuclear DNA (45,46). In another model, injection of mtDNA into the joints of mice induced arthritis mediated by monocytes and macrophages (37). Recent studies have also indicated mtDNA may mediate inflammation through activation of the NLRP3 inflammasome.

Studies have demonstrated oxidized mtDNA in the cytosol can directly bind and activate the

NLRP3 inflammasome, inducing production and release of IL-1b (47,48).

1.3 Juvenile idiopathic arthritis:

1.3.1 Clinical characteristics:

Juvenile idiopathic arthritis (JIA) is one of the most common chronic diseases affecting children (49,50). Describing a heterogeneous group of diseases, it is characterized by joint inflammation which persists for more than 6 weeks, with an age of onset before 16 years of age.

Clinically, JIA is classified into seven subtypes based on clinical and laboratory features in an expert-based classification system developed by the International League of Associations for

Rheumatology (ILAR). The ILAR classification adopted the term juvenile idiopathic arthritis, replacing the terms juvenile rheumatoid arthritis used by the American College of Rheumatology

(ACR) and juvenile chronic arthritis used by the European League Against Rheumatism (EULAR).

These subtypes include systemic arthritis (sJIA), enthesitis related arthritis (ERA), oligoarthritis, rheumatoid factor-positive (RF+) polyarthritis, rheumatoid factor-negative (RF-) polyarthritis, psoriatic arthritis, and undifferentiated arthritis (51).

Between the seven subtypes, the clinical presentation of JIA varies widely. Diagnosis takes into consideration the numbers of joints with active arthritis or limited range of motion, the presence of various serological markers, and other symptoms such as fever (51). While classification attempts to identify homogeneous, mutually exclusive categories, most subtypes are still fairly heterogeneous. However, recent advances have indicated two subtypes, sJIA and ERA, are quite distinct from the others.

Systemic JIA patients have a characteristic inflammatory response with prominent systemic features including fever, rash, and serositis (52). According to ILAR criteria, diagnosis of sJIA requires the presence of arthritis and daily fever persisting for at least 2 weeks plus at least one of the following symptoms: evanescent rash, generalized lymphadenopathy, enlargement of

18 liver or spleen, or serositis (51). Accounting for 5 – 15% of all children with JIA seen in North

America and Europe, males and females are affected with equal frequency, with a broad peak of onset between 1 and 5 years of age (52). In sJIA patients, arthritis is often symmetrical, polyarticular, and may develop after presentation of the initial systemic symptoms during disease course. Patients often have very high erythrocyte sedimentation rate (ESR), C-reactive protein

(CRP) concentration, and platelet count in laboratory investigations. In addition, children with sJIA may develop macrophage activation syndrome (MAS), a potentially fatal complication characterized by macrophages exhibiting haemophagocytic activity and hypersecretion of pro- inflammatory cytokines by continually active T lymphocytes and macrophages (53). MAS is characterized by a massive hypersecretion of pro-inflammatory cytokines, including IL-1β, leading to an overwhelming inflammatory reaction (54). IL-1β is also thought to contribute to the pathophysiology of MAS as patients respond well to anti-IL-1 therapies (54,55).

Another distinctive JIA subtype is ERA, which represents a very diverse group of patients.

Often used as an umbrella term to describe many children, children in the ERA subtype include those who meet the traditional definitions of juvenile spondylarthropathy (SpA) (56). Children classified under ERA and SpA have overlapping genetic disposition as well as clinical features.

Males are generally affected more than females, with an older mean age of onset. ERA patients are characterized by the association of arthritis with enthesitis (51). Enthesitis is defined as inflammation of an enthesis, the site where a tendon or ligament attaches to bone. Currently, enthesitis is diagnosed exclusively through clinical observations such as localized pain, tenderness, and swelling at entheses; imaging is not typically used, making diagnosis of enthesitis difficult.

However, magnetic resonance imaging (MRI) is rapidly gaining acceptance in clinical practice as an adjunct to the clinical exam (57). While the prevalence of enthesitis is not well described, one study reported approximately 10% JIA patients experience enthesitis (58). Unlike other JIA

19 subtypes, the sacroiliac, knee, ankle, and small joints of the toes and feet are commonly affected.

Children with ERA may also develop sacroiliitis, which can advance to spondylitis and continue to progress along the axial skeleton. Furthermore, many ERA patients are HLA-B27 positive and often develop acute anterior uveitis and inflammatory bowel disease during their disease course.

As well, ERA patients are usually seronegative with absence of antinuclear antibody (ANA) and

RF antibodies.

Disease activity in JIA is assessed through a core set of clinical parameters measured at each visit. The core set of variables includes the following: number of joints with active arthritis; number of joints with limited range of motion; ESR or CRP level; physician global assessment of disease activity (PGA) measured on a 10 cm visual analog scale (higher score indicates greater disease activity); patient or parent global assessment of overall well-being measured on a 10 cm visual analog scale (higher score indicates worse well-being); and a measure of functional ability through the Childhood Health Assessment Questionnaire (CHAQ) where higher scores indicate worse function (59). The Juvenile Arthritis Quality of Life Questionnaire (JAQQ) is also used as a measure of quality of life (60). Thus, as most of the core set variables are clinical observations, it is often difficult to recognize subclinical levels of disease activity in children with JIA.

1.3.2 Immunological characteristics:

While the cause and pathogenesis of JIA remains poorly understood, it is known that the immune system plays a critical role. It is thought that both genetic and environmental components contribute to the pathogenesis of JIA. Classical autoimmune diseases are thought be at one end of the immunological spectrum. Autoimmune diseases are associated with the adaptive immune response featuring autoreactive lymphocytes, high titres of autoantibodies, and strong associations with MHC alleles (15). Most JIA subtypes are considered to be classic autoimmune diseases, as they are characterized by the presence of autoantibodies and strong genetic predispositions to genes within the HLA or major histocompatibility complex (MHC) locus on chromosome 6. The

HLA gene complex is the most significant gene region which is associated with susceptibility to

JIA, where the HLA-DRB and HLA-DQA/B alleles have been consistently implicated in conferring risk of JIA (61). Antinuclear antibodies (ANA) are mainly associated with early-onset oligoarthritis, and have been reported to react against a variety of nuclear targets. Children with oligoarthritis are also associated with multiple HLA alleles, including polymorphisms in HLA-A2 and HLA-DRB1 (50). One study also showed children with oligoarthritis were associated with

HLA-DRB1*13, which has also been associated with ANA positivity in JIA (62). Similarly, children with RF+ polyarthritis are characterized by the presence of IgM rheumatoid factor and have been associated with multiple HLA alleles, including HLA-DR4 (50,63). In JIA patients from the UK, psoriatic arthritis was associated with HLA-DRB1 and DLA-DQA1 (62). While multiple associations with HLA alleles have been observed, few non-MHC genes have been associated with

JIA. The most significant gene outside the MHC region which is associated with susceptibility to

JIA, and other autoimmune diseases including rheumatoid arthritis, is PTPN22 which encodes for the lymphoid protein tyrosine phosphatase which is involved in modulating T cell receptor signaling and cytokine signal transduction through the JAK/STAT signaling pathways (64,65).

In contrast, autoinflammatory diseases are thought to be at the opposite end of the immunological spectrum, and are associated with abnormalities in innate immune pathways, where monocytes and neutrophils are the predominant effector cells (15). There is consensus that sJIA is a unique subtype of JIA, and likely should be classified as an autoinflammatory disease. Patients with sJIA have recurrent fevers and multisystem inflammation, and both features are characteristic of autoinflammatory diseases. In sJIA patients, there is no evidence of auto-reactive T-cells or autoantibodies which are characteristic of autoimmune diseases. In addition, sJIA patients are not typically associated with classic HLA polymorphisms, although a few studies have identified an association with the HLA-DRB1*11 allele (62,66,67). Instead, sJIA is associated with genetic risk factors in the promoter elements and genes for various cytokines, including TNF-a, macrophage migration inhibitor factor (MIF)-1, IL-6, IL-1, IL-1R, and IL-10 (52,67-70). Microarray studies in peripheral blood mononuclear cells (PBMCs) provide evidence for innate activation in sJIA patients, where genes associated with the activation of innate cells, including monocytes, macrophages, and neutrophils, were upregulated (15,71,72). Therefore, sJIA patients exhibit a gene expression signature with strong upregulation of innate immunity pathways; this signature is similar to those seen in other autoinflammatory diseases (73). While many cytokines chiefly made by innate cells are elevated in sJIA patients, such as TNF-a and IL-6, studies suggest that pro- inflammatory cytokines IL-1b and IL-18 are central to disease pathogenesis. These cytokines are predominantly produced and secreted by macrophages after inflammasome activation. Patients with active sJIA have high serum levels of bioactive IL-18, compared to patients in other JIA subtypes or with other inflammatory diseases (15). IL-1b is characteristic of autoinflammatory diseases, and has been implicated as a critical pro-inflammatory cytokine in sJIA. Although increased gene expression of IL-1b was not found in active sJIA patients, one study indicated that serum from sJIA patients induced the expression of IL-1 genes (IL-1B, IL-1R1, IL-1R2, and IL-

1RN) in PBMCs from healthy patients (74). Further evidence suggesting IL-1b may be involved in the pathogenesis of sJIA is due to the successful treatment of sJIA with IL-1 inhibitors.

Treatment with Anakinra, a recombinant soluble IL-1 receptor antagonist (IL-1RA), is recommended by the ACR for sJIA patients who have active fever and systemic features (75,76).

Many sJIA patients respond to early treatment with IL-1 inhibitor therapy. Thus, evidence suggests the mechanisms of disease in sJIA differs from the other JIA subtypes, where innate activation and

IL-1b plays a central role.

ERA is another interesting subgroup of JIA with multiple factors contributing to its pathogenesis. Both autoimmune and autoinflammatory features have been implicated. The major genetic factor strongly associated with ERA is the MHC class I allele, HLA-B27. The mechanisms by which HLA-B27 is involved in the pathogenesis of ERA has been under much debate.

Traditionally, HLA-B27 was thought to play a role in the presentation of arthritogenic peptides to auto-reactive CD8+ T-cells, as it is a class I MHC molecule. Studies have proposed molecular mimicry, where it is thought that HLA-B27 or the peptides it presents resemble bacterial proteins or peptides, allowing cross-reactivity with T-cells (77). However to date no “arthritogenic peptides” have been identified (56,57).

Instead, increasing evidence suggests spondyloarthropathies, including ERA, are further along the autoinflammatory spectrum of diseases, rather than the autoimmune spectrum. Recent studies have indicated HLA-B27 may play a central role in the pathogenesis of spondyloarthropathies through an intracellular effect, independent of its role in antigen presentation. Evidence has indicated the HLA-B27 heavy chain tends to misfold in the endoplasmic reticulum (ER), resulting in ER stress and degradation in the cytosol by proteasomes through the ER-associated degradation (ERAD) pathway (78). However, cells may also respond

23 to ER protein misfolding through activation of the unfolded protein response (UPR), where the

UPR can contribute to the pathogenesis of diseases (78). HLA-B27 misfolding, and subsequent activation of the UPR, was observed in cells derived from a HLA-B27 transgenic rat model of ankylosing spondylitis, a type of adult spondyloarthropathy (79). The UPR has also been associated with the activation of innate immunity, where HLA-B27 misfolding in macrophages can result in production of IL-23 which induces Th17 cells to release IL-17, a pro-inflammatory cytokine (80). IL-17 can also stimulate the production of other cytokines from macrophages including TNF-a, IL-6, and granulocyte-macrophage colony-stimulating factor (GM-CSF) (80).

Other studies supporting the role of protein misfolding in innate immune responses have shown evidence that ER stress can also promote inflammation through activation of the NLRP3 inflammasome, leading to secretion of IL-1b (81). Therefore, increasing evidence has indicated autoinflammatory mechanisms of disease may also contribute to the pathogenesis of ERA, where

HLA-B27 misfolding leads to ER stress which can act as a danger signal resulting in the activation of inflammasomes, promoting inflammation.

Thus while most JIA subtypes are thought to be classic autoimmune diseases, both sJIA and ERA subtypes are unique as they are thought to be further along the autoinflammatory spectrum of diseases. However, chronic inflammation is the fundamental pathological process common through all categories of JIA. Actively inflamed joints exhibit the cardinal signs of inflammation including swelling, pain, heat, and loss of function. In all JIA subtypes, products of activated T-cells and macrophages, such as pro-inflammatory cytokines TNF-a, IL-17, IL-6, and

IL-1b, are involved in the pathogenesis of synovitis (82). These cytokines produced in the synovium contribute to local cartilage and bone damage, and the recruitment of more inflammatory

24 cells. In children, chronic arthritis can lead to disturbances in growth and bone or joint development.

Rationale:

Juvenile Idiopathic Arthritis (JIA) is characterized by inflammatory arthritis, where patients have elevated levels of pro-inflammatory cytokines including IL-1β. Chronic, persistent inflammation may lead to joint and tissue damage, and subsequent cell death, resulting in the release of cellular products, such as proteins and nucleic acids, into the circulation. Elevated levels of circulating nucleic acids (CNAs), including mitochondrial DNA (mtDNA), have been detected in the plasma of patients with autoimmune conditions including inflammatory arthritis. In addition, previous studies have shown that endogenous mtDNA may act as a DAMP to activate innate immune cells such as macrophages, leading to inflammasome activation and an inflammatory response through secretion of pro-inflammatory cytokines including IL-1β. Furthermore, intra- articular injection of mtDNA induced arthritis in mice.

Hypothesis:

Mitochondrial DNA is a danger signal leading to immune activation in inflammatory arthritis in

JIA.

Experimental aims: To examine the role of circulating mtDNA in the immunopathogenesis of JIA, we aimed to investigate whether mtDNA can act as a danger signal to activate macrophages in vitro.

Therefore, this project had two main objectives:

1. To determine whether mtDNA can function as a danger signal and mediate production of IL-1b

We will aim to:

• Determine whether stimulation with mtDNA upregulates IL-1b gene expression

• Determine whether stimulation with mtDNA upregulates pro-IL-1b protein expression

• Determine whether macrophages stimulated with mtDNA secrete IL-1b

2. To examine the relationship between circulating mtDNA and disease activity in JIA

We will aim to:

• Quantify the amount of circulating mtDNA in plasma from JIA patients

• Quantify the amount of circulating nucleic acids in plasma from JIA patients

• Examine the relationship between circulating mtDNA and JIA subtypes

• Examine associations between circulating mtDNA and clinical measures of disease activity

• Examine associations between circulating mtDNA and inflammatory cytokine levels

Methods: 5.1 Generation of DNA fragments: To generate mitochondrial DNA (mtDNA) fragments, sequences from various mitochondrial genes were chosen and screened in the Basic Local Alignment Search Tool

(BLAST, National Institutes of Health) and UCSC genome browser databases to ensure the sequences are not present in bacterial and genomic DNA sequences as pseudogenes (83) . Primers were generated for six sequences of variable length and CpG content from mtDNA genes (Life technologies, Carlsbad, CA) (39,84). For proof of concept, two fragments from mtDNA genes human cytochrome oxidase II (Cox II) and 16s ribosomal RNA-transfer RNA-Leucine (16s) were tested. PCR was performed using Cox II and 16s primers with the following thermo profile: 10 minute denaturation at 95oC, 30–35 amplification cycles (95°C for 15 seconds, 57°C for 1 minute), and final extension at 72°C for 7 minutes. All of the sequences for the primers used to generate the DNA fragments are located in Table 1 and Table 2 (see end of methods section). mtDNA fragments were amplified with a human genomic DNA template and verified through 2% agarose gel electrophoresis. Fragments were then inserted into a pCR 2.1 TOPO TA vector and transformed into competent E. coli TOP10 cells, following manufacturer’s instructions (Thermo

Fisher Scientific, Waltham, MA). Transformation was confirmed with blue/white and ampicillin screening. Plasmids were purified from E. coli cells using the QIAprep Spin Miniprep Kit (Qiagen,

Valencia, CA), according to the manufacturer’s protocol. To verify the identity of the cloned insert,

FastDigest EcoRI restriction digestion analysis (Thermo Fisher Scientific) was performed according to manufacturer’s instructions, followed by visualization by 2% agarose gel electrophoresis. Plasmids were also sequenced by The Centre for Applied Genomics (TCAG, The

Hospital for Sick Children Research Institute, Toronto, ON) on a ABI 3730XL instrument (Life

Technologies, Carlsbad, CA) to ensure no mutations were present in the mtDNA fragments.

27 28

Plasmids were then used as template to PCR amplify copious amounts of mtDNA fragments using

16s and Cox II primers, as described earlier. PCR amplified mtDNA fragments were visually confirmed on a 2% agarose gel to be the correct length, and purified using the QIAquick PCR purification kit according to manufacturer’s instructions (Qiagen). Briefly, 300 uL PCR product was loaded onto each column with 1500 uL Buffer PB, washed, and eluted in 30 uL Buffer EB.

Final concentration was determined through spectrometry using a Denovix DS-11 spectrophotometer (Wilmington, DE). As a specificity control, GAPDH and CD4 fragments were also generated by PCR as described above (Table 1), with primers generated by TCAG (The

Hospital for Sick Children Research Institute) (Table 2) (45).

5.2 Cell culture: THP-1 cells, a human monocyte cell line, (American Type Culture Collection, Manassas,

VA) were cultured in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum, 10 mM sodium pyruvate, non-essential amino acids, 50 uM 2-mercaptoethanol, 2 mM L- glutamine, and 10 mM HEPES (complete RPMI+ medium) (Thermo Fisher Scientific). Cells were

6 6 o maintained in T-75 flasks between 0.2x10 – 3x10 cells/mL in an incubator at 37 C with 5% CO2.

Cells were used at the lowest passage number achievable until a maximum of six passages, after which the cells were discarded.

For all experiments, THP-1 monocytes were serum-starved for 16 hours in T-75 flasks before cells were plated in either 12- or 24-well cell culture plates. Cells were seeded at 1x106 cells/mL in 1 mL or 0.5 mL, respectively, in complete RPMI+ medium with 10 ng/mL phorbol

12-myristate 13-acetate (PMA) (Invivogen, San Diego, CA) for 24 hours to differentiate the monocytes into macrophages. Cells were seeded in 12-well plates for subsequent RNA extraction

29 or protein lysis, and seeded in 24-well plates for subsequent analysis by Enzyme-linked

Immunosorbent Assays (ELISAs). After 24 hours differentiation, cells were washed twice with 1x phosphate buffered saline (PBS) and rested in complete RPMI+ medium for 2 hours. THP-1 differentiated macrophages were stimulated with various combinations of reagents as indicated in figures. DNA transfection with Lipofectamine 2000 reagent (Thermo Fisher Scientific) was performed according to manufacturer’s instructions, with 4 uL Lipofectamine 2000 reagent used

6 o per 1x10 cells. During incubations, cells were placed in an incubator containing 5% CO2 at 37 C.

To determine whether mtDNA fragments can induce upregulation of IL-1b gene expression and protein expression, cells were stimulated with either lipofectamine, lipofectamine and Cox II mtDNA, or 10 ng/mL LPS (Sigma-Aldrich, St. Louis, MO). Kinetic analysis was performed and cells were stimulated for various times as indicated in figures. A dose response was performed and cells were simulated with various concentrations of Cox II mtDNA. To determine whether mtDNA fragments activate THP-1 macrophages to produce an inflammatory response and release IL-1β, THP-1 differentiated macrophages were stimulated with either lipofectamine, various concentrations of 16s or Cox II mtDNA fragments and lipofectamine, or 10 ng/mL LPS for 6 hours (signal 1), followed by 10 mM adenosine 5′-triphosphate (ATP) (Sigma-Aldrich) for 1 hour (signal 2). In testing the immunogenicity of the mtDNA fragments, kinetic analysis and dose response experiments were performed and cells were stimulated with various concentrations of signal 1 stimulants for various times as indicated, followed by ATP. THP-1 macrophages were also stimulated with specificity controls such as GAPDH and CD4 fragments (see 5.1).

5.3 Quantitative real-time reverse-transcriptase PCR:

To determine the whether mtDNA stimulation upregulates IL-1b gene expression, cells were stimulated for 6 hours as described earlier (see 5.2), and total RNA was extracted using Trizol

Reagent (Thermo Fisher Scientific) following manufacturer’s instructions. RNA concentrations were determined by spectrophotometric analysis using a Denovix DS-11 spectrophotometer.

Complementary DNA (cDNA) synthesis was performed with Moloney Murine Leukemia Virus reverse transcriptase (Applied Biosystems) according to manufacturer’s instructions. Briefly, samples were incubated at 65oC for 4 minutes to anneal the oligo(dT) primer, followed by incubation at 42oC for 30 minutes. Converted cDNA was analyzed in duplicates via quantitative real-time PCR using primers and probes for GAPDH (Applied Biosystems), and IL-1b (Taqman gene expression assay, Thermo Fisher Scientific). The relative amounts of gene products were compared to a standard curve for IL-1b, generated using cDNA obtained from LPS-stimulated

THP-1 macrophages, and expressed as a ratio against the housekeeping gene GAPDH.

5.4 Western blot analysis:

To determine the ability of mtDNA to induce IL-1b production in THP-1 macrophages,

Western blotting and densitometry analyses were performed after culturing cells with various stimulants (see 5.2). After treatment for 12 hours, cells were washed twice with ice-cold 1x PBS and lysed in 150 uL protein lysis buffer, collected, and stored at -80oC overnight. Protein lysis buffer was prepared by diluting Halt protease inhibitor cocktail (Thermo Fisher Scientific) 100x in RIPA Buffer (Sigma-Aldrich). Protein lysates were centrifuged at 14,000 x g for 15 minutes at

4oC to remove insoluble material, and total protein concentrations were determined using the Bio-

Rad DC Protein Assay (Hercules, CA) according to manufacturer’s protocol. Samples were

31 prepared with Bolt LDS sample buffer and Bolt reducing agent according to manufacturer’s instructions, and subjected to SDS PAGE on a Bolt 4-12% Bis-Tris Plus Gel in Bolt MES SDS running buffer (Thermo Fisher Scientific) for 2 hours at 135 V. Proteins were transferred to polyvinylidene difluoride membrane at room temperature for 2 hours at 40 V using the Mini-

Transblot Electrophoretic Cell (Bio-Rad Laboratories). The membrane was then blocked at 4oC overnight in 1x Tris Buffered Saline plus 0.1% Tween-20 (1x TBST) with 5% milk powder

(Blocking buffer). After blocking, the membrane was washed three times with 1x TBST and probed with anti-IL-1b (Santa Cruz) and anti-GAPDH (clone 6C5, Ambion) primary antibodies for 1 hour at room temperature, followed by HRP-anti-rabbit IgG or HRP-anti-mouse IgG (Jackson

ImmunoResearch Laboratories, West Grove, PA) antibodies respectively, for 1 hour at room temperature. Proteins were detected using enhanced chemiluminescent reagent (PerkinElmer,

Waltham, MA) and the chemiluminescence signal of the protein bands was visualized by autoradiography. Intensity of pro-IL-1β signals relative to GAPDH controls were calculated by densitometry using ImageJ software (National Institutes of Health) and graphed.

5.5 Enzyme-linked immunosorbent assays: To determine whether stimulation with mtDNA fragments can lead to immune cell activation, THP-1 differentiated macrophages were stimulated for 6 hours with signal 1 stimuli followed by 1 hour with signal 2 stimulus as described above (see 5.2). Following stimulations, supernatants were collected and centrifuged at 420 x g for 5 minutes, aliquoted, and stored at -

80oC until needed for cytokine analysis. A human IL-1β Enzyme-linked Immunosorbent Assay

(ELISA) Ready-Set-Go! Kit (eBioscience, San Diego, CA) was used to detect human

IL-1β in the supernatants collected from THP-1 cell cultures, according to manufacturer’s instructions.

5.6 Patient samples: Biological specimens and clinical data was collected from 134 patients with new-onset JIA enrolled in the Biologically Based Outcome Predictors in JIA (BBOP) study from 11 sites across

Canada. Children included in this study satisfied the ILAR classification criteria, were within 6 months of disease diagnosis, and had not received medications other than nonsteroidal anti- inflammatory drugs. Informed consent was obtained from patients and/or their parents, in accordance with and approved by the Research Ethics Board at the Hospital for Sick Children.

Clinical data was collected using standardized clinical reporting forms to capture all key features in the ILAR classification criteria and components of the American College of Rheumatology pediatric core set of measures of disease activity, including standard laboratory markers and questionnaire-based assessments of function, global disease activity, and quality of life (51,59).

5.7 Biological sample collection: Peripheral blood samples were collected in P100 tubes containing a proprietary blend of protein stabilizers (BD Biosciences, San Jose, CA) and Tempus Blood RNA tubes (Thermo Fisher

Scientific). Blood samples were transported at room temperature to the central study biobank at the Hospital for Sick Children Research Institute. Samples were processed within 5 days according to manufacturer’s instructions.

5.8 Microarray analysis: Tempus Blood RNA tubes were vortexed for 10 seconds after blood was drawn and stored at -80oC until RNA was extracted using the Tempus spin RNA isolation kit (Thermo Fisher

Scientific), following manufacturer’s instructions. After RNA isolation, the quantity and quality of the RNA was checked using the Agilent Bioanalyzer (Santa Clara, CA) by TCAG (The Hospital for Sick Children Research Institute). Gene expression of 20,149 genes was determined using the

Affymetrix Human Primeview Microarray (Santa Clara, CA) by TCAG (The Hospital for Sick

Children Research Institute). The microarray contained 48,807 probe sets, where each probe set contained 9–11 probes. Several IL-1b, IL-18, and inflammasome-associated genes were analyzed in our cohort (Table 3).

5.9 Cytokine and chemokine analysis: Briefly, P100 tubes were centrifuged at 2500 x g for 20 minutes to activate the separator device, separating the plasma and cellular elements. After processing, platelet poor plasma samples were stored at −80°C in aliquots of 200 uL. A panel of 47 cytokine and chemokine concentrations were measured from plasma using the EMD Millipore multiplex ELISA kits (Etobicoke, ON) according to manufacturer’s specifications and analyzed on a Luminex100 LabMAP system

(Austin, TX) at the University Health Network Microarray Centre (Toronto, ON) as previously described by Eng et al. (85) (Table 4).

5.10 Quantification of CNAs:

CNAs were isolated from plasma samples using the Norgen Biotek plasma/serum cell-free circulating DNA purification mini kit (Thorold, ON) following manufacturer’s instructions.

Briefly, 120 ug proteinase K was added to 50 uL plasma diluted in 1x PBS to a final volume of

200 uL and incubated at 55oC for 10 minutes to activate the proteinase K. Samples were combined with binding buffer and loaded into columns, washed, and eluted in 50 uL of Buffer B. CNAs were aliquoted and stored at -80oC. To quantify the total concentration of CNAs, the Quant-iT

PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific) was used according to manufacturer’s instructions. Briefly, a standard curve was generated using Lambda DNA standard and loaded in triplicate into a 96-well plate. Each well contained 5 uL CNAs samples diluted in 1x TE buffer to a final volume of 100 uL. An equal volume of PicoGreen reagent was added to each well, and the plate was incubated at room temperature protected from light for 5 minutes. Fluorescence was measured on a Spectramax Gemini EM Fluorescent Microplate Reader (Molecular Devices,

Sunnyvale, CA) at standard fluorescein wavelengths (excitation @ 480 nm, excitation @ 520 nm).

To determine the amount of mtDNA present, isolated CNAs were analyzed in duplicates through quantitative PCR (qPCR) using primers and probes for Cox II (Table 2), and PCR mastermix containing Taq DNA polymerase, dNTPs, reaction buffer, MgCl2, KCl, and a PCR stabilizer

(Norgen Biotek). A standard curve for Cox II was produced using Cox II plasmids generated as described earlier (see 5.1). qPCR was set up in a reaction volume of 20 uL, with 5 uL CNAs added to each reaction mixture. Real-time qPCR was done on an Applied Biosystems 7900HT Fast Real-

Time PCR system (Life Technologies) using the following thermo profile: incubation for 2 min at

50oC, denaturation for 10 min at 95oC, and 40 cycles of 95oC for 15s and 60oC for 1 minute.

Mitochondrial DNA copy number was determined as described by Chiu et al. (39). Briefly, the molecular weight of double-stranded DNA (660 g/mol) was multiplied by the length of the plasmid

(3931 bp) plus the length of the Cox II mtDNA fragment (81 bp), and divided by Avogadro’s number (6.023 x 106 molecules/mol) to get the weight of each plasmid molecule. The concentration of the cloned Cox II plasmid was then divided by the weight of each plasmid molecule to obtain the number of copies of Cox II mitochondrial DNA per mL of plasma, and a standard curve was constructed using this calculation. To determine the copies of Cox II mtDNA/mL plasma, the relative amounts of Cox II mtDNA in samples were compared to the standard curve.

5.11 Statistical analysis:

Where appropriate, statistical significance was calculated using two-tailed student’s t-tests or one-way ANOVA with Tukey’s multiple comparisons test with GraphPad Prism 6 (GraphPad

Software Inc., La Jolla, CA). P values <0.05 were considered significant. In certain experiments, values were reported as the mean ± standard error. Correlations between mtDNA copies/mL plasma and various parameters were determined by calculating Pearson correlation coefficients using GraphPad Prism 6 (GraphPad Software Inc.).

Table 1. Sequences of mtDNA and gDNA fragments synthesized

Length No. of DNA Fragment Sequence (bp) CpGs Human 5’-CCCCACATTAGGCTTAAAAACAGATGCAATTCCCGG cytochrome ACGTCTAAACCAAACCACTTTCACCGCTACACGACCGG oxidase II 81 5 GGGTATA-3’ (Cox II)

16s ribosomal 5’-AGGACAAGAGAAATAAGGCCTACTTCACAAAGCGC RNA-transfer CTTCCCCCGTAAATGATATCATCTCAACTTAGCATTATA RNA-Leucine CCCACACCCACCCAAGAACAGGGTTTGTTAAGATGGCA 172 4 (16s) GAGCCCGGTAATCGCATAAAACTTAAAACTTTACAGTCA GAGGTTCAATTCCTCTTCTTA-3'

5'-AGGGCCCTGACAACTCTTTTCATCTTCTAGGTATGA GAPDH 100 1 CAACGAATTTGGCTACAGCAACAGGGTGGTGGACCTCA TGGCCCACATGGCCTCCAAGGAGTAA-3'

5’-GCATCTTCTTCTGTGTCAGGTGCCGGCACCGAAGGG CD4 85 2 TGAGTAACCCCACACCTGGTCCCCACAAGGCCCTCAA ACCCCTGAGTCC-3’

Table 2. Sequences of primers and probes

Primer Sequence

Cox II- Forward 5'-CCCCACATTAGGCTTAAAAACAGAT-3′ Cox II- Reverse 5'- TATACCCCCGGTCGTGTAGC-3′

Cox II- Probe 5’-FAM-CAATTCCCGGACGTCTAAACCAAACCACTTTC-TAMRA-3’

16s- Forward 5'-AGGACAAGAGAAATAAGGCC-3' 16s- Reverse 5'-TAAGAAGAGGAATTGAACCTCTGACTGTAA-3' 16s- Probe 5'-FAM-TTCACAAAGCGCCTTCCCCCGTAAATGA-TAMRA-3'

GAPDH- Forward 5'-AGGGCCCTGACAACTCTTTT-3′ GAPDH- Reverse 5'-TTACTCCTTGGAGGCCATGT-3′

CD4- Forward 5'-GCATCTTCTTCTGTGTCAGGTG-3′ CD4- Reverse 5'-GGACTCAGGGGTTTGAGG-3′

Table 3. Subset of genes measured by microarray

Genes IL-1B NLRP1 IL-1R1 NLRP2 IL-1R2 NLRP3 IL-1RN NLRC4 IL-18 NLRP8 IL-18BP NLRP12 IL-18R1 PYCARD IL-18RAP CASP1

Table 4. Cytokines and chemokines measured by multiplex ELISA

Cytokines / Chemokines

EGF IL-7 MMP-8 EGF-2 IL-8 MMP-9 Eotaxin IL-10 MMP-10 FGF-2 IL-12p40 MMP-12 G-CSF IL-12p70 MMP-13 GM-CSF IL-13 OPG IFNα2 IL-15 RANKL IFNγ IL-17 RANTES IL-1α IP-10 TIMP-1 IL-1β MCP-1 TIMP-2 IL-1ra MIP-1a TIMP-3 IL-2 MIP-1b TIMP-4 IL-3 MMP-1 TNF-α IL-4 MMP-2 TNF-β IL-5 MMP-3 VEGF IL-6

Results 6.1 Generation of mtDNA fragments through PCR

To determine whether mtDNA acts as a danger signal leading to IL-1b production, mtDNA fragments were generated for in vitro stimulation assays with THP-1 differentiated macrophages.

Short sequences from mitochondrial genes, Cox II and 16s, were PCR amplified with primers generated by TCAG (the Hospital for Sick Children Research Institute). Briefly, PCR generated mtDNA fragments were cloned through standard TOPO cloning protocols, and proper insertion of mtDNA fragments into transformed plasmids was validated through EcoRI restriction enzyme digestion and visualized by 2% agarose gel electrophoresis under ultraviolet light (Figure 3A).

Verified plasmids were also sequenced and used as template for subsequent PCR amplification and purification of fragments at a larger scale (Figure 3). As a proof of concept, two mtDNA fragments (Cox II and 16s) of intermediate lengths and a different number of CpG motifs were successfully generated through the above methods (Figure 3B). In addition, two genomic DNA fragments (GAPDH and CD4) were generated under similar conditions by PCR as specificity controls (Figure 3C). These fragments were used in subsequent in vitro stimulation assays.

38 39

A. Neg. ctrl Cox II

– 81 bp

EcoRI: + - + -

– 172 bp

– 81 bp

Cox II 16s

–100 bp – 85 bp

GAPDH CD4

Figure 3. Verification of DNA fragments by gel electrophoresis.

A. PCR amplified Cox II mtDNA fragments were inserted into a pCR 2.1 TOPO TA vector and transformed into competent E. coli TOP10 cells. Miniprep extracted plasmids were analyzed by EcoRI restriction digestion analysis and visualized on a 2% agarose gel. Water was used as a negative control for restriction digest, and the proper size band (Cox II; 81bp) was seen. B. Extracted plasmids were used as template to PCR amplify mtDNA fragments, Cox II (81 bp) and 16s (172 bp). mtDNA fragments were purified and a stock was generated, and gel electrophoresis confirmed the size and identity of mtDNA fragments. C. Nuclear DNA fragments, GAPDH (100 bp) and CD4 (85 bp), were PCR amplified and a purified stock was generated, and visualized by gel electrophoresis. Representative gels are shown for each verification step in the process required to generate DNA fragments.

6.2 PMA stimulation differentiates THP-1 monocytes into macrophages

To study inflammasome activation and the production and secretion of IL-1b, we evaluated the potential to use THP-1 differentiated macrophages as our preliminary experiments showed they were able to secrete more robust levels of IL-1b when stimulated with LPS and ATP, known inflammasome activators, compared to THP-1 monocytes (data not shown). Thus, THP-1 monocytes were differentiated into macrophages with PMA for various times as indicated, stimulated with LPS and ATP, and levels of secreted IL-1b were analyzed by ELISA from supernatants collected (Figure 4). After stimulation with PMA, cells were examined under a microscope to confirm cell adhesion and spreading, morphological characteristics which are hallmarks of macrophages. Differentiation with PMA for 24 hours resulted in robust secretion of

IL-1b from cells (Figure 4). Therefore, THP-1 cells were differentiated with PMA for 24 hours prior to stimulation with various reagents in subsequent experiments.

800 4 hour 600 16 hour 24 hour

400 (pg/mL) β

IL-1 200

0 media LPS + ATP

Figure 4. Stimulation with PMA for 24 hours results in robust IL-1b secretion from macrophages.

Serum-starved THP-1 monocytes were differentiated into macrophages with PMA (10 ng/mL) for 4, 16, or 24 hours before resting in new RPMI+ media for 2 hours. Cells were then cultured with LPS (10 ng/mL) for 6 hours, followed by ATP (10 mM) for 1 hour. Supernatants were collected and analyzed for IL-1b by ELISA. A representative graph from n=3 independent experiments is shown.

6.3 Stimulation with mtDNA upregulates IL-1b gene and protein expression

Previous studies indicated that endogenous mtDNA may act as a danger signal to activate inflammasomes in macrophages, inducing the secretion of IL-1b (48). Other reports have suggested that mtDNA may act as a danger signal stimulating TLR-9 and leading to the activation of the NFkB pathway (46). In these studies, inflammatory cytokines including TNFa and IL-6 were used as an outcome measurement. However, IL-1b production was not examined. Thus, we hypothesized that mtDNA can act as the first signal and prime macrophages for inflammasome activation through activating the NFkB pathway, leading to upregulation of IL-1B gene expression.

To study our hypothesis, kinetic analysis was first performed on THP-1 differentiated macrophages stimulated with 2 ug/mL Cox II mtDNA, transfected with lipofectamine. LPS was used as a positive control (not shown). After stimulation for 6 or 9 hours, RNA was extracted from lysates, cDNA synthesized, and qPCR analysis was performed. The relative amounts of gene products were compared to an IL-1B standard curve and expressed as a ratio against the expression of housekeeping gene GAPDH (Figure 5). The largest difference in IL-1B expression was seen when cells were treated with Cox II mtDNA for 6 hours, as IL-1B expression increased approximately 3 fold, when compared to lipofectamine alone (Figure 5). Differences in IL-1B gene expression was reduced after stimulation for 9 hours, as IL-1B expression increased only 2 fold in cells stimulated with Cox II mtDNA compared to lipofectamine alone (Figure 5).

0.20

0.15 6 hour 9 hour

0.10

IL-1B / GAPDH / GAPDH IL-1B 0.05

0.00 Media 0 2

Lipofectamine + Cox II (ug/mL)

Figure 5. Stimulation with mtDNA for 6 hours increased IL-1B gene expression.

THP-1 differentiated macrophages were incubated with lipofectamine and/or Cox II mtDNA (2 ug/mL) for 6 or 9 hours before lysates were collected in Trizol reagent. LPS was used as a positive control (not shown). Isolated RNA was converted into cDNA for analysis by qPCR to determine IL-1B expression, expressed relative to GAPDH. Kinetic analysis indicated stimulation with mtDNA for 6 hours resulted in the largest increase in IL-1B gene expression, compared to lipofectamine control.

Following kinetic analysis, THP-1 macrophages were stimulated for 6 hours with various concentrations of Cox II mtDNA, or LPS as a positive control (not shown). IL-1B gene expression was analyzed by qPCR and normalized to GAPDH expression (Figure 6). Upregulation of IL-1B expression was dose-dependent, where the largest fold increase in IL-1B expression was observed when cells were stimulated with 1 ug/mL Cox II mtDNA; IL-1B expression was approximately 10 fold higher in cells stimulated with 1 ug/mL Cox II than cells stimulated with lipofectamine alone, as shown in a graph representative of n = 3 independent experiments (Figure 6).

15 *

12 / e on l a ease

r e

c 9 n n i i m d l a t o

f 6

ec f B 1 - po L li I 3

0 media 0 0.125 0.25 0.5 1 2 4 8

Lipofectamine + Cox II (ug/mL)

Figure 6. Stimulation with mtDNA upregulates IL-1B gene expression in a dose-dependent manner.

THP-1 differentiated macrophages were cultured for 6 hours with various concentrations of Cox II mtDNA (transfected with lipofectamine) before RNA was extracted, converted into cDNA, and qPCR analysis was done. Fold increase of IL-1B gene expression over cells stimulated with lipofectamine alone is shown. IL-1B gene expression was normalized to GAPDH. LPS was used as a positive control for IL-1B gene expression (not shown). Data shown is mean ± standard error, from n=3 independent experiments. Statistical significance was determined by student’s t-test (*p=0.02).

We also determined whether mtDNA stimulation upregulates intracellular protein levels of pro-IL-1b. THP-1 macrophages stimulated with lipofectamine and 2 ug/mL Cox II mtDNA, or

LPS positive control, were collected at various time points as indicated in Figure 7. Pro-IL-1β protein expression was detected by western blot analysis and expressed relative to GAPDH control

(Figure 7). Levels of pro-IL-1b did not increase prior to 12 hours stimulation with Cox II mtDNA

(Figure 7). However, kinetic analysis indicated that pro-IL-1b expression increased approximately

4 fold after 12 hours stimulation with Cox II mtDNA, when compared to cells stimulated with lipofectamine alone (Figure 7). Increase in levels of pro-IL-1b were reduced after 14 hours stimulation with Cox II mtDNA (Figure 7).

pro-IL-1β 31 kDa

GAPDH 37 kDa

Lipofectamine: + + - + + - + + - + + - Cox II (2ug/mL): - + - - + - - + - - + - LPS: - - + - - + - - + - - + 6 hour 9 hour 12 hour 14 hour

16 6 hour 12 9 hour 12 hour 14 hour / GAPDH / GAPDH 8 β

4 pro-IL-1

0 0 2 LPS

Lipofectamine + Cox II (ug/mL)

Figure 7. Pro-IL-1b levels increased after stimulation with mtDNA for 12 hours.

THP-1 macrophages were cultured with lipofectamine and/or Cox II mtDNA (2 ug/mL) for 6, 9, 12, or 14 hours before cell lysates were analysed by Western blot. Cells were stimulated with LPS as a positive control. Membranes were probed with pro-IL-1b and GAPDH antibodies. A blot and its associated densitometry graph are shown with pro-IL-1b levels relative to GAPDH.

As the largest change in pro-IL-1b expression was observed after 12 hours stimulation with

Cox II mtDNA, subsequent dose response experiments were done at this time point. THP-1 macrophages were stimulated with lipofectamine and various concentrations of Cox II mtDNA for

12 hours, with LPS as a positive control (not shown). Levels of intracellular pro-IL-1b were detected by western blot and subsequent densitometry analysis was plotted relative to GAPDH

(Figure 8). Upregulation of pro-IL-1b protein expression was dose-dependent, where macrophages stimulated with increasing concentrations of Cox II mtDNA transfected with

45 lipofectamine led to increased levels of pro-IL-1b (Figure 8). Similar to changes observed in gene expression, a significant increase in pro-IL-1b protein expression was observed when cells were stimulated with 1 ug/mL Cox II mtDNA, where levels of pro-IL-1b were approximately 15 fold greater than cells stimulated with lipofectamine alone (Figure 8B).

pro-IL-1β 31 kDa

GAPDH 37 kDa B. 25 media 0 0.125 0.25 0.5 1 2 4 *

Lipofectamine + Cox II (ug/mL) / 20 e ease on r l c a

e 15 1.0 i n d i l o m f a

0.8 t 10 1 ec - f IL - po o li

0.6 r 5 p / GAPDH / GAPDH β 0.4 0 media 0 0.125 0.25 0.5 1 2 4

pro-IL-1 0.2 Lipofectamine + Cox II (ug/mL)

0.0 media 0 0.125 0.25 0.5 1 2 4 Lipofectamine + Cox II (ug/mL)

Figure 8. Pro-IL-1b levels increased in a dose-dependent manner after mtDNA stimulation.

A. THP-1 macrophages were stimulated with lipofectamine and various concentrations of Cox II mtDNA for 12 hours before lysates analyzed by Western blot for pro-IL-1b protein expression, shown relative to GAPDH. LPS was used as a positive control (not shown). A representative blot and its associated densitometry graph is shown from n=3 independent experiments. B. Fold increase of pro-IL-1b protein expression over cells stimulated with lipofectamine alone. Pro-IL- 1b protein expression was normalized to GAPDH. Data shown is mean ± standard error, from n=3 independent experiments. Statistical significance was determined by student’s t-test (*p=0.04).

6.4 Stimulation with mtDNA induces release of IL-1b from macrophages

The previous results (see 6.3) indicated that stimulation with mtDNA results in upregulation of IL-1b gene and protein expression. These results support the idea that mtDNA can prime macrophages and act as a first signal in inflammasome activation. Thus, we wanted to determine whether mtDNA stimulation, followed by another inflammasome stimulus such as ATP, can activate macrophages to secrete IL-1b. To begin, we performed kinetic experiments where

THP-1 differentiated macrophages were stimulated with known NLRP3 inflammasome activators,

LPS and ATP, for various times as indicated. Culture supernatants were collected and levels of secreted IL-1b were measured by ELISA (Figure 9A). Macrophages stimulated with LPS and

ATP secreted robust amounts of IL-1b after 4 hours stimulation, where concentrations of secreted

IL-1b peaked after 18 hours (Figure 9A). Similarly, when cells were stimulated with lipofectamine, Cox II mtDNA (1 ug/mL), and ATP, robust levels of IL-1b were secreted after 6 hours stimulation, where concentrations of secreted IL-1b also peaked after 18 hours stimulation

(Figure 9B).

Based on our kinetic analyses, levels of secreted IL-1b were analyzed from culture supernatants collected after cells were stimulated with signal 1 stimuli (mtDNA fragments or LPS) for 6 hours in subsequent dose response experiments. This time point was chosen as high levels of

IL-1b were secreted after 6 hours stimulation, without reaching peak levels of secreted IL-1b seen after 18 hours stimulation. As shown in Figure 10A, macrophages stimulated with lipofectamine, various concentrations of Cox II mtDNA, and ATP secreted IL-1b in a dose-dependent manner.

Cells stimulated with lipofectamine, 1 ug/mL Cox II mtDNA, and ATP secreted significantly higher levels of IL-1b (96 pg/mL) compared to cells treated with lipofectamine and ATP alone (4

pg/mL) (Figure 10A). The dose-dependent response of mtDNA-induced macrophage secretion of

IL-1b is similar to the response observed in earlier experiments on mtDNA-induced upregulation

of IL-1b gene and protein expression (see 6.3). To validate these results, we also stimulated cells

with lipofectamine and another mtDNA fragment, 16s, followed by ATP. Secreted levels of IL-1b

were analyzed from supernatants by ELISA (Figure 10B). Similar to cells stimulated with Cox II

mtDNA, macrophages cultured with 16s mtDNA also secreted IL-1b in a dose-dependent manner

(Figure 10B). Concentrations of IL-1b secreted from macrophages stimulated with either 1 ug/mL

Cox II or 2 ug/mL 16s mtDNA and ATP were significantly higher than concentrations of IL-

1bsecreted by cells stimulated with lipofectamine and ATP alone (Figure 10).

A. B.

2000 200 4 hour 4 hour 1500 6 hour 150 6 hour 18 hour 18 hour 24 hour 1000 100 24 hour (pg/mL) (pg/mL) β β IL-1 500 IL-1 50

0 0 media LPS + ATP media 0 1

Lipofectamine + Cox II (ug/mL) + ATP Figure 9. Kinetics of IL-1b secretion from macrophages.

A. THP-1 differentiated macrophages were cultured with LPS (10 ng/mL) for 4, 6, 18, or 24 hours and ATP (10 mM) for 1 hour before supernatants were collected, and analyzed for IL-1b by ELISA. B. Similar to A., except cells were incubated for various times with lipofectamine plus Cox II mtDNA (1 ug/mL) before stimulation with ATP (10 mM) for 1 hour.

A. B. 120 100 *** *** 80 90 ) ) L L m m /

/ 60 pg pg

( 60 (

1 1 40 - - L L I I 30 20

0 0 mmeeddiiaabuffebufferr 00 0.20.255 0.0.55 11 22 4 88 mmeeddiiaabuffebufferr 0 0.2525 0.0.5 1 2 4 8

Lipofectamine + Cox II (ug/mL) + ATP Lipofectamine + 16s (ug/mL) + ATP

Figure 10. Stimulation with mtDNA induces dose-dependent release of IL-1b from macrophages.

A. THP-1 differentiated macrophages were stimulated with lipofectamine and various concentrations of Cox II mtDNA, or LPS positive control (not shown), for 6 hours, followed by ATP (10 mM) for 1 hour. Supernatants were collected and concentration of secreted IL-1b was analyzed by ELISA. Cells were stimulated with lipofectamine plus elution buffer (from the Qiagen PCR purification kit) and ATP as a negative control. B. Similar to A., except concentrations of secreted IL-1β were analysed from culture supernatants by ELISA after THP-1 macrophages were stimulated with lipofectamine and various concentrations of 16s mtDNA for 6 hours, followed by ATP for 1 hour. Data shown is mean ± standard error, with representative graphs shown from n=3 independent experiments. Statistical significance was determined by student’s t-test (***p<0.0001).

To determine whether secretion of IL-1b by macrophages is specifically induced by mtDNA, we performed a specificity experiment. Macrophages were stimulated with either mtDNA fragments (Cox II and 16s) or genomic DNA fragments (GAPDH and CD4) transfected with lipofectamine for 6 hours, followed by ATP for 1 hour. Supernatants were collected and the concentration of secreted IL-1b was analyzed by ELISA. Concentrations of IL-1b secreted by cells stimulated with gDNA fragments were not significantly different from concentrations of IL-1b secreted by cells stimulated with mtDNA fragments (Figure 11). Furthermore, concentrations of

IL-1b secreted from cells cultured with lipofectamine and either mtDNA or gDNA fragments were significantly higher than cells stimulated with lipofectamine alone (Figure 11).

160 *** *

* ** 120

80 (pg/mL) β IL-1 40

0 media buffer lipo Cox II 16s GAPDH CD4

Lipofectamine + nucleic acids (1 ug/mL) + ATP

Figure 11. Stimulation with mtDNA or gDNA induces release of IL-1b from macrophages.

THP-1 differentiated macrophages were stimulated with lipofectamine and nucleic acid fragments (1 ug/mL) for 6 hours, followed by ATP (10 mM) for 1 hour before supernatants were collected and IL-1b concentrations were analyzed by ELISA. Cox II and 16s are mtDNA fragments, GAPDH and CD4 are gDNA fragments. Data shown is mean ± standard error, with a representative graph shown from n=3 independent experiments. Statistical significance was determined by one-way ANOVA, where cells treated with nucleic acids were compared to cells treated with lipofectamine alone (Tukey’s multiple comparisons test; *p=0.01, **p=0.006, ***p<0.0001).

6.5 Patient cohort demographics:

Data from a cohort of 134 children with new-onset JIA enrolled in the Biologically Based

Outcome Predictors in JIA (BBOP) study was used. Information was collected at study enrollment

(pre-treatment) and 6-month (post-treatment) visits. Biological specimens collected included saliva and blood for gene expression and protein/cytokine expression analyses. In addition, detailed clinical descriptions were collected from each visit, including the core set of variables used to measure disease activity in JIA (59). A total of 121 treatment-naïve patients met the criteria for this study. Demographic data on the patient cohort is summarized in Table 5.

Table 5. Characteristics and measures of BBOP patients at enrollment*

Enthesitis Oligo- RF- RF+ Systemic Psoriatic related Undifferentiated arthritis polyarthritis polyarthritis arthritis arthritis arthritis n (%) 26 (22%) 43 (36%) 11 (9%) 16 (13%) 11 (9%) 9 (7%) 5 (4%)

Females : males 17:9 32:11 10:1 8:8 6:6 7:2 3:2

6 10.6 13.6 7.4 9.8 12 9 Age at onset, years (1.4-13.7) (1-15.4) (1.3-15.3) (3.2-13.5) (6.3-15.1) (2.3-15.4) (8.7-13.9)

Age at study 6 (2-16) 12 (1-17) 14 (2-16) 8 (3-14) 13 (9-15) 12 (3-16) 10.5 (9-15.2) enrolment, years Time from onset to study enrollment, 3 (0-72) 7 (2-131) 4 (2-14) 2 (1-7) 9.5 (2-81) 15.5 (3-37) 5 (3-13) months ANA positive, 6 (50) 15 (60) 4 (66.7) 2 (28.6) 2 (40) 1 (25) 2 (50) n (% pos./tested)

Baseline measures: Active joint count 2 (0-9) 9.5 (0-35) 23 (6-41) 2 (0-15) 4 (0-13) 4 (2-19) 2 (1–5) Effused joint 2 (0-6) 6 (0-28) 18 (1-24) 2 (0-6) 4 (0-8) 3 (0-17) 1 (0-2) count Limited ROM joint 1 (0-8) 5 (0-33) 16 (1-24) 2 (0-15) 4 (0-15) 6.5 (1-16) 2 (1-3) count 2.6 3.9 4.9 4 3.9 3.9 3.4 PGA, cm (0.2-7.3) (0.9-7.2) (1.1-9.1) (1.3-7.9) (0.4-8.1) (0.9-6.8) (0.5-6.3) 1.1 (0.3- CHAQ score 0.4 (0-1.6) 0.63 (0-2.6) 1.3 (0.1-3) 0.8 (0-2.75) 0.8 (0-1.5) 0.4 (0-0.75) 1.6) 2.8 3.4 3.3 3.3 4.2 2.9 2.8 JAQQ score (0-4.8) (0-6.8) (0-7) (0-5.85) (1.5-5.6) (1.8-5.9) (2.15-3.85) Pain VAS, cm 2 (0-4) 2 (0-10) 2 (0-6) 2 (0-6) 2 (0-10) 0 (0-4) 2 (0-5)

ESR, mm/hour 21 (0.5-84) 21 (1-107) 41 (5-93) 33 (2-125) 52 (4-105) 16 (5-55) 17 (3-46)

CRP level, 5 3.8 32.4 30.1 14 6 6.9 units/liter (0.2-41.1) (0-2.6) (0.4-88.1) (0-113.3) (1-77) (0.9-6.8) (0.8-23.4) Platelet count, 372.5 326.5 384 446 374 328 337 units/liter (214-650) (163-811) (225-638) (245-944) (253-491) (231-449) (225-439) White blood cell 8.9 7.1 8.9 17.8 10.1 7.9 7 count, cells/liter (3.8-17.7) (3.5-17.6) (3.7-15.3) (6.1-31.3) (5.5-12.4) (5.9-12.9) (5.5-9.2) *Values are the median (range) unless otherwise indicated. RF= rheumatoid factor; ANA= antinuclear antibody; ROM= range of motion; PGA= Physician’s global assessment of activity; CHAQ= Childhood Health Assessment Questionnaire; ESR= erythrocyte sedimentation rate; CRP= C-reactive protein; JAQQ= Juvenile Arthritis Quality of Life Questionnaire; VAS= visual analog scale.

6.6 Circulating mtDNA is significantly decreased after treatment in autoinflammatory subtypes

Previous studies demonstrated patients with various diseases, including autoimmune diseases such as systemic lupus erythematosus, have elevated levels of circulating nucleic acids when compared to healthy controls (86). In rheumatoid arthritis patients, significantly elevated levels of circulating mtDNA were detected in the plasma and synovial fluid (38). Therefore, we wanted to quantify the amount of circulating mtDNA in patient plasma in JIA. We began by analyzing the stability of the CNAs extracted from plasma collected from patients in P100 tubes, which contain a proprietary blend of protease stabilizers. Aliquots of processed plasma were thawed on ice from storage at -80oC, and left at room temperature for various times as indicated before isolation of total CNAs was performed. The amount of circulating mtDNA was quantified from CNAs isolated from plasma through qPCR using a standard curve for Cox II, and plotted according to time left at room temperature (Figure 12). Plasma from a healthy control contained lower levels of circulating mtDNA compared to plasma from a JIA patient, and CNAs were relatively stable at room temperature for up to two hours (Figure 12). No difference was seen in the stability of CNAs isolated from the JIA patient compared to the healthy control (Figure 12).

5 JIA patient 4 healthy control

2 mtDNA (Log10copies/mL plasma ) plasma (Log10copies/mL mtDNA

1 0 10 20 30 40 50 60 70 80 90 100 110 120 time (minutes) Figure 12. CNAs are stable at room temperature after extraction from patient plasma.

Plasma was collected in P100 tubes from a JIA patient or healthy control, processed, aliquoted, and stored at -80oC. After plasma aliquots were thawed on ice and incubated at room temperature for various times as indicated, CNAs were extracted from all aliquots together using a plasma cell- free circulating DNA mini kit. Absolute amounts of circulating mtDNA was quantified through qPCR using a Cox II standard curve.

To determine whether circulating mtDNA is associated with JIA disease activity, we quantified the amount of circulating mtDNA in plasma collected from JIA patients at both enrollment (pre-treatment) and 6-month (post-treatment) visits. Similar to above, CNAs were isolated from plasma and the amount of circulating Cox II mtDNA was quantified through qPCR.

In our entire patient cohort, the levels of circulating Cox II mtDNA were significantly higher at pre-treatment, compared to post-treatment (Figure 13). When the data was analyzed by ILAR subtype, levels of circulating Cox II mtDNA decreased significantly after treatment in sJIA and

ERA patients, but not in patients of the other ILAR subtypes (Figure 13).

entire cohort systemic JIA enthesitis related arthritis oligoarthritis 7 7 7 7 * ** *** 6 6 6 6

5 5 5 5

4 4 4 4

3 3 3 3 Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) pre-treatment post-treatment pre-treatment post-treatment pre-treatment post-treatment pre-treatment post-treatment

RF- polyarthritis RF+ polyarthritis psoriatic arthritis undifferentiated arthritis 7 7 7 7

6 6 6 6

5 5 5 5

4 4 4 4

3 3 3 3 Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) pre-treatment post-treatment Cox II (Log10 copies/mL plasma) pre-treatment post-treatment pre-treatment post-treatment pre-treatment post-treatment

Figure 13. Circulating levels of mtDNA significantly decreased after treatment in sJIA and ERA subtypes.

Circulating Cox II mtDNA significantly decreased after treatment in the entire cohort. Subtype analysis indicates that levels of circulating mtDNA are significantly decreased in the plasma of sJIA and ERA patients after treatment. There are no significant differences between pre- and post- treatment levels of circulating mtDNA in the other JIA subtypes. n=121 (entire cohort); n=16 (sJIA); n=11 (ERA); n=26 (oligoarthritis); n=43 (RF- polyarthritis); n=11 (RF+ polyarthritis); n=9 (psoriatic arthritis); n=5 (undifferentiated arthritis). Box and whisker plots are shown where the box represents the interquartile range and median, and the whiskers represent the 3rd and 97th percentiles. Statistical significance was determined by student’s paired t-test (*p=0.03, **p=0.008, ***p<0.0001).

As our previous in vitro results indicated that other nucleic acids, other than mtDNA, may be able to activate macrophage secretion of IL-1b (see 6.4), we wanted to quantify the concentration of total CNAs isolated from patients at pre- and post-treatment to determine whether a difference could also be observed. CNAs were isolated as described above, and PicoGreen fluorescent nucleic acid stain was used to determine the concentration of total CNAs isolated from patient plasma. However, no significant differences in concentration of CNAs were observed in plasma collected at pre-treatment and post-treatment time points; we did not observe a significant

55 difference when we analyzed our entire cohort, as well as the ILAR subtypes (Figure 14). In sJIA and ERA patients, but not in patients of the other ILAR subtypes, a trend of decreased CNAs concentration was observed after treatment (Figure 14).

entire cohort systemic JIA enthesitis related arthritis oligoarthritis

4 4 4 4

3 3 3 3

2 2 2 2

1 1 1 1

0 0 0 0 DNA concentration (log10 ng/mL) (log10 DNA concentration ng/mL) (log10 DNA concentration pre-treatment post-treatment pre-treatment post-treatment ng/mL) (log10 DNA concentration pre-treatment post-treatment ng/mL) (log10 DNA concentration pre-treatment post-treatment

RF- polyarthritis RF+ polyarthritis psoriatic arthritis undifferentiated arthritis 4 4 4 4

3 3 3 3

2 2 2 2

1 1 1 1

0 0 0 0 DNA concentration (log10 ng/mL) (log10 DNA concentration pre-treatment post-treatment ng/mL) (log10 DNA concentration pre-treatment post-treatment ng/mL) (log10 DNA concentration pre-treatment post-treatment ng/mL) (log10 DNA concentration pre-treatment post-treatment

Figure 14. Concentrations of circulating nucleic acids are not significantly different after treatment.

CNAs concentration is not significantly different in pre-treatment plasma, compared to post- treatment plasma, in the entire cohort. Subtype analysis did not show any significant differences between pre- and post-treatment plasma. n=121 (entire cohort); n=16 (sJIA); n=11 (ERA); n=26 (oligoarthritis); n=43 (RF- polyarthritis); n=11 (RF+ polyarthritis); n=9 (psoriatic arthritis); n=5 (undifferentiated arthritis). Box and whisker plots are shown where the box represents the interquartile range and median, and the whiskers represent the 3rd and 97th percentiles.

6.7 Gene expression significantly differs after treatment in systemic JIA patients

Previous studies have implicated inflammasome activation and IL-1b and IL-18 signaling in the pathogenesis of JIA, and particularly in the pathogenesis of systemic JIA (15,87). As differences were observed in levels of circulating mtDNA after treatment in our patient cohort, and specifically in plasma from sJIA and ERA patients, we wanted to determine whether differences were observed in the gene expression of IL-1b, IL-18, and inflammasome associated genes after treatment. The gene expression of several inflammasome pathway related genes, measured by microarray (see Table 3 in methods), were compared between pre-treatment and post-treatment. When all JIA patients were analyzed, gene expression of all IL-1b and IL-18 associated genes decreased significantly after treatment, with the exception of IL-1B and IL-18 expression which did not significantly change after treatment, and expression of IL-18BP which significantly increased after treatment (Figure 15A). The same set of genes also differed significantly in sJIA patients (Figure 15B). Gene expression in ERA patients did not change significantly after treatment (not shown). When the entire cohort was analyzed after removing sJIA and ERA patients, only IL-18BP gene expression significantly differed following treatment

(Figure 15C).

IL-1B IL-1R1 IL-1R2 IL-1RN A. 12 9 14 10 * 11 * * 9 8 12 10 8 7 10 9 7 6 8 8 6

7 5 6 5 Normalized gene expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment

IL-18 IL-18BP IL-18 R1 IL-18 RAP 8 8.5 12 11

8.0 *** * 10 ** 7 10 7.5 9

6 7.0 8 8 6.5 7 5 6 6.0 6 4 5.5 4 5 Normalized gene expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment

B. IL-1B IL-1R1 IL-1R2 IL-1RN 12 9 14 10 ** ** * 11 8 12 9

10 7 10 8

9 6 8 7

8 5 6 6 Normalized gene expression gene Normalized Normalized gene expression gene Normalized Normalized gene expression gene Normalized Pre-treatment Post-treatment Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment Pre-treatment Post-treatment

IL-18 IL-18BP IL-18 R1 IL-18 RAP 8 8.5 11 11 ** * ** 8.0 10 10 7 7.5 9 9 6 7.0 8 8 6.5 7 5 6.0 6 7 4 5.5 5 6 Normalized gene expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment

C. IL-1B IL-1R1 IL-1R2 IL-1RN 12 9 12 10

11 8 9 10 10 7 8 8 9 6 7

8 5 6 6 Normalized gene expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment

IL-18 IL-18BP IL-18R1 IL-18RAP 7 8.5 10 10

8.0 ** 9 9 6 7.5 8 8

7.0 7 7 5 6.5 6 6

4 6.0 5 5 Normalized gene expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment

Figure 15. Expression of IL-1 and IL-18 associated genes are significantly different after treatment.

A. Gene expression was measured by Affymetrix Human Primeview Microarray and compared between pre- and post-treatment time points. In the entire patient cohort, gene expression of IL-1R1, IL-1R2, IL-1RN, IL-18R1, and IL-18RAP significantly decreased following treatment. Expression of IL-18BP significantly increased after treatment; n=109 patients. B. In sJIA patients, IL-1R1, IL-1R2, IL-1RN, IL-18R1, and IL-18RAP gene expression significantly decreased after treatment, while IL-18BP expression significantly increased following treatment; n=15 patients. C. In the cohort of patients excluding sJIA and ERA patients, IL-18BP gene expression significantly increased following treatment; n=83. Box and whisker plots are shown where the box represents the interquartile range and median, and the whiskers represent the 3rd and 97th percentiles. Statistical significance was determined by student’s paired t-test (*p=0.02–0.04, **p=0.004–0.009, ***p=0.0001).

We also compared treatment effects on gene expression of inflammasome-associated genes

(Figure 16). Gene expression of NLRC4 significantly decreased following treatment in the entire

cohort of JIA patients (Figure 16A), including when sJIA and ERA patients were removed from

the cohort (Figure 16B). In sJIA patients, expression of NLRC4, NLRP2, and NLRP12 decreased

significantly after treatment, while NLRP1 gene expression increased significantly after treatment

(Figure 16C). However, there were no significant differences in gene expression following

treatment in ERA patients (not shown).

A. NLRC4 NLRP2 NLRP12 NLRP1 10 *** 7.5 9 10 9 7.0 9 8 8 6.5 8 7 6.0 7 7 6 5.5

5 5.0 6 6 Normalized gene expression gene Normalized expression gene Normalized expression gene Normalized Normalized gene expression gene Normalized Pre-treatment Post-treatment Pre-treatment Post-treatment Pre-treatment Post-treatment Pre-treatment Post-treatment

B. NLRC4 NLRP2 NLRP12 NLRP1 9 7.5 9 9.5

** 7.0 9.0 8 8 6.5 8.5 7 6.0 8.0 7 6 5.5 7.5

5 5.0 6 7.0 Normalized gene expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment

C. NLRC4 NLRP2 NLRP12 NLRP1 10 9 9.0 9.0 * *** * * 9 8.5 8 8.5 8.0 8 8.0 7 7.5 7 7.5 7.0 6 6 7.0 6.5 5 5 6.0

Normalized gene expression gene Normalized 6.5 Normalized gene expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment expression gene Normalized Pre-treatment Post-treatment Pre-treatment Post-treatment

Figure 16. Inflammasome associated genes are significantly different after treatment.

A. Comparison of pre- and post-treatment gene expression measured by Affymetrix Human Primeview Microarray. In the entire JIA patient cohort, NLRC4 gene expression significantly decreased after treatment; n=109 patients. B. In the entire cohort excluding sJIA and ERA patients, NLRC4 gene expression significantly decreased following treatment; n=83. C. In sJIA patients, NLRC4, NLRP2, and NLRP12 gene expression significantly decreased after treatment, while NLRP1 expression significantly increased following treatment; n=15 patients. Box and whisker plots are shown where the box represents the interquartile range and median, and the whiskers represent the 3rd and 97th percentiles. Statistical significance was determined by student’s paired t- test (*p=0.01–0.04, **p=0.008, ***p<0.0001).

6.8 Associations between circulating mtDNA and clinical measures of disease activity

To examine the relationship between levels of circulating mtDNA and disease activity in

JIA, Pearson correlation coefficients were calculated between mtDNA copy number and clinical measures at both pre- and post-treatment visits. The complete list of clinical measures analyzed is summarized in Table 6. In general, levels of circulating mtDNA were not strongly correlated with the clinical measures of disease activity, both when the entire cohort of patients was analyzed and upon subtype analysis (Table 6). In the entire cohort, weak correlations were found between circulating mtDNA and clinical measures of disease activity (Table 6). In addition, subtype analysis indicated that while slightly higher correlation coefficients were observed when patients were analyzed by ILAR subtype, particularly in sJIA and ERA patients, circulating mtDNA is not highly associated with clinical measures of disease activity (Table 6).

Table 6. Associations between circulating mtDNA and clinical measures of disease activity Enthesitis related Clinical measures of disease activity Entire cohort Systemic arthritis arthritis Hemoglobin Pearson correlation coefficient -0.05 -0.14 -0.24 P 0.44 0.47 0.29 Active joint count Pearson correlation coefficient 0.01 0.19 0.13 P 0.88 0.32 0.58 Effused joint count Pearson correlation coefficient 0.02 -0.03 0.15 P 0.81 0.88 0.52 Limited ROM joint count Pearson correlation coefficient 0.04 0.22 0.05 P 0.56 0.24 0.85 CRP level Pearson correlation coefficient 0.08 -0.13 0.33 P 0.31 0.53 0.16 ESR Pearson correlation coefficient 0.14 -0.07 0.51 P 0.04 0.7 0.01 CHAQ score Pearson correlation coefficient 0.16 -0.09 0.33 P 0.02 0.65 0.16 JAQQ score Pearson correlation coefficient 0.01 -0.06 0.36 P 0.16 0.74 0.13 PGA Pearson correlation coefficient 0.12 0.41 0.20 P 0.06 0.02 0.37 Platelet count Pearson correlation coefficient 0.18 0.15 0.36 P 0.008 0.43 0.1 Neutrophil count Pearson correlation coefficient 0.22 0.2 0.53 P 0.001 0.28 0.01 Lymphocyte count Pearson correlation coefficient 0.24 0.21 0.17 P 0.0005 0.27 0.46 White blood cell count Pearson correlation coefficient 0.32 0.3 0.45 P <0.0001 0.1 0.04 Pain score Pearson correlation coefficient 0.07 -0.14 0.43 P 0.33 0.49 0.06 ROM= range of motion; CRP= C-reactive protein; ESR= erythrocyte sedimentation rate; CHAQ= Childhood Health Assessment Questionnaire; JAQQ= Juvenile Arthritis Quality of Life Questionnaire; PGA= Physician’s global assessment of activity.

6.9 Associations between circulating mtDNA and biological activity levels

To examine the relationship between levels of circulating mtDNA and biological activity levels in JIA patients, Pearson correlation coefficients were calculated between circulating mtDNA copy number and a panel of 45 cytokine/chemokine levels measured by multiplex ELISA from plasma collected at both pre- and post-treatment visits (see Table 4 in methods). We focused our analyses on the sJIA and ERA subtypes, as previous analysis found circulating mtDNA significantly decreased after treatment in these subtypes (see 6.6). Similar to the clinical measures of disease activity, circulating mtDNA was not strongly correlated with the 45 cytokines measured from patient plasma. The associations with the highest correlation coefficients are summarized in

Table 7. Again, while slightly higher correlation coefficients were observed when patients were analyzed by ILAR subtypes, circulating mtDNA is not highly associated with cytokines in JIA patients (Table 7).

Table 7. Associations between circulating mtDNA and patient cytokine levels* Enthesitis related Cytokines measured Entire cohort Systemic arthritis arthritis EGF Pearson correlation coefficient 0.35 0.57 0.36 P <0.0001 0.001 0.09 Eotaxin Pearson correlation coefficient 0.30 0.43 0.51 P <0.0001 0.01 0.01 MMP-8 Pearson correlation coefficient 0.29 0.28 0.40 P <0.0001 0.16 0.06 RANTES Pearson correlation coefficient 0.28 0.51 0.08 P <0.0001 0.003 0.71 G-CSF Pearson correlation coefficient 0.28 0.37 -0.3 P <0.0001 0.04 0.9 *Highest correlation coefficients are shown.

We also explored the associations between circulating levels of mtDNA and pre-treatment gene expression of IL-1b, IL-18, and inflammasome signaling-associated genes. Similar to above,

Pearson correlation coefficients were calculated between circulating mtDNA copy number and gene expression from key genes measured at pre-treatment by microarray (see Table 3 in methods). Again, when the entire cohort of patients was analyzed, weak correlations were observed between levels of circulating mtDNA and gene expression (data not shown). While subtype analyses revealed slightly stronger correlations, overall circulating mtDNA was weakly associated with gene expression at pre-treatment. In sJIA patients, associations were observed with

IL-18 signaling-associated genes, where clinically relevant trends were observed (Figure 17A). A positive correlation was observed between circulating mtDNA and IL-18 gene expression

(coefficient 0.52, p=0.04), while a negative correlation was observed with IL-18BP gene expression (coefficient 0.57, p=0.02) (Figure 17A). Interestingly, levels of circulating mtDNA were also positively associated with pre-treatment expression of IL-18R1 and IL-1R1 (coefficients

0.45; p=0.17, 0.23; p=0.49) in ERA patients (Figure 17B). In addition, a negative correlation was observed between circulating mtDNA and IL-1RN gene expression (coefficient -0.4, p=0.23)

(Figure 17B). In general, circulating mtDNA was not highly associated with biological activity levels. However, subtype analysis demonstrated some moderate associations with pre-treatment gene expression of IL-1b and IL-18 signaling associated genes in sJIA and ERA patients.

A. 12 9 14 10

11 8 12 9

10 7 10 8

9 6 8 7

8 5 6 6 4.0 4.5 5.0 5.5 6.0 6.5 4.0 4.5 5.0 5.5 6.0 6.5 4.0 4.5 5.0 5.5 6.0 6.5 4.0 4.5 5.0 5.5 6.0 6.5 IL-1B normalized gene expression IL-1R1 normalized gene expression IL-1R2 normalized gene expression Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) IL-1RN normalized gene expression Cox II (Log10 copies/mL plasma)

8 8.0 11 11 10 10 7 7.5 7.0 9 9 6 6.5 8 8 5 6.0 7 7

4 5.5 6 6 4.0 4.5 5.0 5.5 6.0 6.5 4.0 4.5 5.0 5.5 6.0 6.5 4.0 4.5 5.0 5.5 6.0 6.5 4.0 4.5 5.0 5.5 6.0 6.5 IL-18 normalized gene expression IL-18R1 normalized gene expression IL-18BP normalized gene expression Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) IL-18RAP normalized gene expression Cox II (Log10 copies/mL plasma)

12 8.5 12 8.5

8.0 8.0 11 11 7.5 7.5 10 7.0 10 7.0 6.5 9 9 6.0 6.5 8 5.5 8 6.0 4.5 5.0 5.5 6.0 6.5 4.5 5.0 5.5 6.0 6.5 4.5 5.0 5.5 6.0 6.5 4.5 5.0 5.5 6.0 6.5 IL-1B normalized gene expression IL-1R1 normalized gene expression IL-1R2 normalized gene expression Cox II (Log10 copies/mL plasma) IL-1RN normalized gene expression Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma)

6.5 8.0 8.5 9.5

8.0 9.0 6.0 7.5 7.5 8.5 5.5 7.0 7.0 8.0 5.0 6.5 6.5 7.5

4.5 6.0 6.0 7.0 4.5 5.0 5.5 6.0 6.5 4.5 5.0 5.5 6.0 6.5 4.5 5.0 5.5 6.0 6.5 4.5 5.0 5.5 6.0 6.5 IL-18 normalized gene expression IL-18R1 normalized gene expression IL-18BP normalized gene expression Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) Cox II (Log10 copies/mL plasma) IL-18RAP normalized gene expression Cox II (Log10 copies/mL plasma)

Figure 17. Circulating mtDNA is associated with pre-treatment gene expression in sJIA and ERA patients.

A. Pearson correlation coefficients were calculated to examine the association between circulating Cox II mtDNA and gene expression at pre-treatment. In sJIA patients, the strongest positive correlation was observed with IL-18 gene expression (R=0.52, p=0.04) and the strongest negative correlation was observed with IL-18BP gene expression (R=-0.57, p=0.02); n=16 patients. B. In ERA patients, the highest positive correlation coefficients were between circulating Cox II mtDNA and IL-18R1 (R=0.45, p=0.17) and IL-1R1 (R=0.23, p=0.49). The strongest negative correlation was observed between circulating Cox II mtDNA and IL-1RN (R= -0.4, p=0.23); n=11 patients.

Discussion:

JIA is one of the most common chronic diseases affecting children. This heterogeneous group of diseases has many long-term consequences, and can significantly affect the quality of life of children with JIA, leading to joint destruction and impaired growth and development (50).

While clinical presentation differs between JIA subtypes, chronic inflammation is common to all

JIA patients, where pro-inflammatory cytokines have been implicated as demonstrated by treatment efficacy with biologics.

Currently, the etiology of JIA is not well understood and continues to be elucidated. There is consensus that sJIA and ERA are unique subtypes as they are considered to be autoinflammatory diseases, where inflammasome activation and elevated levels of IL-1b have been implicated in the pathogenesis. In addition, circulatory mtDNA is a danger signal reflecting cellular injury, and has been implicated to participate in systemic inflammation in multiple diseases, including autoimmune diseases such as rheumatoid arthritis (37). In this thesis, we investigated the role of circulating mtDNA in JIA.

7.1 mtDNA stimulation induces production of IL-1b in macrophages

While previous studies have suggested mtDNA can induce an inflammatory response from innate immune cells, the immune response has not been well characterized. Studies have shown mtDNA is a DAMP and can stimulate TLR-9 and NFkB signaling in neutrophils and macrophages, leading to the secretion of pro-inflammatory cytokines including TNF-a and IL-6 (45,46). It has also been reported that oxidized mtDNA present in the cytoplasm can activate the NLRP3

65 66 inflammasome and cause secretion of IL-1b (44,48). The NLRP3 inflammasome has an important role in the regulation of innate immune responses and inflammation as it is the major mechanism leading to release of IL-1b and IL-18 in macrophages. However, the exact mechanism leading to activation of the NLRP3 inflammasome has been elusive, as multiple diverse and unrelated stimuli have been reported to activate the inflammasome. Recent evidence has indicated that mitochondria may be involved in the regulation of NLRP3 inflammasomes (88). In the present study, we demonstrate that stimulation with mtDNA induced the upregulation of pro-IL-1b gene and protein expression from macrophages in a dose-dependent manner.

While the requirement of TLR-9 was not assessed, published literature has demonstrated that mtDNA can activate TLR-9 and NFkB signaling which may lead to the upregulation of pro-

IL-1b gene and protein expression (45,46). When cells were stimulated with mtDNA fragments alone, without lipofectamine, macrophages did not secrete high concentrations of IL-1b (data not shown). However, when mtDNA fragments were transfected into cells with lipofectamine reagent, macrophages secreted high levels of IL-1b in a dose-dependent manner. Lipofectamine combines with nucleic acids to form lipid-DNA complexes, which enter cells via endocytosis where the intracellular vesicles which may fuse with endosomes (89,90). It is possible that lipofectamine was required in order to localize the mtDNA fragments to the endosome, where mtDNA can be recognized by TLR-9 to activate the NFkB signaling pathway and induce downstream expression of pro-IL-1b. Thus, transfection of mtDNA fragments with lipofectamine reagent was required for the production and secretion of IL-1b from stimulated macrophages. Therefore, our data supports previous studies which indicate that mtDNA can prime macrophages through the activation of

NFkB signaling , acting as a first signal in the activation of the NLRP3 inflammasome.

In addition, previous studies have suggested that mtDNA activates TLR-9 signaling because mtDNA, similar to bacterial DNA, is rich in unmethylated CpG sequences (44,46). To determine whether macrophage secretion of IL-1b is specifically induced by mtDNA, we used lipofectamine to transfect cells with either mtDNA or gDNA fragments followed by ATP, and measured the amount of IL-1b secreted into culture supernatants. Interestingly, the gDNA fragments, which were generated under similar conditions by PCR from GAPDH and CD4 sequences, were also able to induce IL-1b secretion from macrophages at comparable levels to cells stimulated with mtDNA fragments. This suggests that nucleic acids in general, including both mtDNA and gDNA, can act as a danger signal, priming macrophages for inflammasome activation leading to IL-1b release. While TLR-9 was originally discovered to recognize unmethylated CpG

DNA present in bacterial DNA, one study has since challenged this view (91). The data presented by Haas et al. suggests that TLR-9 recognizes double-stranded DNA independently of base sequence and presence of unmethylated CpG motifs, similar to how recognition of RNA by TLR-

3, TLR-7, and TLR-8 is sequence-independent (92). Instead, they propose that TLR-9 recognizes the 2’ deoxyribose containing phosphodiester backbone of DNA (92). This view is supported by another study which reported that DNA lacking CpG motifs can induce an inflammatory response in dendritic cells via TLR-9 signaling, resulting in the production of inflammatory cytokines such as IL-6 (93). Therefore, it is likely that gDNA can also activate TLR-9 signaling. However, under normal circumstances gDNA is unlikely to be found outside of cells, and activation of TLR-9 by gDNA is avoided. Thus, only DNA taken up by cells will be present in the endosome and be immunostimulatory. In our experiments, we also demonstrated that stimulation with both mtDNA and gDNA fragments, followed by ATP, induced IL-1b secretion from macrophages. Because the

DNA fragments were directed to the endosome by lipofectamine, it is likely that TLR-9 was activated by both mtDNA and gDNA fragments, priming macrophages for inflammasome

68 activation. However, future experiments would be required to determine whether mtDNA-induced upregulation of IL-1b gene and protein expression requires TLR-9 and NFkB signaling.

As mtDNA stimulation upregulated pro-IL-1b expression, we wanted to determine whether macrophages secrete IL-1b when stimulated with mtDNA in addition to another inflammasome stimulus. Our data indicates that mtDNA stimulation of macrophages with ATP induced robust secretion of IL-1b. We determined the kinetics of this response, and our data indicated that while levels of secreted IL-1b peaked after mtDNA stimulation for 18 hours, robust levels of secreted

IL-1b were observed after just 6 hours stimulation with mtDNA. However, robust levels of IL-1b were detected in supernatants after 4 hour stimulation with LPS followed by ATP, suggesting that

LPS may be a stronger stimulator of the NLRP3 inflammasome, and mtDNA-induced secretion of

IL-1b is slower under these experimental conditions.

Transfection of nucleic acids with lipofectamine may also result in the release of nucleic acids directly into the cytosol (89,90). Therefore, it is possible that the DNA fragments, when combined with lipofectamine, activated cytoplasmic sensors of double-stranded DNA leading to the activation of macrophages and secretion of IL-1b. The AIM2 inflammasome is one cytosolic sensor which can detect double-stranded DNA in the cytosol. Activation of AIM2 results in the assembly of an inflammasome complex with ASC and pro-caspase-1, leading to caspase-1 activation and secretion of IL-1b (4). Therefore, it would be interesting to determine whether

DNA-induced secretion of IL-1b in macrophages was due to the activation of the AIM2 inflammasome.

Furthermore, one study suggested gDNA does not activate NFkB signaling in macrophages

(37). While we showed gDNA can induce secretion of IL-1b from macrophages, further

69 experiments are required to determine whether gDNA can induce upregulation of IL-1b gene and protein expression. Altogether, our findings suggested that DNA activates macrophages and contributes to an inflammatory response by increasing expression of pro-IL-1b; this primes macrophages, leading to inflammasome-mediated release of IL-1b.

7.2 mtDNA is weakly associated with JIA disease activity

Circulatory mtDNA has been reported to be elevated in several diseases (38,39,84,94,95).

It has been suggested that elevated levels of circulating mtDNA may have a role in autoimmune diseases such as SLE and RA (37,38,96). Therefore, we wanted to determine whether circulating mtDNA is associated with JIA. In the present study, plasma levels of circulatory mtDNA significantly decreased after treatment in our entire cohort of JIA patients (p<0.0001). However upon analysis by ILAR subtype, plasma levels of circulating mtDNA significantly decreased after treatment in only two subtypes, sJIA and ERA, which are unique in JIA and thought to be part of the autoinflammatory spectrum of diseases (p=0.03 and p=0.008, respectively). Previous studies have also demonstrated the potential of CNAs to be used as biomarkers of disease activity in various diseases including SLE (28). Thus, we wanted to quantify the concentration of plasma

CNAs collected from JIA patients at pre-treatment and post-treatment. However, we did not observe any significant differences in the concentrations of CNAs when comparing pre- and post- treatment plasma. Therefore, our data indicates that while the concentration of total CNAs does not significantly differ after treatment, the amount of circulating mtDNA significantly decreases after treatment, suggesting that mtDNA may be a stronger contributor to the immunopathology of

JIA.

We also examined whether changes in gene expression of IL-1b, IL-18 and inflammasome pathway genes were observed after treatment in our JIA patients. Differences in gene expression of IL-1b and IL-18 associated genes were observed. Notably, expression of IL-18BP significantly increased after treatment in our JIA cohort. Analysis by ILAR subtype revealed differences in gene expression were most likely attributed to changes observed in sJIA patients, as the same pattern of differences in gene expression were only observed in sJIA patients. The gene expression pattern observed in sJIA patients is clinically relevant, as decreased expression of IL-18BP at pre- treatment may help explain why sJIA patients with active disease have high serum levels of bioactive IL-18 (15). We also observed a significant decrease in NLRC4 gene expression after treatment in our patient cohort, and specifically in sJIA patients. Subtype analysis revealed additional significant differences in gene expression of NLRP1, NLRP2, and NLRP12 after treatment in sJIA patients. Both NLRP1 and NLRC4 molecules can also form inflammasomes in macrophages, leading to the activation of caspase-1 and secretion of IL-1b and IL-18 (6). Studies have reported that NLRP1 recognizes a bacterial peptidoglycan, muramyl dipeptide, while NLRC4 is activated by microbial proteinaceous ligands such as flagellin (4). Interestingly, a mutation in

NLRC4 has been associated with MAS and overproduction of IL-1b and IL-18 by macrophages, supporting the role of IL-1b and IL-18 in the pathogenesis of sJIA (97). As well, genetic polymorphisms in NLRP1 have been reported to be associated with the susceptibility of various autoimmune diseases including SLE and RA (98). In contrast, one study reported NLRP2 may have a regulatory role as it can negatively regulate NFkB signaling induced by pro-inflammatory cytokines TNF-a and IL-1b (99). As well, they observed that NLRP2 can bind ASC via its Pyrin domain to negatively regulate the activation of caspase-1, therefore decreasing the amount of IL-

1b secreted by THP-1 macrophages (99). NLRP12 also seems to be a negative regulator of

71 inflammation. Although the structure of NLRP12 is similar to NLRP3, studies have reported that

NLRP12 reduces MyD88-dependent activation of NFkB signaling in dendritic cells and macrophages. However, a conflicting report demonstrated co-expression of NLRP12 and ATP activated both NFkB and caspase-1, suggesting NLRP12 can also form an inflammasome similar to the NLRP3 inflammasome (100). Therefore, the molecular function of NLRP12 remains elusive. In addition, mutations in NLRP12 have been associated with susceptibility to familial cold autoinflammatory syndrome 2 (FCAS2), a hereditary periodic fever syndrome which closely resembles FACS1 which is caused by gain-of-function NLRP3 mutations; these autoinflammatory diseases are also treated with anti-IL1R therapy (101). Overall, our gene expression data supports the role of IL-1b and IL-18 in the pathogenesis of sJIA, while suggesting a potential role of other inflammasome-associated genes.

To further explore the relationship between circulatory mtDNA and JIA, we investigated whether a correlation exists between the amount of circulating mtDNA in the plasma and JIA disease activity. In JIA, disease activity is assessed through a core set of clinical parameters (59).

In general, when comparing circulating mtDNA levels and clinical measures of disease activity, weak correlations were observed. Subtype analysis revealed slightly stronger associations with higher correlation coefficients, particularly in sJIA patients where the strongest correlation was observed with the physician global assessment score (coefficient 0.41, p=0.02). While most of the associations with clinical measures were not strong, our data suggests that in sJIA patients, mtDNA may be associated with disease activity.

We also examined the relationship between circulating mtDNA and biological activity levels in JIA patients, examining associations with a panel of 45 cytokines and chemokines measured by multiplex ELISA. Again, the associations observed between circulating mtDNA and

72 cytokines were not very strong. While some cytokines were moderately correlated with levels of circulating mtDNA in sJIA patients, not much is known about the role of these cytokines in JIA.

For instance, the highest correlation coefficient was observed with epidermal growth factor (EGF) levels in sJIA patients. EGF signals through the EGF receptor (EGFR), a member of the ErbB family of tyrosine kinase receptors (102). Activation of the EGFR promotes survival, proliferation, and cytokine production amongst other functions, depending on the cell type. In cancer cells,

EGFR signaling can induce calcium mobilization via activation of protein kinase C (PKC) and

NFkB signaling, suggesting EGF may have a role in inflammation (103). The EGFR has also been implicated as a target in RA, where inhibition of EGFR in a mouse model of RA reduced disease severity through targeting synovial cells (104,105). Therefore, these cytokines may have potential roles in regulating inflammation in JIA.

Associations with pre-treatment gene expression of IL-1b, IL-18, and inflammasome- associated genes were also examined. We demonstrated relatively weak associations with IL-1 and

IL-18 associated genes in sJIA and ERA patients. However, the patterns observed are clinically relevant. In sJIA patients, circulating mtDNA was positively correlated with IL-18 gene expression and negatively correlated with IL-18BP gene expression at pre-treatment. JIA patients of all subtypes have been reported to have elevated plasma levels of IL-18, where patients with active sJIA have significantly higher levels of IL-18 compared to all other subtypes (15,106).

Furthermore, IL-1b has been implicated in the pathogenesis of sJIA, as many patients respond to treatment with IL-1 inhibitors (52). Therefore, the associations of circulating mtDNA with pre- treatment gene expression hint at the involvement of the IL-1b and IL-18 signaling pathways in the pathogenesis of the autoinflammatory subtypes, sJIA and ERA.

Our study included patients from across Canada, which is important as previous studies have reported ethnic differences in JIA (49). However, one limitation of this study was the strict criteria of including only treatment-naïve patients at study enrollment. This criterion produces several limitations, including the potential over-representation of children with mild disease, and a smaller overall population size. Furthermore, most patients enrolled in our study were not truly treatment naïve, as many patients were being treated with NSAIDs before study enrollment. As

JIA represents a heterogeneous group of diseases, it was important to stratify our clinical data by

JIA subtype. However, the sample size for each subtype was then reduced even further. This may account for the weak associations observed between circulating mtDNA and clinical and biological measures of disease activity. To confirm the associations observed, it would be important to increase sample sizes. In addition, we could repeat our analyses in an independent validation cohort.

7.3 Future directions:

While this study demonstrated that mtDNA can increase pro-IL-1b gene and protein expression and induce robust secretion of IL-1b following stimulation with a second stimulus, several questions remain to be answered. It would be interesting to further dissect the pathway of mtDNA-induced macrophage activation leading to an inflammatory response. In particular, we could further investigate the mechanism of how mtDNA induces increased expression of pro-IL-

1b, as previous studies have suggested mtDNA can activate TLR-9 and NFkB signaling pathways

(46). Thus, mtDNA-induced TLR-9 signaling would bear further study. To determine whether mtDNA is activating the AIM2 or NLRP3 inflammasome, it would be important to measure mtDNA-induced changes in IL-18 gene and protein expression, and secretion of IL-18. In addition,

74 it would be interesting to determine whether stimulation with mtDNA induces macrophage production of other pro-inflammatory cytokines downstream of NFkB signaling such as TNF-a,

IL-6, and IL-18.

Studies have reported that mtDNA sequences in circulation are typically present as short fragments (20,29). Thus, the mtDNA and gDNA fragments generated by PCR were controlled to be approximately 100 bp in length, where the primers used to generate the DNA fragments have been previously validated in literature (39,45,84). In addition, we ensured the mtDNA fragments chosen were unique to mtDNA, as mitochondrial DNA sequences have been integrated into and are present in the nuclear genome as pseudogenes (35). The presence of nuclear pseudogenes may lead to accidental overestimation of levels of circulating mtDNA in plasma by qPCR and misinterpretation of analyses. While we ensured our fragments were approximately 100 bp, it would be interesting to determine whether the length of the DNA fragments affects TLR-9 activation. One way to examine this would be to generate larger fragments containing the same sequences, and stimulate cells with these DNA fragments while measuring phosphorylation of

TLR-9 and pro-IL-1b expression.

An interesting finding of this study was that both mtDNA and gDNA fragments could induce secretion of IL-1b from macrophages. Therefore, another important study would be to examine the differential ability of mtDNA, versus gDNA, to activate inflammasomes. As one study suggested gDNA does not activate NFkB signaling in macrophages, it would be important to determine whether gDNA fragments can induce upregulation of pro-IL-1b gene and protein expression via NFkB activation (37). In addition, it would be important to confirm our data which suggests that TLR-9 activation occurs independent of CpG sequence. Future experiments could

75 examine whether macrophages stimulated with gDNA can induce NFkB activation and expression of pro-IL-1b.

Furthermore, it would be important to validate our findings through stimulating cells with other mtDNA and gDNA fragments generated by PCR, or mtDNA and gDNA isolated from cells.

To confirm mtDNA induces activation of inflammasomes, future experiments could examine the effects of mtDNA stimulation on production and secretion of IL-18, another cytokine produced through inflammasome activation. In addition, to further dissect the mechanisms of mtDNA stimulation on NLRP3 or AIM2 inflammasome activation, we can assess changes in NLRP3 expression and formation of ASC speck.

Lastly, we observed treatment-induced differences in levels of circulating mtDNA, where plasma levels of mtDNA significantly decreased after treatment in sJIA and ERA subtypes.

However, we were unable to compare levels of circulating mtDNA in JIA patients to healthy, age matched controls. It would be interesting to determine whether circulatory mtDNA content is significantly elevated in JIA patients compared to healthy, age-matched controls.

Conclusion:

While the exact etiology of JIA remains elusive, there is uniform agreement that inflammation is involved in all JIA subtypes. In particular, inflammatory cytokines such as IL-1b are thought to contribute to the pathogenesis of sJIA, a subtype known to be associated with autoinflammation (52). In this thesis, we have demonstrated that mtDNA transfected with lipofectamine is immunostimulatory, inducing upregulation of pro-IL-1b gene and protein expression in macrophages. Stimulation with lipofectamine and mtDNA primed inflammasomes in macrophages, resulting in robust production and secretion of IL-1b when cells were also stimulated with an inflammasome activator. In children with sJIA, circulatory mtDNA was moderately associated with disease activity and molecules related to inflammasome activation.

These observations are important because they shed light on the mechanisms which contribute to sustained immune responses and inflammation in JIA. Furthermore, circulatory mtDNA may provide a potential biomarker of disease activity in JIA, where subclinical levels of disease activity are hard to recognize and it is difficult to predict overall prognosis in patients.

76 77

danger signal Signal 2

Circulating macrophage nucleic acids

? Inflammasome endosome activation ? TLR activation AIM2 activation

Chronic inflammation

Pro-IL-1β Secreted IL-1β

Figure 18. Proposed model for mtDNA-induced macrophage secretion of IL-1b

Stimulation with mtDNA primes inflammasomes, leading to the upregulation of pro-IL-1b gene and protein expression. Subsequent stimulation of the inflammasome results in assembly of the inflammasome complex, activation of caspase-1, and robust secretion of IL-1b from macrophages. Given the effectiveness of IL-1b inhibitors in sJIA patients (see 1.3.2), mtDNA may contribute to chronic inflammation in JIA.

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