T Cell Receptor Usage and Antigen Discovery in Löfgren’S Syndroεe

T CELL RECEPTOR USAGE AND ANTIGEN DISCOVERY IN LÖFGREN’S SYNDROεE

ANGELA M. MITCHELL

BS, Montana State University, 2009

MS, Montana State University, 2012

A thesis submitted to the

Faculty of the Graduate School of the

University of Colorado in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

Immunology Program

2017

This thesis for the Doctor of Philosophy degree by

Angela M. Mitchell

has been approved for the

Immunology Program

Jill E. Slansky, Chair

Kathryn Haskins

Ross M. Kedl

Lisa A. Maier

Brent E. Palmer

Andrew P. Fontenot, Advisor

Date 12/15/2017

Mitchell, Angela M. (Ph.D., Immunology)

T Cell Receptor Usage and Antigen Discovery in δöfgren’s Syndrome

Thesis directed by Professor Andrew P. Fontenot

ABSTRACT

Sarcoidosis is a granulomatous disease that primarily affects the lungs and is characterized by an accumulation of CD4+ T cells in the bronchoalveolar lavage (BAL).

Previous work has indicated that HLA-DRB1*03:01+ (DR3+) patients diagnosed with the acute form of the disease, δöfgren’s syndrome (δS), have an accumulation of CD4+ T cells bearing TCRs utilizing TRAV12-1. However, the importance of these α-chains in disease pathogenesis and the paired TCR chain remain unknown. This project aimed to identify expanded αTCR pairs expressed on CD4+ T cells derived from the BAL of DR3+ LS patients and to determine their antigen specificity.

Using deep-sequencing approaches, TCRα, TCR, and paired αTCR chains were assessed on BAL CD4+ T cells from LS patients. TRAV12-1 and TRBV2 were the most expanded V region gene segments in DR3+ LS patients, and TRAV12-1 and TRBV2 CDR3 motifs were shared between multiple DR3+ δS patients. When assessing αTCR pairing,

TRAV12-1 preferentially paired with TRBV2, and these TRAV12-1/TRBV2 TCRs displayed

CDR3 homology. These findings suggest that public CD4+ T cell receptor repertoires exist amongst LS patients and that these T cells are recognizing the putative LS-associated antigens in the context of HLA-DR3.

To begin identifying the antigens for which the public TCRs in DR3+ LS patients are specific, an optimized protocol has been established using peptide scanning libraries

(PSLs). PSLs allow for unbiased determination of the amino acids preferred at each position of a given peptide as dictated by the TCR of interest. Preliminary work using a positive control TCR cloned into murine hybridoma cells has established that the methodology is capable of identifying nearly identical mimotopes to the known cognate peptide of the TCR.

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Initial PSL studies using a previously identified LS-associated TCR identified decapeptide mimotopes that were then searched for in human and mycobacterial databases. Assays using matches from the databases in addition to mimotopes designed by traditional deconvolution of the initial PSL screening resulted in several peptides that stimulated the

TCR. Further work using the optimized PSL assays is necessary to determine the antigen specificity of the identified public CD4+ TCRs in DR3+ LS patients.

The form and content of this abstract are approved. I recommend its publication.

Approved: Andrew P. Fontenot

I dedicate this thesis to my friends and family. Their support in and out of my academic

career has been incredible and essential. I also dedicate this thesis to Professor Gary A.

Strobel, for introducing me to the fascinating discipline of research and for instilling in me the

ability to be intrigued by implausible scientific data.

ACKNOWLEDGEMENTS

First, I would like to acknowledge my friends and family for their support throughout the years. I would not have survived 25 years of schooling if I had nothing to look forward to outside of academics. Thank you to my parents, my brother, and all of my family members; you have been supportive every step of the way. Thank you to my numerous cycling friends and teammates, whom I consider family. Without all of those miles logged, hills climbed, and goals achieved, I would not be the person I am today. Thank you for encouraging me to become a better cyclist and a better person.

A special thank you to Dan, for helping me navigate graduate school, supporting me when I needed it most, and helping me see the big picture. I am forever grateful for your insistence on celebrating life’s big (and little) events.

I would like to acknowledge my numerous lab-mates throughout the years. I am honored to have worked with a multitude of researchers from all over the world during my undergraduate research, each of whom introduced me to their own unique perspective in science (as well as in everyday life). Likewise, my Master’s work allowed me to further develop friendships and mentorships from individuals around the globe. For those connections and experiences, I am appreciative, for they have all strengthened me as a scientist and as a person.

I am very grateful to Andrew for allowing me to join his lab and for having faith in me as a graduate student. Thank you for giving me the opportunity to continue with my degree and for guiding me while I attempted to crack the sarcoidosis code. Thank you also to the entire Fontenot lab. I have always been impressed with how well this lab operates, and I am incredibly appreciative of everyone’s help. Thank you for technical help, Doug and Alex, and thank you for keeping the lab so organized and functional, Allison. Thank you for your feedback during lab meetings, Amy, Eric, and Morgan. Thank you, Mickey, for being like a second mentor to me and for being so supportive while I endeavored to figure out ePCR.

You were pivotal in getting the technique up and running, and my Stockholm stint would have been fruitless if not for your efforts.

Thank you to Dr. Johan Grunewald and his group for their hospitality while I visited

Stockholm in the summer of 2016. I had an amazing experience during those six weeks, and I could not have asked for better coworkers. Thank you to Ylva, for being my right-hand

(wo)man during this project. Without your assistance, expertise, and dedication, this project would have floundered.

Lastly, I want to thank Dr. Gary Strobel for allowing me to accompany him on a research expedition in Ecuador during my undergraduate work. I still remember the day he walked in, handed me a headlamp, and said nothing. When I asked what the headlamp was for, he simply responded, “You will likely require it in the jungle.” The Ecuador trip solidified in my mind the fact that I was destined to become a scientist. Thank you.

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TABLE OF CONTENTS

CHAPTER PAGE

I. INTRODUCTION AND BACKGROUND ...... 1

1.1 Sarcoidosis ...... 1

1.2 Granuloma formation in sarcoidosis ...... 2

1.3 Clinical features and diagnosis of sarcoidosis ...... 5

1.4 Treatment of sarcoidosis ...... 9

1.5 δöfgren’s syndrome (LS) ...... 13

1.6 Immunologic characteristics of LS and non-LS sarcoidosis ...... 14

1.7 Genetic characteristics associated with LS and non-LS sarcoidosis ...... 20

1.8 CD4+ T cell response to antigens presented in the context of MHC ...... 25

1.9 CD4+ T cells in the BAL of LS and non-LS patients ...... 28

1.10 T cell receptors (TCRs) associated with LS and non-LS sarcoidosis ...... 32

1.11 Putative etiologies of sarcoidosis ...... 38

1.12 Scope of thesis ...... 42

II. MATERIALS AND METHODS ...... 47

2.1 Patient information ...... 47

2.2 HLA typing ...... 47

2.3 BAL and PBMC preparation ...... 47

2.4 RNA isolation and iRepertoire PCR ...... 49

2.5 Emulsion PCR (ePCR) ...... 49

2.6 Single-cell PCR (scPCR) ...... 53

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2.7 Illumina high-throughput sequencing ...... 56

2.8 Bioinformatic processing ...... 58

2.9 CDR3 motif criteria for DR3+ LS patients ...... 59

2.10 Hybridoma transfection/transduction ...... 59

2.11 Flow cytometry ...... 63

2.12 Peptide stimulation of hybridomas ...... 63

2.13 Peptide scanning libraries (PSLs) for mimotope identification ...... 66

2.14 Statistical analyses ...... 66

III. TCR α AND CHAIN USAGE IN THE BAδ OF SARCOIDOSIS PATIENTS ...... 69

3.1 Identifying TCRα and TCR chains in sarcoidosis patient samples ...... 69

γ.β iRepertoire PCR to identify TCRα and TCR chain usage ...... 70

γ.γ TCRα and TCR expression on blood CD4+ T cells ...... 71

3.4 Relative library and CDR3 diversity in CD4+ T cells from DR3+ LS, sarcoidosis,

and control patients ...... 71

3.5 BAL CD4+ TCRα usage in sarcoidosis patients ...... 76

3.6 BAL CD4+ TCR usage in sarcoidosis patients ...... 78

3.7 CDRγα and CDRγ motifs shared by multiple δS patient BAL CD4+ T cells ...... 87

3.8 Summary ...... 90

IV. CD4+ αTCR PAIRS IN SARCOIDOSIS PATIENT BAL ...... 93

4.1 αTCR pair identification in human patient samples ...... 93

4.2 ePCR as a means of identifying αTCR pairs ...... 94

4.3 αTCR pairing in sarcoidosis patient BAL CD4+ T cells as determined by ePCR ...... 96

4.4 scPCR for determining αTCR pairs ...... 98

4.5 CD4+ αTCR pairing in sarcoidosis patients via scPCR ...... 99

4.6 CD4+ αTCR pairs in δS patients with shared CDRγα/ homology ...... 99

4.7 Summary ...... 106

V. IDENTIFYING MIMOTOPES AND PEPTIDE ANTIGENS ...... 108

5.1 Speculative antigens in sarcoidosis ...... 108

5.2 Hybridoma approach for determining TCR specificity ...... 115

5.3 HLA-DR3- and HLA-DQ2-restricted positive control hybridomas ...... 116

5.4 Sarcoidosis hybridomas generated after sequencing IL-2-expanded BAL CD4+

T cells ...... 120

5.5 ESAT-6, mKatG, and vimentin responses by sarcoidosis hybridomas ...... 123

5.6 Peptide scanning library (PSL) approach to identify mimotopes ...... 128

5.7 Optimizing PSL assays using positive control and sarcoidosis hybridomas ...... 129

5.8 Summary ...... 139

VI. OVERALL CONCLUSIONS AND FUTURE DIRECTIONS ...... 141

6.1 CD4+ T cells in sarcoidosis and LS ...... 141

6.2 Public versus private CD4+ αTCR repertoires ...... 144

6.3 Mimotopes and putative sarcoidosis-associated antigens ...... 147

6.4 Conclusions ...... 151

REFERENCES ...... 153

APPENDIX

A. EMULSION PCR PRIMERS ...... 196

B. UPDATED AND OPTIMIZED PEPTIDE SCANNING LIBRARY PROTOCOL ...... 201

LIST OF TABLES

TABLE PAGE

1-1: Selected TCRα and TCR usage previously associated with sarcoidosis ...... 35

2-1: Characteristics of sarcoidosis and control patients ...... 48

2-2: Hybridoma primers ...... 62

4-1: TRAV12-1/TRBV2 pairs with CDR3 homology in DR3+ LS patients

by ePCR ...... 102

4-2: TRAV12-1/TRBV2 pairs with CDR3 homology in one DR3+ LS patient

by scPCR ...... 103

4-3: Additional related or expanded αTCR sequences by scPCR in one

DR3+ LS patient ...... 105

5-1: Case-control studies evaluating mycobacteria and P. acnes

in sarcoidosis ...... 110

5-2: Positive control hybridomas ...... 117

5-3: Sequences obtained after IL-2 expansion of BAL CD4+ T cells from

LS patients ...... 121

5-4: Optimization of PSL assays ...... 134

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LIST OF FIGURES

FIGURE PAGE

1-1: Clinical and immunological features of δöfgren’s syndrome versus

sarcoidosis ...... 43

1-2: Known and unknown parameters of the trimolecular complex in LS ...... 45

1-3: Thesis project overview ...... 46

2-1: RNA purification and iRepertoire PCR ...... 50

2-2: CD4+ T cell isolation and emulsion PCR (ePCR) ...... 51

2-3: PBMCs encapsulated by emulsion droplets under the microscope after

different vortexing time points...... 52

2-4: Representative ePCR gel ...... 54

2-5: CD4+ T cell sorting and single-cell PCR (scPCR)...... 55

2-6: Representative scPCR gels ...... 57

2-7: Murine T cell hybridomas ...... 60

2-8: Generation of the S-1 hybridoma ...... 64

2-9: Generation of the D2 hybridoma ...... 65

2-10: Peptide scanning libraries (PSLs) ...... 67

3-1: TCRα and TCR usage in sarcoidosis and control patient blood

CD4+ T cells ...... 72

3-2: Relative diversity of BAL CD4+ TCR sequences as determined by

iRepertoire PCR ...... 74

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3-3: CDR3 sharing amongst DR3+ LS, sarcoidosis, and control patient BAL

CD4+ T cells ...... 75

3-4: TCRα usage in sarcoidosis and control patient BAδ CD4+ T cells ...... 77

3-5: TRAV12-1 usage in CD4+ T cells isolated from BAL versus blood ...... 79

3-6: Correlation between TRAV12-1 and CD4/CD8 ratio in sarcoidosis

patient BAL ...... 80

3-7: TRAV12-1 expression in sarcoidosis patient BAL separated by chest

x-ray stage ...... 81

3-8: TCR usage in sarcoidosis and control patient BAL CD4+ T cells ...... 83

3-9: TRBV2 usage in CD4+ T cells isolated from BAL versus blood ...... 84

3-10: Correlation between TRBV2 and CD4/CD8 ratio in sarcoidosis

patient BAL ...... 85

3-11: TRBV2 expression in sarcoidosis patient BAL separated by chest

x-ray stage ...... 86

3-12: Shared TRAV12-1 and TRBV2 CDR3 motifs in sarcoidosis patients ...... 88

3-13: Oligoclonal expansions of TRAV12-1 sequences expressed in DR3+

LS patients ...... 89

3-14: Consensus BAL CD4+ T cell CDR3 motifs shared by DR3+ LS patients as

determined by iRepertoire PCR ...... 91

4-1: Preferential TRAV12-1/TRBV2 pairing in DR3+ LS patients determined

via ePCR ...... 97

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4-β: TCR V usage on BAL CD4+ TRAV12-1+ T cells from one DR3+ LS patient

after scPCR ...... 100

4-γ: Consensus CDRγ motif in BAL CD4+ TRAV12-1+ T cells from one DR3+

LS patient after scPCR ...... 104

5-1: Positive control hybridoma IL-2 responses to cognate peptides ...... 119

5-2: Sarcoidosis hybridoma IL-2 responses to superantigen stimulation ...... 122

5-3: Sarcoidosis hybridoma IL-2 responses to mKatG peptides ...... 124

5-4: Sarcoidosis hybridoma IL-2 responses to ESAT-6 peptides ...... 125

5-5: Sarcoidosis hybridoma IL-2 responses to vimentin peptides ...... 127

5-6: Serum concentration and supernatant incubation optimization

for IL-2 assays ...... 131

5-7: Cell culture serum concentration optimization for IL-2 assays ...... 132

5-8: Mimotope identification via PSL using an HLA-DR3-restricted positive

control hybridoma ...... 135

5-9: Mimotope identification via PSL using a sarcoidosis hybridoma ...... 136

5-10: Screening of sarcoidosis hybridomas with synthesized decamer

peptides ...... 138

LIST OF ABBREVIATIONS

μg microgram μl microliter με micromolar μm micrometer Ag85A antigen 85A BAL bronchoalveolar lavage BCG Bacille Calmette Guerin bp base pair BSA bovine serum albumin C Celsius CD cluster of differentiation cDNA complementary DNA CDR complementary-determining region CMV cytomegalovirus CO2 carbon dioxide C region constant region cRPMI complete RPMI CXCL chemokine (C-X-C motif) ligand DAMP damage-associated molecular pattern DAPI 4',6-diamidino-2-phenylindole DLCO diffusing capacity of the lung for carbon monoxide DNA deoxyribonucleic acid dNTPs deoxynucleotide triphosphates DPBS Dulbecco’s phosphate buffered saline DQ2 HLA-DQB1*0201 DR3 HLA-DRB1*0301 DR11 HLA-DRB1*1101 DRB3 HLA-DRB3*0101 EAE experimental autoimmune encephalomyelitis EBV Epstein-Barr virus ePCR emulsion PCR ESAT early secretory antigenic target FACS fluorescence-activated cell sorting FBS fetal bovine serum FDG-PET F-fluorodeoxyglucose positron emission tomography FEV1 forced expiratory volume in 1 second FoxP3 forkhead box P3 GATA-3 GATA binding protein 3 GM-CSF granulocyte-macrophage colony-stimulating factor HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HIV human immunodeficiency virus HLA human leukocyte antigen HRCT high-resolution computed tomography hsp heat shock protein ICAM intercellular adhesion molecule IL interleukin IMGT international ImMunoGeneTics (IMGT) information system ITAM immunoreceptor tyrosine activation motif IMDM Iscove’s modified Dulbecco’s medium

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KS Kveim-Siltzbach LS δöfgren’s syndrome M-CSF macrophage colony-stimulating factor min minutes mg milligram MHC major histocompatibility complex ml milliliter mM millimolar mKat mycobacterial catalase peroxidase MSCV murine stem cell virus MTB Mycobacterium tuberculosis n/a not applicable or not assessed NLR nucleotide-binding oligomerization domain-like receptor nM nanomolar NOD non-obese diabetic NTM nontuberculosis mycobacteria PAMP pathogen-associated molecular pattern PBMC peripheral blood mononuclear cell PCR polymerase chain reaction PCR-SSP PCR-sequence-specific primer p.i. post injection pMHC peptide-MHC PRR pattern recognition receptor PSL peptide scanning library RA rheumatoid arthritis RANTES regulated on activation, normal T cell expressed and secreted RIG retinoic acid-inducible gene RNA ribonucleic acid ROR RAR-related orphan receptor gamma RPMI Roswell Park Memorial Institute (medium) RT room temperature RT-PCR reverse transcription PCR s seconds SAA serum amyloid A SCAND Scandinavian scPCR single-cell PCR SEA Staphylococcus enterotoxin A SI stimulation index SIV simian immunodeficiency virus SLE systemic lupus erythematosus spp. plural of species TB Mycobacterium tuberculosis T-bet T-box expressed in T cells TCR T cell receptor Th T helper TLR Toll-like receptor TGF transforming growth factor TNF tumor necrosis factor TRAC TCR alpha constant TRAV TCR alpha variable TRBC TCR beta constant

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TRBV TCR beta variable Treg T regulatory cell U units US United States VC vital capacity V region variable region

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CHAPTER I

INTRODUCTION AND BACKGROUND1

1.1 Sarcoidosis

Sarcoidosis is a systemic granulomatous disease that primarily affects the lungs and for which there has been no elucidated etiology. The earliest documented publication describing the disease dates back to the late 1800s and was written by Sir Jonathan

Hutchinson (1). Interestingly, the study was published four years before the causative agent of tuberculosis, bacillus, was identified by Robert Koch (1–3). One famous patient of Dr.

Hutchinson’s was Mrs. Mortimer, who had skin lesions which he termed “εortimer’s εalady” when he published his findings and then subsequently presented the patient to the

Dermatological Society of London (2–4). The Society deemed the condition to be sarcoma and requested a follow-up biopsy, which was never obtained, due to the inability to follow up with Mrs. Mortimer (2). The term “lupus perino” was coined by Ernest Besnier, a dermatologist, in 1889 after seeing a patient with purple swellings on the nose, ears, and fingers that resembled lupus vulgaris, a form of skin tuberculosis (2, 3, 5). Another dermatologist, Cesar Boeck, coined the term “sarkoid” after seeing a patient with lesions that resembled sarcoma, and he later went on to describe the granulomas in more detail as well as describe the multi-organ nature of the disease (2, 3, 6, 7). Sven Löfgren was a pulmonary physician who first described cases consisting of fever, erythema nodosum, and bilateral hilar lymphadenopathy, which he termed “bilateral hilar lymphoma”, now known as

δöfgren’s syndrome (LS), discussed further in section 1.5 (2, 3, 8–10). As will be discussed in section 5.1, a skin test (Kveim-Siltzbach test) was developed in the 19γ0’s when it was

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1 observed that intradermal injection of a preparation made from sarcoidosis tissues would generate granuloma-like responses in patients diagnosed with sarcoidosis but not in control subjects (2, 3, 11–14). However, the antigen(s) driving the reaction in sarcoidosis patients has not been identified, and many questions remain about the immunological responses observed with administration of the test.

After the initial observations of sarcoidosis by those mentioned above and others, sarcoidosis was initially thought to be a disorder of the skin, but once chest x-rays were used routinely, it was discovered that the disease actually afflicts the lungs in about 90% of cases (3, 15). Additionally, sarcoidosis can affect almost any area of the body and may present as asymptomatic disease, acute disease followed by resolution, multi-organ involvement, progressive disease leading to lung fibrosis, or anywhere in between, making diagnosis and treatment difficult. The annual incidence ranges from 1 - 40 per 100,000 people and can affect any race, age, or gender, but there are higher annual incidences in

African-American and northern European populations (16–22).

Despite the wealth of information on the pathological findings, clinical features, immunologic and genetic characteristics, and putative antigens, discussed below, no definitive cause of sarcoidosis has been identified.

1.2 Granuloma formation in sarcoidosis

The main identifiable presentation of sarcoidosis is the presence of noncaseating

(non-necrotic) granulomas at the sites of disease. Granulomas are defined areas of lymphoid-like structures formed by the aggregation of immune cells, typically due to the inability of the immune system to effectively clear an antigen. The most common areas for granulomas to form in sarcoidosis patients are the lungs, lymph nodes, eyes, and skin (23).

As there is no identified etiologic agent in sarcoidosis, and because the granulomas are non-specific inflammatory lesions, the granulomas themselves are not directly diagnostic of

2 the disease versus any other granulomatous disease. However, generalized findings about sarcoidosis-associated granulomas have allowed for a better understanding of the immunopathogenesis of the disease.

The initial trigger for granuloma formation is thought to be conventional presentation of peptides by antigen presenting cells (APCs) to T cells (24). This presentation triggers the release of cytokines and inflammatory mediators and promotes cell-cell interactions, leading to recruitment of inflammatory cells, including macrophages, monocytes, and lymphocytes

(17, 23, 25). Instead of acquiring and processing antigens, presenting peptides, and stimulating T cells, secondarily recruited macrophages begin to accumulate in these areas and are converted into epithelioid and multinucleated giant cells, subsequently beginning the process of granuloma formation (17, 23). Activated CD4+ T cells can be found scattered primarily around and sometimes throughout the granulomas in sarcoidosis (25). Additional

CD4+ T cells are recruited to the area after initial granuloma formation due to chemoattractant factors that are released by the macrophages and monocytes, including interleukin-16 (IL-16) and regulated on activation, normal T expressed and secreted

(RANTES) (26). Sarcoidosis patient macrophages have also been demonstrated as expressing high levels of tumor necrosis factor alpha (TNF-α), Iδ-6, IL-12, IL-1, class II major histocompatibility complex (MHC), and adhesion molecules such as intercellular adhesion molecule-1 (ICAM-1) (17, 23, 26).

It has been known for some time that granuloma formation in general is dependent upon T cell activation, as mice depleted of T cells cannot form granulomas (27). However, it has also been demonstrated that granulomas can be formed in mice in the absence of T cells if particular cytokines are present (i.e., TNF-α and interferon-gamma, IFN-) (28). As reviewed in (29), it has subsequently been shown that IFN- released by both the activated

CD4+ T cells surrounding the granuloma and those within the granuloma allows for maintenance of the granuloma via macrophage release of TNF-α, Iδ-17, macrophage

3 colony-stimulating factor (M-CSF), and granulocyte-macrophage colony-stimulating factor

(GM-CSF). IL-12 and IL-18 release by the macrophages induces and maintains differentiation of CD4+ T cells into T helper 1 (Th1) cells (30). Th1 cells, which will be discussed further in section 1.6, play an important role in granuloma formation. In fact, granulomas are unable to form in a pulmonary granuloma mouse model when an inhibitor of

Th1 chemokine receptors (CXCR3 and CCR5) is administered subcutaneously (31). In contrast, it has been demonstrated that T regulatory cells (Tregs) are capable of eliminating granuloma formation in vitro (32). In that study, the authors found that depletion of CD25hi

Tregs from cultured healthy donor peripheral blood mononuclear cells (PBMCs) accelerated growth of in vitro granulomas surrounding beads coated with Bacille Calmette Guerin (BCG) extracts. The same phenomenon occurred when PBMCs from sarcoidosis patients were used, but only if the patients were in remission. If the patients had active disease, the Tregs were not able to suppress granuloma formation. The authors concluded that Treg suppression of granulomas formation is impaired in active sarcoidosis patients. Another mediator thought to play a role in sarcoidosis granuloma formation is IL-17 (33, 34). This cytokine is increased in sarcoidosis patients (35, 36), it has been detected in granulomatous tissue (but not unaffected tissue) from sarcoidosis patients (36), and IL-17-deficient mice do not form granulomas in a murine BCG model (37). Additionally, if IL-17 is blocked in a granulomatous model using Schistosoma, granuloma formation is greatly diminished (38).

Sarcoidosis-associated granulomas often resolve without treatment, but about 10 –

30% of cases progress to lung fibrosis, which is one of the major causes of morbidity and mortality in sarcoidosis (17, 25, 39, 40). Patients who have progressed to lung fibrosis do not exhibit resolution of the disease (17, 41–43). Macrophage cytokines and a CD4+ T cell switch from Th1 to Th2 are thought to mediate the progression of granulomas to fibrosis in sarcoidosis (17, 25, 29, 44, 45). The polarization of Th1 and Th2 cells is discussed further in section 1.6. One cytokine that has been implicated in pulmonary fibrosis is transforming

4 growth factor beta (TGF-) (42, 46–48). Sarcoidosis patients with active disease involving altered lung function were found to have higher levels of TGF- in bronchoalveolar lavage

(BAL) and alveolar macrophage supernatant than normal controls (49). However, sarcoidosis patients with normal lung function had similar levels of TGF- to those of controls. The authors concluded that pulmonary functional impairment in sarcoidosis is associated with overproduction of TGF- in those patients.

Overall, the noncaseating granuloma is the pathologic hallmark of sarcoidosis, and it develops due to aggregations of macrophages and monocytes, as well as other immune cells, with the primary foci surrounded primarily by Th1-polarized CD4+ T cells (17, 23, 29,

50). However, as discussed in the next section, the clinical features of sarcoidosis are variable and can affect areas outside of the granulomatous inflammation, making diagnosis and treatment difficult.

1.3 Clinical features and diagnosis of sarcoidosis

Sarcoidosis can be limited to one organ, or it may present in multiple organs at once.

The disease can sometimes be first detected in asymptomatic patients during a chest x-ray performed as a diagnostic test for another disease or as part of a screening program (17,

43). However, most patients are symptomatic at the time of diagnosis, often with generalized symptoms such as fatigue, shortness of breath, cough, and fever (17, 43). The clinical features and diagnostic characteristics of sarcoidosis have been extensively reviewed previously (17, 41, 43, 51–54). In this section, the main clinical features associated with common manifestations of sarcoidosis will be discussed, and diagnostic tests that are recommended for the disease will be outlined.

The most common manifestation of the disease is pulmonary sarcoidosis, which is likely why symptomatic patients often initially present with pulmonary symptoms. Chest x- rays are always recommended if there is a suspicion of sarcoidosis (17, 41, 52), and the

5 disease has been organized into categories based on findings from the x-rays. Stage I involves bilateral hilar lymphadenopathy (enlarged lymph nodes of the pulmonary hila), stage II has both enlarged lymph nodes and reticular opacities (due to parenchymal infiltrates), stage III has no lymphadenopathy but does have reticular opacities, and stage IV indicates signs of pulmonary fibrosis (17, 41, 43, 52). The stages are not necessarily progressive, with the exception of patients progressing to fibrosis (stage IV) from other stages, and patients can present with any stage at initial diagnosis. However, it has been shown that, in general, patients with higher chest x-ray staging have a greater number of pulmonary symptoms, more serious impairment of lung function, lower incidences of disease resolution, and a higher mortality (43, 55, 56). High-resolution computed tomography

(HRCT) chest scans are superior to chest x-rays for determining parenchymal involvement, hilar disease, and evidence of fibrosis (41, 43, 52, 54, 57, 58). However, although HRCT is a more sensitive method for identifying pulmonary abnormalities, its use as a clinical management tool is unsupported (59), and the exposure to high radiation due to multiple scans over time is dangerous for the patient (43, 60). Lastly, F-fluorodeoxyglucose positron emission tomography (FDG-PET) can further detect abnormalities in the lungs of sarcoidosis patients and may be useful for determining if treatment is effective in some patients, as reviewed in (41) and (52).

Lung function is impaired in 20 – 80% of sarcoidosis patients and may be higher in patients with stage II, III, or IV disease (43, 52). Pulmonary function tests can be used to determine the severity of disease in terms of respiratory impairment and to follow the course of disease with or without therapeutic intervention (41, 43, 52). Spirometry for vital capacity

(VC), lung volumes, diffusing capacity of the lung for carbon monoxide (DLCO), and the six- minute walk test can all be used to test pulmonary function in sarcoidosis patients.

Bronchoscopy with BAL is often used as a supportive diagnostic tool for sarcoidosis when other findings are indicative of disease; however, the BAL findings are not specific to

6 the disease and require other measures for a diagnosis (17, 41, 52, 61). For instance, although one common finding in the BAL is an elevated CD4/CD8 T cell ratio in the BAL fluid, and a ratio of >3.5 has a specificity of 93 – 96 % while a ratio of >10 has a specificity of

99% for sarcoidosis (61, 62), the ratio is actually normal in 15% of the cases (52).

Additionally, several other diseases have a high CD4/CD8 ratio, including chronic bronchitis, eosinophilic pneumonia, and other granulomatous lung diseases (63). The CD4/CD8 ratio and the BAL cells themselves are important in terms of understanding the immune response to sarcoidosis, and the CD4 alveolitis that occurs with the disease will be discussed further in section 1.9. Biopsies from the skin or peripheral lymph nodes can be taken relatively easily if those tissues are involved, but surgical lung biopsies are rare (52). More common biopsies are endobronchial and transbronchial lung biopsies, and these biopsies can also be used to eliminate the possibility of bacterial or fungal causes of disease (64–66).

Another common organ involved in sarcoidosis is the eye (17, 43, 67). The most common manifestation of sarcoidosis of the eye is uveitis, an inflammation of the uvea (43).

An influx of inflammatory cells can be found in the anterior chamber, vitreous, pars plana, or peripheral retina, leading to cataracts, glaucoma, retinal scarring, and/or permanent vision loss (43). However, anterior uveitis often clears spontaneously within a year (17). Optic neuritis is a less common manifestation, and it often presents first as a loss of vision or color vision in patients (43, 68–70). Cranial and optic nerve involvement can occur with optic neuritis, and papillitis (optic disc inflammation), papilledema (pressure around the brain causing optic nerve swelling), or granulomas of the head and neck can all be associated with optic neuritis caused by sarcoidosis (68, 70). Permanent damage to the eye due to sarcoidosis can occur, but it may be avoided with appropriate treatment (17, 70, 71).

There are two types of skin sarcoidosis: specific lesions with granulomatous inflammation and non-specific lesions with inflammation but no evidence of granulomas

(43). A common skin involvement manifests as erythema nodosum, which is a rash

7 consisting of raised, tender nodules (17, 43). Erythema nodosum (coupled with bilateral hilar adenopathy, fever, and ankle arthritis) is used in the diagnosis of δöfgren’s syndrome, which will be discussed in more detail in section 1.5 (72). Other skin manifestations include lupus perino (hardened skin lesions), annular lesions, plaques, psoriaform lesions, ulcers, maculopapular eruptions, and subcutaneous nodules (17, 43, 73).

Cardiac sarcoidosis is found in only about 5% of patients in the clinic, but autopsies have confirmed cardiac involvement in up to 75% of patients (17, 43, 74–76). As reviewed elsewhere (17, 43, 52), several manifestations of cardiac sarcoidosis can occur. Conduction abnormalities, impairment of ventricular function, and heart failure are all associated with cardiac sarcoidosis. Although a less common manifestation, cardiac sarcoidosis is a serious diagnosis and must be monitored and treated accordingly, as it is potentially fatal (17, 41,

43, 52, 77).

Neurosarcoidosis is observed in about 5 – 10% of patients and can affect any area of the nervous system, including the cranial nerves, brain, leptomeninges, and peripheral nerves, among others (17, 43, 78–82). Involvement of the cranial nerves occurs most commonly, with the facial nerve being the most affected (78–80). Generally, if one cranial nerve is affected, the condition will resolve, but if several cranial nerves are affected, the disease often has a chronic presentation (43, 83). Almost half of neurosarcoidosis patients develop disease involving the brain parenchyma, and about 15% develop a peripheral neuropathy that can cause pain, numbness, or burning sensation that often affects the feet

(43). Seizures, psychiatric symptoms, aseptic meningitis, and hydrocephalus can also occur with neurosarcoidosis (52).

The diagnosis of sarcoidosis is challenging, even with the advent of technologies with advanced capabilities such as HRCT and FDG-PET mentioned above. Although granulomas can be detected more easily than in the past using sophisticated methods, evidence of granulomatous inflammation or granulomas does not preclude the requirement

8 of eliminating potential alternative causes. Biopsies can be useful for determining organ involvement, but they are not necessarily required for a diagnosis of sarcoidosis (51).

However, biopsies are recommended if infection or malignant conditions are suspected as an alternative diagnosis (17).

As there is not an identified cause of sarcoidosis, and because the disease resembles other granulomatous disorders, there is no diagnostic test for sarcoidosis.

Therefore, it is a diagnosis of exclusion, meaning known causes of granulomatous inflammation must be considered and eliminated before a diagnosis of sarcoidosis can be made. Many diseases resemble sarcoidosis clinically and can be due to infections from bacteria/fungi/parasites, occupational and environmental exposure to organic or inorganic agents such as is seen in chronic beryllium disease (CBD) or hypersensitivity pneumonitis, and neoplasia or autoimmune disorders such as Wegener’s granulomatosis, among several other syndromes and infections (17, 52). Testing of patient samples via smears, serology, or cultures can distinguish infectious causes from sarcoidosis, while patient history, presence of metals in the tissue, or lymphocyte proliferation tests can identify occupational and environmental exposure diseases (17). Neoplasia and autoimmune disorders can be tested via histology or antibody levels, as well as by determining whether particular tissues are involved, as these disorders can affect different tissues than sarcoidosis in some cases (17).

Overall, sarcoidosis can affect nearly any tissue of the body, the disease presents with varying symptoms in different patients, and no diagnostic test is available. Therefore, as will be discussed in the next section, treatment of sarcoidosis patients is challenging, and patients often relapse.

1.4 Treatment of sarcoidosis

The treatment of sarcoidosis is complex, and it often is used to simply improve quality of life for the patients or to prevent end-stage disease rather than in an attempt to

9 cure the disease (84). Many patients resolve the disease or have improvement in symptoms without therapeutic intervention, so therapy is often not administered immediately (17, 41).

These patients are withheld from therapy because they are generally asymptomatic, have little to no pulmonary function impairments, and are likely to undergo remission, whereas patients who are indicated for treatment are typically symptomatic, have worsening pulmonary function, and are more likely to have chronic sarcoidosis (41). Corticosteroids remain the first line form of therapeutics, but unfortunately, many sarcoidosis patients have relapses, requiring long-term corticosteroid treatment which then often leads to significant morbidity associated with the therapy (17, 41, 84). Anti-metabolites and biologic agents have also been studied for their effectiveness in treating sarcoidosis. Several of the more common therapies will be discussed below.

Systemic corticosteroids are used in sarcoidosis therapy because they have been shown to downregulate immune responses in general. For the treatment of sarcoidosis, these medications have been demonstrated as effective in improving pulmonary function and reducing chest x-ray findings. However, the dosages, duration of therapy, and effectiveness of corticosteroids for sarcoidosis treatment are all unclear and not well described (17, 41, 84). The initial dose is typically up to 40 mg per day for 6 – 18 months, but the relapse rate is 30 – 80% after tapering (41, 54, 84). Treatment with corticosteroids is on a patient-per-patient basis in terms of dosages and duration, depending on the individual response and tolerance in a given patient (54, 84). Additionally, the response to corticosteroid therapy can vary from organ to organ, so disease manifestations can drive therapeutic strategy as well (41, 84). Treatment of skin or eye sarcoidosis can include topical glucocorticoids, which have fewer side effects than systemic steroids, but they can still lead to toxicity (85, 86).

An alternative or second line therapy for sarcoidosis is the use of anti-metabolite medications. As mentioned above, many sarcoidosis patients do not respond well or

10 consistently with the use of corticosteroids, and these medications have serious side effects.

Anti-metabolites such as methotrexate, azathioprine, leflunomide, and mycophenolate are often used in place of or to replace corticosteroids for treatment of sarcoidosis. Methotrexate is the most commonly used anti-metabolite, and it acts as an anti-inflammatory and immunosuppressive drug by inhibiting synthesis of DNA, RNA, and proteins (17, 41, 84).

Like with corticosteroids, dosages and duration of therapy with methotrexate are unclear and not well-established. However, studies have shown that steroids can be avoided completely with the use of methotrexate alone, or the two can be combined (87–89).

Although the treatment has fewer side effects than corticosteroid therapy, several adverse effects have been reported with the use of methotrexate, including leukopenia, mucositis, nausea, vomiting, and, rarely, drug-induced pneumonitis (90, 91). Hepatotoxicity and infections are also potential adverse reactions to methotrexate therapy, so patients must be monitored for such events while using the drug (89, 92). Azathioprine is another anti- metabolite therapy that inhibits lymphocyte proliferation via the blockage of RNA and DNA synthesis (41). Although the drug has been shown to be as effective as methotrexate for sarcoidosis treatment, it has the potential for a greater number of side effects, some of which are associated with serious complications (93–95). Leflunomide is very similar to methotrexate and may be potentially less toxic in some patients (41, 84). The drug is an anti-lymphocyte agent, and it has been effective in treating pulmonary and extra-pulmonary sarcoidosis (96, 97). Lastly, mycophenolate is also an anti-metabolite therapy that has been effective in treating sarcoidosis, and it acts by inhibiting T and B cells by blocking inosine monophosphate dehydrogenase and de novo guanosine nucleotide synthesis (41, 84).

Biologic agents used for the treatment of sarcoidosis have included anti-TNF agents such as infliximab or adalimumab and B cell depleting antibodies such as rituximab. Anti-

TNF monoclonal antibodies (mAbs) have been successfully used to treat refractory sarcoidosis of a variety of tissues (98–104). These antibodies are likely effective in

11 sarcoidosis because TNF-α is important for granuloma formation, as discussed in section

1.2 above. Additionally, sarcoidosis patients with higher TNF-α often have more severe disease (105, 106). Not all anti-TNF therapies work equivalently, and differences may be attributed to mechanism of action characteristics or dosage recommendations (84, 107,

108). Lastly, Rituximab, which is an antibody against CD20, depletes B cells and has been proven effective as an immunomodulatory agent (109). Rituximab can treat a variety of disorders, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), and it has been used in organ transplantation to reduce antibody production (110–112). It has also been demonstrated to have additional effects on T cell function by downregulating

CD40 ligand on CD4+ T cells (113, 114), increasing the number of Treg cells in SLE patients

(114, 115), and reducing the numbers of Th1 and Th2 cells in pemphigus vulgaris, an autoimmune disorder of the skin and mucous membranes (116). Overall, some of the possible mechanisms of action of rituximab include a decrease or elimination of pathogenic

B cell clones and/or antibodies, decreased cytokine production by B cells, decreased antigen presentation to T cells by B cells (leading to decreased activation of pathogenic T cells), or increased Treg functions (109). Any of these immunological functions could be involved in the pathogenesis of sarcoidosis and may explain the effectiveness of rituximab in the treatment of the disease.

In terms of monitoring disease progression or spontaneous remission, and to determine response to different therapies, a questionnaire has been established to assess sarcoidosis patient health status (52, 117). The questionnaire has five modules with several items in each module: general health status, lung, eye, skin, and medication (117). The questionnaire isn’t used in diagnostic testing, but it is useful for follow-up care, assessment of patient health status, and for research purposes (52).

In summary, several lines of therapy have been used for the treatment of sarcoidosis. Clinically, the disease is heterogeneous, causing current therapies to have

12 varying degrees of effectiveness and making universal therapies difficult to establish.

However, as will be discussed in the next section, patients with a particular form of acute disease have a more homogenous presentation of sarcoidosis and typically recover with no requirement for therapy.

1.5 Löfgren’s syndrome (LS)

Within the numerous manifestations, symptoms, and outcomes amongst sarcoidosis patients in general, there is a subgroup of patients that presents with acute disease displaying very specific and nearly identical symptoms. In 1952, a Swedish pulmonologist,

Sven Löfgren, published articles describing acute sarcoidosis patients presenting with fever, erythema nodosum (a particular skin rash), and bilateral hilar adenopathy (swollen chest lymph nodes) (118, 119). Subsequently, Dr. Löfgren published a series of articles further describing the syndrome and indicating that those with this differential diagnosis fared better than other patients (8, 9). The disease was originally termed “bilateral hilar lymphoma” by

Dr. δöfgren, but it is now known as δöfgren’s syndrome, or δS (2, 3, 118, 119).

LS is typically seen in European patients but is uncommon in Japanese or United

States patients, and distinct clinical characteristics define LS versus non-LS sarcoidosis

(15). Clinical characteristics of LS include the original findings by Dr. Löfgren of fever, bilateral hilar adenopathy, and erythema nodosum, but now the diagnosis also includes arthritis, particularly of the ankle (120). Recent evidence indicates that erythema nodosum may be preferentially present in female versus male LS patients while male LS patients may be more likely to have bilateral ankle arthritis (121). Additionally, the diagnosis is almost always one of an acute disease, with LS patients typically recovering without therapeutic intervention within 2 years (120, 122). The presence of erythema nodosum has been shown to be correlated with a good prognosis (56); however, the disease can be chronic in rare cases (123) or can present with recurring symptoms without progressing to chronic disease

(120, 124). Several immunologic and genetic factors have been associated with LS versus non-LS sarcoidosis, as will be discussed in the following sections.

1.6 Immunologic characteristics of LS and non-LS sarcoidosis

The immunopathogenesis of sarcoidosis has been widely studied, and the cells of both the innate and adaptive immune system have been noted for their roles in the disease course. The innate immune response to sarcoidosis primarily includes contributions by neutrophils and alveolar macrophages, while the adaptive immune response to sarcoidosis involves APCs (e.g., dendritic cells and alveolar macrophages), Th1 cells, Th2 cells, Th17 cells, and Treg cells, among others. As discussed previously, granuloma formation requires the presence of both activated T cells and macrophages. Although T cells are thought to be the main contributors to disease pathogenesis, the cell types discussed below are also recognized as being important for the immunopathogenesis of sarcoidosis. CD4+ T cells involved in sarcoidosis will be discussed in more detail in section 1.9.

The innate immune system evolved to protect organisms from invading microorganisms in a non-specific manner and without long-term protection. Innate immune receptors involved in the recognition of pathogens include pattern recognition receptors

(PRRs), Toll-like receptors (TLRs), nucleotide-binding oligomerization domain-like receptors

(NLRs), and retinoic acid-inducible gene (RIG)-I receptors. These receptors have evolved because microorganisms express molecules arranged in patterns called pathogen- associated molecular patterns (PAMPs) that are then recognized by the innate immune system via the specialized receptors mentioned above. Endogenous ligands that are released during trauma or injury are also recognized by these receptors, and these ligands are termed damage-associated molecular patterns (DAMPs). TLRs and NLRs have roles in granulomatous diseases such as Crohn’s disease and tuberculosis, indicating the importance of these receptors in pathogen detection (125–127). Recently, the role of TLR9

14 in sarcoidosis has been examined. It was observed that alveolar macrophages from sarcoidosis patients had a higher expression of TLR9, and BAL cells from these patients had increased chemokine release when stimulated with TLR9 ligands, perhaps indicating their role in the influx of CD4+ T cells to the BAL during active disease (128). TLRs 1-9 were assessed for expression in skin granulomas and epidermis from sarcoidosis patients via immunohistochemical staining (129). In that study, TLRs 5 and 6 were the most prevalent

TLRs in the granulomas and epidermis of sarcoidosis patients, and expression of TLRs 2, 3,

4, 5, 6, 7, and 8 was increased in sarcoidosis dermis and epidermis versus control samples.

Neutrophils comprise a large percentage of white blood cells, are short-lived, and are the first cell type to respond to inflammation. They are recruited by chemokines such as chemokine (C-X-C motif) ligand 8 (CXCL8) and CXCL5 that are released by proinflammatory cells such as monocytes and macrophages. Neutrophils express PRRs and are effective at phagocytosing microorganisms or particulates, degranulating, and releasing cytokines. Some studies have shown that an increase in the number of neutrophils in BAL correlates with progressive disease in sarcoidosis patients and with the requirement for corticosteroid intervention (130, 131). Although neutrophils may be important in the immune response to potential pathogens involved in sarcoidosis pathogenesis, they may also be responsible for the observed damage to lung via release of reactive oxygen intermediates and proteases.

Alveolar macrophages also express innate immune receptors on the surface, and they respond to stimulation via these receptors by producing pro-inflammatory mediators.

They are the first cell type exposed to airborne antigens, and the number of alveolar macrophages is increased in sarcoidosis (33, 132). Therefore, inhaled antigens may play an important role in the pathogenesis of sarcoidosis. Furthermore, because alveolar macrophages are major players in granuloma formation due to their transformation into multinucleated giant cells, they are one of the key cell types in sarcoidosis pathogenesis in

15 general. In terms of their function in the adaptive immune response to sarcoidosis, alveolar macrophages play a pivotal role in the activation of T cells by their capability of processing and presenting antigens. Costimulatory and adhesion molecules are increased on the surface of alveolar macrophages from sarcoidosis patients with active disease, allowing for better presentation of antigens to T cells and for a proinflammatory state to be maintained

(133–135). Additionally, it has been observed that alveolar macrophages from patients with inactive sarcoidosis do not have enhanced antigen presentation capabilities (136).

Costimulatory molecules that have increased expression on sarcoidosis alveolar macrophages include ligands for CD40 (CD154), CD5 (CD72), CD28 (CD80 and CD86), and CD30L (CD153) (137–142). Adhesion molecules that are highly expressed on sarcoidosis alveolar macrophages include CD54 (ICAM-1), CD11a, CD11b, and CD11c

(136). A general summary of presentation of peptides to T cells that will expand on some of these interactions will be provided in section 1.8 below. Alveolar macrophages have also been shown to spontaneously secrete IFN-, a cytokine important in adaptive immunity due to its effects of augmenting antigen presentation by macrophages, enhancing T cell activation, and downregulating Treg function (143).

Another cell type that excels in antigen presentation is the dendritic cell. Dendritic cell involvement in sarcoidosis has not been well studied, likely because it has been shown that these cells are rarely found in normal BAL or in sarcoidosis granulomas present in the lung (144, 145). A specialized type of dendritic cell, the Langerhans cell, is present in the epidermis, and skin lesions associated with sarcoidosis have been demonstrated to have increased numbers of these cells (145). In contrast to skin dendritic cells, some studies have suggested that there is a trend toward a decrease in dendritic cells in the peripheral blood, possibly indicating that they are homing to sites of disease during active sarcoidosis (146,

147).

T cells comprise an important aspect of the adaptive immune response as well, and most of the sarcoidosis literature suggests that CD4+ T cells play a major role in sarcoidosis pathogenesis. An influx of immune cells into the alveoli of the lungs is known as alveolitis

(25), and the alveolitis in sarcoidosis consists primarily of CD4+ T cells. The CD4+ T cell responses in sarcoidosis are reflective of a traditional T-cell mediated response to antigen.

Therefore, the focus for this section will be on CD4+ T cells and the cytokines that drive different aspects of disease. CD4+ T cells are known as T helper (Th) cells, and their distinction from CD8+ cytotoxic T cells in terms of responses to antigen presented in the context of MHC will be discussed in section 1.8. Additionally, the T cell receptors (TCRs) on the surface of the Th cells associated with sarcoidosis will be discussed in section 1.10.

The three types of Th cells are Th1, Th2, and Th17, and each cell type has different transcription factor expression, polarizing cytokines, and cytokines released upon activation.

Th1 cells are polarized by IL-12, IL-18, and IFN-, express the transcription factor T-box expressed in T cells (T-bet), and release IFN- and Iδ-2 (148). Th2 cells are polarized by IL-

4, express the transcription factor GATA binding protein 3 (GATA-3), and release IL-4, IL-5, and IL-13 (148). Th17 cells are polarized by TGF-, IL-6, IL-21, and IL-23, express the transcription factor RAR-related orphan receptor gamma (RORT), and release IL-17 and

IL-21 (148).

In addition to the differences listed above, Th1 and Th2 cells also express different chemokine receptors, and therefore, respond to different chemokines. Th1 cells express

CXCR3, CCR5, and CXCR6, while Th2 cells express CCR3, CCR4, and CCR8 (29). CD4+ T cells in the lungs of sarcoidosis patients with active disease express CXCR3, CXCR6, T-bet, and release IL-2 and IFN-, indicating that they are Th1 cells (149–157). Additionally, BAL levels of chemokines such as CCR5 are increased in sarcoidosis patients (158). Th1 cells are thought to be recruited from the blood to the lung in sarcoidosis due to the cytokines being produced by alveolar macrophages and other cells in response to the putative

17 sarcoidosis-associated antigen(s). In contrast, no evidence for Th2 cells in sarcoidosis- associated alveolitis has been demonstrated, and there have been no reports of IL-4, IL-5, or IL-13 being increased in sarcoidosis BAL or cell culture supernatants from sarcoidosis patients with active disease (29). However, Th2 cells may play a role in progression to fibrosis, as will be discussed below. Th17 cells are typically involved in the recruitment of neutrophils and macrophages to sites of disease and are important for clearance of bacteria

(148). As reviewed in (29), the role for Th17 cells in pulmonary sarcoidosis is unclear, as most publications looking at these cells are doing so in the blood, rather than the lung.

Some of the studies have seen an increase in the number of Th17 cells in both the blood and BAL, but the cells were activated non-specifically ex vivo prior to identification and cytokine analysis by flow cytometry (35, 36, 159, 160). In one study looking at patients with active disease compared to control subjects, sarcoidosis patients displayed a higher percentage of peripheral Th17 cells, a greater amount of IL-17 released by peripheral alveolar macrophages, and an increased expression of IL-17 by the multinucleated giant cells within granulomas (35).

As eluded to in section 1.2, Th1 cells play an important role in granuloma formation, and a CD4+ T cell switch from Th1 to Th2 is thought to be involved in the transition to fibrosis. The activated T cells surrounding initial granulomas release IFN-, allowing for alveolar macrophages present in the granuloma to become more activated and release IL-

12, IL-18, IL-17, TNF-α, ε-CSF, and GM-CSF (29). Due to the M-CSF and GM-CSF, the macrophages then turn into multinucleated giant cells, while IL-12 and IL-18 maintain the

Th1 predominance (29). The development of fibrosis is thought to occur because of differences in the immune response during acute versus chronic sarcoidosis. In chronic patients, T cell activation is not as prominent as in acute patients (156). As a result, lower

IFN- production by the T cells affects the alveolar macrophages as well as fibroblasts, where it can inhibit matrix production (161). Lower IFN- from alveolar macrophages can, in

18 turn, reduce the Th1 polarization and lead to a Th1 to Th2 shift. Alveolar macrophages in chronic sarcoidosis have been shown to secrete several cytokines associated with fibrosis, including TGF- and the profibrotic chemokine CCL18, indicating that they are M2 macrophages, a subtype associated with fibrosis (29, 46, 162–164).

Tregs are cells typically associated with homeostasis and the prevention of autoimmunity, and they have been shown to suppress early granuloma formation in sarcoidosis (165). Treg cells express CD25, produce cytokines such as IL-10 and TGF-, and rely upon the transcription factor forkhead box P3 (FoxP3) transcription factor for their development and function (166). One study found that there was a negative correlation between the Treg/Th17 ratio and sarcoidosis relapse after corticosteroid therapy was stopped, indicating that the Treg/Th17 ratio in the periphery might be a diagnostic indicator of relapse in sarcoidosis patients (167). Peripheral and BAL Treg cells have been shown to be increased in sarcoidosis patients and, in vitro, the Tregs were able to suppress IL-2 from

CD4+CD25- T cells (168). However, it was also demonstrated that these cells could not inhibit secretion of TNF-α or IFN- from a separate CD4+ T cell population that did not produce IL-2. The authors discuss that the lack of suppression of TNF-α is particularly interesting because anti-TNF-α therapy can be useful for refractory sarcoidosis due to the granuloma-inducing effect TNF-α possesses, as was discussed in section 1.4 above. They argue that because there was a decrease in the ability of sarcoidosis Tregs to block TNF-α secretion, those cells may be unable to control granuloma formation in vivo. However, they also mention that their data do not necessarily indicate that the Tregs are intrinsically defective, but rather, they may be unable to suppress the extensive amounts of TNF-α produced during active disease. Another study showed an increase in BAL Tregs in sarcoidosis patients accompanied by a decrease in peripheral Tregs as the disease progressed (165). However, Tregs were increased in number compared to healthy controls in both compartments. The authors found that sarcoidosis BAL Tregs had lower suppression

19 capabilities than blood Tregs from the same individuals, but Tregs from healthy control BAL and blood were equivalent in terms of suppression capacity. Sarcoidosis blood Treg suppression capacity was not different from that of healthy control blood Tregs, indicating that there is not a general defect in suppressor capacity in sarcoidosis patients. Lastly, the authors found that disease stage did not correlate with suppressor activity but that FoxP3 expression positively correlated with suppressor activity. The authors concluded from the study that the Tregs in sarcoidosis BAL are less capable of suppressing other T cells, have progressed into a memory-like phenotype, and produce inflammatory cytokines that may be involved in the chronic inflammatory state of the lung. Treg production of TGF- may also play a critical role in fibrosis, as TGF- has been implicated in a variety of fibrotic diseases

(46). As reviewed in (29), there are conflicting results pertaining to Tregs and sarcoidosis, but some studies have suggested that high Treg numbers are found in acute disease, while low Treg numbers are found in chronic disease (29). Typically, Tregs allow for remission of a

T cell-mediated disease due to their suppressive capacity against T cells, but in sarcoidosis,

Tregs have often been shown as inadequate in their ability to do so. This defect may contribute to the abundance of lung proinflammatory cytokines and the induction of granuloma formation and/or fibrosis in sarcoidosis patients.

Overall, numerous immune cell types have been studied in sarcoidosis patients, and oftentimes, conflicting results regarding their roles in disease pathogenesis make the immune response to sarcoidosis unclear. These differing results may be due to differences in many factors related to the disease, including the genetic backgrounds of the patients, which will be discussed in the next section.

1.7 Genetic characteristics associated with LS and non-LS sarcoidosis

Genetic contribution to sarcoidosis development and severity occurs, as there is evidence of familial clustering (169), a higher concordance in monozygotic twins versus

20 dizygotic twins (170, 171), and an increased prevalence amongst certain racial groups and within particular geographic locations (16, 18–20). No single gene has been identified as causing sarcoidosis, but several genes are thought to play a role in sarcoidosis occurrence, pathogenesis, and outcome. Differences in genetic backgrounds may also explain, in part, why the disease is so variable in terms of presentation, severity, and progression (172). A variety of genes have been associated with susceptibility or resistance to sarcoidosis, and some of the more well-studied genes will be discussed.

Polymorphisms in B7 or B7-like molecules have been identified, and these molecules signal through unknown receptors to inhibit T cell activation (173). One of these molecules, butyrophilin-like 2 (BTNL2) has been associated with autoimmune and inflammatory diseases such as ulcerative colitis and SLE (174–176). BTNL2 is similar in structure to the co-stimulatory molecule CD80, but it has been shown to be an inhibitory rather than a stimulatory molecule, possibly through its ability to promote de novo FoxP3 expression to induce Treg development (172, 173, 177). In fact, BTNL2 is highly expressed in the gut, where it is believed to play a role in limiting inflammation due to commensal bacteria or dietary antigens (173). Improper BTNL2 signaling may lead to an unregulated and increased

T cell response, allowing for uncontrolled inflammation, such as what is thought to happen during the T cell alveolitis in sarcoidosis. Several splicing variants of BTNL2 that lead to its truncation and inability to insert into the cell membrane have been associated with sarcoidosis and are risk factors for the disease (176, 178–182). Additionally, it has been shown that a particular BTNL2 polymorphism, BTNL2G16071A, is present more often in sarcoidosis patients with persistent disease than those with acute disease, indicating that

BTNL2 polymorphisms may not only be risk factors for sarcoidosis, but that particular

BTNL2 variants may be associated with progressive disease (183).

Th17 cells and IL-23 are thought to be involved with pathogenesis of a variety of autoimmune and inflammatory disorders including inflammatory bowel disease, psoriasis,

21 and ankylosing spondylitis (184). As mentioned above, Th17 cells are expanded by the cytokine IL-23, which is produced by activated macrophages and dendritic cells (185). IL-23 binds to the IL-23 receptor complex comprised of two components: IL-1βR1 and Iδ-23R

(186). In sarcoidosis patients, IL-23R polymorphisms have been identified, including those that are associated with chronic versus acute sarcoidosis, as well as with risk for sarcoidosis in general (182, 187, 188). While the role of Th17 cells in sarcoidosis has not been fully elucidated, as discussed in section 1.6 above, it is thought that they may enhance granuloma formation (189). Additionally, as IL-23 promotes the expansion of Th17 cells, the

IL-23R polymorphisms observed in these patients indicate that Th17 cells are likely involved in sarcoidosis pathogenesis.

As discussed in previous sections, TNF-α is an important cytokine for granuloma formation, and patients with higher TNF-α levels have more severe disease. Two polymorphisms in the human TNFA promoter have been identified that involve substitutions of guanine (TNFA1) to adenosine (TNFA2) in an uncommon form of the allele (190, 191).

Following those discoveries, it has been shown that one of those rare alleles is associated with high TNF-α production as well as a particular major histocompatibility complex (MHC) haplotype (HLA-A1-B8-DR3-DQ2) (192–194). MHC haplotypes associated with sarcoidosis risk will be discussed in further detail below, but the HLA-DRB1*0301 (HLA-DR3) allele

(included in the haplotype listed above) has been associated with LS in particular.

Additionally, one study showed that it may be possible to distinguish LS from non-LS sarcoidosis based on the presence of both TNFA2 and HLA-DR3 in LS (195). Several studies have associated the TNFA2 polymorphisms with increased risk or severity of sarcoidosis, and one study performed a meta-analysis of 7 case-control studies involving the TNFA2 polymorphism (196). The meta-analysis study found that there was a 47% increased risk of sarcoidosis if a patient carried the TNFA2 allele. Therefore, sarcoidosis

22 patients carrying TNFA2, which promotes more TNF-α production, may be more susceptible to increased inflammation and to granuloma formation in general.

Risk of developing sarcoidosis has long been associated with differences in MHC genes, which are highly polymorphic and encode for MHC molecules. MHC molecules are important in the adaptive immune response, particularly in antigen presentation to T cells.

Each MHC variant possesses unique preferences for which peptides will bind and subsequently be presented to T cells. MHC class II is expressed only on specific cell types, including DCs, macrophages, and B cells, while MHC class I is expressed on all nucleated cells. The human MHC is often referred to as the human leukocyte antigen (HLA). HLA-DR,

HLA-DP, and HLA-DQ are the human MHC class II molecules, while HLA-A, HLA-B, and

HLA-C are the classical MHC class I molecules. The MHC genes are polymorphic, and each individual inherits a combination of six class I and six class II alleles. As will be explained further in the next section, CD4+ T cells recognize antigen in the context of MHC class II, while CD8+ T cells respond to antigens presented in the context of MHC class I. As discussed in previous sections, pulmonary sarcoidosis is associated with an increased

CD4/CD8 ratio in the BAL, and one of the hallmarks of the disease is a CD4+ T cell alveolitis. Therefore, MHC class II alleles have been more thoroughly studied in sarcoidosis patients than MHC class I alleles (197). It is presumed that the CD4+ T cells that are expanded in the lungs of these patients are responding to a putative sarcoidosis-associated antigen in the context of MHC II. Therefore, identifying sarcoidosis patients with common

HLA alleles may allow for a better understanding of what antigens may be triggering disease pathogenesis.

Various sarcoidosis patient populations have demonstrated differing associations with particular HLA alleles, some conferring an increased risk and others providing protection from sarcoidosis. Regardless of the patient population, HLA-DR alleles with a hydrophobic residue at position 11 (DR1 and DR4) have been associated with a reduced

23 risk of sarcoidosis (198, 199). Position 11 of the DR molecule falls within the P6 pocket of the antigen binding groove, and it is the only amino acid position in that pocket with variability, meaning it is an important residue for determining antigen binding preferences

(199). As such, a hydrophobic amino acid at that position may affect the number of water molecules allowed in the P6 pocket, possibly influencing the strength of the MHC/peptide bond (199). A multi-center study entitled A Case Control Etiologic Study in Sarcoidosis

(ACCESS) found that there was an increased risk in both African–American and Caucasian populations associated with HLA-DRB1*1101 (DR11), with the highest risk seen in African-

Americans (200, 201). DR5, DR8, and DR9 have been associated with disease in Japanese patients, while DR5 has been associated with chronic disease and DR3 with acute disease in German patients (195, 202, 203). In Scandinavian patients, DR14 and DR15 have been linked to chronic disease, while there is strong evidence for an association between the DR3 allele and acute sarcoidosis (198, 204–208). In fact, evidence for the association between

DR3 and better prognosis has been particularly strong in studies focused on Scandinavian sarcoidosis patients. For example, when assessing whether particular HLA haplotypes were associated with disease prognosis in Scandinavian patients, it was found that DR3 was more common in sarcoidosis patients than control subjects (33% versus 17%, respectively, p < 0.001) (198). Following a two-year period, patients were classified as either chronic or non-chronic, and 65% of the non-chronic patients were those with the DR3 allele, while only

33% of the chronic patients had the allele (p < 0.001). In a separate but similar study by the same group, 89% of DR3+ and 27% of DR3- patients were non-chronic after two years, further supporting the DR3/acute disease linkage (209). Lastly, a larger study from that group showed 76.8% of acute and 9.2% of chronic sarcoidosis patients carried the DR3 allele (208). Numerous additional studies have confirmed the association of sarcoidosis, in particular with an acute onset and/or LS, and the DR3 allele in differing populations (204–

206, 210–214). The DR3 association with LS in Scandinavian patients will be discussed in further detail in subsequent sections.

The HLA-DQB1*0201 (DQ2) allele, which has been associated with autoimmune diseases like Type 1 diabetes mellitus and celiac disease, is in linkage disequilibrium with

DR3 (215, 216). Therefore, most of the DR3+ sarcoidosis patients also carry the DQ2 allele.

In fact, a good prognosis has been associated with the DQ2 allele in sarcoidosis patients

(217, 218). However, although DR3 and DQ2 are in linkage disequilibrium and there is evidence that DQ2 may be linked to LS, the strongest evidence to date ties LS to DR3 (198,

206, 210, 217). Additionally, a minor allele, HLA-DRB3*0101 (DRB3), encodes for an MHC molecule that shares amino acid sequences with DR3 in regions important for antigen binding, allowing the two molecules to present similar and identical peptides (219). The

DRB3 allele has also been positively associated with sarcoidosis (220, 221).

Overall, the MHC associations in sarcoidosis indicate a potential familial clustering of the disease. Furthermore, in conjunction with the numerous other genetic risk factors (some of which are listed above), the MHC associations support a potential genetic contribution to disease pathogenesis.

1.8 CD4+ T cell response to antigens presented in the context of MHC

Two main subtypes of T cells are involved in cell-mediated immunity: CD8+ and

CD4+ T cells. CD8+ T cells are cytotoxic T cells—they respond to peptide presented by

APCs in the context of MHC class I and directly kill infected cells. In contrast, CD4+ T cells are T helper cells (Th cells) which respond to peptides presented by MHC class II and aid in stimulating other immune cells like macrophages, B cells, and cytotoxic T cells. CD4+ T cells can stimulate these cells either via secretion of cytokines or by upregulating costimulatory molecules on their surface. As discussed in section 1.6 above, there are several forms of Th cells (Th1, Th2, and Th17), and each subtype has unique functions.

As discussed in the previous section, MHC associations in sarcoidosis indicate that a particular antigen may be driving disease pathogenesis. Additionally, as CD4+ T cells traffick to and are expanded in the lung during active disease in sarcoidosis patients, a basic overview of CD4+ T cell recognition of antigen in the context of MHC class II will be reviewed in this section.

CD4+ T cells are initially activated when they recognize a peptide presented in the context of MHC class II on the surface of an APC. Peptides are pieces of intact proteins that have been degraded by the APC and are subsequently loaded onto the MHC molecule for presentation. The peptide-MHC (pMHC) on the APC in conjunction with the TCR on the surface of the responding T cell is collectively termed the “trimolecular complex”, and T cells are said to be “restricted” by εHC molecules. Class II MHC molecules are transmembrane heterodimers comprised of an α and a chain, each with two immunoglobulin-like domains close to the membrane and two variable N-terminal domains farther out from the membrane.

The variable domains located on the N-terminal ends of the α and chains form the peptide binding groove where peptides are presented to T cells. MHC class II molecules can accommodate peptides that are usually between 13-17 amino acids long that are not confined within the groove (i.e., amino acids may protrude from the binding groove). The peptide is anchored into the groove in two manners: by the binding of portions of the peptide backbone to pockets within the MHC molecule that are invariable and by the binding of side chains of the peptide to variable pockets within the groove. Certain positions in an MHC molecule’s peptide binding groove can only accommodate (and thus, require) specific amino acids to bind in those positions. These restricted positions within the MHC molecule are called anchor positions (222, 223). For example, the anchor positions for binding to the LS- associated HLA-DR3 molecule and their corresponding preferred amino acids of the peptide are positions 1 (L, I, F, M, V), 4 (D), 6 (K, R, E, Q, N), and 9 (Y, L, F) (224). Although the anchor positions are strongly suggestive of what is preferred for binding at a particular

26 position, the consensus anchor amino acids for DR3 were identified using a small number of known DR3-restricted peptides, some of which did not strictly follow the amino acid preferences at all of the anchor positions (224–227). Therefore, although these anchor positions may lend clues as to the peptides being presented by DR3 in LS, there is no definitive evidence that the putative LS-associated antigens adhere to these preferences.

Chapter V contains a further discussion of suspected sarcoidosis-associated antigens.

Typically, the peptides presented by MHC II are endosome-derived (either from extracellular sources that were ingested by the APC or from intracellular microbes that grow within the endocytic compartment of the APC) and are degraded in lysosomes. MHC II proteins are synthesized and assembled in the lumen of the endoplasmic reticulum, and the processed peptides are loaded into the peptide binding groove in an endosomal compartment, allowing for the pMHC to be expressed on the surface of the APC.

CD4+ T cells have a receptor, designated the TCR, on their surface which recognizes the MHC-bound peptide, and the TCR consists of two chains, TCRα and TCR. Th and cytotoxic T cells are classified as α T cells that possess αTCRs. In contrast, there are T cells which express different chains and are termed T cells, but these cells will not be discussed here. TCRs are formed via recombination events in which different gene segments combine to form unique TCRs capable of recognizing nearly any foreign peptide presented in the context of MHC due to the extreme diversity of the repertoire. TCR rearrangement and the various segments comprising the TCR chains will be discussed further in section 1.10.

In addition to the TCR, T cells require co-receptors in order to recognize the pMHC complex. Th cells express CD4, while cytotoxic T cells express CD8 on the surface, thus the designations of CD4+ and CD8+ T cells have been established, respectively. These co- receptors span the membrane and bind the invariable portions of class II or class I MHC, and the cytoplasmic portions of the co-receptors contain tyrosine kinases (e.g., Lck), which

27 play a role in TCR signaling for activation of the T cell. Another important co-receptor is

CD3, which is a complex of four polypeptide chains (, , , and ζ) that form three distinct heterodimers (, , and ζζ). CD3 molecules span the membrane and contain an N-terminal immunoglobulin-like extracellular domain as well as a cytoplasmic tail consisting of immunoreceptor tyrosine activation motifs (ITAMs). One other important co-stimulator of T cells is CD28, which is expressed on naïve T cells where it binds its ligands CD80 or CD86.

CD28 is important for recruitment of intracellular proteins involved in TCR signaling.

As recently reviewed, TCR signaling is comprised of several molecules that interact with one other, and it will only be briefly reviewed here (228–232). After the αTCR recognizes the pMHC in conjunction with co-receptor (CD4 or CD8) binding to the MHC molecule, phosphorylation of the CD3 ITAMs by the co-receptor-associated molecule Lck occurs. Due to the ITAM phosphorylation, recruitment of the tyrosine kinase Zap70 leads to phosphorylation of δat, which can then assemble the “δat signalosome”. The δat signalosome is a central hub that connects the initial TCR signals to a multitude of signaling pathways delivering signals to the nucleus, which ultimately results in activation of the T cell.

A discussion of the immunologic properties of the CD4+ T cells associated with sarcoidosis will be provided in the next section.

1.9 CD4+ T cells in the BAL of LS and non-LS patients

Although sarcoidosis can affect almost any organ, nearly all sarcoidosis patients have lung involvement. In fact, 90% of sarcoidosis patients have an increase in BAL lymphocytes at the time of diagnosis, and the CD4/CD8 ratio in the BAL is almost always elevated compared to healthy control subjects (61, 63, 142, 233–237). Furthermore, the accumulated CD4+ cells disappear with disease resolution, indicating that they play a role in disease pathogenesis (237, 238). This section will focus on the immunological properties of

28 the expanded CD4+ T cells in the BAL of sarcoidosis patients, while section 1.10 will focus on the TCRs of the expanded BAL CD4+ T cells.

Antigen-specific responses by T cells require TCR interaction with pMHC, as discussed in section 1.8 above. This interaction leads to a signaling cascade resulting in the secretion of cytokines as well as the downregulation of the TCR on the surface of the T cell.

As discussed in section 1.6, different variants of CD4+ Th cells are stimulated by unique cytokines and then subsequently release particular cytokines. It has been shown that the

CD4+ T cells accumulating in the lungs of sarcoidosis patients with active disease are Th1 cells, as reviewed in (239). As discussed in sections 1.2 and 1.6, Th1 cells are important for granuloma formation, are found throughout sarcoidosis granulomas, and secrete proinflammatory cytokines such as IL-2 and IFN-. Additionally, Th1 cells in the BAL of sarcoidosis patients display markers associated with memory T cells (156, 240, 241).

IL-2 is one of the first cytokines released by activated Th1 cells, and upregulation of the IL-2 receptor is indicative an activated phenotype. It has been demonstrated that supernatants from ex vivo lung T cells obtained from sarcoidosis patients contained higher amounts of IL-2 than those from controls and that BAL CD4+ T cells from these patients spontaneously release IL-2 ex vivo (150–152). One study also noted that sarcoidosis patient blood T cells expressed more IL-2 mRNA and surface IL-2 receptor directly ex vivo than control cells (154). Additionally, it was observed that the blood T cells also proliferated to a greater extent in the presence of IL-2 ex vivo than those from control patients, indicating that sarcoidosis patients also have higher percentages of activated T cells in their blood relative to control subjects. Spontaneous IFN- release by BAL CD4+ T cells from sarcoidosis patients has also been observed directly ex vivo, and cells from patients with active disease released more IFN- than those from patients with inactive disease (143). Furthermore, one study showed that sarcoidosis BAL CD4+ T cells have downregulated their TCRs relative to levels found in autologous blood samples (242). This observation indicated that the T cells

29 were recruited and then subsequently activated due to interaction with an antigen presented in the context of MHC II rather than by non-specific chemotaxis. A more recent study also assessed the frequency of BAL CD4+ T cells that produced IL-2, IL-4, IFN-, and TNF-α

(243). In that study, the BAL cells from sarcoidosis patients had greater frequencies of CD4+ cells expressing IFN- and TNF-α and lower frequencies expressing IL-4. All of these studies support the finding that the CD4+ T cells involved in the alveolitis associated with sarcoidosis are activated Th1 cells.

A very early marker for T cell activation is CD69, which has been shown to be increased on BAL CD4+ T cells in sarcoidosis (142, 244, 245). However, another study showed that the CD69 expression on BAL CD4+ T cells was no different between sarcoidosis and control patients (246). The reasons for the discrepancy are not clear, but as

CD69 is upregulated on BAL cells compared to those in the blood in healthy subjects, it may be difficult to find differences in CD69 expression between BAL samples in general, regardless of disease state (247).

The co-stimulatory molecule CD28 is expressed on naïve CD4+ T cells in healthy individuals and interacts with CD80 and CD86 on APCs, as discussed in section 1.8 above.

Downregulation of CD28 has been shown to occur on CD4+ T cells after repeated rounds of antigen-induced proliferation, while absence of CD28 signaling leads to a state of anergy in which naïve T cells are unable to respond to antigenic stimulation (248–251). In contrast, memory T cells do not require CD28 costimulation to respond to antigenic stimulation. Some studies have shown that CD28 is downregulated on BAL CD4+ T cells in sarcoidosis patients, possibly indicating that the chronic nature of the disease leads to the presence of antigen-induced memory T cells in the lungs (142, 246, 252). However, one study found increased frequencies of BAL CD4+ CD28+ T cells in sarcoidosis patients (140). It may be that CD28 expression is an indicator of disease progression. For example, increased CD28

(along with increased CD80 and CD86 on APCs) may be an indicator of recent T cell

30 activation and inflammation, whereas decreased CD28 may suggest chronic inflammation associated with advanced disease, memory T cell formation, or possibly even naïve T cell anergy (246).

Recently, the dysfunction of BAL CD4+ T cells has been assessed in sarcoidosis patients of varying clinical outcomes (237). In that study, antibodies to CD3 and CD28 were used to stimulate the TCRs non-specifically directly ex vivo. After the stimulation, the cells from sarcoidosis patients produced less IL-2 and IFN- and proliferated to a lower extent than cells from healthy controls. The sarcoidosis cells displayed lower expression of the molecules Lck, PKC-θ, and NF-κB, which are key players in the transcription of IL-2. The authors found that Tregs were increased in sarcoidosis patients, but sorting those cells out from the CD4+ T cell population still did not rescue the phenotype of the remaining cells after non-specific TCR stimulation. However, addition of exogenous IL-2 and overexpression of

PKC-θ were independently able to rescue the responses of the CD4+ T cells to non-specific

TCR stimulation. The sorted Treg cells were found to be deficient in their ability to suppress other cells, possibly indicating that there is a general CD4+ T cell deficiency in sarcoidosis that is not limited to the effector cells. Lastly, the authors noted that patients who had spontaneous resolution of the disease had a restoration of IL-2/IFN- production, proliferation after TCR stimulation, and Lck/PKC-θ/NF-κB expression. These restorations were not seen in disease progressors. Overall, the findings indicate that resolution of pulmonary sarcoidosis is dependent upon the restoration of defects in IL-2 signaling that occur with initial disease onset.

In general, the expanded BAL CD4+ T cells in sarcoidosis patients are highly activated Th1 cells, and they spontaneously secrete proinflammatory cytokines that are known to be important in granuloma formation. Because they have downregulated their

TCR, it is likely that the accumulated T cells are responding to antigenic peptides presented by APCs in the context of MHC II. Determining the putative sarcoidosis-associated

31 antigen(s) that the expanded cells are responding to requires information about the TCRs that are most frequently expressed on the CD4+ T cells in the BAL. Previously identified BAL

CD4+ TCRs that have been expanded in differing subsets of sarcoidosis patients will be discussed in the next section.

1.10 T cell receptors (TCRs) associated with LS and non-LS sarcoidosis

T cells recognize pMHC via a TCR expressed on the surface of the cell, as discussed in previous sections, and the antigenic specificity of any given TCR is determined by the amino acid sequence of the molecule. However, a TCR is not encoded by a single germline-encoded gene; rather, it is generated by the combination of noncontiguous gene segments via a process called V(D)J recombination, leading to a diverse repertoire of TCRs in a given individual. Nontemplate insertions and deletions also account for the diversity of the TCR repertoire. Furthermore, as discussed previously, the αTCR is comprised of two chains, α and , each of which also contributes to the diversity of the repertoire. It would be conceivable, then, that each TCR was specific for one peptide. Indeed, the estimated potential diversity of the TCR repertoire in humans is greater than 1020; however, because the number of T cells in a human (~1011) is far lower, every TCR must have some ability to cross-react with several different peptides (253–257). In pulmonary sarcoidosis patients, it is of interest to identify the BAL CD4+ TCR repertoire, because knowing that component of the trimolecular complex would allow for a better understanding of the potential peptide(s) driving disease pathogenesis. Before discussing the TCRs previously found to be associated with sarcoidosis, it is important to explain how TCRs are formed, what drives their diversity, and what determines their specificity. In this section, a very brief overview of

αTCR recombination will be reviewed, followed by a discussion about the proposed

αTCRs involved in sarcoidosis.

Progenitor cells in the thymus give rise to T cells where the process of V(D)J recombination occurs. The α and chains of the TCR are each comprised of variable (V) and constant (C) regions. The TCRα locus consists of Vα and Jα gene segments, while the

TCR locus contains V, D, and J gene segments. First, the TCR chain undergoes somatic recombination of the D-J segments followed by V-DJ recombination. For the alpha chain, a V-J recombination event occurs (with no D segments contributing to the alpha chain). The V(D)J V region exon is transcribed and spliced to join either C or Cα, and the mRNA is translated to form the TCR or TCRα chain, respectively. Pairing of the two chains forms the heterodimeric αTCR. During the recombination events, N and P nucleotide additions occur due to enzymatic addition of random nucleotides and repair of the double stranded breaks required for recombination, respectively. Additionally, nucleotides are also deleted during the process, leading to further diversity of the TCR repertoire. As the C region of each chain is identical for any given species, the V region is of most interest in determining the antigen specificity of a given TCR. In fact, direct contact with the peptide presented by MHC occurs within the hypervariable complementary-determining region 3

(CDR3) loops of the V regions, and the CDR3 loops are considered the primary determinant of antigenic specificity (258–262). In contrast, the CDR1 and CDR2 loops are more directly in contact with the tops of the MHC helices. As such, V region usage and the limited CDR3 sequencing that has previously been reported for sarcoidosis BAL and blood CD4+ T cells will be discussed below. All nomenclature will be in agreement with the international

ImMunoGeneTics (IMGT) information system database (TRAV/TRBV) with some references to the Arden (263) nomenclature in parentheses (Vα/V).

As depicted in Table 1-1, early work identifying common TCRs in sarcoidosis patients focused primarily on the TCR chain. One of the oldest descriptions of V usage in sarcoidosis patient blood and BAL was performed using a mAb to TRBV12 (V8) and looking by flow cytometry for staining of T lymphocytes, as well as CD8+ and CD4+ cells

33 individually (264). Normal controls showed >5% TRBV12+ lung and blood T cells, while sarcoidosis patients showed >7% in both compartments and an enrichment in the lung versus the blood. A later publication from the same group utilized PCR to amplify any of 20

V genes for which primers were available (265). The authors looked at lung and blood mononuclear cells, so differentiation between CD4+ and CD8+ T cells could not be performed. Despite that caveat, the sarcoidosis patients displayed an average CD4/CD8 value of 6.8 in the lung, indicating that the vast majority of the PCR products originated from

CD4+ cells. Some of the sarcoidosis patient samples (lung or blood) had biased usage of several TRBV genes including TRBV5 (V5), TRBV1β (V8) TRBVβ4 (V15), TRBV14

(V16), and TRBV18 (V18), with many patients exhibiting biases only in the lung relative to the blood. Additional expansion of the cells by IL-2 caused even further skewing of the repertoire. Normal control subjects had no biased usage of any TRBV segment in lung versus blood. Lastly, the authors performed limited sequencing analyses on several patients’ unstimulated or Iδ-2-expanded cells. No CDRγ motif was shared amongst multiple patients, although a TRBV18 motif containing the amino acid sequence RGR did show up in one patient in both the lung and the blood. In a similar study, a different group looked at unstimulated blood and IL-2-expanded BAL cells using six V mAbs and found that TRBV12, TRBV7 (V6) and TRBV10 (V12) were expanded in the IL-2-stimulated BAL cells of one sarcoidosis patient each but in no control patients (266). By PCR, some sarcoidosis patients had expansions of TRBV12 and TRBV20-1 (Vβ) in Iδ-2-expanded BAL cells versus PBMCs. When assessing sequences in BAL and blood before and after IL-2 expansion, several repeated TRBV12, TRBV20-1, and TRBV10 sequences were found in each of the three individuals evaluated, but no common CDRγ motifs were seen amongst two or all three patients. As mentioned in section 1.1 and further described in section 5.1, the Kveim-Siltzbach test involves intradermal injection of a suspension of sarcoidosis tissue to which sarcoidosis patients develop a granulomatous reaction (11–13, 267). It is of interest

Table 1-1. Selected TCRα and TCRβ usage previously associated with sarcoidosis. Cell Patient TRAV TRBV source group Method Expansion Citation No preferential Grunewald, et 1 12-1 usage BAL SCAND mAb n/a al., 1992(268) No preferential Grunewald, et 2 12-1 usage BAL SCAND mAb n/a al., 1994(269) No preferential mAb, Grunewald, et 3 12-1 usage, 4, 18 BAL SCAND PCR n/a al., 1995(270) Grunewald, et 4 12-1 n/a BAL SCAND mAb n/a al., 2000(221) Grunewald, et 5 12-1 n/a BAL SCAND mAb n/a al., 2002(271) Grunewald, et 6 12-1 2 BAL SCAND mAb n/a al., 2016(272) BAL, Moller, et 7 n/a 12 PBMC US mAb n/a al., 1988(264) 5, 12, 24, BAL, Forman, et 8 n/a 14, 18 PBMC US PCR IL-2 al., 1994(265) BAL, mAb, Forrester, et 9 n/a 12, 20-1 PBMC US PCR IL-2 al., 1994(266) 20-1, 28, KS, 4 weeks p.i. (KS), Klein, et 10 n/a 7, 12 PBMC US PCR n/a (PBMC) al., 1995(273) Ahlgren, et 11 n/a 2, 12, 14 BAL SCAND mAb n/a al., 2014(274)

Abbreviations: TRAV/TRBV: TCRα/TCR chains in IεGT nomenclature (www.imgt.org) BAL: bronchoalveolar lavage PBMC: peripheral blood mononuclear cells KS: Kveim-Siltzbach reaction biopsy US: United States sarcoidosis patients SCAND: Scandinavian sarcoidosis patients mAb: monoclonal antibody staining, evaluated by flow cytometry PCR: polymerase chain reaction, utilizing specific Vα or V primers n/a: not applicable or not evaluated p.i.: post injection

35 to determine whether the T cells that are responding to the injection and helping to form the granuloma are oligoclonal in nature (i.e., share CDR3 homology, indicating that they recognize similar or identical peptides). One study looking at RNA isolated from the reaction sites used PCR to identify potential biases in the repertoire four weeks after injection (273).

The authors observed TRBV20-1, TRBVβ8 (Vγ), TRBV7, and TRBV12 biases. Subsequent sequencing of the TCRs revealed two patients with a Kveim reaction TRBV12 RGR CDR3 motif and one patient with the same TRBV18 RGR motif as was found in the study referenced above either before or after IL-2 culture of lung or blood cells. A more recent paper looking at V repertoires in BAδ T cells of sarcoidosis patients found TRBVβ (Vββ),

TRBV12, and TRBV14 expansions in the BAL of several patients, but overall they did not find any segments that were significantly more highly expressed on CD4+ T cells of the lung versus blood and saw no differences between sarcoidosis patient and control patient V usages (274).

The primary TRAV chain associated with sarcoidosis has been TRAV12-1, and in particular, it has been strongly linked to LS. Several studies looking at TRAV12-1 in these patients will be briefly reviewed here and are summarized in Table 1-1. In a very early study, a strong association was seen between HLA-DR3+ sarcoidosis patients and expansions of

BAL CD4+ TRAV12-1+ T cells (relative to blood) via mAb staining and flow cytometry (268).

No other Vα expansions were seen in sarcoidosis patients or controls. Additionally, in four patients analyzed 6 or more months later, two of the resolved patients showed normalized levels of TRAV12-1-expresing BAL CD4+ T cells, indicating their negative correlation with disease resolution. Indeed, a recent study found that the percentage of BAL CD4+ TRAV12-

1+ T cells decreased with clinical resolution, as did the percentage of BAL lymphocytes and the BAL CD4/CD8 ratio (238). The study also used 6 V mAbs and did not find a preferential usage of any particular V by BAL CD4+ T cells. Another study found that there was a significant increase in the number of BAL CD4+ TRAV12-1+ T cells in sarcoidosis patients

36 with short (<2 year) versus long (>2 year) disease duration (221). In a study by the same group, a 100% correlation between the DR3/DQ2 haplotype and BAL CD4+ TRAV12-1+ T cell expansion was observed (269). The expansion was not seen in DR3- sarcoidosis or

DR3- healthy control patients. Interestingly, one former DR3+ sarcoidosis patient whose disease had resolved did not have the expansion. Lastly, 3 DR3+ allergic alveolitis as well as

6 DR3+ healthy controls also did not have significant TRAV12-1 expansions. One observation the authors noted was that the DR3+ patients, regardless of health status, tended to have a preference for TRAV12-1 in the BAL versus blood, but with the sarcoidosis patients exhibiting a much greater expansion than the controls. The DR3+ healthy controls were all hospital workers, and the authors speculated that perhaps they may have been exposed to a putative sarcoidosis-associated antigen, leading to the slight TRAV12-1 expansion in the BAL. One study sequenced BAL CD4+ TRAV12-1+ T cells and found a shared CDR3α motif in two patients (CVV-DY-GGSQGN) and an additional one within a single patient (CVV-RHMDS) (270). None of these studies identified a potential TRBV chain that was also expanded in the majority of the DR3+ sarcoidosis patients. However, a new study by the same group did look at co-expression of TRBV chains on BAL CD4+ TRAV12-

1+ T cells with various TCR mAbs and found a small but significant population of cells co- staining with TRBV2 (272). In contrast, DR3- patients did not have expansions of either chain separately or of both chains co-expressed. When the cells from DR3+ patients were assessed for TRAV12-1 and TRBV2 sequences, no dominant CDR3 motifs were found on either chain. However, the CD4+ cells were not sorted on TRAV12-1+TRBV2+, a population which potentially could possess more oligoclonality.

Overall, the issues with these studies are that many of the earlier studies relied upon

IL-2 expansion, which likely skewed the repertoires toward cells responsive to IL-2 stimulation and possibly eliminated those cells that are of the activated memory and/or anergic/exhausted phenotype that has been observed in sarcoidosis patients (as discussed

37 in section 1.9 above). Additionally, many private TCR repertoires were found within individual patients by sequencing, but no consensus CDR3 motifs were found amongst the majority of sarcoidosis patients. Only one of the studies addressed αTCR pairing and did so solely by mAb staining. Although the study did sequence using TRAV12-1 and TRBV2 primers, the co-expressing cells were not sorted prior to sequencing, leaving open the possibility that the TCR sequences obtained were not derived from chains paired together as complete TCRs. Therefore, as this thesis will address, determination of αTCR pairing and shared TCR repertoires in a larger cohort of patients is necessary to begin identifying potential disease-associated antigens in sarcoidosis.

1.11 Putative etiologies of sarcoidosis

As has been discussed up until this point, many aspects of sarcoidosis have been identified, including clinical findings, immunologic characteristics, genetic associations, and the role of CD4+ T cells in disease pathogenesis. Despite these advances in our understanding of the disease, the etiologic agent of sarcoidosis has yet to be identified. The current understanding is that the development of sarcoidosis requires four main events: exposure to an antigen, an acquired immune response directed at the antigen via APC presentation of peptides to CD4+ T cells, a non-specific inflammatory response mediated by effector cells that appear at the sites of disease, and formation of granulomas (17, 275).

Sarcoidosis-associated granulomas are primarily found in the lungs in nearly all patients, indicating that the putative antigen may be acquired via inhalation (25). Therefore, investigations of potential environmental/occupational, infectious, and autoimmune antigens have been performed, but as of yet, no conclusive evidence has been found for a direct association between one antigen and sarcoidosis. In this section, some of the more well- studied putative antigens will be discussed in further detail.

In regard to a role for environmental exposures in the development of sarcoidosis, studies have made the observation that there is a seasonal clustering of cases in the late winter or early spring months (276, 277). Interestingly, one study found that there was an increase in incidence during these seasons only in DR3+ individuals and did not see a difference in date of onset in DR3- patients (206). However, there are other studies showing conflicting results in terms of seasonality and sarcoidosis. For instance, one study based in

Turkey found that spring had the highest incidence while summer had the lowest (278), while another study from India saw the highest incidence in summer and lowest in winter

(279). A Croatia-based study found the incidence highest in spring and summer (280), and a

US-based study found no seasonal variation in incidence (281). There are several other examples of conflicting results in the literature, meaning either there is no general seasonal variation in disease onset or there are unique environmental triggers within different regions that drive a particular seasonality in a given region. A third possibility accounting for the differences is that patients may be seen in clinic closer to the onset of disease if they have more apparent symptoms. Overall, these studies support potential regional-specific environmental antigens that may be playing a role in disease onset.

The US-based study ACCESS, first mentioned in section 1.7, had the goal of identifying environmental, occupational, or other demographic factors that were associated with increased or decreased sarcoidosis risk (200). In that study, no factors could be identified as having a greater than 2-fold risk (odds-ratio) when >5% of the subjects were exposed. The study did find that exposure to mold/mildew, pesticides, and insecticides had a positive association with sarcoidosis (1.5-fold increase risk). Studies utilizing ACCESS data found associations with military service, educational occupations, or exposure to organic dust, wood burning, metal dust, building materials, or gardening supplies (282, 283).

Diseases such as chronic beryllium disease (CBD) and hypersensitivity pneumonitis are good examples of similar granulomatous disease to sarcoidosis in which inorganic and

39 organic agents can induce disease, respectively (284, 285). Therefore, it is conceivable that sarcoidosis pathogenesis might be caused by an environmental or occupational exposure.

Alternatively, several lines of evidence indicate that there may be a microbial source of antigen in sarcoidosis. One clue to a potential microbial source is the Kveim reaction, discussed briefly in section 1.1 and again in more detail in section 5.1. In this test, injection of preparations of sarcoidosis tissues leads to a granulomatous inflammatory response at the site of injection in sarcoidosis patients but few control patients. This observation suggests that there is a possibility of the presence sarcoidosis-associated antigens in the

Kveim reaction preparations. Another clue to a potential infectious source in sarcoidosis is that naïve transplant recipients have developed sarcoidosis after receiving a solid organ transplant from donors with a history of sarcoidosis (286). The most well-studied microbial sources in relation to sarcoidosis are Mycobacterium spp., which will be reviewed in section

5.1. In essence, studies indicate that mycobacterial DNA or RNA is present in sarcoidosis tissues, that T cells respond to mycobacterial proteins and peptides, and that sarcoidosis patients have circulating antibodies to mycobacterial proteins. Likewise, as will also be discussed in further detail in section 5.1, other commonly-studied sarcoidosis-associated microbes are Propionibacterium spp. As with Mycobacterium spp., findings with

Propionibacterium spp. show possible evidence of DNA/RNA in sarcoidosis tissues, T cell responses to peptides, and circulating antibodies. However, no consensus exposure to any infectious agent has been reported in the majority of patients, and conflicting results or poorly controlled studies leave the potential microbial association uncertain. For instance, there is no indication that an active mycobacterial infection is associated with sarcoidosis, and treatment with corticosteroids or other immunosuppressive therapies does not induce a reactivation of latent mycobacteria in sarcoidosis patients (50). Lastly, some pathogens can induce lung lesions that resemble those caused by sarcoidosis, and as such, infections by

40 pathogens such as Chlamydia, Cryptococcus, Histoplasma, and Sporothrix spp. must be ruled out during the diagnosis of sarcoidosis (50).

Proposed noninfectious etiologies of sarcoidosis have included serum amyloid A

(SAA) proteins. SAA proteins are highly-conserved acute-phase reactants that increase in concentration during inflammation by up to 1000-fold (287, 288). There is evidence that SAA is abundant in tuberculosis, nontuberculosis mycobacterial infection, leprosy, and even sarcoidosis tissue (289–292). In one study, SAA was found to be the only amyloid, prion, or amyloid precursor expressed within sarcoidosis granulomas by immunohistochemistry and from BAL by ELISA, and there was a positive correlation between SAA levels and chest x- ray stage (292). Additionally, the SAA levels in the sarcoidosis granulomas were higher than those found in granulomas from other diseases that were assessed. Furthermore, SAA levels correlated with the number of CD3+ T cells within granulomas, and SAA stimulated sarcoidosis BAL cells to secrete more TNF and IL-18 than those from control patients. SAA has also been reported as being increased in sarcoidosis patient blood (293, 294). It has been hypothesized that a microbial pathogen triggers a hyperimmune Th1 response which clears the invading pathogen but triggers SAA accumulation at the sites of granuloma formation, allowing for a progressive self-aggregation of SAA within the granulomas (50).

The authors of that review suggested that there may be a feed-forward amplification that contributes to the Th1 response and ongoing inflammation in progressive sarcoidosis.

Other suspected noninfectious etiologies of sarcoidosis include autoimmune targets such as zinc finger protein 688 and mitochondrial ribosomal protein L43 (295). In one study,

HLA-DR-bound peptides were eluted from DR3+ LS patient BAL cells, and autoantigens were identified (296). ATP synthase and vimentin, a cytoskeletal protein, were eluted from

HLA-DR3 in these patients, indicating that there may be a potential role for autoantigens in sarcoidosis. It should be noted, however, that both ATP synthase and vimentin are a widely expressed in normal individuals, and, in particular, epithelial cells in normal lung tissue have

41 high expression of vimentin (297). Additionally, no control BAL samples were utilized in the study. Therefore, while there is evidence for an autoimmune association with sarcoidosis, further studies are necessary to confirm the previous studies’ findings in order to determine whether the identified autoantigens are truly pathogenic in sarcoidosis.

1.12 Scope of thesis

In summary, sarcoidosis is a granulomatous disease of unknown etiology that can affect almost any organ of the body. Clues from genetic, environmental, and immunologic associations may help elucidate the putative sarcoidosis-associated antigen(s). The disease itself has a wide range of clinical manifestations and can either resolve spontaneously or become a chronic condition, making diagnosis and treatment difficult. Because of the heterogeneity of the disease, antigen discovery and the etiologic agent(s) driving disease pathogenesis have yet to be determined. As depicted in Figure 1-1, one exception to the heterogeneity of the disease is LS, in which patients present with nearly identical clinical manifestations comprised of distinct clinical symptoms (erythema nodosum, bilateral hilar adenopathy, and ankle arthritis). LS patients also frequently share the HLA-DR3 allele, and there is ample evidence that oligoclonal CD4+ TRAV12-1+ T cells traffick to the lung during active disease in these patients. Lastly, there is a greater prevalence of LS in certain geographic areas as well as seasonal clustering of the disease during the spring, indicating that common exposure to an environmental antigen may be driving disease pathogenesis.

Therefore, the work presented in this thesis will focus primarily on the homogenous LS rather than the heterogeneous non-LS patient population.

Figure 1-1: Clinical and immunological features of Löfgren’s syndrome versus sarcoidosis. Schematic showing generalized similarities and differences between δöfgren’s syndrome (LS) sarcoidosis and other (non-LS) sarcoidosis patients in terms of clinical presentation and immunologic characteristics. In summary, LS patients almost always carry the HLA-DR3 allele, exhibit specific symptoms, and recover within two years. In contrast, there is not strong evidence of a predominant HLA allele in non-LS patients, their symptoms are variable, and the disease is often recurrent. In both cases, CD4+ T cells traffic to the lung, express a variety of activation markers, and produce several inflammatory cytokines.

One major goal of current LS research in general is to determine the predominant

αTCR pairs that are accumulating in the lungs of patients during active disease. As shown in Figure 1-2, two components of the trimolecular complex are thought to be known in LS patients: HLA-DR3 and TRAV12-1. However, the associated Jα, V, D, and J chains, as well as the putative antigen(s), have yet to be elucidated. Furthermore, an unbiased approach to studying the paired αTCR repertoire in these patients has not been performed.

Consequently, the overall goals of this thesis project were to: 1. Determine the αTCR pairs in the BAL of DR3+ LS patients in an unbiased manner, 2. Establish whether there are predominant αTCR pairs that are exclusively shared amongst this patient group versus non-LS and control patients, and 3. Utilize T cell hybridomas and peptide scanning libraries

(PSLs) to determine the preferred peptides of the shared TCRs.

As depicted in Figure 1-3, an extended goal of the research project includes designing tetramers comprised of DR3 in association with the LS-associated peptides for use in the clinical setting as a diagnostic test, to assess disease severity, and/or for determination of the effectiveness of therapeutics over time.

Figure 1-2: Known and unknown parameters of the trimolecular complex in LS. Schematic depicting components comprising the recognition of the putative LS-associated antigenic peptide by BAL CD4+ T cells when presented in the context of MHC class II. Known associations in LS are boxed in black with respective designations listed outside of the boxes. Unknown parameters are boxed in red.

Figure 1-3: Thesis project overview. Schematic depicting the overall goals of the thesis project as well as the basic methodology utilized to accomplish the goals. The chapters in which the steps are discussed are indicated.

CHAPTER II

MATERIALS AND METHODS2

2.1 Patient information

Thirteen newly diagnosed sarcoidosis patients (9 LS and 4 non-LS) were included in the study (Table 2-1). All patients were diagnosed with sarcoidosis according to criteria established by the World Association of Sarcoidosis and Other Granulomatous Disorders

(72). These criteria included typical clinical and radiographic manifestations, an elevated

BAL CD4/CD8 ratio and, if required, biopsy evidence of granulomatous inflammation, as well as exclusion of other diagnoses. Informed consent was obtained from all subjects, and ethical approval was granted from the Stockholm County Regional Ethical Committee.

2.2 HLA typing Genomic DNA was extracted from whole blood samples of all patients. HLA-DRB1 and DRB3 alleles were subsequently determined by the PCR-sequence-specific primer

(PCR-SSP) technique (Olerup SSP-DR Low Resolution Kit) as previously described (298).

2.3 BAL and PBMC preparation

Bronchoscopy with BAL and PBMC isolation were performed as previously described

(274, 298). Cells for iRepertoire, emulsion, and non-emulsion analyses were thawed and washed in complete RPMI (cRPMI): RPMI (Hyclone) supplemented with 10% fetal bovine serum (FBS; Hyclone), 5% sodium pyruvate (Gibco), 5% Penicillin/Streptomycin/Glutamine

(Gibco), and 5% Hepes (Gibco). BAL cells were incubated in 6 or 12 well plates for 20

Table 2-1. Characteristics of sarcoidosis and control patients.

LS patients Non-LS patients Control patients (n = 9) (n = 4) (n = 3)

Gender (M/F) 9/0 3/1 2/1 Age (years) 36 (33-42) 31 (29-44) 66 (46-68) 1 Chest radiographic stage 0/6/3/0/0 0/0/2/0/1* N/A 0/I/II/III/IV Smoking status (non- 3/4/2 2/1/0* 0/2/1 smoker/former/current) 2 VC (% of predicted) 98.0 (77.8-102.8) 79.0 (75.5-83.0) 94.0 (91.5-96.5)** 3 DLCO (% of predicted) 102.0 (97.0-104.0) 76.0 (75.0-77.0) 87.0 (87.0-87.0)** 4 FEV1 (% of predicted) 85.0 (82.0-92.3) 68.0 (56.0-75.5) 95.0 (93.0-98.5) 6 BAL concentration (10 278.1 (198.4- 284.2 (282.3- 316.7 (249.0- cells/L) 472.3) 389.4) 704.9) % BAL macrophages 79.3 (61.5-85.4) 66.6 (48.5-72.5) 88.3 (52.3-91.0) % BAL lymphocytes 16.4 (12.0-36.5) 29.3 (24.8-47.5) 10.6 (8.2-46.4) % BAL neutrophils 1.0 (0.8-2.2) 4.1 (2.6-4.1) 0.6 (0.3-0.9) BAL CD4/CD8 ratio 13.1 (6.3-16.8) 6.0 (5.5-9.7) 1.5 (1.5-4.1) + - HLA-DRB1*03 /DRB1*03 + - DRB3*01 /DRB1*03 7/2/0 1/2/1 1/1/1 - DRB3*01 1 Chest radiography staging as follows: stage 0 = normal chest radiography, stage I = enlarged lymph nodes, stage II = enlarged lymph nodes with parenchymal infiltrates, stage III = parenchymal infiltrates without enlarged lymph nodes, and stage IV = signs of pulmonary fibrosis 2 VC = vital capacity 3 DLCO = diffusing capacity of the lung for carbon monoxide 4 FEV1 = forced expiratory volume in 1 second *Unknown for one patient **N/A for healthy control patient All percentage values are denoted as median (p25-p75) Controls include one healthy and two (non-sarcoidosis) fibrotic lung disease patients

48 minutes at 37°C to adhere alveolar macrophages. CD4+ T cells were obtained after positive selection cell sorting (Dynabead kit; Life Technologies) and incubated overnight at 37°C in c-RPMI supplemented with a low concentration (30 U/ml) of IL-2 to prevent cell death and to ensure cell viability before sorting. After 16-18 hours, the cells were aliquoted equivalently into three 1.7 ml tubes for iRepertoire, emulsion, or non-emulsion RT-PCR reactions.

2.4 RNA isolation and iRepertoire PCR

RNA was purified from CD4+ T cells according to the manufacturer’s instructions in the RNeasy Mini Kit (Qiagen). Two rounds of PCR using the iRepertoire human TCR alpha and beta kits (iRepertoire) were performed according to the manufacturer’s instructions and as described in Figure 2-1. PCR products were gel-purified and quantified using a QuBit

Fluorometer (Life Technologies).

2.5 Emulsion PCR (ePCR)

Cells for emulsion and non-emulsion reactions were washed twice in MACS buffer:

DPBS (Hyclone) with 0.5% FBS (Hyclone) and 1% HEPES (Gibco). As shown in Figure 2-2, cells were centrifuged and resuspended in 50 μl of RT-PCR master mix [1x OneTaq One-

Step RT-PCR buffer and enzyme (New England Biolabs), 10 U RNase out (Invitrogen), 2.5 mg/ml acetylated BSA (Sigma-Aldrich), 400 nM each of the C region primers and Stepout primers, and 40 nM each of the V region primers as described in Appendix A]. The emulsion cells had γ00 μl of the oil mixture added according to the specifications in the εicellula

Emulsion and Purification Kit (CHIMERx). The tubes were vortexed at 4°C for 2 minutes, and representative emulsion droplets containing cells after differing periods of vortexing are displayed in Figure 2-3. Non-emulsion cells (in 50 μl RT-PCR mix) were added to PCR tubes, and the vortexed emulsion cells (in 50 μl RT-PCR mix plus γ00 μl oil) were divided

Figure 2-1: RNA purification and iRepertoire PCR. Schematic depicting iRepertoire PCR methodology. BAL CD4+ T cells were isolated from patient samples, RNA was purified from the cells, and two rounds of PCR were performed on the RNA. Bottom pictures depicting iRepertoire PCRs were obtained from iRepertoire.com.

Figure 2-2: CD4+ T cell isolation and emulsion PCR (ePCR). Schematic depicting ePCR methodology. BAL CD4+ T cells were isolated from patient samples, cells were resuspended in RT-PCR mixture with the first set of primers, and three rounds of PCR were performed before deep sequencing and subsequent analyses. Adapted from Turchaninova, et al. Eur J Immu 2013 (299).

Figure 2-3: PBMCs encapsulated by emulsion droplets under the microscope after different vortexing time points. PBMCs were labeled with CFSE, spun, and resuspended in RT-PCR master mix. γ00 μl of the oil mixture from the εicellula kit was added on top, and the contents were vortexed for 7, 5, 2, 1, or 0.5 minutes. Images were taken at 20X.

52 into three PCR tubes. The cycling for PCR1 was as follows: 65°C – 2 min, 48°C – 40 min,

94°C – 2 min, [94°C – 30 sec, 54°C – 60 sec, 68°C – 2 min] x 35 cycles, 68°C – 10 min.

The separated triplicate emulsion samples were pooled together for purification using the εicellula Emulsion and Purification Kit (CHIεERx) according to the manufacturer’s instructions. The non-emulsion samples were purified using the GeneJET PCR Purification

Kit (Thermo Scientific).

The first nested PCR reaction (PCRβ) utilized β μl of the non-emulsion PCR1 product or β0 μl of the emulsion PCR1 product in a 100 μl reaction. The master mix included

1x Standard Taq buffer and enzyme, 10 mM dNTPs, 100 nM of each nested primer

(Appendix A), and 1.6 με blocking oligos (Appendix A). The cycling for PCR2 was as follows: 95°C – 30 s, [95°C – 30 s, 54°C – 30 s, 68°C – 1 min] x 20 cycles, 68°C – 10 min.

The second nested PCR reaction (PCRγ) utilized β μl of either non-emulsion or emulsion PCR2 product in a 50 μl reaction. The master mix included 1x LongAmp buffer and enzyme, 10 mM dNTPs, and 100 nM of each nested primer (Appendix A). The cycling for

PCR3 was as follows: 94°C – 30s, [94°C – 30 s, 53°C – 30 s, 65°C – 1 min] x 25 cycles,

65°C – 10 min.

As a control, non-emulsion reactions were performed in which all primers and regents were present, but no oil mixture was used during PCR1. This setup allowed for any

TCRα product to combine with any TCR product regardless of which CD4+ T cell each product originated. PCR products were gel-purified and quantified using a QuBit

Fluorometer (Life Technologies), and a representative ePCR gel is shown in Figure 2-4.

2.6 Single-cell PCR (scPCR)

BAL cells were thawed and placed in culture at 2.5 x 106 cells/ml at 37C overnight in standard tissue culture medium supplemented with IL-2 as described in section 2.3. As demonstrated in Figure 2-5, single cell sorting of DAPI-, CD3+, CD4+, TRAV12-1+ stained T

Figure 2-4: Representative ePCR gel. A representative 2% agarose gel displaying a DNA ladder (L) with corresponding base pair (bp) bands and the products obtained after the third round of ePCR.

Figure 2-5: CD4+ T cell sorting and single-cell PCR (scPCR). Schematic displaying scPCR methodology. BAL CD4+ TRAV12-1+ T cells were single-cell sorted by flow cytometry, RNA was purified from the cells, and cDNA was made. The cDNA was then split into two plates for PCR1, which used V gene primers for either TCRα or TCR PCR amplification. Two additional rounds of PCR were performed, with PCR3 adding barcodes for identification and pairing of each cell’s TCRα and TCR chains after deep sequencing and subsequent analyses. Adapted from Michels, et al. Diabetes 2017 (300).

55 cells was performed on a BD FACS Aria (Becton Dickinson). Cells were directly sorted as one cell/well into reverse transcription buffer in 96 well polypropylene plates. For cDNA synthesis and subsequent PCRs, cells were subjected to methodology as previously described (300). Briefly, cells were lysed and RNA reverse transcribed using a combination of random hexamers and TCRAC/BC gene specific primers. Superscript was added, and the sealed plates were incubated in an oven for 75 minutes at 55C. For each T cell, TCRA and

TCRB gene expression was separately ascertained in a series of three PCR reactions performed in 96 well PCR plates, as previously described (300). The initial PCR was a multiplex reaction (40 TRAV and 33 TRBV primers plus constant region primers, each at 0.5

με) using Advantage β polymerase mix in a 50 μl reaction volume for 35 cycles. The first round PCR products were diluted, and β.5 μl of template was added to a β5 μl second round

PCR reaction utilizing nested TCRAC and TCRBC primers on the γ’ end. The 5’ primer for the second round PCR reaction was a common sequence that was added to the 5’ end of each of the V region primers used in PCR round 1 (designated the Illumina short primer).

After 35 cycles of amplification, the products were diluted and used as template in a third round of PCR. This final amplification of 18-20 cycles incorporated barcodes on both ends of each PCR product to enable identification of sequences run on the Illumina MiSeq platform.

Thus, unique pairs of forward and reverse primers were added to each well of a 96 well plate that specified each T cell by its position on the plate. PCR products were gel-purified and quantified using a QuBit Fluorometer (Life Technologies), and representative scPCR gels are shown in Figure 2-6.

2.7 Illumina high-throughput sequencing

PCR products were gel-purified and quantified using a QuBit Fluorometer (Life

Technologies). Samples were pooled separately for each application (iRepertoire, ePCR, or scPCR) and sequenced on a Mi-Seq instrument (Illumina) for either paired-end 2 x 250

Figure 2-6: Representative scPCR gels. Representative 2% agarose gels displaying DNA ladders on each end and TCRα (A) or TCR (B) products obtained after the third round of scPCR.

(ePCR and scPCR) or paired-end 2 x 150 (iRepertoire) reading. The TCR sequences reported in this thesis are listed according to IMGT nomenclature and have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo, under accession number GSE1005558.

2.8 Bioinformatic processing

The open-source Galaxy Platform (usegalaxy.org) was utilized to process the raw sequencing files, as outlined below and as previously described (301). For each of the iRepertoire or scPCR samples, the raw read 1 and read 2 sequences were paired in the

Galaxy using the Pear function, and the samples were then split into individual files via the

Barcode Splitter function and utilized for MiTCR identification (see below). The emulsion and non-emulsion read 1 and read β sequences were paired at the γ’ end in Galaxy using the

FastQ Joiner function to generate 500 nucleotide-length products with small portions of the

C region at either end of the products. The products were trimmed using the Trim

Sequences function in Galaxy to create an alpha read and a beta read, which were both exported and utilized for TCR identification and pairing. The TCR identification for all methods was performed using MiTCR (github.com/milaboratory/mitcr) and the command prompt instructions given by the developer. MiTCR is an open-source bioinformatics software that identifies TCR V, D, and J gene segment usage (IMGT nomenclature; www.imgt.org) and extracts the CDR3 sequence for each sequencing read. For the emulsion and non-emulsion samples, the alpha and beta chains were paired based on identical cluster identifications (cluster IDs) using MiTCRCombine as described previously

(301). The results for all samples were analyzed further in Microsoft Excel. A cutoff of 10 reads was utilized for all samples, (i.e., any sequence with a read count of ≤9 was not included in any of the analyses).

iRweb (iRepertoire.com) was utilized to calculate the number of CDR3s shared amongst patient groups and for determining D50 values. CDR3 sharing was calculated by the CDR3 Algebra function from iRweb. Sequences found in any other patient group were excluded. Thus, the numbers reflect only those CDR3 sequences shared exclusively in the patients indicated. CDR3 sequences were required to be identical (but with no restrictions on V or J usage) and must have been found in ≥β patients in the given group. For D50, the number of unique CDR3s in a given sample was determined, and the following formula was used: D50 = (# of unique CDR3s that make up 50% of the total reads * 100) / # of unique

CDR3s.

2.9 CDR3 motif criteria for DR3+ LS patients

CDR3 motifs were found amongst DR3+ LS patients by utilizing a defined set of criteria. First, the motifs were required to be expanded (i.e., present at ≥10 reads in each patient as per the read cutoff defined above and encoded by ≥β unique nucleotide variants).

The sequences were then required to share an identical V region and have the same CDR3 length. Identical J usage was prioritized for determining whether two sequences were highly related, but sequences with different J usage were considered part of the same motif if the above stipulations were met. Finally, any sequences meeting all of the above criteria that were also found in control subjects were excluded.

2.10 Hybridoma transfection/transduction

As depicted in Figure 2-7, TCRs of interest originated from patient BAL cells, and generation of hybridoma lines were performed as previously described, but with the designated changes described below (302–304). Once target TCRs were identified, RT-

PCR was performed on the RNA or synthetic DNA (gBlocks) were obtained that consisted of sequences for TCRα, TCR, or the two chains linked by a βA peptide. Primers for

Figure 2-7: Murine T cell hybridomas. Construction of murine T cell hybridomas after sequencing TCRs from BAL CD4+ T cells isolated from sarcoidosis patients. First, TCRA and TCRB gene products or gBlock fragments (either separate or linked together with a 2A peptide) are ligated into retroviral expression vectors. Phoenix cells are then transfected with the vectors, and the cells produce supernatants containing the retrovirus (not shown). TCR negative murine hybridoma cells are transduced with the supernatants to form the cells expressing human TCR variable chains attached to murine constant regions.

60 amplification of gBlocks are listed in Table 2-2. All products and murine stem cell virus

(MSCV) vectors were digested with appropriate restriction enzymes (NEB) overnight at 37°C according to the manufacturer’s instructions. Digested products were heat inactivated and gel purified. δigation reactions (10 μl) were performed for MSCV plus TCR chains using the

T4 ligation kit (NEB) according to the manufacturer’s instructions, either separately or together (if connected by the 2A peptide). DH5α max efficiency cells (50 μl) were transformed with the ligated products (1 μl) according to the manufacturer’s instructions.

Colonies were picked the following day for growth overnight (~18 hours), and mini preps

(Qiagen) were performed for each colony. Sequences were confirmed via standard Sanger sequencing (QuintaraBio) using primers listed in Table 2-2.

Phoenix cells were plated in Iscove’s modified Dulbecco’s medium (IMDM, Thermo

Scientific), supplemented with 1% sodium pyruvate (Thermo Scientific), 1%

Penicillin/Streptomycin (Thermo Scientific), and 10% FBS at 225,000 cells/ml (2 ml) in 6 well plates pre-coated with 100 μg/ml poly-D-lysine (Sigma-Aldrich). At ~18 hours (day 2), media was removed, cells were washed with DPBS (Thermo Scientific), and DNA was added with

δipofectamine β000 reagent (Thermo Scientific) according to the manufacturer’s instructions. Serum was added 4 hours later for a final concentration of 10%. Media was replaced (10% serum) on day 3. On day 4, supernatants were collected, spun, and filtered through a 0.45 μm tip. Supernatants were frozen at -20°C for future use or used immediately.

Supernatants were used for spin-fection transfection of murine hybridoma cells

(54ZC, 5KC-hu CD4, 5KC-no CD4, 5415, or MN279, all originally TCR-). The hu CD4 line expresses human CD4, the no CD4 line does not express human CD4, the 5415 line has a mutated human CD4 that better associates with the TCR, and the MN279 line has the mutated CD4 plus multiple copies of murine CD3 complex. 100,000 hybridoma cells were spun for 90 minutes at 2000 rpm with 1 ml supernatant (500 μl TCRα + 500 μl TCR, or 1 ml

Table 2-2. Hybridoma primers. Name For/Rev1 Sequence Source2 XhoI gBlock F TAC GCG CTC GAG ACC ACC ATG IDT MBC1 gBlock R TTG GGT GGA GTC ACA TTT CTC AG IDT CGC GCT CGA GAC CAC CAT GAG GCT GGT GGC Dβα F IDT AAG AGT A Dβα R ACA CAG CAG GTT CCG GAT TCT GGA TG IDT CGC GGT CGA CAC CAC CAT GGG CAC CAG GCT Dβ F IDT CCT CTT C Dβ R TTG GGT GGA GTC ACA TTT C IDT MSCV F CTT GAA CCT CCT CGT TCG A IDT M13 (MSCV) R CAG GAA ACA GCT ATG AC IDT

1 Forward or reverse primer 2IDT = Integrated DNA Technologies

αTCR supernatant if linked by the βA peptide) and 15 μg/ml polybrene (Millipore). Cells were then resuspended in fresh IMDM (10% serum) for culture. At 72 hours, cells were checked by flow cytometry for TCR expression and flow sorted to obtain lines that were

≥95% TCR+.

Agarose gels depicting DNA products purified for assembling two hybridomas (the S-

1 sarcoidosis and the D2 positive control) are shown in Figures 2-8 and 2-9, respectively, with flow cytometry plots confirming expression of the TCRs on the transduced hybridoma lines.

2.11 Flow cytometry

Hybridoma cells were stained with the following antibodies: TCR-PE (BD

Biosciences) and CD4-APC (Invitrogen). The hybridoma cells were subsequently gated on

FSC/SSC (lymphocytes), Vector+ (GFP), TCR+ (PE), and then CD4+ (APC) if applicable.

BAL cells for scPCR were stained with the following antibodies: TRAV12-1-FITC (Endogen),

CD3-APC Cy7 (BioLegend), CD4-PerCP Cy5.5 (BioLegend), and DAPI (BD Biosciences).

Cells for scPCR were sorted on FSC/SSC (lymphocytes), DAPI-/CD3+, and then

CD4+/TRAV12-1+. Flow cytometry was done on a FACS Canto II, cell sorting was performed on a FACS Aria, and analyses were performed using FlowJo V10.

2.12 Peptide stimulation of hybridomas

DAP3 fibroblast cells were transduced with either HLA-DR3 or HLA-DQ2 and used as antigen presenting cells (APCs). Unless otherwise indicated, the following was put into each well of a 96-well plate: 1e5 APCs, 1e5 hybridoma cells, and peptide or peptide mixtures (0.1 ng/ml – β00 μg/ml). Plates were incubated overnight at 37°C in 5% CO2.

Murine IL-2 in the supernatant was measured by the “Ready-SET-Go!” ELISA kit

Figure 2-8: Generation of the S-1 hybridoma. (A) PCR amplification of DNA gBlocks for TCRα and TCR chains of S-1. (B) Digestion of εSCV vectors p18β9 (α) and 18γ0 (). TCRα was column-purified after digestion (not shown), and TCR digestions are displayed. (C) Digest screens of ligated TCRα/p18β9 and TCR/p18γ0. Red boxes indicate digested TCRα or TCR products for ligation, blue boxes indicate digested εSCV vectors for ligation, and white boxes indicate a representative sample possessing the correct sequences for insert + vector. (D) Plots depicting S-1 TCRs after transduction into the 5KC murine cell lines with or without human CD4.

Figure 2-9: Generation of the D2 hybridoma. (A) PCR amplification of DNA gBlocks for TCRα and TCR chains of Dβ. (B) Digestion of TCRα, TCR, and εSCV vectors p18β9 (α) and 1830 (). (C) Digest screens of ligated TCRα/p18β9 and TCR/p18γ0. Red boxes indicate digested TCRα or TCR products for ligation, blue boxes indicate digested εSCV vectors for ligation, and white boxes indicate a representative sample possessing the correct sequences for insert + vector. (D) Plots depicting D2 TCRs after transduction into the 5KC murine cell lines with or without human CD4.

(eBioscience). Supernatants were incubated for 2 hours at RT for the ELISA unless otherwise indicated.

2.13 Peptide scanning libraries (PSLs) for mimotope identification

As displayed in Figure 2-10, the decapeptide PSL consists of 10 sublibraries, each of which has one position of the decapeptide fixed as a given amino acid, with all other positions being randomized to any amino acid. Sublibrary 1 is shown as an example, in which the first position of the decapeptide is fixed with each of the 20 amino acids, giving 20 samples comprising sublibrary 1. Therefore, there are 200 samples tested in total for the first round of the PSL screening. Hybridomas were stimulated as described in 2.12 above.

However, optimization of the PSL protocol and delineations from the methods listed in 2.12 are outlined in Appendix B.

Biometrical analyses score the peptides so that numerical values are assigned to each amino acid at every position of the 10-mer based on the logarithm of the IL-2 (pg/ml) when the given mixture is present. The projected ability of a given peptide to stimulate the

TCR is calculated by adding all of the values associated with each amino acid at every position of the 10-mer. When the maximal stimulatory amino acid values are added for each of the positions of a given 10-mer, that value is used to identify and rank any peptides found in a UniProt database, as previously described (207, 302, 305–307).

2.14 Statistical analyses

Two-way ANOVA with Dunnett’s multiple comparisons test correction was performed on iRepertoire V usage data. Two-tailed unpaired t tests were utilized for D50 comparisons,

PBMC versus BAL, and chest x-ray stage comparisons. Pearson scores were determined for correlations between TCR V region usages and BAL CD4/CD8 ratios. Kruskal-Wallis test with Dunn’s post-test was performed for TRAV12-1/TRBV2 and TRAV26-1/TRBV20-1

Figure 2-10: Peptide scanning libraries (PSLs). Schematic displaying how PSLs are constructed. A decapeptide PSL contains 10 sublibraries, each having one position of the decapeptide fixed with a particular amino acid and the other remaining positions randomized. Within each sublibrary, there are 20 samples to be tested, as there are 20 amino acids that can be used in the fixed position, as shown on the right for Sublibrary 1.

67 preferential pairing testing. For analysis of preferential pairing between TRAV12-1/TRBV2 and TRAV26-1/TRBV20-1, the following was calculated: P(A) = % pairs using TRAV12-1 or

TRAV26-1 in a sample, P(B) = % pairs using TRBV2 or TRBV20-1 in a sample, and P(AB) =

% pairs using both TRAV12-1/TRBV2 or TRAV26-1/TRBV20-1 in a sample. If the chains were preferentially pairing, P(AB) > P(A) * P(B); if the chains were pairing by chance, P(AB)

= P(A) * P(B). Therefore, to test for preferential pairing, a one-sample t-test was utilized to calculate whether P(AB) – P(A) * P(B) was significantly different than 0 when all 6 DR3+ LS patients were included. GraphPad Prism v6.07 was utilized for all statistical analyses. A p value < 0.05 defined statistical significance.

CHAPTER III

3 TCR α AND β CHAIN USAGE IN THE BAL OF SARCOIDOSIS PATIENTS

3.1 Identifying TCRα and TCRβ chains in sarcoidosis patient samples

Several methodologies have been established over the last few years that have greatly enhanced the ability to identify TCRα, TCR, and paired αTCR sequences from bulk sample and single-cell suspensions. As reviewed in (308), paired TCRs have been identified from type 1 diabetes mellitus, Epstein-Barr virus, multiple sclerosis, and psoriasis human patient samples, among other diseases. However, in the case of identifying TCRs in sarcoidosis, many of these methodologies have not yet been applied. There are several instances of specific antibodies for TCR Vα and V chains used to successfully to identify V region usage on bulk cell samples from sarcoidosis patients (221, 264–266, 268–270, 274).

Additionally, PCR analyses of bulk cell populations using specific V region primers have been performed on sarcoidosis patient samples (265, 266, 270). As discussed in section

1.10, from these and other studies, the predominant TCRα chain identified as being expanded in the BAL of DR3+ LS patients is TRAV12-1 (Vα2.3) (221, 271). Importantly, the

TRAV12-1-bearing CD4+ T cells disappear from the lungs upon disease resolution, indicating their involvement in disease pathogenesis and/or resolution (238, 268). Previous work has shown that TRAV12-1-expressing CD4+ T cells also accumulate in the lungs of

DRB3+ LS patients (221, 271).

Compared to the strong association between TRAV12-1 and LS, identification of shared TCR chains amongst sarcoidosis patients has been less consistent. TCR chains expanded and associated either with LS or sarcoidosis have included TRBV5 (V5),

______

TRBVβ4 (V15), and TRBV18 (V18) (265), as well as TRBV5-5 (V5-3) (269), TRBV12

(V8) (266, 274), and TRBV2 (Vββ) (274). These particular TCR chains have been previously demonstrated to be expanded in differing sarcoidosis patient populations, as discussed in section 1.10.

Identifying shared V region usage for TCRα and TCR in sarcoidosis patients has been instrumental in coming one step closer to determining the antigens for which the TCRs are specific. However, as discussed in section 1.10, the CDR3 regions of the TCR encompassing the V(D)J segments of both chains are directly associated with the pMHC

(258–262). As such, CDR3 sequences for both chains are invaluable in determining shared specificity between sets of TCRs as well as for assessing repertoire diversity of a given sample. Although limited sets of data have been published on several different CD4+ TCRα and TCR sequences found in LS and non-LS patient samples, no study has used deep sequencing approaches and been able to identify shared and consensus CDR3 motifs for either chain found in multiple patients. Additionally, no study has assessed αTCR pairing in sarcoidosis patient samples.

3.2 iRepertoire PCR to identify TCRα and TCRβ chain usage

One method for determining αTCR usage is iRepertoire PCR, wherein multiplex

PCRs separately amplify all Vα or V regions of the TCR for a given sample. As described in section 2.4 and displayed in Figure 2-1, iRepertoire is a commercially-available kit that supplies barcoded primers for all possible Vα and V chains. For these analyses, BAL cells and PBMCs were collected from LS, non-LS, and control subjects (Table 2-1), and two rounds of iRepertoire PCR were performed on RNA from sorted CD4+ T cells. In this manner, semi-quantitative analyses of TCR usage was assessed for each chain separately.

+ 3.3 TCRα and TCRβ expression on blood CD4 T cells

PBMCs were available from a subset of patients, and CD4+ T cells were isolated from the samples for evaluation of TCR V usage by iRepertoire PCR. Regardless of diagnosis, no significant TCRα (Figure γ-1A) or TCR (Figure γ-1B) was expanded in any

PBMC sample. Most TRAV and TRBV were expressed on ≤5% of all CD4+ T cells, with only a few being expressed on up to ~20%. All samples had a relatively slightly higher percentage of CD4+ T cells expressing TRAV26-1 and TRBV20-1, which may have been due to common exposures or vaccines. Overall, although all possible TCRα and TCR V regions were detected after assessing the PBMC samples, no V region was utilized to a greater extent in one patient group versus another in the blood. These results indicate that iRepertoire PCR allows for an unbiased assessment of the TCR repertoire in a human patient sample and does not overtly overamplify any given V region. However, one major caveat to using iRepertoire PCR is that no αTCR pairing information is available. For example, although a particular TCRα chain may be the most highly expressed in a given sample, one cannot assume that it is paired with the top TCR chain in the same sample at a high frequency.

3.4 Relative library and CDR3 diversity in CD4+ T cells from DR3+ LS, sarcoidosis,

and control patients

CD4+ T cells traffick to the lungs of sarcoidosis patients during active disease, presumably in response to the putative sarcoidosis-associated antigen. Therefore, the diversity of the TCRs on the accumulated BAL cells was of interest. In order to determine the relative diversity of the TCR sequences within each patient, D50 values were calculated for each patient sample. D50 measures the relative diversity of any given sample, and the more diverse a sample, the closer the value will be to 50. For all PBMC samples (regardless

+ Figure 3-1: TCRα and TCRβ usage in sarcoidosis and control patient blood CD4 T cells. Usage of TCR Vα (A) or V (B) segments in four patient groups is shown, with δS patients divided by HLA genotype (HLA-DR3+ or HLA-DRB3+). Bottom graphs display TCRα (A) and TCR (B) chains comprising <5% of PBMC CD4+ T cells in all patient groups. Numbers in parentheses indicate the number of patients in each group. Bars represent means.

72 of diagnosis), the TCR sequences expressed on CD4+ T cells were significantly more diverse compared to those TCRs expressed on BAL CD4+ T cells of DR3+ LS patients (D50 range, 5.3 – 21.1 and 0.7 – 6.4, respectively, p = 0.005 for TCRα) (D50 range, γ.4 – 24.4 and 0.8 – 5.4, respectively, p = 0.0β5 for TCR) (Figure 3-2). Similarly, sarcoidosis patients that were not DR3+ LS patients also had significantly less diverse sequences in their BAL than the PBMC samples (1.9 – 11.8 and 1.5 – 5.1, TCRα and TCR respectively, p < 0.05 for both versus PBMC) (Figure 3-2). BAδ from two fibrosis patients also had lower TCRα and TCR diversity compared PBεCs (data not shown).

To analyze the extent of CDR3 homology within subject groups, a CDR3 sharing metric from iRweb (iRepertoire.com) was used. This tool does not take into account V or J usage but rather identifies identical CDR3 motifs in any given patient group. As seen in

Figure 3-3A, the CD4+ T cell compartment in the BAL of DR3+ LS and sarcoidosis patients had high numbers of CDRγα chains exclusively present in each group when assessing sequences shared in ≥β patients within the group (801 and 677 CDRγs, respectively).

However, BAL CD4+ T cells from DR3+ δS patients shared more identical CDRγα chains than sarcoidosis patients when the number of patients sharing the CDR3s was increased to

≥γ, ≥4, or ≥5 patients (Figure 3-3A, right). Similarly, a smaller number of CDRγs were exclusively shared in DR3+ LS or sarcoidosis patients (277 and 227, respectively) (Figure 3-

3B). As was the case with CDRγαs, more identical CDRγs were shared in ≥γ, ≥4, and ≥5

DR3+ LS patients than sarcoidosis patients (Figure 3-3B, right). In fact, no CDRγs were shared in ≥4 sarcoidosis patients.

Overall, these data suggest that BAL CD4+ T cells from DR3+ LS patients have a more restricted TCR repertoire compared to those cells in the circulation and that CD4+ T cells in the BAL of these patients contain a higher proportion of shared CDRγα and CDRγ sequences. It may not be unexpected that the BAL repertoire was less diverse than that of

Figure 3-2: Relative diversity of BAL CD4+ TCR sequences as determined by iRepertoire PCR. D50 values, measuring sequence diversity (with numbers closer to 50 indicating more diverse samples), were calculated for each patient’s TCRα (A) and TCR (B) sequences derived from BAL CD4+ T cells. CD4+ T cells isolated from PBMCs were obtained from a subset of control and sarcoidosis/LS patients and used as a comparison. Lines indicate means ± SD. p values were calculated based on two-tailed unpaired t tests; ** = p < 0.01, * = p < 0.05.

Figure 3-3: CDR3 sharing amongst DR3+ LS, sarcoidosis, and control patient BAL CD4+ T cells. The number of identical BAL CD4+ T cell CDR3s shared in at least 2 patients exclusively within each patient group is depicted in the Venn diagrams for TCRα (A) and TCR (B). The bar graphs display the number of identical CDRγs shared in more than β, γ, 4, and 5 DR3+ LS or other sarcoidosis patients.

75 the blood, as the lungs are frequently exposed to numerous antigens via inhalation. This exposure in the lungs would conceivably restrict the BAL TCR repertoire compared to the blood, where fewer antigens are present at any given time. However, the finding that there was a more restricted TCR repertoire in DR3+ LS patients versus other sarcoidosis patients was intriguing, as it suggests that DR3+ LS patients may share a common antigenic exposure that is driving an expansion of TCRs with shared CDR3 regions. As such, the shared TCRα and TCR chains were assessed further and will be discussed in the next section.

+ 3.5 BAL CD4 TCRα usage in sarcoidosis patients

Although CDR3 homology can indicate similar antigen specificity of TCRs (303, 304),

V region usage is an important aspect of T cell specificity as well. Therefore, V region usage was assessed on BAL CD4+ T cells in an unbiased fashion at the gene level to determine whether TCRs expressed on BAL CD4+ T cells of DR3+ LS patients also shared V region homology. TRAV12-1 expansions have previously been associated with DR3+ LS patients

(221, 238, 268, 269), and after determining overall TRAV usage in each patient group, significantly increased expression of TRAV12-1 was observed in DR3+ LS patients versus control patients (17.3% ± 2.5 vs. 2.5% ± 0.6, p < 0.001; Figure 3-4). Two DRB3+ LS patients also had an increased percentage of BAL CD4+ T cells expressing TRAV12-1. Non-LS sarcoidosis patients did not have significantly altered TRAV12-1 expression on BAL CD4+ T cells, and no other TRAV expansion was seen for any patient group versus control subjects

(Figure 3-4). However, as was seen in the blood, all patients (regardless of HLA genotype or diagnosis) had greater than 10% of the BAL CD4+ T cells expressing TRAV26-1.

As TRAV12-1 expansions were seen in the BAL of DR3+ LS patients, these expansions were evaluated to determine if they were significant over blood values. For these evaluations, LS and non-LS patients were separated, since previous work has

+ Figure 3-4: TCRα usage in sarcoidosis and control patient BAL CD4 T cells. Usage of TCR Vα segments in four patient groups is shown, with δS patients divided by HδA genotype (HLA-DR3+ or HLA-DRB3+). Bottom graph displays TCRα chains expressed by <5% of BAL CD4+ T cells in all patient groups. Numbers in parentheses indicate the number of patients in each group. Bars represent means ± SD. p values were calculated based on two-way ANOVA calculations versus the control patient samples with correction for multiple comparisons; **** = p < 0.0001.

77 demonstrated a strong correlation between LS and the expression of TRAV12-1 in the BAL

(221, 238, 268, 269, 272, 274). In DR3+/DRB3+ LS patients, TRAV12-1 expansions were approximately 7.5-fold higher than in blood (Figure 3-5). Additionally, non-LS sarcoidosis and patients with other lung diseases did not have expansions of TRAV12-1 above blood levels (Figure 3-5). These data support the hypothesis that CD4+ T cells utilizing TRAV12-1 are actively recruited to the BAL of DR3+/DRB3+ LS patients. Based on the selective expansion of TRAV12-1 in the BAL of DR3+ LS patients, it was determined whether expansions of CD4+ TRAV12-1+ T cells in the BAL of DR3+ LS patients correlated with disease characteristics. Expansions were compared to BAL CD4/CD8 ratio, age, and percentage of BAL lymphocytes. A significant correlation was found between TRAV12-1 usage and CD4/CD8 ratio in DR3+ LS patients (r2 = 0.856; Figure 3-6, left). There was no significant correlation between TRAV12-1 in non-LS and control patients and CD4/CD8 ratio

(r2 = 0.002; Figure 3-6, right). No correlations were found between TRAV12-1 and either age or percentage of BAL lymphocytes (data not shown).

Disease severity of sarcoidosis is categorized by chest radiographic staging at the time of diagnosis, and the samples utilized for this study were obtained from patients with differing stages of disease (Table 2-1). Although there was a significant difference in

TRAV12-1 for both stage I and stage II DR3+ LS patients versus controls, there was no significant difference between the two stages amongst these patients (Figure 3-7, left). No differences were seen for non-LS patients in regard to TRAV12-1 usage versus control patients for any stage or between stages (Figure 3-7, right).

+ 3.6 BAL CD4 TCRβ usage in sarcoidosis patients

Significantly increased expression of TRBV2 on BAL CD4+ T cells was exclusively seen in DR3+ LS patients relative to controls (12.8% ± 1.1 vs. 2.9% ± 0.7, p < 0.001; Figure

Figure 3-5: TRAV12-1 usage in CD4+ T cells isolated from BAL versus blood. (A) TRAV12-1 usage as a percentage of all CD4+ T cells in LS patients (left) and non-LS/control patients (right) is shown in BAL versus PBMCs. (B) Percentages for BAL cells were normalized to PBMC percentages, and fold change was determined for TRAV12-1 in LS and non-LS/control patients. Bars represent means ± SD. p values were calculated based on two-tailed unpaired t tests; ** = p < 0.01.

Figure 3-6: Correlation between TRAV12-1 and CD4/CD8 ratio in sarcoidosis patient BAL. TRAV12-1 usage was plotted versus BAL CD4/CD8 ratio in DR3+ LS patients (left) and other sarcoidosis patients and controls (right). p values were calculated based on Pearson scores.

Figure 3-7: TRAV12-1 expression in sarcoidosis patient BAL separated by chest x-ray stage. Patients were separated based on chest x-ray stages, and the percentage of CD4+ T cells in the BAL utilizing TRAV12-1 in DR3+ LS patients (left) and other sarcoidosis patients (right) were compared to control patient samples. Bars represent means ± SD. p values were calculated based on two-tailed unpaired t tests; *** = p < 0.001, * = p < 0.05.

3-8), as has been previously reported (272, 274). DRB3+ LS patients did not have increased expression of TRBV2 on CD4+ T cells in the BAL (Figure 3-8). Significant CD4+ T cell expansions utilizing TRBV10-3, TRBV24-1, TRBV5-1, and TRBV7-3 were also seen in DR3+

LS patients (Figure 3-8, bottom); however, these expansions comprised a small percentage of all CD4+ T cells in the BAδ (mean values were ≤β.5% of all BAL CD4+ cells). As was seen in the blood, all patients (regardless of HLA genotype or diagnosis) had greater than 10% of the BAL CD4+ T cells expressing TRBV20-1.

Although DR3+ LS patients had significant expansions of TRBV2 in the BAL as shown in Figure 3-8, TRBV2 expression in the BAL of DR3+ LS and DRB3+ LS patients was not significantly different compared to blood when the two groups were combined (Figure 3-

9, left). Additionally, non-LS sarcoidosis and patients with other lung diseases did not have expansions of TRBV2 above blood levels (Figure 3-9, right).

As was performed with TRAV12-1 expression, TRBV2 usage was compared to BAL

CD4/CD8 ratio, age, and percentage of BAL lymphocytes. No correlations were seen between TRBV2 and age or percentage of lymphocytes (data not shown). TRBV2 did not correlate with CD4/CD8 ratio in any patient group (r2 = 0.004 LS and 0.026 non-LS/control;

Figure 3-10), as was seen for TRAV12-1 in DR3+ LS patients.

Similar to what was seen with TRAV12-1, although there was a significant difference in TRBV2 usage for both stage I and stage II DR3+ LS patients versus controls, there was no significant difference between the two stages amongst these patients (Figure 3-11, left).

Again, no differences were seen for non-LS patients in regard to TRBV2 usage versus control patients for any stage or between stages (Figure 3-11, right).

+ Figure 3-8: TCRβ usage in sarcoidosis and control patient BAL CD4 T cells. Usage of TCR V segments in four patient groups is shown, with LS patients divided by HLA genotype (HLA-DR3+ or HLA-DRB3+). Bottom graph displays TCR chains expressed by <5% of BAL CD4+ T cells in all patient groups. Numbers in parentheses indicate the number of patients in each group. Bars represent means ± SD. p values were calculated based on two-way ANOVA calculations versus the control patient samples with correction for multiple comparisons; **** = p < 0.0001, *** = p < 0.001, ** = p < 0.01, * = p < 0.05.

Figure 3-9: TRBV2 usage in CD4+ T cells isolated from BAL versus blood. (A) TRBV2 usage as a percentage of all CD4+ T cells in LS patients (left) and non-LS/control patients (right) is shown in BAL versus PBMCs. (B) Percentages for BAL cells were normalized to PBMC percentages, and fold change was determined for TRBV2 in LS and non-LS/control patients. Bars represent means ± SD. p values were calculated based on two-tailed unpaired t tests.

Figure 3-10: Correlation between TRBV2 and CD4/CD8 ratio in sarcoidosis patient BAL. TRBV2 usage was plotted versus BAL CD4/CD8 ratio in DR3+ LS patients (left) and other sarcoidosis patients and controls (right). p values were calculated based on Pearson scores.

Figure 3-11: TRBV2 expression in sarcoidosis patient BAL separated by chest x-ray stage. Patients were separated based on chest x-ray stages, and the percentage of CD4+ T cells in the BAL utilizing TRBV2 in DR3+ LS patients (left) and other sarcoidosis patients (right) were compared to control patient samples. Bars represent means ± SD. p values were calculated based on two-tailed unpaired t tests; **** = p < 0.0001, *** = p < 0.001.

+ 3.7 CDR3α and CDR3β motifs shared by multiple LS patient BAL CD4 T cells

Despite the presence of shared CDR3s in each patient group (Figure 3-3), only DR3+

LS patients had skewing of the TCR repertoire with expansions of BAL CD4+ T cells expressing TRAV12-1 and TRBV2. Therefore, TRAV12-1 and TRBV2 sequences were interrogated further in these patients to identify disease-specific, shared CDR3 motifs (as defined in section 2.9). Several similar and identical TRAV12-1 CDR3 motifs utilizing

TRAJ42 were expanded in DR3+ LS patients (Figure 3-12A). For example, in Patients 1079 and 1088, 13 and 14 different nucleotide combinations were utilized to generate the CVV-

PR-GGSQGNLIF sequence, respectively. Interestingly, with only one exception (a single sequence in Patient 1229, a healthy DR3+ individual), these TCRα sequences were not present in BAL CD4+ T cells isolated from DR3-/DRB3- sarcoidosis patients or other control subjects. Figure 3-13 shows a detailed view of the nucleotides used to generate the amino acids of three different TRAV12-1 CDR3 motifs, using the sequences from Patient 1088 as a reference. Similar results were found for each of the motifs shown in Figure 3-12A (Figure 3-

13, and data not shown). For reference, the germline encoded nucleotides are shown for

TRAV12-1 and TRAJ42 in Figure 3-13D. The sequence diversity occurred in the N region,

N-terminal V region, and C-terminal J region, suggesting that the nucleotide differences seen in multiple LS patients were due to antigen selection and precluding the possibility of

PCR contamination or artifact.

When searching for shared TRBV2 motifs within sarcoidosis patients, identical CDR3 motifs in multiple DR3+ LS patients were less common than those seen for TRAV12-1

(Figure 3-12B). Although the TRBV2 sequences of interest were not found in DR3-/DRB3- sarcoidosis, control, and even non-LS patients, the expansions were expressed at lower frequencies at the nucleotide level relative to those seen in the TRAV12-1 motifs. However, there were several CDRγ sequences that were closely related and exclusively found in the

DR3+ LS patients.

Figure 3-12: Shared TRAV12-1 and TRBV2 CDR3 motifs in sarcoidosis patients. A subset of the TRAV12-1 (A) and TRBV2 (B) TCR CDR3 motifs that were shared and expanded in LS and non-δS patients versus control (Other and Healthy) patients is shown. Orange (α) or blue () color indicates that the sequence was present in the patient sample, whereas gray indicates that the sequence was not found in the patient sample. Numbers indicate the unique nucleotide variants encoding the amino acid sequence in the specified patient, with darker colors representing higher numbers of nucleotide variants in a patient sample.

Figure 3-13: Oligoclonal expansions of TRAV12-1 sequences expressed in DR3+ LS patients. Shared and expanded CDR3 motifs in CD4+ T cells purified from the BAL of DR3+ LS patients were determined after iRepertoire PCR and deep sequencing. The three most expanded motifs that were shared amongst DR3+ LS patients are shown at the nucleotide level below the deduced amino acid sequences (A, B, C). Variants found in patient 1088 are shown, and patients with identical sequences are listed in the far right column. Nucleotides highlighted in blue, red, and black are encoded by TRAV12-1, TRAJ42, and non-template bases, respectively. Germline-encoded nucleotides are shown as a reference (D).

When examining all identical and highly related TRAV12-1 CDRγα and TRBVβ

CDRγ motifs exclusively expanded in BAL CD4+ T cells of DR3+ LS patients, two consensus CDRγα motifs were identified using an online sequence alignment tool (309):

CVV-PR-GGSQGNLIF and CVV-NRR-GGSQGNLIF (Figure 3-14A). All sequences in the motif utilized TRAV12-1 (CVV) and TRAJ42 (GGSQGNLIF); however, all sequences had 2 or 3 n or p nucleotide additions and all deleted both the germline asparagine (N) of TRAV12-

1 and the tyrosine (Y) of TRAJ4β. Additionally, two consensus CDRγ motifs were deduced from the shared TRBV2 sequences in DR3+ LS patients: CASS-EQGR-EEQFF and CASS-

EQGGR-ETQYF (Figure 3-14B). Sequences in the TCR motif all shared TRBVβ (CASSE) usage, but there were several sequences with a deletion of the glutamic acid (E) of TRBV2 and a utilization of a glycine (G) at that position. Moreover, most of the TRBV2 motif sequences shared TRBV2-5 (ETQYF) usage, but many of the sequences also had insertions and deletions in the J region, sometimes corresponding to differing J usage. All sequences comprising the TRBV2 motifs had either 3 or 4 n or p nucleotide additions between the V and J regions.

3.8 Summary

Overall, no TRAV or TRBV segments were found to be expanded in the blood when assessed by iRepertoire PCR, and the TCR diversity of the blood CD4+ T cells was higher than what was seen for sarcoidosis and LS BAL CD4+ T cells. Both DR3+ LS and sarcoidosis patient groups each shared a larger number of CDRγα and CDRγ sequences in their BAL than control patients when the cutoff was 2 patients. Furthermore, DR3+ LS patients had more shared sequences than sarcoidosis patients when the cutoff was raised to 3, 4, or 5 patients. BAL CD4+ T cells expressing either TRAV12-1 or TRBV2 were expanded DR3+ LS patients only, and several CDRγα and CDRγ motifs were shared in the

Figure 3-14: Consensus BAL CD4+ T cell CDR3 motifs shared by DR3+ LS patients as determined by iRepertoire PCR. Graphical representations of CDRγα (A) or CDRγ (B) motifs shared amongst DR3+ LS patients are shown after utilizing a web-based sequence alignment tool (weblogo.berkeley.edu). Amino acids are color-coded based on properties, and black color designates the conserved C-terminal CVV and CASS, as well as the N- terminal F, amino acids. Letter size reflects frequency of appearance of a specific residue at a certain position.

91 majority of the DR3+ δS patients, including consensus CDRγα motifs (CVV-PR-

GGSQGNLIF, CVV-NRR-GGSQGNδIF) and CDRγ motifs (CASS-EQGR-EEQFF, CASS-

EQGGR-ETQYF).

CDRγα motifs have previously been identified in BAδ CD4+ T cells from sarcoidosis patients, some of which resembled the CVVN-RY-GGSQGNLIF and CVV-IG(X)-

GGSQGNLIF sequences identified here in multiple DR3+ LS patients (Figure 3-12A), including CVV-DY-GGSQGN (270) and CVV-IGS-GGSQGNLIF (272). TCR sequences derived from skin biopsies of Kveim reactions (273) and from BAL CD4+ T cells after IL-2 culture (266) have not revealed shared or consensus CDRγ motifs. However, a previous study showed several TRBV2 motifs in the BAL of DR3+ LS patients (272), and while the

CDRγ motifs were fairly heterogeneous amongst patients, two individual sequences in that study (CASS-EQGR-GETQYF and CASS-GPGGR-TEAFF) were identical to or resembled the consensus TRBV2 sequences identified in multiple patients in the present study via iRepertoire PCR (Figure 3-12B). While there were numerous motifs shared across patient groups in the current study, focus was placed on motifs found only in LS DR3+ patients utilizing TRAV12-1 and TRBV2. Although the sequences included in the determination of the

CDR3 motifs were not expanded in control subjects or non-LS patients, one caveat is that

TCR motifs utilizing other V regions may have been overlooked. Nonetheless, future studies are necessary to determine the specificity of the identified shared TCR chains.

Unfortunately, iRepertoire PCR provides no information on pairing of the two chains.

Therefore, although BAL CD4+ T cells expressing TRAV12-1 and TRBV2 are expanded, that does not necessarily posit that they are paired together on those cells.

CHAPTER IV

+ 4 CD4 αβTCR PAIRS IN SARCOIDOSIS PATIENT BAL

4.1 αβTCR pair identification in human patient samples

V, D, and J gene segments combine together to form the complete αTCR, and, as discussed in section 1.10, the CDR3 regions of both chains are important for interactions with the pMHC and for antigen specificity. Traditionally, multiplex PCR protocols have had the capacity to amplify and sequence only one chain at a time, losing the pairing information for the complete αTCR and, subsequently, the ability to determine the antigen specificity of a given TCR. However, there have been several recent studies in which both chains of the

TCR were identified either in bulk samples or on single cells (299–301, 310–314).

Additionally, sophisticated statistical analyses have been developed to identify paired chains based on the probability of pairing due to frequencies of the chains in a given sample (315,

316). As reviewed in (317), there are several issues with each of these approaches, including the possibility that lower-frequency clones may be overlooked, α and chains can be promiscuous (299, 310, 312, 318), many TCRs utilize more than one α or chain (310,

319–321), as well as general PCR amplification and sequencing errors.

Another issue associated with previous methodologies for assessing TCR repertoires is the inability to determine TCR repertoires directly ex vivo and the necessity of culturing primary T cells in IL-2 for extended periods before sequencing. Selected examples demonstrating that long-term culture with IL-2 can cause biases include studies showing changes in the molecular signature of PBMCs (322), the TCR repertoire of BAL CD4+ T cells (323), and the TCR repertoire of tumor infiltrating lymphocytes (324). Therefore, in

______

93 order to accurately assess the TCR repertoire of a given sample, methodology that uses T cells directly ex vivo with no manipulation via long-term IL-2 culture is necessary.

4.2 ePCR as a means of identifying αβTCR pairs

One methodology that has been recently developed for the identification of αTCR pairs is emulsion PCR (ePCR) (299, 301, 325), as described in Figure 2-2. ePCR has several advantages over traditional high-throughput sequencing approaches, including the ability to sequence large numbers of cells on a single-cell level, the capability of sequencing

T cells directly ex vivo without cloning or expanding T cells using cytokines such as IL-2, and the capacity to amplify small amounts of template DNA. The PCR reactions are similar to those previously established for PCR sequencing of TCR chains, but one key difference is that the reactions link the TCRα chain to the TCR chain for a given cell, generating a single

αTCR product which can be sequenced together on a per-cell and per-patient sample basis.

As described in section 2.5, bulk cells are resuspended in PCR1 master mix, and an oil mixture is added on top. The contents are then vortexed for a period of time (optimized as described in section 2.5 and shown in Figure 2-3) so that the cells are each encapsulated in separate oil-in-water emulsion droplets. The droplets provide a micro-reactor for each cell during the first PCR in which cDNA is generated, the two TCR chains are amplified separately, and the two chains are physically linked by the complementary overlap extension sequences. The next PCR step requires the emulsion to be broken for further amplification of the linked TCR chains by means of nested common C region primers. A major issue with the second step is that any unpaired α or chains from the droplets subsequently have access to all other unpaired chains from the other droplets. To prevent unpaired chains from cross-reacting and forming de novo TCRs, blocking oligonucleotides

(oligos) are added to the second PCR reaction. The blocking oligos are complementary to

94 the overlap extension sequences that would be exposed on any unpaired but amplified α or

chain. The blocking oligos suppress further amplification of the unpaired chains, as they are phosphorylated on the γ’ end. The third PCR uses nested C region primers that are each tagged with differing barcode sequences, and each sample is given its own set of primers (i.e., barcodes) so as to enable demultiplexing of the samples after sequencing.

Challenges with ePCR are abundant, and optimization of the protocol has been performed previously (299, 325–328) along with additional improvements described in section 2.5. For the first PCR, the size of the emulsion droplets can be an issue in terms of separating T cells into individual micro-reactors. Therefore, optimal oil mixtures and vortexing times must be determined. As such, CHIMERx has developed an optimized oil mixture that is commercially available in the Micellula Emulsion and Purification Kit, and, as shown in Figure 2-3, vortexing times were tested using CFSE-labeled PBMCs. Additionally,

PCR primers for the first PCR must be tested for their ability to equally amplify all possible

TRAV and TRBV segments as well as represent accurate TCR repertoires for a given sample. The ePCR primers listed in Appendix A were tested for equivalent amplification of all possible TRAV and TRBV sequences using PBMCs previously (work performed by

Daniel Munson, data not shown).

Another challenge is to demonstrate the ability of ePCR to correctly pair αTCRs without cross-reactions occurring between TCRs originating from different T cells, as discussed above. Previous work from a collaboration between our group and another laboratory utilized ePCR on hybridoma cells with known TCRs and demonstrated that correct pairing of TCRα and TCR chains occurred with an efficiency of 85% (301). In contrast, when the emulsion step was not performed, the percentage of correctly matched pairs dropped to 5-10%. Indeed, when emulsion samples were compared to the non- emulsion samples, high percentages of particular αTCR pairs in the emulsion samples were seen, but no expansions were observed in the non-emulsion samples (data not

95 shown). However, as has been stated in previous work utilizing this method, ePCR is not directly quantitative, and over-amplification of the top αTCR pairs likely occurs for most emulsion samples. For example, when considering all samples, the most highly expressed

TCR in a given sample comprised an average of 25.6% of the CD4+ cells in the BAL and

31.9% of the CD4+ cells in the blood (301). Furthermore, the top αTCR pair in the blood of one patient represented 76.23% of all TCRs expressed on CD4+ T cells. Given those values, the output hierarchy is skewed toward the top αTCR pairs. Thus, the data was utilized in a non-quantitative fashion, and αTCR pairs of interest were searched for based on those

TCRs expanded in the iRepertoire data.

+ 4.3 αβTCR pairing in sarcoidosis patient BAL CD4 T cells as determined by ePCR

The initial data generated by iRepertoire analyses suggested that the TRAV12-1 and

TRBV2 chains might be paired together on lung-accumulated CD4+ T cells in DR3+ LS patients. Although one study has shown a small but significant percentage of CD4+ T cells from the BAL of LS patients co-expressing TRAV12-1 and TRBV2 by flow cytometry (272), no previous studies have sequenced and identified complete αTCR pairs on BAδ CD4+ T cells from these patients. Therefore, to address αTCR pairing on BAδ CD4+ T cells, ePCR was performed (299, 301).

As described in section 2.5, section 4.2, and Figures 2-2, 2-3, and 2-4, cells were resuspended in RT-PCR reaction master mix with reagents and primers for amplifying TCRα and chain genes, and following the addition of an oil mixture and vortexing, the contents separated into a water-in-oil emulsion, capturing one cell per micelle. After three rounds of

PCR, single bands with barcoded and joined αTCR products were gel-purified for each patient.

When analyzing TRBV usage on BAL CD4+ TRAV12-1+ T cells from DR3+ LS patients, approximately 30% of those cells co-expressed TRBV2 (Figure 4-1A). When

Figure 4-1: Preferential TRAV12-1/TRBV2 pairing in DR3+ LS patients determined via ePCR. (A) Six DR3+ δS BAδ samples were subjected to ePCR, and V usage on TRAV1β- 1-utilizing BAL CD4+ T cells is shown. (B) Vα usage on TRBVβ-utilizing BAL CD4+ T cells from six DR3+ LS patients is shown. (C) TRAV12-1 usage on TRBV2+ cells, TRBV2 usage on TRAV12-1+ cells, TRAV26-1 usage on TRBV20-1+ cells, and TRBV20-1 usage on TRAV26-1+ cells are shown for comparison amongst patient groups. Bars represent means ± SD. p values were calculated based on Kruskal-Wallis tests with Dunn’s post-test; * = p < 0.05.

97 assessing BAL CD4+ TRBV2+ T cells from these patients, ~40% expressed TRAV12-1

(Figure 4-1B). The higher percentage of TRAV12-1/TRBV2 pairing could be due to random chance, as the relative abundance of each chain in these patients is high. Only two V regions were highly expressed in most BAL samples regardless of diagnosis, TRAV26-1 and TRBV20-1 (Figures 3-4, 3-8). Therefore, pairing between TRAV26-1 and TRBV20-1 was interrogated for all patient groups to determine if highly expressed V regions preferentially pair together simply due to chance. TRAV26-1 and TRBV20-1 did not pair together at a high percentage in any patient group, as was seen exclusively in DR3+ LS patients between TRAV12-1 and TRBV2 (Figure 4-1C). Using the preferential pairing equation as described in section 2.14, the averaged value for TRAV12-1/TRBV2 pairing was significantly different than 0 (0.066 ± 0.059; p = 0.04) while the value for TRAV26-1/

TRBV20-1 was not different (-0.0105 ± 0.0257; p = 0.36). These data suggest that TRAV12-

1 and TRBV2 are preferentially paired on the expanded BAL CD4+ T cells in DR3+ LS patients, likely due to antigen selection.

4.4 scPCR for determining αβTCR pairs

Another methodology for identifying TCR pairs is single-cell PCR (scPCR). scPCR has several advantages over ePCR for determining TCR repertoires, including the near

100% accuracy of αTCR pair determination and direct quantification of αTCR pairs in a given sample. The PCR reactions, as described in section 2.6 and Figure 2-5, include similar steps to those for ePCR and were optimized previously (300). The first round of PCR is performed on cells which have been single-cell sorted into individual wells of a 96 well plate. The wells contain all reaction materials for generating cDNA. Subsequently, each well’s cDNA contents can then be split equivalently into two plates for PCR1, one for TCRα determination and one for TCR determination. PCR2 and PCR3 steps are similar to ePCR in that nested C region primers are used for further amplification and barcoding of the

98 samples. After sequencing and subsequent analyses, α and chains from the cDNA that originated in a given well can be paired based on barcodes related to the location of that well on a given plate.

General disadvantages of scPCR include the higher expense (mainly due to the necessity of a greater number of custom primers), the need for a biased repertoire, and the limitation of the ability to only assess a small number of single cells (hundreds versus hundreds of thousands or millions for ePCR, for example). In the case of LS, the TRAV12-1 association in DR3+ patients supports the use of scPCR, as the BAL CD4+ TRAV12-1+ T cells from those patients can be sorted from bulk BAL cells. Additionally, as data from iRepertoire and ePCR both confirmed TRAV12-1 expansions in all DR3+ LS patients, scPCR was a logical next step for confirmation of αTCR motifs and pairs identified by those two methodologies.

+ 4.5 CD4 αβTCR pairing in sarcoidosis patients via scPCR

In order to identify αTCR pairs in a more quantitative fashion as well as to validate the αTCR pairs obtained by ePCR, single-cell PCR (scPCR) was performed on DR3+ LS patient 1244. As described in section 2.6 and shown in Figures 2-5 and 2-6, BAL CD4+ T cells expressing TRAV12-1 were single-cell sorted into 96 well plates (Figure 4-2A), cDNA was generated, and three rounds of PCR were performed (300). A total of 86 complete

αTCR pairs were obtained. Similar to the ePCR-derived results in Figure 4-1B, TRBV2 was the most prevalent TCR chain paired with TRAV1β-1 (Figure 4-2B). TRBV7-9 and TRBV5-

1 were the next two TCR chains most frequently paired with TRAV12-1.

+ 4.6 CD4 αβTCR pairs in LS patients with shared CDR3α/β homology

αTCR pairs containing identical or similar CDRγ regions were first searched for in the ePCR data by utilizing the shared TRAV12-1 and TRBV2 CDR3 motifs from Figures 3-

+ + + Figure 4-2: TCR Vβ usage on BAL CD4 TRAV12-1 T cells from one DR3 LS patient after scPCR analysis. (A) BAL cells were obtained from one DR3+ LS patient BAL sample and sorted by flow cytometry into 96-well plates. Cells were sequentially gated on lymphocytes (not shown), CD3+DAPI-, and CD4+TRAV12-1+, which were subsequently sorted into 96-well plates at one cell per well for scPCR. (B) The V usage on the TRAV1β- 1+ cells was determined for each well.

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12 and 3-14 as references. Thirteen pairs with highly-related CDRγα and CDRγ sequences were found in multiple DR3+ LS patients (Table 4-1). In many cases, the pairing was exclusive (i.e., a particular TCRα chain paired with the listed TCR chain at a high frequency relative to pairing with other TCR chains, and vice versa), as shown in pairs 5, 6, and 13.

However, several of the pairs were less exclusive, either on the TCRα side, TCR side, or both. The αTCR pair designated as clone 5 was exclusively found in the BAδ of four DRγ+

LS patients. Importantly, different nucleotides were utilized in these four patients to generate identical amino acid sequences (data not shown), suggesting the presence of a public TCR repertoire. Although other shared αTCRs were found exclusively in DRγ+ LS patients, the sequences either did not utilize TRAV12-1/TRBV2 or did not share CDR3 homology in either chain (data not shown).

To validate the CDR3 homology found by ePCR, the CDRγ homology was assessed amongst the TRAV12-1/TRBV2 sequences in the scPCR data (Table 4-2).

Although pairs with similar CDRγ sequences in both TCRα and TCR (e.g., pairs β/γ and

7/8) were observed, the TRAV12-1 CDR3 regions were generally heterogeneous and utilized different TRAJ regions. However, the dominant TRBVβ CDRγ motif found in patient

1244 (CASS-EGSR-GTAFF) (Figure 4-3) resembled both the motif identified by iRepertoire in the other seven DR3+ LS patients (CASS-EQGR-EEQFF, Figure 3-14) as well as three pairs by ePCR (pairs 8, 9, 10, Table 4-1). Additional pairs with CDRγα and/or CDRγ similarity are shown in Table 4-3. Taken together, these results show that BAL CD4+ T cells bearing TCRs with CDR3 homology are expanded in DR3+ LS patients. The data suggest a critical role for the TRAV12-1/TRBV2 TCRs in recognizing the putative DR3-peptide complex in LS patients.

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Table 4-1. TRAV12-1/TRBV2 pairs with CDR3 homology in DR3+ LS patients by ePCR.

% α % β Pair 1 1 CDR3α CDR3β Pt. paired paired # 2 3 with β with α

1 CVS QRGG SYIPTF:TRAJ6 CASS GQGN SPLHF:TRBJ1-6 1088 63.6 35.5

2 CVV TLGG QNFVF:TRAJ26 CASS GQGN SPLHF:TRBJ1-6 1088 1.2 30.0

3 CVV TLGG QNFVF:TRAJ26 CASS GTGG NQPQHF:TRBJ1-5 1088 1.6 86.5

4 CVV TLGG QNFVF:TRAJ26 CASS GTGG RATQYF:TRBJ2-3 1088 24.1 29.7

5 CVV TLGG QNFVF:TRAJ26 CASS ETGG NQPQHF:TRBJ1-5 1088 99.5 99.7

1147 100.0 100.0

1162 95.1 100.0

1215 100.0 100.0

6 CVV NMGN DYKLSF:TRAJ20 CASS GTGG RATQYF:TRBJ2-3 1088 75. 9 70.3

7 CVV PLGN TPLVF:TRAJ29 CASS EARG SGYTF:TRBJ1-2 1146 3. 5 6.1

8 CVV PLGN TPLVF:TRAJ29 CASS GQGR LGAFF:TRBJ1-1 1146 19.6 55.4

9 CVV NSMS GNQFYF:TRAJ49 CASS GQGR TEAFF:TRBJ1-1 1146 0.5 2.2

10 CVV SVTG ANNLFF:TRAJ36 CASS EAGR TEAFF:TRBJ1-1 1146 0.6 0.2

11 CVV NPDG QKLLF:TRAJ16 CASS EASG QETQYF:TRBJ2-5 1147 100.0 30.0

12 CVV PRSG GSYIPTF:TRAJ6 CASS GTGG RTEAFF:TRBJ1-1 1146 1.2 38.8

13 CVV PSGG SQGNLIF:TRAJ42 CASS EESR EKLFF:TRBJ1-4 1146 84.2 80.4

1Amino acids forming shared or similar CDR3 motifs are colored and bolded 2 The percentage that the listed TCRα is paired with the listed TCR is shown (% α paired with ), as opposed to pairing with other TCR in a given patient sample 3 The percentage that the listed TCR is paired with the listed TCRα is shown (% paired with α), as opposed to pairing with other TCRα in a given patient sample

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Table 4-2. TRAV12-1/TRBV2 pairs with CDR3 homology in one DR3+ LS patient by scPCR.1

Pair # CDR3α CDR3β # wells

1 TRAV12-1:CVV SPSG TYKYIF:TRAJ40 TRBV2:CASS EASR GAAFF:TRBJ1-1 1

2 TRAV12-1:CVV NIFT GNQFYF:TRAJ49 TRBV2:CASS EEAR VQAFF:TRBJ1-1 1

3 TRAV12-1:CVV NAFT GNQFYF:TRAJ49 TRBV2:CASS EESR ETQYF:TRBJ2-5 1

4 TRAV12-1:CVV NSAS GTYKYIF:TRAJ40 TRBV2:CASS EEGR VTAFF:TRBJ1-1 2

5 TRAV12-1:CVV NNGN DYKLSF:TRAJ20 TRBV2:CASS EGGR GTAFF:TRBJ1-1 1

6 TRAV12-1:CVV NRYN NARLMF:TRAJ31 TRBV2:CASS GEAR NTIYF:TRBJ1-3 1

7 TRAV12-1:CVV NNPG ANNLFF:TRAJ36 TRBV2:CAST GAGG RGQPQHF:TRBJ1-5 1

8 TRAV12-1:CVV NAPG ANNLFF:TRAJ36 TRBV2:CASS GAGG RGGELFF:TRBJ2-2 2

1Shown are cells expressing αTCR pairs with shared or similar TRAV1β-1 and TRBV2 CDR3 motifs and the number of wells that the pairs were found in. Shared or similar CDR3 motifs are colored and bolded.

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+ + + Figure 4-3: Consensus CDR3β motif in BAL CD4 TRAV12-1 T cells from one DR3 LS patient after scPCR. An online multiple sequence alignment tool (weblogo.berkeley.edu) was utilized to generate a graphical representation of a consensus CDRγ motif found in multiple wells. Amino acids are color-coded based on chemical properties, and black color designates the conserved C-terminal CASS, as well as the N-terminal F, amino acids. Letter size reflects frequency of appearance of a specific residue at a certain position.

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+ 1 Table 4-3. Additional related or expanded αβTCR sequences by scPCR in one DR3 patient.

Pair # wells # CDR3α CDR3β

1 TRAV12-1:CVV NKGA AGNKLTF:TRAJ17 TRBV2:CASS FQDR AGYTF:TRBJ1-2 2

2 TRAV12-1:CVV HKGA TGGGNKLTF:TRAJ10 TRBV2:CASS VQDR GGYTF:TRBJ1-2 2

3 TRAV12-1:CVA PRGG GSYIPTF:TRAJ6 TRBV7-2:CASS LDRG QGYTF:TRBJ1-2 1

4 TRAV12-1:CVA FRGG SYIPTF:TRAJ6 TRBV7-2:CASS LDRG QPQHF:TRBJ1-5 1

5 TRAV12-1:CVV MGSG GSYIPTF:TRAJ6 TRBV7-2:CASS QDRG RGPGHF:TRBJ1-5 1

6 TRAV12-1:CVP FKGG SYIPTF:TRAJ6 TRBV7-2:CASS RDRG PYEQYF:TRBJ2-7 1

7 TRAV12-1:CVV NRFG GSQGNLIF:TRAJ42 TRBV11-3:CASS FLQG PLHF:TRBJ1-6 1

8 TRAV12-1:CVV NAYG GSQGNLIF:TRAJ42 TRBV11-3:CASS FLSG TPKLFF:TRBJ2-2 2

9 TRAV12-1:CVV NTGF QKLVF:TRAJ8 TRBV19:CASS PRSG RGTDTQYF:TRBJ2-3 1

10 TRAV12-1:CVV NGGF QKLVF:TRAJ8 TRBV19:CASS PRSG VGTDTQYF:TRBJ2-3 1

11 TRAV12-1:CVV NRGG SYIPTF:TRAJ6 TRBV7-9:CASG GTLS NEQFF:TRBJ2-1 4

12 TRAV12-1:CVC AVSG SARQLTF:TRAJ22 TRBV6-5:CASS KRTG TGHSPLHF:TRBJ1-6 3

13 TRAV12-1:CVV IKSG GSYIPTF:TRAJ6 TRBV2:CASQ QGSG QPQHF:TRBJ1-5 2

14 TRAV12-1:CVA GEGS YIPTF:TRAJ6 TRBV2:CASS EGGQ GETQYF:TRBJ2-5 2

15 TRAV12-1:CVV SRGN QFYF:TRAJ49 TRBV3-1:CASS QMNN QPQHF:TRBJ1-5 2

16 TRAV12-1:CVV NLSG NQFYF:TRAJ49 TRBV5-1:CASS TFND VEQFF:TRBJ2-1 2

17 TRAV12-1:CVV NMAG NQFYF:TRAJ49 TRBV5-1:CASS APDG TQYF:TRBJ2-5 2

1Shown are cells expressing αTCR pairs with shared or similar TRAV1β-1 and various TRBV CDR3 motifs. The number of wells the pairs were found in is shown, with shared or similar CDR3 motifs bolded.

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4.7 Summary

αTCR sequencing in sarcoidosis has been primarily limited to single-chain analyses in which the predominant TRAV usage in DR3+ LS patients has been identified as 12-1, as discussed in sections 1.10 and γ.1. The TCR chain paired with the TRAV12-1-expressing cells has remained unidentified. Overall, when assessing all possible αTCR pairs via ePCR, DR3+ LS patients had a higher percentage of cells co-expressing TRAV12-1 and

TRBV2 than any other pairing. One study did show a small population of BAL CD4+ T cells co-expressing TRAV12-1 and TRBV2 (272), and the data presented in this chapter confirm that there is an expansion of BAL CD4+ T cells in DR3+ LS patients that express TRAV12-

1/TRBV2 TCRs. Additionally, these two chains, although not expanded individually to the same extent as was found in DR3+ LS patients, still did not preferentially pair in DRB3+ LS, non-LS, or control patients. iRepertoire analyses showed that TRAV26-1 and TRBV20-1 were also expressed frequently in all patient samples; however, there was no preferential pairing of these two chains in DR3+ LS patients or any other patient population. Lastly, scPCR data from one DR3+ δS patient indicated that when assessing all TCR chains pairing with BAL CD4+ TRAV12-1+ T cells, the predominant chain was TRBV2.

These data suggest that the TRAV12-1/TRBV2 BAL CD4+ T cells that share CDR3 homology are present and expanded in the lungs of DR3+ LS patients as a result of being recruited to the site of disease in response to presentation of conventional antigens.

Additionally, the consensus motif amongst the TRBV2 sequences paired with TRAV12-1- expressing cells in patient 1244 was very similar to the consensus motif found in multiple patients via iRepertoire and to sequences found by ePCR in several patients, indicating that cells expressing this TRBV2 motif are likely responding to the putative LS-associated antigens. However, in all methodologies employed in this study, DR3+ LS patients shared several other TCRα, TCR, and αTCR CDRγ motifs not considered as part of the consensus motifs, due to utilization of other TRAV/TRBV genes, length of the CDR3s, or

106 other factors. These other sequences may also play an important role in antigen recognition in these patients, and they cannot be ruled out as possible disease-associated TCRs.

Recently, our group has used scPCR in a similar manner with BAL CD4+ T cells from

CBD patients (Falta, et al., unpublished data). Since peptides completing the beryllium + pMHC/TCR complex are almost always present in assay medium supplemented with serum

(e.g., plexin A4 peptides) (302), testing the TCRs for beryllium specificity can be performed with each new TCR. As such, CBD TCRs that were identified via scPCR as either expanded or that shared CDR3 similarity to other TCRs in a given sample have been tested for beryllium specificity by cloning the TCRs into hybridoma cells, exposing them to HLA-DP2- expressing fibroblasts in the presence of beryllium, and measuring IL-2 production. In the majority of cases, the TCRs that fell into either category (expanded or with CDR3 homology) were beryllium-specific (Falta, et al., unpublished data). Testing of the DR3+ LS patient

TCRs identified by ePCR and scPCR is not feasible using known antigens, as sarcoidosis- associated antigens have not been identified to date. However, the results from the beryllium study indicate that the most logical choice would be to study TCRs that: 1. Are found in several DR3+ LS patients, 2. Share CDR3 homology, and/or 3. Represent the most expanded pairs found in individuals. TCRs falling into one or more of these categories would likely be associated directly with disease, and determining their antigen specificity would allow for the potential identification of the disease-causing agent.

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CHAPTER V

IDENTIFYING MIMOTOPES AND PEPTIDE ANTIGENS

5.1 Speculative antigens in sarcoidosis

In 1935, Drs. Williams and Nickerson noted that intradermal injection of a mixture generated from sarcoidosis skin lesions induced granulomatous inflammation of the skin in individuals suspected of having sarcoidosis but not in normal controls (14). Several years later, Dr. Kveim reported using a similar test but injecting lymph node tissue (267). After the initial discovery, derivations of the original test were used on several patient groups to determine its effectiveness in diagnosing sarcoidosis (11–13). The Nickerson-Kveim test

(also known as Kveim test or Kveim-Siltzbach test) utilized mixtures from skin lesions, spleens, lymph nodes, or a combination of tissues originating from sarcoidosis patients.

Once the mixture was injected subcutaneously, the site was monitored for development of a granulomatous lesion about 4-6 weeks later. If the reaction was positive (about 50-80% of cases in the initial studies), a patient was diagnosed with sarcoidosis. Anywhere from 70-

90% of LS patients have a positive Kveim test (120, 329). The site of granulomatous inflammation typically consists of infiltrated CD4+ T cells with a somewhat restricted V repertoire (273). The results of the tests were interpreted to mean that there are tissue antigens targeted by the adaptive immune system in these patients. However, as reviewed in (329), several issues have led to inconclusive results, confusing interpretations, and safety concerns for patients, abrogating the use of the Kveim-Siltzbach test for diagnosis.

It has been long speculated that the putative sarcoidosis-associated antigens are derived from a microbial, viral, or autoimmune source. As reviewed in (330), several antigens have been indicated as potential sarcoidosis-causing agents. Two bacterial sources of potential sarcoidosis antigens that have been widely studied over the years are

Mycobacterium spp. and Propionibacterium spp., which will be further discussed below.

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As shown in Table 5-1A, Mycobacterium tuberculosis (MTB) and nontuberculosis mycobacteria (NTM) have both been evaluated as potential causes of sarcoidosis. As reviewed in (330) and (331), various methodologies have identified MTB, NTM, and other mycobacterial DNA and RNA in tissue samples from sarcoidosis patients (332–341). In contrast, other studies have shown little or no evidence of mycobacterial nucleic acids in sarcoidosis tissues (342–344). In terms of immunologic reactions to various mycobacterial proteins and peptides, studies have focused on several different proteins, including mycobacterial catalase peroxidase (mKatG), early secretory antigenic target (ESAT-6), antigen 85A (Ag85A), superoxide dismutase A (SodA), and numerous heat shock protein

(hsp) components.

In one study, sarcoidosis tissues were separated and analyzed by mass spectrometry, and mKatG was identified as the most abundant protein (345). Additionally, the study utilized immunoblotting and in situ hybridization to identify mKatG protein in about half of the sarcoidosis tissues but no control tissues. The authors determined that serum IgG from sarcoidosis patients was able to bind recombinant mKatG via immunoblot analyses.

Additionally, several studies have demonstrated that PBMCs or CD4+ T cells from some sarcoidosis patients are stimulated by mKatG as detected by the release of cytokines (e.g.,

IFN-) as well as by proliferation (346–350).

PBMCs from sarcoidosis patients have been shown to produce IFN- when exposed to Ag85A (351); however, some healthy control subjects also responded. ESAT-6 and SodA have also been demonstrated to stimulate sarcoidosis BAL cells to produce Th1 cytokines, whereas sarcoidosis PBMCs do not respond (340, 346–348, 350, 352–355). ESAT-6 was also found to be associated with granulomatous inflammation in sarcoidosis specimens

(354). Lastly, MTB heat shock proteins have been shown to stimulate sarcoidosis PBMCs compared to healthy control cells, induce antibody responses, and have been associated with the disease in general (353, 356–358).

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Table 5-1A. Case–control studies evaluating the role of mycobacteria in sarcoidosis.*

Sarcoidosis patients Non-sarcoidosis controls First author, Country Molecular technique Year (Ref.) Type of Type of n/N n/N microorganism microorganism

PCR of 65 kDa Bocart, France mycobacterial antigen and 2/22 MTBC 0/22 - 1992 (359) IS6110 Hofland, NAAT for Mycobacteria Netherlands 0/32 - 2/86 1 MTBC, 1NTM 2014 (360) and Culture Robinson, PCR for 16S rDNA, hsp65 USA 2/30 NTM 1/30 NTM 2013 (361) and rpoB Oswald-Richter, Mycobacterium USA MALDI-IMS for ESAT-6 5/15 0/4 - 2012 (354) spp Svendsen, BD ProbeTec IS6110 Denmark 1/52 MTBC 0/50 - 2011 (362) amplification PCR of 65 kDa Mootha, 10 MTBC, 3 India mycobacterial antigen and 13/27 2/40 NTM 2010 (363) NTM IS6110 Zhou, Real-time PCR of IS986 China 20/104 MTBC 7/55 MTBC 2008 (341) and human -blobin gene Dubaniewicz, BD ProbeTec IS6110 Poland 3/50 MTBC 0/10 - 2006 (364) amplification Fite, PCR of IS6110 and Spain 9/23 MTBC 1/23 MTBC 2006 (365) Southern blot hybridization Yasuhara, Japan PCR of IS6110 0/6 - 0/6 - 2005 (366) Song, USA PCR of MTB 16S rRNA 6/16 MTBC 0/16 - 2005 (345) Marcoval, Spain NAAT for rRNA of MTBC 0/35 - 0/39 _ 2005 (344) Lee, Nested PCR for Taiwan 7/21 NTM 0/16 - 2002 (367) mycobacterial hsp65 DNA Drake, PCR of 16S rRNA, rpoB 11 MTBC, 3 USA 15/25 0/25 _ 2002 (340) and IS6110 NTM, 1 both PCR of Gazouli, IS6110/IS1245/IS900/IS90 Greece 33/46 MTBC 0/20 - 2002 (368) 1, 16S rRNA, MPB64 and mtp40 Eishi, Japan PCR of IS6110/IS900 5/108 MTBC 2/86 MTBC 2002 (343) Klemen, PCR of IS6110 and Austria 3/4 NTM 0/39 _ 2000 (339) mycobacterial chaperonin

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Table 5-1A, continued.*

Sarcoidosis patients Non-sarcoidosis controls First author, Country Molecular technique Year (Ref.) Type of Type of n/N n/N microorganism microorganism

PCR of 65 kDa Li, 2 MTBC, 14 USA mycobacterial antigen and 16/20 0/20 _ 1999 (336) NTM RFLP analysis Ishige, Japan PCR of IS6110 3/15 MTBC 1/15 MTBC 1999 (369) PCR of IS6110, nested Wilsher, PCR to amplify 85 bp NZ 0/31 _ 0/10 - 1998 (370) sequence within the 123 bp product 4 NTM, 13 39 Di Alberti, Hemi-nested PCR for 16S Italy 17/38 Mycobacterium 39/113 Mycobacterium 1997 (371) rRNA spp spp Vokurka, PCR of IS6110 and DR France 0/15 _ 0/27 _ 1997 (372) region Ozcelik, Turkey PCR of IS6110 5/11 MTBC 2/15 MTBC 1997 (373) PCR of 65 kDa Popper, Austria mycobacterial antigen and 11/35 NTM 0/39 - 1997 (334) IS6110 PCR of IS900/IS902, El-Zaatari, USA MAC-specific PCR assay 7/7 NTM 13/38 NTM 1996 (374) and Western blot PCR of 65 kDa Fidler, UK mycobacterial antigen and 7/16 MTBC 1/16 MTBC 1993 (375) IS6110 Thakker, UK PCR of groEL 1/14 MTBC 1/11 MTBC 1992 (376) Gerdes, Germany PCR of 16S rDNA 0/14 - 0/10 - 1992 (377) Mycobacterial rRNA Mitchell, UK detection by liquid phase 5/5 MTBC 0/5 - 1992 (333) hybridization Saboor, PCR of IS986/IS6110 and 10 MTBC, 4 UK 14/20 5/22 3 MTBC, 2 NTM 1992 (332) groEL NTM Lisby, Denmark Nested PCR for IS900 0/18 - 0/18 - 1993 (378) Grosser, Germany PCR of IS986/IS6110 35/65 MTBC 1/34 MTBC 1999 (379) Vago, Italy PCR of IS6110 2/30 MTBC 0/17 - 1998 (380)

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Table 5-1A, continued.*

Sarcoidosis patients Non-sarcoidosis controls First author, Country Molecular technique Year (Ref.) Type of Type of n/N n/N microorganism microorganism

Richter, PCR of mycobacterial 16S Germany 1/24 MTBC 3/57 MTBC 1996 (381) rDNA Ghossein, PCR of 65 kDa USA 0/10 - 0/10 - 1994 (342) mycobacterial antigen Cannone, Italy PCR of IS6110 2/30 MTBC 0/10 - 1997 (382)

*Modified from the open-access publication: Esteves, T., G. Aparicio, and V. Garcia-Patos. 2016. Is there any association between Sarcoidosis and infectious agents?: a systematic review and meta-analysis. BMC Pulm. Med. 16: 165. n Mycobacteria-positive samples, N total samples, PCR polymerase chain reaction, 65 kDa 65-Kilodalton mycobacteria antigen, IS6110 insertion sequence to identify Mycobacterium tuberculosis complex (MTBC), NTM non-tuberculous mycobacteria, NAAT nucleic acid amplification test, 16S rDNA ribosomal DNA common to all mycobacteria, rpoB RNA polymerase -subunit gene, MALDI-IMS matrix-assisted laser desorption ionization as a mass spectrometry imaging, ESAT-6 6 kDa early secretory antigenic target produced by Mycobacterium tuberculosis, IS986 insertion sequence to identify MTBC, rRNA ribosomal RNA, IS1245/IS900/IS901/IS902 insertion sequence to identify Mycobacterium avium complex, MPB64 mycobacterial protein, mtp40 Specific primers of MTB species, RFLP restriction fragment length polymorphism DR direct repeat, groEL gene encoding 65 kDa antigen

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Table 5-1B. Case–control studies evaluating the role of P. acnes in sarcoidosis.*

First author, Sarcoidosis Controls Country Molecular technique Year (Ref.) n/N n/N

Robinson, USA PCR for bacterial 16S rDNA 7/30 1/30 2013 (361) Oswald-Richter, USA MALDI-IMS for propionibacterial proteins 7/15 1/4 2012 (354) Yasuhara, Japan PCR for 16S rRNA 2/6 0/6 2005 (366) Gazouli, Greece PCR for 16S rRNA 0/46 0/20 2002 (368) Eishi, Japan PCR for 16S rRNA 93/108 25/86 2002 (343) Ishige, Japan Quantitative PCR for 16S rRNA 12/15 3/15 1999 (369) Negi, Immunohistochemical methods (PAG and TIG antibodies) and Japan 149/196 0/79 2012 (383) western blot Yamada, Japan Quantitative real-time PCR for 16S rRNA 8/9 2/9 2002 (384) Eishi, Japan PCR for P. acnes DNA 36/39 12/29 1994 (385) Abe, Japan Isolation of P acnes in culture 31/40 38/180 1984 (386) Hiramatsu, Japan Nested PCR for 16S rRNA 21/30 7/30 2003 (387)

16S rDNA ribosomal DNA, MALDI-IMS matrix-assisted laser desorption ionization as a mass spectrometry imaging, rRNA ribosomal RNA

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When analyzing peptides eluted from HLA-DR3, the MHC allele most highly associated with LS, a study found that no mycobacterial antigens were isolated (296).

Additionally, one study found that sarcoidosis patient responses to ESAT-6 were no different than those seen in control subjects with nongranulomatous lung disease (388). Overall, as extensively reviewed in (331), although at least 50 manuscripts have been published describing potential connections between mycobacterial proteins and sarcoidosis, several studies have refuted those claims based on little or no evidence of associations between the two.

As shown in Table 5-1B, Propionibacterium acnes has long been associated with sarcoidosis, with the first report of isolation of the bacterium from sarcoidosis patients in

1978 (389). Since that publication, several groups have isolated P. acnes or its bacterial

DNA from sarcoidosis tissues, and cellular responses to the bacterium or bacterial proteins have been demonstrated (343, 390–399). However, as discussed in (331) and (50), P. acnes is an extremely common commensal bacterium in normal control patients and is typically found in sarcoidosis tissues not associated with granulomatous areas (237, 400).

As mentioned previously, ACCESS was a multi-center study conducted in the United

States that aimed to identify sarcoidosis-associated factors in four possible areas: genetics, environment, infectious agents, and autoimmunity (200). In the study, questionnaires were given to over 700 sarcoidosis patients and more than 700 age-, race-, and sex-matched controls. The only positively-correlated factors with sarcoidosis were possible environmental factors such as employment in the agricultural industry, occupational exposure to insecticides, or mold and mildew exposure. As reviewed in the study, several publications have also associated sarcoidosis with some of these environmental and occupational exposures. However, despite identifying several factors that were positively-associated with sarcoidosis, a single agent could not be found that, alone, indicated a “cause” for the disease.

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Recently, self-proteins have been investigated for their role in sarcoidosis, including vimentin, a known auto-antigen associated with diseases such as rheumatoid arthritis and mesothelioma (23, 401–405). It has been shown that peptides from vimentin can be eluted from HLA-DR molecules that are affinity purified from the BAL of DR3+ LS patients (296).

Vimentin peptides have also been identified via SDS-PAGE followed by mass spectrometry in Kveim-Siltzbach reaction extracts (406). Additionally, studies have directly stimulated

PBMCs or BAL CD4+ T cells from sarcoidosis patients with vimentin peptides (406–408). An issue with several of the studies is that some of the sarcoidosis patient samples did not respond to these peptides while a few of the normal healthy control samples did respond.

Nevertheless, a recent publication depicted a three-dimensional computer-generated model of a theoretical TRAV12-1/TRBV2 TCR in complex with HLA-DR3-vimentin (272). The model predicted that specific residues on the HLA-DR3 molecule could directly interact with

TCRα residues on the CDR1α and CDRβα loops and that the TRBV2 residues could be important for HLA-DRγ recognition and for linkage to the TCRα chain. Although the model predicted a binding topology that agrees with the current speculations presented here, it is important to state that the antigens recognized by these TCRs and the mechanisms by which these TCRs bind to the HLA-DR3-peptide complex remain unknown.

5.2 Hybridoma approach for determining TCR specificity

It is difficult and oftentimes impossible to maintain antigen-specific T cells in culture indefinitely or long enough to clone them for determination of their antigen specificity.

Therefore, approaches have been established for expressing TCRs on immortalized murine hybridoma cell lines that express no endogenous TCR. The hybridoma cells are readily transduced with a given TCR in a stable manner, grow in basic cell culture media to large numbers very rapidly, and do not depend on the functional state of the cell from which the

TCR of interest originated. Although not useful for studying cytokine responsiveness,

115 functional state, or T cell classification (Th1, Th2, etc.) of the original T cells, the transfected hybridomas can provide useful information regarding the antigen specificity of a given TCR.

As described in section 2.10, synthetic DNA is generated that encodes the sequences of the TCRs of interest, typically with the two chains linked by a 2A peptide. The chains are cloned into MSCV vectors, bacterial cells are transformed with the DNA, and transfection of a retroviral producer cell line generates supernatants. The supernatants are then used for spin-fection of the hybridoma line of choice. As discussed in section 2.10, several hybridoma lines are available, and each line has advantages as well as disadvantages.

Hybridomas have been utilized previously in many systems, and they have been used to identify disease-driving antigens in CBD, a similar disease to sarcoidosis. αTCR pairs were identified from the lungs of HLA-DP2+ CBD patients and were cloned into hybridoma cells (303, 323, 409). From these studies, beryllium-dependent mimotopes and self-peptides have been identified that are recognized by BAL CD4+ T cells in human CBD patients (302, 410).

5.3 HLA-DR3- and HLA-DQ2-restricted positive control hybridomas

As discussed in section 1.7, the HLA molecule associated most closely with LS is

HLA-DR3. However, that allele is in linkage disequilibrium with HLA-DQ2, meaning that in many patients, both molecules are present. Therefore, the putative LS-associated antigen(s) could potentially be presented by either molecule. As such, two positive control hybridomas were required to verify that the assay system using APCs expressing HLA-DR3 or HLA-DQ2 works efficiently and correctly. As such, murine TCR- hybridoma cells were transfected with

TCRs of known specificities, and the sequences are listed in Table 5-2. A well- characterized, HLA-DR3-restricted TCR clone designated RP15 is specific for peptide 3-13,

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Table 5-2. Positive control hybridomas. Name TRAV N TRAJ TRBV NBDN TRBJ

RP15 19 CA VGAWS SGTYKYIFG AJ40 5-3 CASS FGQGS EQYFG BJ2-7

D2 26-1 CIV L GGADGLTF AJ45 7-2 CASS FRF TDTQYF BJ2-3

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KTIAYDEEARR, from heat shock protein 65 (hsp65) of Mycobacterium leprae (225, 226,

411, 412).

These studies and others have delineated minimal epitopes of the hsp65 peptide that are required for DR3 binding and RP15 stimulation. It was determined that the only required amino acids of the peptide for DR3 binding were P2-I and P5-D (226). The only required peptides for RP15 stimulation were P1-T, P3-A, and P4-Y. Substitutions at various positions were shown to be tolerated in most cases, but the cognate peptide was identified as

KTIAYDEEARR. Therefore, the RP15 TCR was cloned into a murine hybridoma, and when the hsp65 (3-13) peptide was presented to RP15 in the context of HLA-DR3, the TCR responded with a maximal IL-2 response (Figure 5-1A, left). In contrast, when the same peptide was presented by HLA-DQ2, no response was elicited (Figure 5-1A, left).

The RP15 TCR was transfected into several hybridoma lines to determine which line would be ideal for antigen discovery when using sarcoidosis TCRs with unknown specificities. As shown in Figure 5-1B, three hybridoma lines (5415, MN279, and 54ZC) responded to peptide stimulation at very low concentrations. The 5415 line was the most sensitive line (EC50 = 3.235 pg/ml), MN279 was the next most sensitive (EC50 = 19.06 pg/ml), and 54ZC was the least sensitive (EC50 = 142.0 pg/ml).

The other positive control TCR is designated D2, and it was identified as being specific for the α-gliadin (62-70) peptide in a DQ2-restricted manner (413). In that study, biopsies were taken from the small intestines of celiac disease patients and treated with gluten for several days, with IL-12 and IL-15 added to the cultures later. Several rounds of restimulation were performed until stable polyclonal T cell lines were generated. The lines were cloned by limiting dilution, and proliferation assays were performed to find clones reactive to DQ2.5-glia-α1a or DQ2.5-glia-αβ peptides of α-gliadin. RNA was extracted from the proliferated clones, cDNA was generated, and Vα and V primers were used in multiplex

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Figure 5-1: Positive control hybridoma IL-2 responses to cognate peptides. (A) Responses of the HLA-DR3-restricted RP15 and HLA-DQ2-restricted D2 hybridomas to cognate peptide stimulation. DAP3 DR3 or DAP3 DQ2 fibroblast cells (1e5/well) presented either hsp65 (10 μg/ml) or α-gliadin (6 μg/ml) peptides to RP15 or Dβ T cell hybridomas (1e5/well), respectively. Differing hybridoma cell lines (5415, MN279, 54ZC, hu CD4, and no CD4) were transduced with either the RP15 (B) or the D2 (C) TCR, and DAP3 DR3 or DAP3 DQ2 fibroblasts (1e5/well), respectively, presented cognate peptide at several dilutions to the hybridomas (1e5/well). Tables below graphs display EC50 values for each hybridoma line. Cells were stimulated overnight, and IL-2 production was measured by ELISA.

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PCRs to determine the sequences of the TCRs. The clone designated D2 expressed

TRAV26-1 and TRBV7-2 and was reactive toward the DQ2.5-glia-αβ peptide PQPEPδYPQ.

As shown in Figure 5-1A, right, the D2 TCR responded robustly to presentation of the α-gliadin (62-70) peptide in the context of HLA-DQ2. As expected, the TCR was DQ2- restricted, as it did not respond when the peptide was presented in the context of HLA-DR3.

The D2 TCR was transfected into several hybridoma lines and tested for sensitivity to peptide. As shown in Figure 5-1C, the MN279 and 5415 lines were nearly equivalent in terms of EC50 (5.298 and 4.784 pg/ml, respectively), while the 5KC line expressing human

CD4 (5KC hu CD4) had a higher estimated EC50 (105.9 pg/ml), and the 5KC line without

CD4 (5KC no CD4) did not respond well to low concentrations of antigen (an exact EC50 could not be calculated).

The RP15 and D2 hybridomas responded to their respective cognate peptides only when presented in the context of the correct restricting MHC, providing reliable positive controls for further studies involving antigen discovery with sarcoidosis TCR hybridomas.

5.4 Sarcoidosis hybridomas generated after sequencing IL-2-expanded

BAL CD4+ T cells

BAL T cell clones from sarcoidosis patients designated A, P, and S were isolated after IL-2 expansion, and their TCRs were sequenced via PCR amplification of RNA using specific TCR primers. The TCRs sequenced from each clone all utilized TRAV12-1 and

TRBV5, but the J region usages differed, as listed in Table 5-3. There was not significant

TCR homology on either chain (α or ), and the lengths of the CDRγs varied for each clone.

Once the TCRs were transfected into hybridoma lines, they were tested with a superantigen specific for TRBV5 family members (Staphylococcus enterotoxin A, SEA). As shown in

Figure 5-2, all sarcoidosis hybridomas responded to stimulation by SEA in the context of both HLA-DQ2 (left) and HLA-DR3 (right). However, the S-1 hybridoma was re-derived

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Table 5-3. Sequences obtained after IL-2 expansion of BAL CD4+ T cells from LS patients. Patient TRAV TRBV N TRAJ NBDN TRBJ Clone 12-1 5 A CVV GGA YKYIFG AJ40 CASS FTDGK ETQYFG BJ2-5 1F6 A CVVN ILK TGANNLFFG AJ36 CASS SGLVS QETQYFG BJ2-5 2C9 P CVVN KD GGSQGNLIFG AJ42 CASS LPEGW SYEQYFG BJ2-7 2G5 S CVV MGGPG SNYQLIWG AJ33 CASS PGQRV GANVLTFG BJ2-6 S-1

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Figure 5-2: Sarcoidosis hybridoma IL-2 responses to superantigen stimulation. DAP3 DQ2 (A) or DAP3 DR3 (B) fibroblast cells (1e5/well) presented SEA at various concentrations to 1F6, 2C9, 2G5, or S-1 T cell hybridomas (1e5/well). The cells were stimulated overnight, and IL-2 production was measured by ELISA.

122 several times, and the endogenous response in the absence of peptide remained high when

HLA-DR3-expressing APCs were cultured with the hybridoma. None of the other TCRs had significant endogenous responses when cultured with HLA-DQ2- or HLA-DR3-expressing

APCs in the absence of peptide. These data show that the sarcoidosis hybridomas are capable of producing IL-2 after stimulation.

5.5 ESAT-6, mKatG, and vimentin responses by sarcoidosis hybridomas

As discussed in section 5.1, several mycobacterial and endogenous proteins have been attributed to T cell responses in sarcoidosis patients. To test whether the sarcoidosis hybridomas would respond to speculative peptides studied previously, three of the sarcoidosis hybridomas were cultured overnight with HLA-DQ2- or HLA-DR3-expressing

APCs and overlapping peptides. Peptides spanning P1 – P380 of mKatG were presented to the hybridomas in the context of HLA-DQ2 (Figure 5-3A) or HLA-DR3 (Figure 5-3B), but very few elicited any IL-2 responses above 5 pg/ml. Several of those peptides were presented to RP15 and D2 to determine specificity of the responses by the sarcoidosis hybridomas. As shown in Figure 5-3C, three of the peptides also stimulated RP15 and/or

D2, sometimes in the context of the incorrect MHC molecule (e.g., mKatG 266-280 presented by DR3 to D2). Therefore, the low-level stimulation by these particular peptides is non-specific and likely due to the preparation of the peptides. The purity of the mKatG peptides was about 75%, on average, meaning 25% of the preparations could contribute to the non-specific stimulation of all hybridomas tested.

Overlapping ESAT-6 peptides spanning P1 – P95 were tested in a similar manner, and the majority of the peptides did not stimulate the hybridomas when presented in the context of HLA-DQ2 or HLA-DR3 (Figure 5-4A and 5-4B, respectively). Two of the peptides did stimulate two out of the three hybridomas to a low extent (i.e., ~10 pg/ml IL-2). The two peptides (61-75 and 66-80) were tested for the ability to stimulate the RP15 and D2

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Figure 5-3: Sarcoidosis hybridoma IL-2 responses to mKatG peptides. DAP3-DQ2 (A) or DAP3-DR3 (B) fibroblast cells (1e5/well) presented overlapping mKatG peptides 1-380 (10 μg/ml) to 1F6, βC9, or βG5 T cell hybridomas (1e5/well). The cells were stimulated overnight, and IL-2 production was measured by ELISA. Peptides that are not shown elicited <5 pg/ml IL-β from all hybridomas. Selected mKatG peptides (10 μg/ml) were presented to the D2 or RP15 hybridoma (1e5/well) by DAP3-DQ2 or DAP3-DR3 fibroblast cells (1e5/well) as a comparison, with Dβ and RP15 cognate peptide stimulation shown (α-gliadin [62-70] and hsp65 [3-13], respectively, 10 μg/ml) (C).

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Figure 5-4: Sarcoidosis hybridoma IL-2 responses to ESAT-6 peptides. DAP3-DQ2 (A) or DAP3-DR3 (B) fibroblast cells (1e5/well) presented overlapping ESAT-6 peptides 1-95 (10 μg/ml) to 1F6, βC9, or βG5 T cell hybridomas (1e5/well). The cells were stimulated overnight, and IL-2 production was measured by ELISA. Selected ESAT-6 peptides (10 μg/ml) were presented to the D2 or RP15 hybridoma (1e5/well) by DAP3-DQ2 or DAP3-DR3 fibroblast cells (1e5/well) as a comparison, with D2 and RP15 cognate peptide stimulation shown (α-gliadin [62-70] and hsp65 [3-13], respectively, 10 μg/ml) (C).

125 hybridomas (Figure 5-4C). Although the peptides did not stimulate the RP15 hybridoma, they did stimulate the D2 hybridoma. ESAT-6 (66-80) stimulated D2 equivalently in the context of HLA-DQ2 and HLA-DR3, indicating that, as was seen with the mKatG peptides, impurities in the preparations are likely causing non-specific responses from several hybridomas. ESAT-6 (61-75) stimulated D2 in the context of HLA-DQ2, but the stimulation was lower than was seen for two of the sarcoidosis hybridomas. Further investigation of that particular peptide may be warranted. However, overall, the majority of the ESAT-6 peptides did not stimulate the sarcoidosis hybridomas to a great extent.

As discussed in section 5.1, vimentin is another protein with potential links to sarcoidosis. Therefore, vimentin peptides were tested for their ability to stimulate the sarcoidosis hybridomas. Vimentin peptides TH2 and JW1 that had been eluted from HLA-

DR3 (296, 414) and several variations of the vimentin mutant JW0 (415) were available for testing. The peptides are listed in Figure 5-5A along with their designations and sources.

Two commercially available vimentin peptides were tested as well (sequences also shown in

Figure 5-5A). For the antigen screening, a different hybridoma line was utilized for the 1F6,

2C9, and 2G5 TCR transfections (MN279). As discussed in section 2.10, the MN279 hybridoma line has a mutated human CD4 molecule (as does the 5415 line), but it also contains multiple copies of the murine CD3 complex, giving the TCR both a higher affinity and a higher avidity for peptide. As shown in Figure 5-5B, when the sarcoidosis hybridomas were stimulated with the different vimentin peptides, none of the responses were above background (i.e., no peptide). Additionally, when several of the mKatG and ESAT-6 peptides were tested with the hybridomas in the new line, none of the responses were above background. Therefore, the 1F6, 2C9, and 2G5 sarcoidosis hybridomas are not specific for mKatG, ESAT-6, or vimentin.

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Figure 5-5: Sarcoidosis hybridoma IL-2 responses to vimentin peptides. DAP3-DR3 fibroblast cells (1e5/well) presented various vimentin peptides (β5 μg/ml) to 1F6-MN279, 2C9-MN279, or 2G5-MN279 T cell hybridomas (1e5/well) (A). The cells were stimulated overnight, and IL-2 production was measured by ELISA (B). Selected ESAT-6 and mKatG peptides (β5 μg/ml) were presented to the hybridomas as a comparison.

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5.6 Peptide scanning library (PSL) approach to identify mimotopes

Although several methodologies have been established for the generation of peptide libraries, one approach using PSLs has been successfully utilized for the identification of mimotopes and antigens in CBD and other diseases (301, 307, 416–418). PSLs are comprised of mixtures of peptides of a given length in which each position is considered individually for amino acid preference by the TCR of interest. By using this unbiased approach, all potential peptides of a given length can be assessed for their ability to stimulate a given TCR. Details of the composition of the mixtures are listed in section 2.13 and are depicted in Figure 2-10.

PSLs can identify mimotopes, which are peptides that are not identical in sequence to a given peptide epitope but that bind to the MHC molecule of interest and are recognized by the T cells of interest. After identification of mimotopes by PSL screens, the sequences can be screened against human, viral, bacterial, and fungal protein databases to determine if the sequences are similar to known peptides. Instances of PSLs identifying mimotopes similar to naturally-occurring peptides have been published previously (207, 302, 305–307).

Additionally, PSL biometrical analyses have been utilized for the identification of T cell epitopes associated with several different diseases (302, 305, 306, 416, 419–424).

Advantages of PSLs are that the analyses are unbiased (i.e., the TCR defines the preferred amino acids at any given position) and that the PSLs contain all possible amino acid combinations comprising a peptide of a given length. Potential complications include false positive results at one or more positions (leading to identification of incorrect mimotopes) or a lack of any response to the mixtures (allowing for false negative results). As such, several optimizations have been applied to the PSL assay protocol to increase the sensitivity of the

TCRs while limiting background and false positive responses. The optimizations are discussed in the next section as well as in Appendix B.

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5.7 Optimizing PSL assays using positive control and sarcoidosis hybridomas

As indicated in the previous section, antigen discovery aided by the use of PSLs has been successful for identifying disease-associated antigens in several diseases, including

CBD. However, in the case of CBD, beryllium plus an additional peptide must be present in the system. Therefore, optimization of the assays in that case was performed in which an endogenous response needed to be eliminated in the absence of beryllium so that the PSL responses could be detected above basal IL-2 levels. Similarly for the sarcoidosis and positive control TCRs, an optimized protocol was necessary in order to determine whether a discernible signal-to-noise ratio could be achieved with these TCRs.

One initial issue with the PSL assays was that the signal-to-noise ratio was low due to the signals being very low above the background IL-2 levels. To increase the signal over background, different hybridoma lines were tested for their ability to respond to PSL peptides. The 5415 and MN279 lines with RP15, D2, or the 1F6 TCR responded well to

P4/P5, but 5415 had a somewhat lower response to the peptides with a slightly lower background response (data not shown). The other cell lines (54ZC, no CD4, hu CD4) did not have a high signal in general, although the background was somewhat lower than the 5415 and MN279 lines (data not shown).

The cell culture medium used previously for transfected hybridoma and fibroblast lines was IMDM with 10% serum. However, when using the PSL peptides for assays using

10% serum IMDM, background responses (no peptide) often reached 50 pg/ml IL-2 with the

MN279 hybridoma line, while stimulated responses ranged from 25-100 pg/ml IL-2 (data not shown). Reducing the serum percentage in the medium was not ideal, as the transfected cells grew poorly and often did not expand in IMDM with low serum concentrations.

Therefore, alternate mediums designed for low or no serum conditions were considered.

One such medium, OptiPRO, was tested with several concentrations of serum (0%, 0.5%,

1%, 5%, 10%) for PSL (P4/P5 sublibraries) and cognate peptide assays. In addition, to

129 circumvent the issue that the APCs may have been presenting peptides originating from the serum, effectively outcompeting the PSL peptides for binding, the fibroblast APCs were adapted over the course of several weeks to be grown in 1% or 0% serum OptiPRO medium. However, the APCs also did not grow well or expand proficiently in the low serum conditions. Nonetheless, two representative experiments are shown in Figures 5-6A and 5-

6B, where APCs adapted to 1% serum presented low concentrations of hsp65 and α-gliadin peptides more efficiently to RP15 and D2, respectively, than those grown in 10% serum.

Another optimization for enhancing IL-2 detection above background was increasing the length of the incubation step of the IL-2-containing supernatants during the ELISA. The standard ELISA protocol includes a 2 hour room temperature incubation of the supernatants. However, supernatants incubated overnight at 4°C were tested to see whether lower amounts of IL-2 could be detected without increasing IL-2 levels for the background wells (no peptide) as well as the negative control wells (no supernatant added or no recombinant IL-2 added for the standard curve). As seen in Figures 5-6C and 5-6D, when the RP15 and D2 hybridomas, respectively, were tested with APCs adapted to 10% and 0% serum with either a 2 hour RT or overnight 4°C incubation, the overnight incubation was superior for detecting low concentrations of IL-2 versus 2 hour RT incubation. In addition, the 0% serum was superior to 10% serum, especially in the case of RP15 at low concentrations of peptide.

Another option for optimization was to grow the cells in 10% or 1% serum IMDM and wash them several times in 0% serum OptiPRO before using a low or no serum OptiPRO medium for the stimulation period. The cells grew more robustly in the 10% serum IMDM, and when differing concentrations of assay serum (0% or 10%) were used in OptiPRO, the

10% culture serum  0% assay serum or 1%  0% both worked very well versus the 10%

 10% condition (Figure 5-7, and data not shown).

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Figure 5-6: Serum concentration and supernatant incubation optimization for IL-2 assays. Graphs displaying IL-2 responses as detected by ELISA after the RP15 (A) or D2 (B) hybridomas (1e5/well) were subjected to differing concentrations (0 – 100 ng/ml) of their respective cognate peptides presented by DAP3 fibroblast cells (1e5/well) that had been grown in IMDM with 10% (blue bars) or OptiPRO with 1% (red bars) serum. Graphs displaying IL-2 responses as detected by ELISA after the RP15 (C) or D2 (C) hybridomas (1e5/well) were subjected to differing concentrations (0 – 50 μg/ml) of their respective cognate peptides presented by DAP3 fibroblast cells (1e5/well) that had been grown in IMDM with 10% (blue bars) or OptiPRO with 0% (red bars) serum with supernatants incubated for 2 hours at RT (left, dark bars) or overnight at 4°C (right, light bars). All assays were performed in media using the same concentration of serum that the APCs were grown in originally.

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Figure 5-7: Cell culture serum concentration optimization for IL-2 assays. Graph displaying IL-2 responses as detected by ELISA after the RP15 hybridoma (1e5/well) was subjected to varying concentrations (0 – 50 μg/ml) of hsp65 (γ-13) peptide presented by DAP3 DR3 fibroblast cells (1e5/well) that had been either grown in IMDM with 1% (blue bars) or 10% (red and gray bars) serum. Fibroblasts were then washed twice in 0% serum OptiPRO medium, and the assays were performed in either 0% (blue and red bars) or 10% (gray bars) serum. All supernatants were cultured overnight at 4°C during the ELISA.

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Several other experiments were performed with differing combinations of the following conditions (data not shown): RP15, D2, 1F6, 2C9, and 2G5 TCRs transfected into several hybridoma lines, varying concentrations of serum for the assay, fibroblast APCs adapted to different serum concentrations, IMDM or OptiPRO mediums for cell culture and assays, and varied incubation times for the ELISA supernatant step. All combinations of each of these conditions are listed in Table 5-4, with optimal conditions highlighted in orange. An updated and optimized protocol is included in Appendix B.

After optimizing the IL-2 assay system using both cognate and P4/P5 PSL sublibrary peptides with the various hybridomas available, the RP15 hybridoma was used to determine whether the full set of PSL sublibraries (P1-P10) could accurately predict the known cognate peptide for which the TCR is specific. Hybridoma and fibroblast cells were grown in 10% medium, and after washing the cells twice in 0% serum medium, the final concentration of serum for the assays was either 0% or 0.5%. When 0% serum was used with the PSL peptides, the correct hsp65 (3-13) amino acids could not be distinguished from other possible amino acids at each position (data not shown). However, as shown in Figure 5-8, when 0.5% serum was used, amino acids at positions 1, 2, 3, 4, 6, 7, and 8 were correctly identified (T, I, A, Y, E, E, A, respectively) from the KTIAYDEEARR cognate peptide.

Position 5 showed a glutamic acid (E) instead of an aspartic acid (D), while positions 9 and

10 showed either D (P9 and P10) or E (P10 only) instead of two arginine (R) residues.

Overall, the optimized conditions allowed for the identification of a nearly identical mimotope to the cognate RP15 peptide. Further studies would be necessary to determine whether the

E at P5 and the D/E would be more optimal at P9 and P10, as those mutations have not been tested.

Next, one of the sarcoidosis hybridomas was tested in three independent experiments with the P1-P10 sublibraries with the same conditions as was performed for

RP15, described above. As shown in Figure 5-9A, some positions showed preferred amino

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Table 5-4. Optimization of PSL assays. Cell culture medium IMDM OptiPRO Cell culture medium serum concentration 10% 5% 1% 0.5% 0% Assay medium IMDM OptiPRO Assay medium serum concentration 10% 5% 1% 0.5% 0% Hybridoma line 54ZC 5KC 5415 MN279 ELISA supernatant incubation 2 hours RT 4°C ON

IεDε: Iscove’s εodified Dulbecco’s εedium (Thermo Scientific) OptiPRO: OptiPRO serum free medium (Thermo Scientific) RT: room temperature ON: overnight Boxes shaded orange indicate optimal conditions for PSL screening

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Figure 5-8: Mimotope identification via PSL using an HLA-DR3-restricted positive control hybridoma. Graphs displaying IL-2 responses after the RP15 hybridoma (1e5/well) was subjected to sublibraries 1-10 (β00 μg/ml) presented by DAPγ DRγ fibroblast cells (1e5/well). Letters in blue indicate a match with hsp65 (3-13) KTIAYDEEARR, the cognate peptide for RP15, whereas red letters indicate a mismatch. The IL-2 responses displayed represent values above background (i.e., no peptide). All assays were performed using fibroblast and hybridoma cells grown in 10% serum medium and washed twice in 0% serum medium before being used in the assay at a final serum concentration of 0.5%.

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Figure 5-9: Mimotope identification via PSL using a sarcoidosis hybridoma. Graphs displaying IL-2 responses after the 1F6 hybridoma (1e5/well) was subjected to sublibraries 1-10 (β00 μg/ml) presented by DAPγ DRγ fibroblast cells (1e5/well) (A). The Iδ-2 responses displayed represent values above background (i.e., no peptide). Graphs display means ± SD from 3 independent experiments. Summary data displaying the stimulation index (SI: mixture IL-2/media IL-2) for a given amino acid in the designated position (B).

136 acids, while others did not. The positions that did not have a preference may need additional positions of the peptide fixed (e.g., anchor residues) before the preferred amino acid can be identified. The predominant preferred amino acids that were evident after the three assays are listed in Figure 5-9B, and many of the positions preferred D or E. This phenomenon could be due to one of three possibilities: 1. The assays did not work correctly, giving D and

E at multiple positions, possibly due to the TCR pairing being incorrect or those mixtures having higher concentrations of peptides, 2. D and E are preferred at some positions, but the peptide is allowed to shift by one position in each direction when only one position of the peptide is fixed, or 3. Each of the D and E residues are preferred at the indicated positions.

To test whether the predicted residues were indeed preferred at each of the positions, traditional deconvolution of the results from the three assays was performed so that the maximally stimulatory amino acids at each position were determined, and decapeptides were designed according to the rankings of each amino acid at all positions. In addition, as sarcoidosis could potentially be driven by a self-protein being presented to the T cells, a human protein database was searched for peptides that resembled the potential mimotopes listed in Figure 5-9B, and decapeptides were designed based on close or identical matches in the database. Furthermore, as Mycobacterium tuberculosis is also one of the suspected antigen sources driving sarcoidosis, a tuberculosis (TB) database was referenced for potential peptides matching the mimotopes. As shown in Figure 5-10A, 52 decapeptides were designed from these three sources, based on the data in Figure 5-9A.

When the peptides were tested with the 1F6 hybridoma, several of the peptides produced

IL-2 responses above background (Figure 5-10B). The top nine peptides are shown in

Figure 5-10C, many of which did contain several D and E residues. The top three ranking peptides came from a human leiomodin protein, a TB protein, and a traditional deconvolution peptide, respectively.

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Figure 5-10: Screening of sarcoidosis hybridomas with synthesized decamer peptides. Traditional deconvolution (TD), a human protein database (Hu), and a Mycobacterium tuberculosis protein database (TB) were used to determine appropriate peptides for screening after initial PSL results with the 1F6 hybridoma (A). Graphs displaying IL-β responses after the 1F6 hybridoma (1e5/well) was subjected to peptides (β5 μg/ml) presented by DAP3 DR3 fibroblast cells (1e5/well). The IL-2 responses displayed represent values above background (i.e., no peptide). Shown is a table depicting the most stimulatory peptide names, the sequences (with amino acids colored by property), the average IL-2 produced after no peptide values were subtracted, and the protein source (if known) (C).

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Although the decapeptide assays showed that several of the peptides gave responses above background (no peptide), none of the peptides were stimulatory enough to declare any of the peptides a true mimotope, as the top responses were only ~35 pg/ml above background. In addition, the 1F6, 2C9, and 2G5 TCRs were identified after continuous IL-2 stimulation of sarcoidosis BAL T cells. Therefore, it is unclear whether these

T cells are truly expanded in vivo. One of two steps could be performed in the future: 1. 1F6,

2C9, and 2G5 could be tested with PSL sublibraries where 2, 3, or more positions are fixed to further determine the true preferred amino acids at each position, or 2. The shared TCRs determined by iRepertoire, ePCR, and scPCR could be used, as it has been shown that they are expanded in the BAL during active disease and are shared amongst multiple sarcoidosis patients but not in control patients. These possibilities for future directions are discussed in further detail in chapter VI.

5.8 Summary

Antigen discovery in the sarcoidosis research field has been focused mainly on

Mycobacterium spp., Propionibacterium spp., and self-antigens. However, no study has elucidated a single disease-associated antigen, nor has any study established a common exposure or infection in sarcoidosis patients. Because a diagnostic test is not available for sarcoidosis, and the condition is primarily chronic (with the exception of LS patients), it is of interest to determine whether sarcoidosis-associated antigens exist. If antigens are identified that recruit and stimulate the BAL CD4+ T cells in the lungs of patients during active disease, those antigens could be used to generate tetramers for use in diagnostic screening. Additionally, if the antigens were known, preventative measures could perhaps be taken in many cases, effectively preventing development of the disease. Furthermore, the identity of the antigens could help determine better therapeutics for those with active disease.

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In order to assess the antigen specificity of a given TCR, sensitive methodologies must be employed. The use of hybridomas to study TCR specificity has been instrumental in identifying antigens in other diseases, as discussed above, and its use with sarcoidosis

TCRs may prove fruitful as well. Positive control hybridomas were essential in determining whether the hybridoma assays could be verified. Cloning the RP15 and D2 TCRs into the

MSCV vectors and then transfecting the hybridoma lines confirmed that the approach can generate murine hybridoma lines that are antigen-specific. Each of the TCRs responded to their cognate antigen in the correct MHC presentation setting only. Additionally, using superantigens with the sarcoidosis TCRs of unknown specificity allowed for the confirmation that the transfected TCRs were capable of making IL-2 when stimulated.

RP15 and D2 were also instrumental in determining that the low-level mKatG and

ESAT-6 responses from the sarcoidosis hybridomas were, in fact, non-specific and likely due to impurities in the preparations. The sarcoidosis hybridomas also did not respond above background to several different vimentin peptides. Therefore, although no

Propionibacterium peptides have been tested thus far, the three sarcoidosis hybridomas that have been generated are not specific for mKatG, ESAT-6, or vimentin.

The next experiments involved an unbiased approach to antigen specificity via PSLs.

RP15 and D2 were again used to optimize the system for maximal TCR sensitivity, minimal background, and accurate detection of the correct cognate antigen or mimotope. Once the system was optimized, one of the sarcoidosis TCRs was screened using the full PSL peptide set, and mimotopes were generated based on the results. Several candidate antigens came from those experiments, including self-protein and Mycobacterium peptides.

Overall, the PSL approach was validated using positive control hybridomas, and its use with the newly-sequenced TCRs described in chapters III and IV will be invaluable for determining the antigen specificity of the BAL CD4+ T cells in sarcoidosis patients.

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CHAPTER VI

OVERALL CONCLUSIONS AND FUTURE DIRECTIONS5

6.1 CD4+ T cells in sarcoidosis and LS

The accumulation of CD4+ T cells and other mononuclear inflammatory cells at the sites of disease initiates the formation of noncaseating granulomas and can lead to fibrosis and progressive organ failure in sarcoidosis patients (17, 23, 26, 29, 425). Patients with sarcoidosis have a higher percentage of BAL CD4+ T cells versus the blood, a larger BAL

CD4/CD8 ratio than normal controls, and more CD4+ T cells in the lung than normal controls

(150, 151, 153, 239). The CD4+ T cells that accumulate in the lungs of sarcoidosis patients during active disease proliferate in vitro when cultured in IL-2-supplemented medium and spontaneously secrete IL-2 and other cytokines (143, 150–154, 426, 427). In the BAL of

DR3+ LS patients, the infiltrated CD4+ T cells consist of oligoclonal TCR expansions, suggesting that these T cells are recruited to the lung in response to conventional antigen stimulation (264, 266, 268, 270, 272).

In LS patients, it has been shown previously that the predominant TCR α-chain expressed on CD4+ T cells in BAL is TRAV12-1, and a strong correlation exists between

HLA-DR3 and the expression of TRAV12-1 on BAL CD4+ T cells (221, 238, 268, 269).

Additionally, expansions of several TCR -chains in distinct sarcoidosis populations have been described (264–266, 269, 272, 274). However, rarely have related TCRα or chains been identified among multiple sarcoidosis patients, and to date, no study has successfully characterized complete αTCR pairs expressed on T cells derived from the lungs of multiple sarcoidosis patients.

______

141

Using iRepertoire PCR, TCRα and TCR chains sharing CDRγ homology were found amongst several DR3+ LS patients, and a few of the sequences were also shared with non-LS sarcoidosis patients. Although CDRγα homology was observed amongst the BAL

CD4+ T cells when utilizing iRepertoire, the cells with paired TRAV12-1 and TRBV2 TCRs had differing CDRγα motifs in some cases when determined by ePCR and scPCR.

Instances of antigen-driven TCR specificity with dependence on one TCR chain versus another have been demonstrated in the literature. For example, the TCRs of anti-insulin B:9-

23 clones have a conserved Vα/Jα chain with differing N regions, while another similar clone shares the Vα (but not the Jα) segment (428–430). When only the B:9-23-specific TCRα chain expressing the conserved Vα and Jα chains was expressed in non-obese diabetic

(NOD) mice and was allowed to pair with any TCR, the mice produced high levels insulin autoantibodies, indicating a central role for the TCRα chain in the development of diabetes

(431). In that study, the authors also found that a conserved TCR chain was able to influence the phenotype of the mice when introduced alone and subsequently allowed to pair with any TCRα. The authors hypothesized that autoantigenic peptides exist that drive the T cell response targeting human islet beta cells and that the TCRs recognizing those peptides share Vα and Jα homology, while the TCR chain is primarily influencing the degree of insulitis. It would be of interest to express either the TRAV12-1 TCRα or TRBVβ

TCR (or both) chains with the CDRγ motifs identified from δS DRγ+ patients here in mice without other TCR chains present, as was performed with the B:9-23-specific TCRs mentioned above. Several mouse models of granulomatous disease resembling sarcoidosis have been demonstrated, as reviewed in (432). The models primarily involved injection of either Mycobacterium tuberculosis or Propionibacterium acnes via various routes of administration into C57BL/6 mice. The mice had varying degrees of granuloma formation and CD4+ T cell responses. Expressing only the TRAV12-1+ and/or TRBV2+ TCRs found in this study in these or other murine granuloma models might demonstrate whether the TCRs

142 are sufficient to induce granulomatous disease similar to that found in LS and whether the

TCRα or TCR chain is more important for disease development and severity.

The disproportionate TCRα and TCR chain roles in antigen recognition and disease development has also been demonstrated in CBD (304). TCR chains expressing TRBV5-1 in CBD patients pair with different TRAV chains to generate beryllium-specific TCRs that recognize the same antigenic epitope (304, 433). Using site-specific mutagenesis along the

CDRs of the TCRα and TCR genes, it was found that T cell recognition was dependent on several TCR residues in CDR1, CDRβ, and CDRγ, while only one amino acid of the

CDRγα region was required for beryllium recognition. Given the nearly identical TCR chains shared by LS patients, the data presented here might support a similar model to that observed in CBD, where recognition of the LS-associated antigen being presented by HLA-

DRγ could be dependent on the TCR CDR1, CDR2, and CDR3 regions. The role in antigen recognition for the TCRα chains on sarcoidosis BAL CD4+ T cells with differing

CDR3 sequences might be minimal, and perhaps the CDR1 and CDR2 regions of the TCRα chain are more directly contacting the HLA-DR3 molecule, driving the linkage between DR3 and TRAV12-1-expressing CD4+ T cells possessing varying CDR3 regions. As discussed previously, a recent publication depicted a three-dimensional computer-generated model of a TRAV12-1/TRBV2 TCR in complex with HLA-DR3-vimentin (272). Although the model predicted a binding topology that agrees with speculations presented here, it is important to state that the antigens that are recognized by the TCRs identified here and the mechanisms by which the TCRs bind to the HLA-DR3-peptide complex remain unknown.

Previous work characterizing lung-accumulated CD4+ T cells in sarcoidosis patients has been hindered by a lack of suitable methodology for determining both α- and -chain usage on single cells. In the present study, this limitation was overcome by using iRepertoire

PCR to identify CD4+ TCR α and gene usage as well as ePCR and scPCR to elucidate

αTCR pairing on CD4+ T cells derived from the lungs of DR3+ LS patients. The present

143 study shows that TRAV12-1 preferentially pairs with TRBV2 and identifies public TRAV12-

1/TRBV2 TCRs that likely play a critical role in antigen recognition in the lungs of LS patients.

+ 6.2 Public versus private CD4 αβTCR repertoires

Public T cells are characterized by the expression of identical or highly related TCR

Vα and/or V genes that are present in the majority of subjects, dominate the response to a specific epitope, and dictate disease severity, despite being restricted in nature (434–441).

In order for a public T cell response to occur, the T cells must be selected from approximately 1020 different potential TCRs in humans after thymic selection (253–256).

Furthermore, only ~1011 T cells are estimated to be circulating in a human (253–257).

Therefore, it is a rare occurrence for any given TCR to be repeated in an individual unless there is a selection bias occurring, due to antigen selection or other phenomena.

This type of TCR bias has been infrequently demonstrated in CD4+ T cells obtained from blood or the target organ of human subjects, due in large part to unknown stimulatory antigens. αTCR pairs have been derived from the lungs of HLA-DP2-expressing CBD patients that recognize an identical HLA-DP2-peptide/Be complex (303). In addition, the inverse relationship between expansion of CD4+ T cells expressing these public TCRs and lung function suggests a pathogenic role for these T cells in CBD (303). In contrast, increased expression of TRAV12-1 on CD4+ T cells in the BAL of LS patients has been associated with a better prognosis, and the disappearance of these public CD4+ T cells from the lungs of LS patients occurs with disease resolution (238, 268). Thus, the differences between public T cells in LS and CBD likely relates to antigen clearance from the lung. For example, in CBD, public T cells may drive the loss of lung function and the development of fibrosis due to the persistence of beryllium within the lung, while in LS, public T cells may be intimately involved in clearance of an as of yet unknown antigen.

144

A recent paper described a public CD4+ TCR repertoire in human immunodeficiency virus (HIV) positive individuals (442). The authors looked at T cell responses to a specific

HIV protein, Gag293, which had been shown previously to be very immunostimulatory to

HIV-infected patient cells. Gag293 peptide was used to stimulate CD4+ T cells from PBMCs, and then Gag293 tetramers were used to sort tetramer-binding CD4+ T cells. TCR diversity of the cells was performed on purified RNA by using TRAV- and TRBV-specific primers for

RT-PCR reactions. TRAV24 and TRBV2 segments were overrepresented in patients who controlled HIV infection without therapy versus those on therapy, and the corresponding

PCR products were sequenced for identification of CDR3 similarities and other metrics.

Public motifs (18 utilizing TRAV24 and 18 utilizing TRBV2) were found in Gag293-specific

CD4+ cells in HIV controllers. However, no assessment of the αTCR pairs was performed, so it is unclear whether the public TCRs in these patients co-express TRAV24/TRBV2.

However, the authors did co-express several of the sequences together on TCR- mutant

Jurkat cells, and they found that the assembled TCRs were specific for the GAG293 tetramer, could recognize HIV antigens, and responded to Gag293 peptide. Of note, the

TRBV2 public repertoire motif (CASSRRTSGGDEQFF) did not match any TRBV2 motifs identified in DR3+ LS patients in the present study.

As well reviewed in (434) and (436), public CD8+ T cell responses have also been characterized in both cytomegalovirus (CMV) and Epstein-Barr virus (EBV) infections (443–

452). Other public CD8+ T cell responses have been identified in HIV (453, 454), simian immunodeficiency virus (SIV) (455), experimental autoimmune encephalomyelitis (EAE)

(456–458), and a multitude of other diseases or targets.

Several explanations of why public TCR repertoires exist have been discussed in the literature, as reviewed in (434). Proposed structural reasons for public T cell occurrences include that the shape of the peptide-MHC can drive a bias by allowing only a certain proportion of TCRs direct contact points (459–464) or that structural changes to the TCR

145 itself during binding can lead to biases (465, 466). Explanations of public T cell repertoires based on sequences include the idea that TCRs with sequences almost identical to germline

V, D, and J segments may be biasing the repertoire (443, 467). Some studies have proposed that the reason germline-like sequences are more likely to form public repertoires is that they are more degenerate in recognition of antigens versus those that have not evolved over generations (i.e., those that include random nucleotide additions and deletions)

(468, 469). Although this could be one explanation, there are several studies which refute the idea, as public repertoires identified previously were observed as having many nucleotide additions (445, 450, 470). One explanation for biases in the T cell repertoire is the idea of “convergent recombination”, explained in detail in (434) and first proposed in

2006 (470). The concept behind convergent evolution is that different recombination patterns of V(D)J segments can converge to produce the same nucleotide sequence, while several nucleotide sequences can also converge to encode the same amino acid sequence.

Instances of recombinatorial biases (i.e., preferential V(D)J recombinations) are reviewed in

(436), and the authors explain that, in particular, several instances of TCR skewing toward certain V, D, and/or J segment pairings have been observed. Although any of these explanations are possible as the reason behind the occurrence of public T cell repertoires, no definitive explanation has been established.

As previously reviewed in (434) and (436), private TCRs found within individuals also exist and can be antigen-specific. The authors explain that private TCRs are on the other extreme of TCR sharing from public TCRs, and these TCRs likely have a different driving force behind their expansion and presence in a given individual. A public TCR could be present in multiple individuals regardless of its avidity for a given pMHC, as discussed above. However, a private TCR likely requires a very high avidity for pMHC, as the TCR is relatively scarce in any given population. Therefore, the TCRs with the highest avidity for a given pMHC in an individual will outcompete other TCRs in that individual, giving rise to a

146 private TCR repertoire. In contrast, public TCRs could have a higher frequency in the thymus, meaning they will be more common in a population. The public TCRs, therefore, will outcompete other TCRs simply because they are present more often in a given population.

Within the iRepertoire, ePCR, and even the scPCR data from one patient generated in the current study, private TCR repertoires were observed (data not shown).

Overall, sharing of TCRs has been demonstrated in multiple individuals, as well as within individuals, for a variety of diseases. Public and private TCR repertoires can respond to the same pMHC, meaning that the public TCRs identified might only represent a small fraction of the sarcoidosis antigen-specific TCRs in DR3+ LS and/or non-LS patients. Further analyzation of the multitude of sequences obtained from this cohort of patients is necessary to determine whether private repertoires occur frequently. In addition, determining whether those TCRs have the same antigen specificity as the public TCRs will need to be assessed.

6.3 Mimotopes and putative sarcoidosis-associated antigens

A major advancement in the sarcoidosis research field would be the identification of mimotopes of peptide antigens that are presented to the infiltrated CD4+ T cells during active disease. Although several proposed antigens have been studied over the years, no common exposure (or consensus response to an antigen) has been elucidated. Some of the peptides tested have included those of mycobacterial, propionibacterial, or human self-protein origin.

Whether these peptides or proteins are involved with the initiation or perpetuation of sarcoidosis remains unclear. In the present study, sarcoidosis TCRα and TCR chains that were sequenced after BAL cells from LS DR3+ patients were cultured for several weeks in

IL-2 were cloned into murine hybridomas. The hybridomas were tested for specificity to mKatG, ESAT-6, and vimentin peptides, but no stimulation was found above background.

Several of the ESAT-6 and mKatG peptides did stimulate above background, but they were

147 found to also stimulate positive control hybridomas that expressed TCRs unrelated to sarcoidosis and of known specificity.

As the screens with the mKatG, ESAT-6, and vimentin peptides were negative, one of the sarcoidosis hybridomas was screened with an unbiased PSL. Several D and E residues showed up in the initial screen, and decapeptides were generated to match the preferred amino acids after the initial screening. Although most of the decapeptides stimulated the hybridoma above background, none of the candidates induced a response to the same extent that would be expected after presentation of the cognate peptide. Based on the data from the positive control TCRs (RP15 and D2), cognate responses are often 100-

1000 pg/ml over background when using the hybridoma system described in the current study. Mimotopes can produce lower responses in general, but until other sarcoidosis hybridomas are tested with the decapeptides, it is not clear whether any of the tested mimotopes are similar to the cognate peptides involved in sarcoidosis pathogenesis. The other sarcoidosis hybridomas, 2C9 and 2G5, could be tested either with the PSL or with the decapeptides. However, based on their TCR sequences compared to each other and to the

1F6 hybridoma (see Table 5-3), they likely would not respond to the same peptides.

Furthermore, as all three sequences were obtained after BAL cells were cultured for several weeks in IL-2, it is unclear whether the CD4+ T cells bearing those TCRs were expanded in the lung or if they just represented robust clones that expanded well after ex vivo stimulation.

The next steps in terms of determining potential mimotopes or sarcoidosis- associated antigens will include cloning the public TCRs from the current study into murine hybridomas and first subjecting them to stimulation by the mKatG, ESAT-6, and vimentin peptides. The TCRs that would be used for future studies should include several TCRs with related α or chains, but it would be redundant to generate hybridomas with nearly identical

α and chains. For example, in Table 4-1, good candidates for cloning would be pairs 2 and

148

5-13. It would not be necessary to generate pairs 1, 3, and 4 as hybridomas, since they are very similar in sequence to other listed TCRs.

If the initial screens with known peptides are negative, the PSL approach could be used to determine which amino acids are preferred at each position of the peptide for any given TCR. After the initial screening, decapeptide screening could be performed, or biased libraries where two or more positions are fixed could be done. Additionally, if the initial PSL screens do not show preferences at particular positions, a biased library with known HLA-

DR3 anchor residues in the peptides could be made. For example, DR3 has the following preferred amino acids: P1 (L, I, F, M, V), P4 (D), P6 (K, R, E, Q, N), and P9 (Y, L, F) (224).

The RP15 peptide KTIAYDEEARR fits this motif loosely, as the I could be P1, so the D at P4 and E at P6 match the preferred amino acids. It would be of interest to design a biased PSL library for the DR3-restricted positive control hybridoma, RP15, with those anchor residues fixed. The initial PSL results for RP15 indicated that P5-E, P9-D, and P10-D/E may actually be maximally stimulatory, so whether that preference would hold up after anchor positions were fixed would be of great use in future experiments with TCRs of unknown specificity. In fact, the initial study describing the preferred amino acids for DR3 binding of hsp65 and for

RP15 stimulation by the peptide noted that the P9 and P10 tolerated K and Y/F, respectively, for DR3 binding, but they did not test whether D or E would have bound better in either or both positions (226). That study showed that P5-E bound about 50% as well as

P5-D. Furthermore, after performing amino acid substitutions at each position of the peptide, it was shown that RP15 required only P1-T, P3-A, and P4-Y. The authors did not look at P2 or P5, due to those being preferred for DR3 binding, so it is unknown whether the P5-E found here might be more stimulatory than the P5-D in the hsp65 (3-13) peptide. Therefore, screens using a modified decapeptide based on the initial PSL screening

(TIAYEEEAD[D/E]) might indicate that the modified peptide is more stimulatory than the hsp65 (3-13) peptide. These experiments could be performed to better understand the

149 specificity of the PSL approach and to determine whether the next steps after initial screening with one amino acid fixed at each position would be: A. Biased libraries based on the initial screens; B. Biased libraries based on known preferred anchor residues of the HLA molecule, or C. Direct decapeptide screening based on the initial screens. Additionally, only a few PSL experiments were performed using the HLA-DQ2-restricted hybridoma, D2 (data not shown). Future studies could focus on that hybridoma as a second positive control in the setting of antigen presentation by a different MHC.

After PSL assays are optimized further, and a streamlined approach for determining next steps after initial screening has been established, testing of the public TCRs from DR3+

LS patients will be useful in determining whether putative LS-associated antigens exist and whether they relate to a particular exposure, infection, or autoimmune disorder. The results in the current study are promising in terms of identifying the cause of the CD4+ T cell alveolitis in LS, as they demonstrate that there are several TCRs with CDR3 homology expanded in the lungs of and shared amongst LS individuals who not only possess an identical MHC allele, but who present with nearly identical clinical symptoms. As the TCRs are not found in non-LS or control patients, the mimotopes that are discovered will be of vital importance for determining the causative agent in LS. Furthermore, there are also sequences within the data that are shared amongst all sarcoidosis patients (LS and non-LS) but not in controls, so further analyzation of the data could lead to identification of a disease- causing agent in the majority of sarcoidosis patients.

If the PSL screens identify mimotopes and/or disease-causing agents, class II MHC tetramers could be constructed using HLA-DR3 in combination with the LS-associated antigens or mimotopes. As depicted in Figure 1-3, the tetramers could then be utilized for multiple purposes, including being used as a diagnostic test for patients suspected of having

LS. As there is no single confirmatory diagnostic test for any form of sarcoidosis, using tetramers to identify LS-associated CD4+ T cells in the blood or BAL of patients would be a

150 substantial advance in the clinic. Additionally, the progression of disease and the response to therapy could be assessed using tetramers to track the CD4+ alveolitis in patients with active disease and in those being treated, respectively.

6.4 Conclusions

The link between LS and the accumulation of CD4+ T cells expressing TRAV12-1 has been known for more than 20 years (268). However, progress toward the delineation of the underlying mechanism(s) responsible for the recruitment of these cells to the lung has been hampered by the inability to generate a large number of BAL T cell clones and the failure to identify the paired TCR chain. The ability to detect public LS-associated TCRs in the current study was aided by the use of deep sequencing technologies including ePCR and scPCR. These approaches have several advantages, including their ability to determine

αTCR pairs on T cells directly ex vivo and without cytokine-aided expansion. This is particularly important in a target organ where the majority of T cells are terminally- differentiated and thus incapable of vigorously undergoing repeated rounds of stimulation

(471). Conversely, T cell cloning is an ineffective approach for obtaining large numbers of clones expressing unique αTCRs, predominantly selecting those T cells that retain proliferative capacity, thus biasing the TCR repertoire. The current approach, in particular scPCR, obviates the need for T cell cloning and can be utilized to delineate the most expanded αTCRs expressed on T cells derived from any target organ. Disadvantages of this approach are expense and the requirement for a biased or skewed TCR repertoire.

Thus, this deep sequencing approach will likely not be applicable to the analysis of a diverse

TCR repertoire, such as exists in blood.

A weakness of the current study is the small number of patient samples. This likely accounted for the inability to identify a correlation between CD4/CD8 ratio and the expression of TRBV2 on BAL CD4+ T cells in LS patients as previously observed by

151

Grunewald et al. (221). However, as shown here, large numbers of patients are not required when using deep sequencing αTCR repertoire methodologies due to the depth of the analyses.

In conclusion, the findings presented here identify public αTCRs expressed on BAL

CD4+ T cells in LS patients, and the data strongly suggest that these public TCRs recognize putative LS-associated antigens and drive disease pathogenesis. Importantly, the delineation of complete αTCR pairs with a distinct association to HδA and prognosis will enable future studies on antigen recognition.

152

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452. Day, E. K., A. J. Carmichael, I. J. M. ten Berge, E. C. P. Waller, J. G. P. Sissons, and M. R. Wills. 2007. Rapid CD8+ T cell repertoire focusing and selection of high-affinity clones into memory following primary infection with a persistent human virus: human cytomegalovirus. J. Immunol. 179: 3203–13.

453. Gillespie, G. M. A., G. Stewart-Jones, J. Rengasamy, T. Beattie, J. J. Bwayo, F. A. Plummer, R. Kaul, A. J. McMichael, P. Easterbrook, T. Dong, E. Y. Jones, and S. L. Rowland-Jones. 2006. Strong TCR conservation and altered T cell cross-reactivity characterize a B*57-restricted immune response in HIV-1 infection. J. Immunol. 177: 3893– 902.

454. Yu, X. G., M. Lichterfeld, S. Chetty, K. L. Williams, S. K. Mui, T. Miura, N. Frahm, M. E. Feeney, Y. Tang, F. Pereyra, M. X. Labute, K. Pfafferott, A. Leslie, H. Crawford, R. Allgaier, W. Hildebrand, R. Kaslow, C. Brander, T. M. Allen, E. S. Rosenberg, P. Kiepiela, M. Vajpayee, P. A. Goepfert, M. Altfeld, P. J. R. Goulder, and B. D. Walker. 2007. Mutually exclusive T-cell receptor induction and differential susceptibility to human immunodeficiency virus type 1 mutational escape associated with a two-amino-acid difference between HLA class I subtypes. J. Virol. 81: 1619–31.

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456. Fazilleau, N., C. Delarasse, C. H. Sweenie, S. M. Anderton, S. Fillatreau, F. A. Lemonnier, D. Pham-Dinh, and J. M. Kanellopoulos. 2006. Persistence of autoreactive myelin oligodendrocyte glycoprotein (MOG)-specific T cell repertoires in MOG-expressing mice. Eur. J. Immunol. 36: 533–43.

457. Fazilleau, N., C. Delarasse, I. Motta, S. Fillatreau, M.-L. Gougeon, P. Kourilsky, D. Pham-Dinh, and J. M. Kanellopoulos. 2007. T cell repertoire diversity is required for relapses in myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis. J. Immunol. 178: 4865–75.

458. Menezes, J. S., P. van den Elzen, J. Thornes, D. Huffman, N. M. Droin, E. Maverakis, and E. E. Sercarz. 2007. A public T cell clonotype within a heterogeneous autoreactive repertoire is dominant in driving EAE. J. Clin. Invest. 117: 2176–85.

459. Miles, J. J., D. Elhassen, N. A. Borg, S. L. Silins, F. E. Tynan, J. M. Burrows, A. W. Purcell, L. Kjer-Nielsen, J. Rossjohn, S. R. Burrows, and J. McCluskey. 2005. CTL recognition of a bulged viral peptide involves biased TCR selection. J. Immunol. 175: 3826– 34.

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468. Gavin, M. A., and M. J. Bevan. 1995. Increased peptide promiscuity provides a rationale for the lack of N regions in the neonatal T cell repertoire. Immunity 3: 793–800.

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470. Venturi, V., K. Kedzierska, D. A. Price, P. C. Doherty, D. C. Douek, S. J. Turner, and M. P. Davenport. 2006. Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination. Proc. Natl. Acad. Sci. U. S. A. 103: 18691–6. 194

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APPENDIX A

EMULSION PCR PRIMERS

A. Emulsion PCR 1 TRAV and TRBV primers. Overlap extension sequences indicated in red.

Name Sequence (5’ to 3’)

TRAV1 GGCGCGCCATGGGAATAGGAGCACCCACATTTCTKTCTTACAATG

TRAV10 GGCGCGCCATGGGAATATCATGACTTTCAGTGAGAACACAAAGTCG

TRAV11 GGCGCGCCATGGGAATAGACCTTAATTCAATCAAGCCAGAAGGAGC

TRAV12 GGCGCGCCATGGGAATAGAAGATGGAAGGTTTACAGCACAGSTC

TRAV13 GGCGCGCCATGGGAATAATTATAGACATTCGTTCAAATGTGGGCGA

TRAV14 GGCGCGCCATGGGAATACAACAGAAGGTCGCTACTCATTGAATTTCC

TRAV15 GGCGCGCCATGGGAATAGTGTCTTTGACCTTAATTCAATCAAGCCAGATG

TRAV16 GGCGCGCCATGGGAATACTCCAGTTACTCTTGAGACACATCTCTAGAG

TRAV17 GGCGCGCCATGGGAATACAGGTAGAGGCCTTGTCCACC

TRAV18 GGCGCGCCATGGGAATACCTGAGCTCCTCCTGAAAAGTTCAG

TRAV19 GGCGCGCCATGGGAATAGTGGAGAATTGGTTTTCCTTATTCGTCGG

TRAV2 GGCGCGCCATGGGAATAGTTAAAGGCTCAAAGCCTTCTCAGCAG

TRAV20 GGCGCGCCATGGGAATAGAATTCCTCTTCACCCTGTATTCAGCTG

TRAV21 GGCGCGCCATGGGAATACTCACATCTCTGTTGCTTATTCAGTCAAGTCAG

TRAV22 GGCGCGCCATGGGAATACATTCCCTCAGGGACAAAACAGAATGG

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TRAV23 GGCGCGCCATGGGAATAGCATTATTGATAGCCATACGTCCAGATGTG

TRAV24 GGCGCGCCATGGGAATACCGAGGCCTTGTTTGTAATGACTTTAAATGG

TRAV25 GGCGCGCCATGGGAATAGATACAGTTAGTGAAGAGTGGAGAAGTGAAG

TRAV26 GGCGCGCCATGGGAATAACAGMTTCACTCCCAGGGKCCA

TRAV27 GGCGCGCCATGGGAATACCTGGTGACAGTAGTTACGGGTG

TRAV28 GGCGCGCCATGGGAATAGTGCAGTGGTTTCATCAAAAGCCTG

TRAV29 GGCGCGCCATGGGAATACCTGCTGAAGGTCCTACATTCCTG

TRAV3 GGCGCGCCATGGGAATAGGGATAACCTGGTTAAAGGCAGCTATG

TRAV30 GGCGCGCCATGGGAATACACCCGTCTTCCTGATGATATTACTGAAGG

TRAV31 GGCGCGCCATGGGAATAGGTCTCCCAATGGGAAGATTATTTTCCTC

TRAV32 GGCGCGCCATGGGAATACGGGGAAGGCCCTAATATCTTAATGG

TRAV33 GGCGCGCCATGGGAATAGCAACCTCCCAGTGAAGAGATGG

TRAV34 GGCGCGCCATGGGAATAGTCTTATCTTCTTGATGATGCTACAGAAAGGTGG

TRAV35 GGCGCGCCATGGGAATAGGGGAAGGTCCTGTCCTCTTG

TRAV36 GGCGCGCCATGGGAATAGCTCCCACATTTCTATTTATGCTAACTTCAAGTG

TRAV37 GGCGCGCCATGGGAATACTGAGGAAGGCCTCATTTCCCTG

TRAV38 GGCGCGCCATGGGAATACGCCAAGAAGCTTATAAGCAACAGAATGC

TRAV39 GGCGCGCCATGGGAATAGTTTGTGTTGCTATCAAATGGAGCAGTG

TRAV4 GGCGCGCCATGGGAATAGCCAAGGACCACGATTTATTATTCAAGGATAC

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TRAV40 GGCGCGCCATGGGAATACCTCTGCAGCTTCTTCAGAGAGAG

TRAV41 GGCGCGCCATGGGAATAGCATGGAAGATTAATTGCCACAATAAACATACAGG

TRAV5 GGCGCGCCATGGGAATACCTGGAGCAGGTCTCCAGTTG

TRAV6 GGCGCGCCATGGGAATAGGTACCGACAAGATCCAGGAAGAG

TRAV7 GGCGCGCCATGGGAATAGGATGGGTCCCAAACACCTATTATCC

TRAV8 GGCGCGCCATGGGAATAGYTTTGAGGCTGAATTTAWVAGGAGYVAAWYYTCC

TRAV9 GGCGCGCCATGGGAATAGACAAGGGAAGSAACAAAGGTTTTGAAG

TRBV10 TATTCCCATGGCGCGCCCATGGGCTGAGGCTGATCYATTAC

TRBV11 TATTCCCATGGCGCGCCGTTGCCTAAGGATCGATTTTCTGCAGAG

TRBV12 TATTCCCATGGCGCGCCGATGATTCGGGGATGCCSGTA

TRBV13 TATTCCCATGGCGCGCCGATGCAGAGCGATAAAGGAAGCATCC

TRBV14 TATTCCCATGGCGCGCCGTGAAAGAGTCTAAACAGGATGAGTCCG

TRBV15 TATTCCCATGGCGCGCCCCTGATAACTTCCAATCCAGGAGGC

TRBV16 TATTCCCATGGCGCGCCGGTATGCCCAAGGAAAGATTTTCAGC

TRBV17 TATTCCCATGGCGCGCCGATTTCCTTCCAGTACCAAAACATTGCAGTTG

TRBV18 TATTCCCATGGCGCGCCCAGAGGAAGGTCTGAAATTCATGGTTTATCTC

TRBV19 TATTCCCATGGCGCGCCCAGGGCAAGGGCTGAGATTG

TRBV2 TATTCCCATGGCGCGCCCTCAGAGAAGTCTGAAATATTCGATGATCAATTCTC

TRBV20 TATTCCCATGGCGCGCCCACATACGAGCAAGGCGTCG

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TRBV23 TATTCCCATGGCGCGCCCGGAGATGCACAAGAAGCGATTCTC

TRBV24 TATTCCCATGGCGCGCCGCCTACGGTTGATCTATTACTCCTTTGATG

TRBV25 TATTCCCATGGCGCGCCGGAACTACACCTCATCCACTATTCCTATGG

TRBV27 TATTCCCATGGCGCGCCGACTGATAAGGGAGATGTTCCTGAAGG

TRBV28 TATTCCCATGGCGCGCCGGGCTACGGCTGATCTATTTCTCATATG

TRBV29 TATTCCCATGGCGCGCCGCTCTGAGGCCACATATGAGAGTG

TRBV3 TATTCCCATGGCGCGCCGAAACAGTTCCAAATCGCTTCTCACCT

TRBV30 TATTCCCATGGCGCGCCCCAGCTGCTCTTCTACTCCGTTG

TRBV4 TATTCCCATGGCGCGCCGCTAAGAAGCCACTGGAGCTCATG

TRBV5 TATTCCCATGGCGCGCCGAGGMAACTTCCCTSMTMGATTCTCAG

TRBV6 TATTCCCATGGCGCGCCGGTATCGACAAGACCCAGGCA

TRBV7 TATTCCCATGGCGCGCCTTGGTACCGACAGGSCCTG

TRBV9 TATTCCCATGGCGCGCCCGATTCTCCGCACAACAGTTCC

RT AC CTTGAAGTCCATAGACCTCATGTCTAGC

RT BC GTGCTGACCCCACTGTGC

Stepout TCRA TATTCCCATGGCGCGCC

Stepout TCRB GGCGCGCCATGGGAATA

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B. Emulsion PCR 2 C region primers.

Name Sequence (5’ to 3’)

1st nested AC AATCGGTGAATAGGCAGACAGACTTG

1st nested BC CTTCTGATGGCTCAAACACAGCG

Alpha block TTTTTTTGGCGCGCCATGGGAATATTT/γ’PHOS/

Beta block TTTTTTTATTCCCATGGCGCGCCTTT/γ’PHOS/

C. Emulsion PCR 3 C region primers. Underlined is the bridge priming site. The INDEXX is a 6 base barcode specific to Illumina. The INDX is a 4 base identifier that allows for multiplexing each Illumina barcode further. The NN bases are either 0, 4, 6, or 8 bases of randomized sequence to help diversify the reads prior to moving through the common priming site.

Name Sequence (5’ to 3’)

CAA GCA GAA GAC GGC ATA CGA GAT INDEXX GTG ACT GGA GTT CAG ACG 2nd nested AC TGT GCT CTT CCG ATC TGG GTC AGG GTT CTG GAT AT

AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC GCT 2nd nested BC CTT CCG ATC TNN INDX CCT CGG GTG GGA ACA C

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APPENDIX B

UPDATED AND OPTIMIZED PEPTIDE SCANNING LIBRARY PROTOCOL

DAP3 Cell Culture 1. Thaw DAP3 fibroblasts in 10% serum IMDM (see notes below) 2. Wash twice in media, count cells 3. Plate all cells in 1 – 2 T75 flasks 4. Pass cells every 2 – 3 days into new T75 flasks and eventually into T175 flasks 5. To pass cells in T175 flasks (should be ~100% confluent): a. Label 50 ml tubes, 1 per T175 flask, add 10 ml IMDM (10% serum) b. Remove old media from T175 flask c. Rinse the cells gently with 10 – 12 ml warm PBS d. Remove PBS and discard e. Coat surface of flask with 2 ml trypsin-EDTA i. Rock back and forth gently to ensure complete coverage f. Allow trypsin to remain on cells for 30 – 45 seconds g. Remove trypsin and discard h. Hit the flask firmly from each side several times to loosen cells i. Use 8 ml IMDM (10% serum) to rinse the cells to the bottom of the flask j. Transfer the 8 ml containing trypsinized cells into the labeled 50 ml tube k. Optional: Repeat steps i and j if necessary l. Invert the tube several times to mix m. Spin at 1400 rpm for 5 min n. Remove supernatant, resuspend in 8 – 10 ml IMDM (10% serum) o. Pass 0.5 – 2 ml of cells per new T175 flask, bring volume to 20 ml total p. Optional: With remaining cells, if transferring to 0.5% serum for experiment: i. Add OptiPRO medium (0% serum) to the 50 ml line of the 50 ml tube ii. Invert to mix iii. Spin cells at 1400 rpm, 5 min

Hybridoma Cell Culture 1. Thaw hybridoma cells in appropriate 10% serum IMDM (see notes below) 2. Wash twice in media, count cells 3. Plate all cells in 1 – 2 T75 flasks 4. Pass cells every 2 – 3 days into same T75 flasks, replace flasks every 7 – 10 days 5. To pass cells (should be cloudy, but media still red): a. Transfer all cells from flask into a labeled 50 ml tube b. Add appropriate volume of new media to flask c. Pass 0.5 – 1 ml of collected cells into flask to obtain a total of 20 ml/flask d. Optional: With remaining cells, if transferring to 0.5% serum for experiment: i. Add OptiPRO medium (0% serum) to the 50 ml line of the 50 ml tube ii. Invert to mix iii. Spin cells at 1400 rpm, 5 min

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Notes A. Trypsin-EDTA (0.25%), Gibco B. 500 ml base IMDM, Gibco, supplemented with: a. 1% sodium pyruvate (Gibco) i. 5 ml per 500 ml bottle IMDM b. 1% thio/pen/strep i. 5.5 ml frozen aliquots of: 65 μl monothioglycerol (Sigma) added to 100 ml bottle of penicillin/streptomycin (Invitrogen), mixed and sterile filtered c. 10% FBS (Gibco) i. 50 ml per 500 ml bottle IMDM C. DAP3 DQ2 media a. Base IεDε + β00 μg/ml hygromycin B (Invitrogen) D. DAP3 DR3 media a. Base IεDε + 600 μg/ml G418 (Sigma) E. 5KC hu CD4 media a. Base IεDε + 0.5 μg/ml puromycin (Sigma) F. 5KC no CD4 media a. Base IMDM G. 5415 media a. Base IMDM (optional: + 200 μg/ml hygromycin B) H. 54ZC media a. Base IεDε + 600 μg/ml G418 I. MN279 media a. Base IMDM J. 0% serum OptiPRO a. 1 L OptiPRO SFM (Gibco) b. 2% (2X) GlutaMAX (Gibco) K. Freeze media a. 1 ml DMSO (Sigma) b. 1 ml FBS c. 3 ml base IMDM d. Resuspend cells in 1.8 ml per vial

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Experimental Setup for PSL Screening 1. Store PSL peptides at 4°C if using for <2 weeks, otherwise, freeze at -80°C 2. While performing next steps, bring chilled PSL peptides to RT or thaw frozen PSL peptides by placing boxes with lids removed on paper towels in a 37°C incubator 3. Pass DAP3 cells as described above, following the optional step 5p 4. After completing the optional step, discard supernatant and resuspend cells in 10 ml 0% OptiPRO medium 5. Vortex briefly and add 0% OptiPRO medium to a total of 50 ml 6. Spin cells again at 1400 rpm, 5 min 7. Discard supernatant, resuspend cells in 10 – 20 ml 0% serum OptiPRO 8. Count cells 9. Spin cells, resuspend in appropriate volume of 0.5% FBS OptiPRO for experiment a. See plate diagram in template below for plate setup and total cell # needed 10. Plate DAP3 cells a. For DAPγ: Want 1e5 cells/well in 100 μl total, i.e., 1e6/ml 11. Thawed PSL peptides can be removed from the incubator 12. Each strip of 8 peptides corresponds to Rows A-H on a particular plate (see diagram below) a. Align the strips in correct order for a given plate b. With caps securely on, vortex a strip for 6 seconds on full speed c. Quickly and carefully (so as not to contaminate other tubes) remove the strip cap and place it next to the tubes d. Using an 8-well multichannel pipette set to β0 μl, quickly pipet up and down 3 times using 1 tip per tube e. Transfer the β0 μl to appropriate wells, pipet up and down γ times, and release contents into well f. Quickly remove used tips and repeat steps d – e using fresh tips for next row i. ALWAYS: use new tips for each row ii. ALWAYS: vortex immediately prior to using a strip in duplicate iii. Replace caps carefully and accurately to avoid cross-contamination of peptide mixtures g. See diagram below for control wells and to see which wells are for the ELISA standard (STN, pg/ml listed to the right) i. Column 11 gets DAPγ, β0 μl ddH2O (sterile-filtered), and hybridoma cells ii. Column 12 gets nothing until ELISA steps 13. Pass hybridoma cells as described above, following the optional step 5d 14. Follow steps 4-9 from this section using hybridoma cells 15. Plate hybridoma cells a. For hybridomas: Want 1e5 cells/well in 80 μl total, i.e., 1.β5e6/ml b. Total volume should now be β00 μl/well 16. Incubate overnight at 37°C, 5% CO2 17. Continue with murine IL-β EδISA protocol as per manufacturer’s instructions, except: a. Allow supernatant step to run overnight at 4°C i. NOT 2 hours at RT b. ELISA kit: Ready-SET-Go! (eBioscience)

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Experiment number: __AM 001-50____ Dates: __5-8-16 – 5-10-16__ Name: _PSL 2040 P1-P10, 0.5% serum_

1 2 3 4 5 6 7 8 9 10 11 12 pg/ml

A1 A1 K1 K1 T1 T1 F2 F2 P2 P2 Hyb STN 200 A

C1 C1 L1 L1 V1 V1 G2 G2 Q2 Q2 + STN 100 B

D1 D1 M1 M1 W1 W1 H2 H2 R2 R2 water STN 50 C

E1 E1 N1 N1 Y1 Y1 I2 I2 S2 S2 with STN 25 D

F1 F1 P1 P1 A2 A2 K2 K2 T2 T2 No STN 12.5 E

G1 G1 Q1 Q1 C2 C2 L2 L2 V2 V2 mix- STN 6.25 F

H1 H1 R1 R1 D2 D2 M2 M2 W2 W2 tures STN 3.125 G

I1 I1 S1 S1 E2 E2 N2 N2 Y2 Y2 STN 0 H Plates (5 total): Positions 1/2, 3/4, 5/6, 7/8, and 9/10

APCs: DAP3 DR3 1e5/well, 100 μl/well  1e6/ml, each has 88 x _5_ plates = _440_ wells, = _44.0_ e6 cells in _44.0_ml FBS for 0.5% serum (48/47.76 + 240 μl FBS) Counts:

Antigens: PSL 2040 P1 – P10 20 μl/well  2 wells/concentration, = 40 ul each

2 mg/ml stock  20 ul/200 ul total = 200 ug/ml final

Hybridomas: RP15-5415; Aria sorted, not cloned 1e5/well, 80 μl/well  1.25e6/ml, each has 88 x_5_ plates = _440_ wells, = _44.0_ e6 cells in _35.2_ml FBS for 0.5% serum (48/38.21 + 192 μl FBS) Counts:

ELISA standards: (msIL-2) 200  3.125 pg/ml, 100 ul/well 100 ul + 7400 ul = β00 pg/ml, 1:β dilutions…

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