Advances in Immunology Volume 92

Contents

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Systemic Lupus Erythematosus: Multiple Immunological Phenotypes in a Complex Genetic Disease Anna-Marie Fairhurst, Amy E. Wandstrat, and Edward K. Wakeland 1. 2. 3. 4. 5.

Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Immunological Alterations in SLE. . . . . . . . . . . . . . . . . . . . . . . . . . Genetics of SLE Susceptibility in Humans . . . . . . . . . . . . . . . . . . . Murine Models of SLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeling Disease Development . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 5 22 28 40 45

Avian Models with Spontaneous Autoimmune Diseases Georg Wick, Leif Andersson, Karel Hala, M. Eric Gershwin, Carlo Selmi, Gisela F. Erf, Susan J. Lamont, and Roswitha Sgonc Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 2. Chicken Genomics and Its Application to the Genetic Dissection of Autoimmune Disorders . . . . . . . . . . . . . . . . . . . . . . . 75 3. The OS Chicken: Model for Human Hashimoto Disease . . . . . . . . 83 4. The UCD-200 Line of Chickens: A Model for Human Systemic Sclerosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5. The SL Chicken Model for Human Autoimmune Vitiligo . . . . . . . 102 6. Conclusions and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

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Functional Dynamics of Naturally Occurring Regulatory T Cells in Health and Autoimmunity Megan K. Levings, Sarah Allan, Eva d’Hennezel, and Ciriaco A. Piccirillo Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Phenotype of CD4þCD25þ nTreg Cells . . . . . . . . . . . . . . . . . . . . . 3. Factors Regulating the Expansion and Specificity of nTreg Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Innate and Adaptive Inflammatory Signals Dictating the Function of nTreg Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Growth Factor–Mediated Control of nTreg Cell Development, Function, and Homeostasis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Control of Autoimmune Responses by nTreg Cells . . . . . . . . . . . . . 7. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

119 120 122 126 129 134 138 145 146

BTLA and HVEM Cross Talk Regulates Inhibition and Costimulation Maya Gavrieli, John Sedy, Christopher A. Nelson, and Kenneth M. Murphy 1. 2. 3. 4. 5. 6. 7. 8.

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of BTLA and HVEM Ligand Discovery . . . . . . . . . . . . . Structural Characterization of BTLA Bound to HVEM . . . . . . . . . Viral Modulators of the HVEM–BTLA Pathway . . . . . . . . . . . . . . . Expression and Regulation of BTLA, LIGHT, and HVEM on T Cells and APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mechanisms of the CD28 Family Inhibitory Receptors . . . . . . . . . Consequences of HVEM Ligation . . . . . . . . . . . . . . . . . . . . . . . . . . BTLA and HVEM in Models of Disease . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

157 158 164 164 165 167 173 176 178 179

The Human T Cell Response to Melanoma Antigens Pedro Romero, Jean-Charles Cerottini, and Daniel E. Speiser Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 2. Melanoma Antigens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

c o nt e n t s 3. Measurement of Antigen-Specific T Cell Responses. . . . . . . . . . . . 4. Naturally Acquired Tumor Antigen-Specific T Cell Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Vaccine-Induced T Cell Responses . . . . . . . . . . . . . . . . . . . . . . . . . 6. Regulation of Tumor Antigen-Specific T Cell Responses . . . . . . . . 7. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vii 190 196 202 206 207 209

Antigen Presentation and the Ubiquitin-Proteasome System in Host–Pathogen Interactions Joana Loureiro and Hidde L. Ploegh Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Host–Pathogen Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Manipulation of the Host Response by Pathogens: Some General Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Antigen Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Class I MHC Antigen Presentation . . . . . . . . . . . . . . . . . . . . . . . . . 5. Pathogen Recognition by CD8þ T Cells and NK Cells . . . . . . . . . 6. Class II MHC Antigen Presentation . . . . . . . . . . . . . . . . . . . . . . . . 7. Ubiquitin-Proteasome System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. The Ubiquitin Conjugation Cascade . . . . . . . . . . . . . . . . . . . . . . . . 9. Ubiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10. Ubiquitin Chains and Ubiquitin-Like Modifiers (Ubls) . . . . . . . . . 11. Deubiquitinating Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12. The Proteasome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13. ER Quality Control and Degradation . . . . . . . . . . . . . . . . . . . . . . . 14. ERAD Substrate Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15. ERAD E3 Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16. Mammalian ERAD E3s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17. The Elusive Dislocon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18. Driving Dislocation and the Ub-Binding Route to the Proteasome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19. Peptide N-Glycanase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20. Viral Interference with Class I MHC Antigen Presentation . . . . . . 21. Human Cytomegalovirus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22. HCMV Interference with Class I MHC Antigen Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23. Dislocation from the ER: HCMV US11 and US2. . . . . . . . . . . . . . 24. Signal Peptide Peptidase Is Required for Dislocation from the ER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

226 227 229 230 230 232 233 234 234 236 237 239 239 240 241 243 244 247 247 249 250 251 251 254 255

viii 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47.

c on t e n ts SPP and Generation of HLA-E Epitopes . . . . . . . . . . . . . . . . . . . . SPP and Processing of the Hepatitis C Virus Core Protein . . . . . . SPP and Calmodulin Signaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . SPP Peptide Peptidase and Development . . . . . . . . . . . . . . . . . . . . SPP and ER Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Three Routes of Pathogen-Mediated ER Protein Disposal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathogen Interference with Class II MHC Antigen Presentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inhibition of Recognition at the Surface of the APC . . . . . . . . . . . Class II MHC Downregulation from the Surface of the APC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CD4 Downregulation from the Surface of the CD4þ T Cell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathogen Manipulation of the Ubiquitin-Proteasome System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interference with Proteasomal Proteolysis . . . . . . . . . . . . . . . . . . . . Control of Infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Virus Budding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bacterial Chromosome Integration . . . . . . . . . . . . . . . . . . . . . . . . . ISGylation and deISGylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Control of Inflammation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Posttranscriptional Gene Silencing. . . . . . . . . . . . . . . . . . . . . . . . . . Downregulation of Cell Surface Receptors by Pathogen-Encoded E3s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Programmed Cell Death in Plants . . . . . . . . . . . . . . . . . . . . . . . . . . Cytokine Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathogen-Encoded DUBs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

256 256 257 257 258 261 264 264 265 266 269 270 271 272 273 274 275 275 276 277 277 278 280 280

Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Contents of Recent Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325

Contributors

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Sarah Allan (119), Department of Surgery, University of British Columbia and Immunity and Infection Research Centre, Vancouver Coastal Health Research Institute, Vancouver V6H 3Z6, Canada Leif Andersson (71), Department of Medical Biochemistry and Microbiology, Uppsala Biomedical Center, Uppsala University, SE 75124 Uppsala, Sweden; and Department of Animal Breeding and Genetics, Swedish University of Agriculture Sciences, SE 75124 Uppsala, Sweden Jean-Charles Cerottini (187), Division of Clinical Onco-Immunology, Ludwig Institute for Cancer Research, Lausanne Branch, University Hospital (CHUV), Lausanne, Switzerland; and National Center for Competence in Research (NCCR), Molecular Oncology, Epalinges, Switzerland Eva d’Hennezel (119), Department of Microbiology and Immunology, McGill University Health Center, Montreal, Quebec, Canada H3A 2B4; and McGill Centre for the Study of Host Resistance, McGill University Health Center, Montreal, Quebec, Canada H3A 2B4 Gisela F. Erf (71), Center of Excellence for Poultry Science, University of Arkansas, Fayetteville, Arkansas Anna-Marie Fairhurst (1), Center for Immunology, The University of Texas Southwestern Medical Center, Dallas, Texas Maya Gavrieli (157), Department of Pathology and Center for Immunology, Howard Hughes Medical Institute, Washington University School of Medicine, St. Louis, Missouri M. Eric Gershwin (71), Department of Internal Medicine, Division of Rheumatology, Allergy and Clinical Immunology, Genome and Biomedical Sciences Facility, University of California, Davis, California

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Karel Hala (71), Division of Experimental Pathophysiology and Immunology, Biocenter, Innsbruck Medical University, A-6020 Innsbruck, Austria; and Faculty of Agriculture, University of South Bohemia, Cˇeske´ Budeˇjovice, Czech Republic Susan J. Lamont (71), Department of Animal Science, Iowa State University, Ames, Iowa Megan K. Levings (119), Department of Surgery, University of British Columbia and Immunity and Infection Research Centre, Vancouver Coastal Health Research Institute, Vancouver V6H 3Z6, Canada Joana Loureiro (225), Whitehead Institute, 9 Cambridge Center, Cambridge, Massachusetts Kenneth M. Murphy (157), Department of Pathology and Center for Immunology, Howard Hughes Medical Institute, Washington University School of Medicine, St. Louis, Missouri Christopher A. Nelson (157), Department of Pathology and Center for Immunology, Howard Hughes Medical Institute, Washington University School of Medicine, St. Louis, Missouri Ciriaco A. Piccirillo (119), Department of Microbiology and Immunology, McGill University Health Center, Montreal, Quebec, Canada H3A 2B4; and McGill Centre for the Study of Host Resistance, McGill University Health Center, Montreal, Quebec, Canada H3A 2B4 Hidde L. Ploegh (225), Whitehead Institute, 9 Cambridge Center, Cambridge, Massachusetts Pedro Romero (187), Division of Clinical Onco-Immunology, Ludwig Institute for Cancer Research, Lausanne Branch, University Hospital (CHUV), Lausanne, Switzerland; and National Center for Competence in Research (NCCR), Molecular Oncology, Epalinges, Switzerland John Sedy (157), Department of Pathology and Center for Immunology, Howard Hughes Medical Institute, Washington University School of Medicine, St. Louis, Missouri Carlo Selmi (71), Department of Internal Medicine, Division of Rheumatology, Allergy and Clinical Immunology, Genome and Biomedical Sciences Facility, University of California, Davis, California Roswitha Sgonc (71), Division of Experimental Pathophysiology and Immunology, Biocenter, Innsbruck Medical University, A-6020 Innsbruck, Austria Daniel E. Speiser (187), Division of Clinical Onco-Immunology, Ludwig Institute for Cancer Research, Lausanne Branch, University Hospital (CHUV), Lausanne, Switzerland; and National Center for Competence in Research (NCCR), Molecular Oncology, Epalinges, Switzerland

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Edward K. Wakeland (1), Center for Immunology, The University of Texas Southwestern Medical Center, Dallas, Texas Amy E. Wandstrat (1), Rheumatic Diseases Division, The University of Texas Southwestern Medical Center, Dallas, Texas Georg Wick (71), Division of Experimental Pathophysiology and Immunology, Biocenter, Innsbruck Medical University, A-6020 Innsbruck, Austria

Systemic Lupus Erythematosus: Multiple Immunological Phenotypes in a Complex Genetic Disease Anna‐Marie Fairhurst,* Amy E. Wandstrat,† and Edward K. Wakeland*


Center for Immunology, The University of Texas Southwestern Medical Center, Dallas, Texas † Rheumatic Diseases Division, The University of Texas Southwestern Medical Center, Dallas, Texas

1. 2. 3. 4. 5.

Abstract............................................................................................................. Introduction ....................................................................................................... Immunological Alterations in SLE .......................................................................... Genetics of SLE Susceptibility in Humans ............................................................... Murine Models of SLE ........................................................................................ Modeling Disease Development............................................................................. References .........................................................................................................

1 2 5 22 28 40 45

Abstract Systemic lupus erythematosus (SLE) is a complex polygenic autoimmune disease characterized by the presence of anti‐nuclear autoantibodies (ANAs) that are often detectable years prior to the onset of clinical disease. The disease is associated with a chronic activation of the immune system, with the most severe forms progressing to inflammatory damage that can impact multiple organ systems in afflicted individuals. Current therapeutic strategies poorly control disease manifestations and are generally immunosuppressive. Recent studies in human patient populations and animal models have associated elements of the innate immune system and abnormalities in the immature B lymphocyte receptor repertoires with disease initiation. A variety of cytokines, most notably type I interferons, play important roles in disease pathogenesis and effector mechanisms. The genetic basis for disease susceptibility is complex, and analyses in humans and mice have identified multiple susceptibility loci, several of which are located in genomic regions that are syntenic between humans and mice. The complexities of the genetic interactions that mediate lupus have been investigated in murine model systems by characterizing the progressive development of disease in strains expressing various combinations of susceptibility alleles. These analyses indicate that genetic epistasis dramatically impact disease development and support the feasibility of identifying molecular pathways that can suppress disease progression without completely impairing normal immune function.

1 advances in immunology, vol. 92 # 2006 Elsevier Inc. All rights reserved.

0065-2776/06 $35.00 DOI: 10.1016/S0065-2776(06)92001-X

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1. Introduction Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that is classically associated with the production of pathogenic autoantibodies to a spectrum of nuclear antigens. SLE usually presents with a diverse array of clinical symptoms, which often reflect the consequences of injury to multiple organ systems. This clinical heterogeneity results from tissue damage targeted by autoantibody and inflammatory processes initiated as a consequence of deposition of complement‐fixing immune complexes (ICs). Severe complications, which ultimately develop in about 50% of lupus patients, can manifest as a variety of clinical problems, including nephritis, central nervous system vasculitis, pulmonary hypertension, interstitial lung disease, and stroke. Current treatments for SLE involve a variety of immunosuppressive drug therapies, including hydroxychloroquine, steroids, and cytotoxic drugs. Although these therapies allow management of disease severity for many patients, a variety of deleterious drug side effects and therapy‐resistant disease symptoms significantly diminish the quality of life for many SLE patients. The extensive clinical heterogeneity exhibited by this disease complicates diagnosis and has led to the development of guidelines requiring that patients fulfill any 4 of 11 criteria to be diagnosed with SLE (Hochberg, 1997; Tan et al., 1982). Currently, more than 400,000 individuals in the United States are diagnosed with SLE, and possibly an equal number fulfill 2 or 3 of the 11 criteria but are not diagnosed with SLE. Further, the incidence of disease appears to have increased threefold over the last 40 years, possibly due to improvements in the detection of mild disease (Lawrence et al., 1998; Uramoto et al., 1999). There is a significant variation in both the incidence and severity of disease among ethnic groups, with African Americans, African Caribbeans, Hispanic Americans, and Asians all having a higher incidence and greater disease severity than European and American Caucasians (Hochberg, 1985; Jimenez et al., 2003; Lawrence et al., 1989; Serdula and Rhoads, 1979; Vilar and Sato, 2002). The majority of patients with SLE have disease onset between the ages of 16–55. Although the factors responsible for the initiation of SLE are poorly understood, genetic predisposition is firmly established as a key element in susceptibility. However, despite over a decade of intensive investigations of the genetic basis for SLE susceptibility in humans, very few causative disease alleles have been clearly identified. This is due to a variety of factors, both technical and biological, which have impacted the successful application of classic analytical approaches to the discovery of causative disease alleles in complex systems. Despite this complexity, new insights have been obtained through the analysis of animal models and focused studies on disease progression in human patient populations. Furthermore, recent advances in our understanding of the human and mouse genomes are providing tools that will significantly

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enhance future genetic analyses of SLE susceptibility. Here, we will overview our current understanding of the immunological pathogenesis and the genetic basis for susceptibility to SLE of both human and murine lupus. 1.1. Clinical Presentation A typical patient with SLE is a young woman in her childbearing years who presents with intermittent fatigue, joint pain and swelling, skin rashes, low white blood cell count, and chest pains due to pleuritis. Approximately one‐ half of lupus patients will manifest the more severe complications of the disease, which can include nephritis, central nervous system vasculitis, pulmonary hypertension, interstitial lung disease, and stroke. The most characteristic clinical feature of SLE is the production of high‐titered anti‐nuclear autoantibodies (ANAs). Although a variety of organ systems can be targeted in human SLE, targeting of the kidneys is the most severe clinical pathology. Kidney pathology is initiated predominantly through the deposition of ANA‐containing ICs, which initiates a robust inflammatory response, leading to destruction of the glomeruli and subsequent nephritis and proteinuria. Many of the clinical manifestations correlating with morbidity and death are associated with renal failure (Balow, 2005). The disease course of SLE is quite variable, with pathogenesis commonly following a relapsing and remitting course, with slow progression to more severe clinical disease. This variability is typified by the diagnosis criteria set by the American Rheumatism Association (ARA), which are presented in Table 1. The presence of any 4 disease features from a list of 11 qualifies a diagnosis of ‘‘definitive’’ SLE (Table 1; Tan et al., 1982). These features are diverse and include molar rash, discoid rash, photosensitivity, oral ulcers, arthritis, serositis (pleuritis or pericarditis), renal disorder (persistent proteinuria or cellular casts), neurological disorder (seizures or psychosis), hematological disorder (hemolytic anemia with reticulocytosis, leukopenia, lymphopenia, or thrombocytopenia), immunological disorder (anti‐DNA, anti‐SM, anti‐phospholipid antibodies), and anti‐nuclear antibody (ANA; Hochberg, 1997; Tan et al., 1982). The heterogeneity in pathogenesis is also demonstrated by the diverse array of methods proposed for the measurement of disease activity—all of them are complex. The main five methods are the Systemic Lupus Erythematosus Activity Index (SLEDAI), the Systemic Lupus Activity Measure (SLAM), the British Isles Lupus Assessment Group (BILAG) index, the revised Systemic lupus Activity Measure (SLAM‐R), and the European Consensus Lupus Activity Measure (ECLAM) (Bae et al., 2001; Bombardier et al., 1992; Hay et al., 1993; Liang et al., 1989). The SLEDAI method is primarily used in the United States. It is a static measure over a timeframe of 10 days assessing 24 components each weighted a value of 1, 4, 6, or 8 (Bombardier et al., 1992). Certainly, this

A N N A ‐ M A R I E FA I R H U R S T E T A L .

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Table 1 The ARA Criteria for Lupus with Incidence in Patients from 1980 Criteria 1. Malar rash 2. Discoid rash 3. Photosensitivity 4. Oral ulcers 5. Arthritis 6. Serositis

7. Anti‐nuclear antibody (ANA blood test) 8. Renal disorder

9. Neurologic disorder 10. Hematologic alterations 11. Immunological alterations

Specific symptoms ‘‘Butterfly rash’’ across the nose and cheeks Scarring rashes with scaling and plugging of hair follicles Abnormal light sensitivity Small sores in the mucosal lining of the mouth Nondeforming and nonerosive joint inflammation Inflammation in the linings of the heart, lung, or abdominal cavity; usually manifested as pain Positive test Excessive protein in the urine or the presence of red cells, white cells or casts in the urine Seizures or psychosis Hemolytic anemia, leukopenia, thrombocytopenia LE cell tests, anti‐DNA, anti‐phospholipid or anti‐Sm antibodies

Prevalence in patients 27–63% 21% 38–60% 16% 42–95% 25–45%

99% 22–50%

2–25% 81% 27–56%

References (Hochberg, 1997; Hopkinson et al., 1994; Houman et al., 2004; Jacobsen et al., 1998; Tan et al., 1982; Uramoto et al., 1999). For a complete description of the changes made to the 1982 criteria see the following link: http://www.rheumatology.org/publications/classification/SLE/1982SLEupdate.asp?aud¼mem

demonstrates that the diagnosis and measure of disease activity is complex and reflects the heterogeneity of progressive disease pathogenesis. 1.2. Gender Bias in Disease Incidence Disease incidence and susceptibility is strongly gender biased in both humans and many animal models of SLE. The incidence of SLE in women may be increased as much as from 10‐ to 15‐fold over that of men among adults, although this gender bias appears to be lower among children (3‐fold) (Lahita, 1999). Studies in animal models have demonstrated that the female hormones estrogen and prolactin may play a role in this gender bias (Grimaldi et al., 2005a,b). The role of female hormones in the development of SLE in humans has also been investigated, most notably within the context of the use of oral contraceptives by SLE patients (Pando et al., 1995). In one study, oral

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contraceptive treatment was found to be therapeutic. However, more than 10 years earlier, estrogen was shown to exacerbate the disease symptoms (Jungers et al., 1982). More recently, the debate has continued, with two groups presenting data suggesting that contraceptive use has no effect on the disease (Petri et al., 2005; Sanchez‐Guerrero et al., 2005). Since the majority of these patients had no active disease or were in a stable state, this leaves open the question of the role of contraceptives in severe flares, particularly since pregnancy increases the incidence of flares and severity of disease (Bermas, 2005; Moroni and Ponticelli, 2005). In addition, murine models suggest that estrogen accelerates the disease, while androgens delay the onset of the phenotype (Roubinian et al., 1978, 1979). 2. Immunological Alterations in SLE Numerous immunological alterations have been characterized in patients with SLE. A current model of the initiation of SLE proposes that self‐antigens, in the form of nucleosomes, ss‐ or dsDNA, or apoptotic cell bodies are taken up by B cells and DCs and presented to T cells through the MHC molecule. Apoptotic cell bodies, which are prevalent throughout the immune system and are generated as consequence of normal immune responsiveness, are thought to be the major source of lupus autoantigens (Casciola‐Rosen and Rosen, 1997; Casciola‐ Rosen et al., 1994, 1999). The activation of autoreactive T cells results in T–B cell interactions, costimulation of the B cells, IgG class switching, affinity maturation, and IgG antibody production. The antibodies produced by plasma and B cells bind self‐antigen with high affinity and form ICs. Normally, ICs are cleared in a normal immune response by the complement and FcgR systems and phagocytes. However, in the case of SLE, an accumulation of IC deposits can be found in the kidney with ensuing nephritis. The recruitment of phagocytes into the area then adds to the inflammatory response, as they may release proteases, activating cytokines and other harmful products which target the cells of the glomeruli. A variety of studies has documented abnormalities in virtually all immune cell lineages as a component of lupus pathogenesis. Overall, it is apparent that the chronic immune activation that is a hall mark of this disease ultimately leads to abnormalities throughout the immune system. We will discuss the major immune deviations associated with each cell lineages separately. 2.1. B Cells The production of high titers of autoantibodies specific for nuclear antigens is a key hallmark of SLE and is a primary element, supporting the crucial role of B cells, in SLE pathogenesis. The diversity of nuclear autoantigens recognized

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by the sera of lupus patients has been investigated extensively, and autoantibodies to specific autoantigens have been associated with the development of severe pathogenesis (Tan, 1997; Tan and Kunkel, 2006). The mechanisms involved in the loss of B cell tolerance to nuclear antigens, and the potential importance of receptor editing in these mechanisms are well documented (Li et al., 2001; Radic and Weigert, 1995; Shlomchik et al., 1987a,b). A body of data developed over more than three decades clearly documents the importance of humoral autoantibody production in the pathogenesis of SLE. The importance of B cells in human disease was clearly demonstrated recently through the efficacy of Rituximab in the treatment of SLE (Leandro et al., 2002a,b). Rituximab is a chimeric human/mouse monoclonal antibody to CD20, an antigen expressed on B cells (Reff et al., 1994). A number of studies have subsequently confirmed the initial efficacy of Rituxmab. Meta‐analysis of these studies shows that patient’s response depends on the success of B cell depletion (Sfikakis et al., 2005). This correlates with murine data which show that B cells are necessary for the progression of disease (Chan and Shlomchik, 1998; Shlomchik et al., 1994). Phenotypic alterations in the peripheral blood of patients include an increase in the presence of autoreactive B cells, IgG memory cells, and plasma cells (Jacobi et al., 2003; Odendahl et al., 2000; Pugh‐Bernard et al., 2001). The B cell lineages of SLE patients are hyper activated, having increased surface expression of costimulatory molecules which include CD40L (CD154), CD80, and CD86 with increased proliferation and MAPK phosphorylation (Balow and Tsokos, 1984; Citores et al., 2004; Folzenlogen et al., 1997; Grammer et al., 2004; Tanaka et al., 1988). Recent studies have found a decrease in the expression of Lyn in SLE patients, together with increased ubiquitination, impaired translocation to lipid rafts (Flores‐Borja et al., 2005). This subcellular protein acts in a negative feedback loop following BCR stimulation (Cornall et al., 1998). Consistent with this, the altered expression is associated with an increase in proliferation, increased IL‐10 production, and circulating anti‐dsDNA antibodies (Flores‐Borja et al., 2005). In addition, the expression of specific chemokine receptors is altered in patients with SLE suggesting an alteration in homing or migration (Henneken et al., 2005). CXCR5, CXCR4, and CCR6 are all downregulated, while the expression of CXCR3 is increased. B cells and plasma cells are responsible for the elevation in circulating levels of ANAs and the increase in IgG, termed hypergammaglobinemia. A large number of studies have tried to link abnormalities in V(D)J recombination with SLE, but these have been unsuccessful. It appears that somatic hypermutation, the degree of receptor editing, and positive and negative selections are more important (Dorner and Lipsky, 2005). More recently, a study with a

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small number of patients demonstrated defects in two discrete early checkpoints in B cell development (Yurasov et al., 2005). In addition, Cappione, III, et al. (2005) have shown that there is an alteration in the germinal center reaction leading to the release of autoreactive B cells. The impact of genetic polymorphisms on B cells has been shown to relate to disease occurrence and success in therapy. The genotype of FcgRIIIa on B cells in SLE patients correlates with the success of response to Rituximab treatment (Anolik et al., 2003). FcgRIIIa is a low‐affinity receptor for IgG and has two allotypes. The fcgr3a gene has a dimorphism located at the amino acid position 158, with either phenylalanine (F) or a valine (V). Patients with a homozygous allotype of FcgRIIIa‐158V (VV) have a greater efficiency of B cell depletion in response to Rituximab (Anolik et al., 2003). The homozygous FcgRIIIa‐VV has a higher affinity for Rituximab than FcgRIIIa‐158F (Dall’Ozzo et al., 2004). It is expressed on NK cells and macrophages and is important in antibody‐dependent cellular mediated cytotoxicity (ADCC). Recent evidence has suggested that NK cells demonstrate ADCC in the presence of Rituximab, unlike T or monocytes, and that the difference in binding may result in a decrease in efficiency of targeted B cell death (Dall’Ozzo et al., 2004). It is possible that FcgRIIIa may not be the only IgG receptor involved in the depletion of B cells in vivo. Murine studies have shown that neutrophils and complement are important in Rituximab efficacy, but this must be taken cautiously since the FcgR repertoire has a large number of differences in types of receptor, expression, and function (Daeron, 1997; Golay et al., 2006; Hernandez‐Ilizaliturri et al., 2003; Mestas and Hughes, 2004). Examination of the B cell coreceptor, CD154, revealed an increase in the frequency of polymorphisms in the untranslated region (UTR) in patients with SLE (Citores et al., 2004). Since this is an important costimulatory receptor in DC and T cell interaction, it presents important consequences if the function is altered. Moreover, CD154, and its interaction with CD40, is essential in the generation of IgG‐producing B cells, therefore an upregulation or increase in function may contribute to the IgG and ANA in SLE (Grammer et al., 2003). 2.2. T Cells Evidence from murine studies has demonstrated that CD4þ T cells are key for the production of autoantibodies in most lupus models (Busser et al., 2003). This supports the long‐standing belief that a loss of tolerance in both B and T cells is necessary for the development of severe autoimmunity (Shlomchik et al., 2001). In 1989, Datta and colleagues demonstrated that there was an expansion of double negative (CD4CD8) TCRabþ T cells in the peripheral

8

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blood of patients with SLE and that this T cell population could augment the CD4þ T helper (Th)‐mediated production of IgG autoantibodies by B cells (Shivakumar et al., 1989). In ensuing studies, however, this group cloned T cells from SLE patients and demonstrated that 15% of T cell clones retained the capacity of autoantibody production of B cells and that over 80% of these were CD4þ (Rajagopalan et al., 1990; Shivakumar et al., 1989). Although CD4þ Th cells are thought to be the main reactive T cell subset, autoreactive CD8þ and DN TCRþ cells were found. Analyses of autoreactive peripheral T lymphocytes in SLE patients have shown that they respond to autoantigens such as dsDNA, nucleohistones, and DNase I (Filaci et al., 1996; Rajagopalan et al., 1990). In addition, activated nucleosome‐specific T cells have been found in patients with SLE, unlike other autoimmune disorders (Bruns et al., 2000). Once activated, T cells proliferate, provide help to self‐reactive B cells, and support a sequence of events potentiating the maturation and class switch of autoantibody production. For a more detailed review on T cell alterations see Hoffman (2004). Data on genetic alterations of T cells is limited in human studies. There has been a great interest in examining polymorphisms in cytotoxic T lymphocyte antigen 4 (CTLA‐4) (Aguilar et al., 2003; Blanco et al., 2001; Fernandez‐ Blanco et al., 2004; Lee et al., 2001; Parks et al., 2004; Pullmann, Jr., et al., 1999). This is a receptor primarily expressed on T cells after cellular activation and is continuously expressed on regulatory CD25þCD4þ T cells (Stockinger et al., 2001). It interacts with CD80/CD86 on antigen‐presenting cells, and therefore acts as a competitive inhibitor of the coreceptor CD28, and therefore when upregulated, acts to regulate T cell activation and expansion (Brunner et al., 1999; Oosterwegel et al., 1999). The Ctla4 gene lies on chromosome 2 at 2q33, in a region found to be associated with SLE in several genetic linkage studies (Gaffney et al., 1998; Moser et al., 1998; Quintero‐Del‐Rio et al., 2002, 2004). However, the results from polymorphic gene analyses have not been conclusive and suggest that the dependency on CTLA‐4 depends on ethnicity and environmental factors (Parks et al., 2004). Linkage with SLE has been shown with polymorphisms in Korean, Slovakian, Spanish, Japanese, and African American populations (Ahmed et al., 2001; Fernandez‐Blanco et al., 2004; Hudson et al., 2002; Parks et al., 2004; Pullmann, Jr., et al., 1999). Within these studies, there is conflicting data on the polymorphisms implicated. Moreover, there is conflicting data on the same polymorphism. There have been four regions with polymorphisms identified, three within the promoter regions at 1722, 1661, and 318 and one in exon 1, at þ49. Linkage in the Korean population showed a decrease in the frequency of the C allele and an increase in the TT genotype in 1722 in SLE (Hudson et al., 2002). Another Korean study did not examine this

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polymorphism and did not find any association in other sites (Lee et al., 2001). Investigations in a Spanish population also found a significant alteration in the allelic frequency in 1722; however, the results were exactly the opposite from the Korean study, with an increase in the frequency of the C allele (Fernandez‐Blanco et al., 2004). However, another Spanish study did not find any association in any of these sites. More recently, a meta‐analysis assessed linkage of these polymorphisms in 14 publications (Lee et al., 2005). They determined that there is linkage between the polymorphism in exon 1 and SLE, particularly within the Asian population. They did not, however, find any evidence of linkage in 1722, demonstrating the necessity for larger studies and meta‐analyses in polymorphism assessment. Single nucleotide polymorphisms (SNPs) have also been found in the T cell receptor b and z chains (TCRb and TCRz). Harley and colleagues have demonstrated polymorphisms in the TCRb chain are associated with the production of Sjoegrens syndrome A (SSA) antibody and that this was also linked with HLA‐DQ allelic distribution (Frank et al., 1990; Scofield et al., 1994). These were, however, relatively small studies (~70 patients in each case) which remain to be repeated. The gene for TCRz is located at 1q22–23, in a region that has been linked with SLE (Moser et al., 1998). Mutations have been found in the UTR and the promoter region in SLE patients, resulting in an overall decrease in TCRz protein on the cell surface (Nambiar et al., 2001). This was a small study but is supported by a detectable loss of TCRz chain protein in SLE patients (Brundula et al., 1999; Liossis et al., 1998; Takeuchi et al., 1998). There have been other reports of mutations within the coding region of TCRz. Two groups report a linkage with SLE (Takeuchi et al., 1998; Tsuzaka et al., 1998), but another group did not find any association with SLE (Wu et al., 1999). 2.3. Monocytes and Polymorphonuclear Cells Although autoreactive B and T cells play dominant roles in the initiation of autoantibody production, recent murine data suggest that myeloid cells and DCs may also play a significant role in the initiation and progression of disease pathogenesis. Phenotypic alterations in receptor expression have been reported in peripheral blood monocytes and neutrophils of SLE patients. In addition, microarray analysis of peripheral PBMCs has shown that there is an increase in mRNA of genes associated with neutrophils, termed a granulopoeisis signature, in patients with SLE (Bennett et al., 2003). There are a number of conflicting reports on the expression of the LPS receptor, CD14, in SLE. While one group has reported a decrease in expression on freshly isolated monocytes (Steinbach et al., 2000), another suggests that the expression is lower after 7 days in culture

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(Herrmann et al., 1998). Furthermore, Bijl et al. (2006) report no difference in either freshly isolated or cultured monocytes. The discrepancies in these research articles may reflect variations among patient disease profiles, the nature of the receptor itself, the activating mediators in the sera and/or the isolation procedures used to extract this cell type. CD14 is a glycophosphoinositol (GPI)‐linked molecule and may be cleaved from the cell surface on activation. In 1986, Kimberly et al. (1986) demonstrated a loss in the function of Fc‐ and complement‐mediated phagocytosis by peripheral blood mononuclear phagocytes in patients with SLE. Since then, there have been a number of groups who have reported a loss in the ability of SLE phagocytes to engulf and destroy apoptotic material (Bijl et al., 2006; Cairns et al., 2001; Herrmann et al., 1998; Ren et al., 2003). Furthermore, there are an increasing number of studies suggesting that apoptotic cells may provide a source of self‐antigen for presentation by DCs and for T and B cell hyperreactivity, which supports the original studies by Rosen, Casciola‐Rosen, and coworkers (Baumann et al., 2002; Blanco et al., 2001; Casciola‐Rosen et al., 1994; Frisoni et al., 2005). Recently, this defect in phagocytosis has been suggested to reflect the actions of a suppressive element within serum (Bijl et al., 2006). In these studies, non‐SLE control serum added to macrophage cultures completely restored their phagocytic function, suggesting that the phenotype results from a deficiency in the sera of SLE patients, rather than the presence of an inhibitor. This loss in phagocytic ability correlated with a decrease in the levels of circulating complement factors, C1q, C3, and C4 (Bijl et al., 2006). A decrease in the uptake of apoptotic cells has also been associated with a loss of CD44 on monocytes and neutrophils (Cairns et al., 2001). In other studies, increases in the levels of circulating apoptotic mononuclear cells and monocytes and polymorphonuclear cells (PMNs) have been correlated with the inability to remove apoptotic material (Cairns et al., 2001; Courtney et al., 1999a,b; Lorenz et al., 1997; Perniok et al., 1998). In one study this increase in apoptotic neutrophils correlated positively with the SLAM score of disease severity (Courtney et al., 1999a). The SLAM index measures various elements of organ function, and neutrophils are recruited into sites of inflammation, which supports the validity of this association. However, another study measuring an association with the SLEDAI measure of disease severity did not find a correlation (Klint et al., 2000). Finally, sera from SLE patients has been shown to induce apoptosis in a number of cell types (Bengtsson et al., 2004; Klint et al., 2000). This effect appears to be specific to SLE, since sera from patients with other autoimmune disorders or bacterial infection do not have this activity. The increase in apoptotic cells also may be correlated with a deficiency of serum C1q and C4 (Bengtsson et al., 2004; Klint et al., 2000). Whether this relates to an activation

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of the complement system and increases in complement products, such as C3b and C5a, is not known. Analysis has shown that the apoptosis is partly dependent on TNF‐a, since Etanercept, an anti‐TNF inhibitory monoclonal antibody reduces this serum effect (Bengtsson et al., 2004; Pitidhammabhorn et al., 2006). There are several apoptotic genes that are upregulated in the peripheral blood cells of SLE patients, including Caspases 3 and 8, Bad and Bax, Bcl‐x, and Calpain (Pitidhammabhorn et al., 2006). Polymorphisms in apoptosis‐related genes have also been found in a Taiwanese population in poly(ADP‐ribose) polymerase (PARP) and Fas ligand (FasL) (Chen et al., 2006). Another study has shown that monocytes of SLE patients do not downregulate their expression of FasL in response to metalleoprotease‐induced cleavage, and therefore the cells present a higher surface expression (Eneslatt et al., 2001). These studies are consistent with an increased propensity for apoptosis in the peripheral blood cells of SLE patients. Overall, the bulk of these findings are consistent with the presence of several mechanisms that promote apoptosis and dysregulate the normal processing of apoptotic cells. Although the significance of apoptotic cells in the pathogenesis of human lupus is difficult to ascertain, data from murine studies suggest that apoptotic material can induce autoantibody production and promote glomerulonephritis (GN; Denny et al., 2006). 2.4. Dendritic Cells The significance of DCs in SLE pathophysiology has been shown in numerous studies over the last few years. Banchereau, Pascual and colleagues showed that monocytes from SLE patients stimulated autologous CD4þ T cell proliferation in culture, which is a functional phenotype typical of DCs (Blanco et al., 2001). When SLE serum was added to monocytes from controls they found that these cells also developed DC‐function and DC‐like morphology, together with an upregulation of surface molecules, CD80 and CD86. In addition these cultured monocytes were able to present antigen from apoptotic cells to autologous T cells in vitro. This response was found to be largely dependent on interferon‐alpha (IFN‐a). Investigations by Vallin and colleagues showed that sera from SLE patients stimulated the production IFN‐a from PBMCs and that the combination of anti‐dsDNA antibodies and immunostimulatory plasmid DNA could replicate this response, suggesting that antibody–DNA complexes were responsible (Vallin et al., 1999a,b). The findings of Pascual and colleagues have been replicated by another group who cultured normal monocytes with SLE serum and found that they also developed an IFN‐a–dependent DC‐like

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phenotype with an upregulation of the surface molecules, MHC I, CD38, CD123 (Dall’era et al., 2005). The source of the IFN‐a was hypothesized to come from plasmacytoid dendritic cells (pDCs), which are the primary producers of IFN‐a among immune cell lineages (Pascual et al., 2003). Studies have shown that the production of IFN‐a by PMBCs after incubation with SLE sera can be inhibited by antibodies to the BDCA‐2 and BDCA‐4 receptors, which are expressed by pDC (Blomberg et al., 2003). This suggests that additional serum factors may be involved in triggering pDCs to secrete IFN‐a. Activated pDCs would be predicted to increase B cell activation and induce plasma cell differentiation by producing IFN‐a and IL‐6, which potentially would increase circulating IgG and ANAs in patients with SLE (Jego et al., 2003; Poeck et al., 2004). A number of studies have demonstrated a decrease in the levels of pDCs and mDCs in the peripheral blood of patients with SLE (Gill et al., 2002; Migita et al., 2005; Robak et al., 2004; Scheinecker et al., 2001; Zhuang et al., 2005). It is currently hypothesized that this results from an increased migration of pDCs into the skin and potentially other organs (Blomberg et al., 2001; Farkas et al., 2001; Pascual et al., 2003). Overall, research strongly supports a key role for dendritic cells in SLE pathogenesis, although the details of their specific activities remain to be fully elucidated. Research focused on identifying the serum components responsible for stimulating pDCs of SLE patients has determined that the combination of apoptotic cells and purified autoreactive IgG leads to IFN‐a production (Bave et al., 2003). By digesting the IgG and removing the Fc component, Ronnblom and colleagues showed that Fc reactivity was essential for the effect. Moreover, by using inhibitory antibodies to FcgRI (CD64), FcgRII (CD32), and FcgRIII (CD16), they demonstrated that FcgRII was the receptor responsible. Furthermore, FcgRII is present on the cell surface of ~50% of pDCs. Since pDCs do not express FcgRIIb or FcgRIIc mRNA, but have detectable levels of FcgRIIa, the receptor contributing to the IFN‐a production is probably FcgRIIa. Further work by this group has shown that the essential apoptotic cell element is present in supernatants from both apoptotic and necrotic cells and that sera from patients with antiribonucleoproteins has a greater effect on IFN‐a induction (Lovgren et al., 2004). RNase treatment removed the effect from both apoptotic and necrotic supernatants; however, DNase only reduced the effect from apoptotic cells. This, together with the kinetic difference in response, with necrotic extracts having a much more rapid response, suggests that there are different pathways involved in IFN‐a stimulation. Means et al. (2005) have also shown that the IFN‐a response of

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PBMCs and pDCs by SLE sera is dependent on FcgRII and the presence of DNA. Furthermore, by using an inhibitory cytosine phosphoguanine (CpG), they showed a dose‐dependent reliance on TLR9. By using GFP tagged FcgRII and YFP tagged TLR9 in U373 cells, they demonstrated with Alexa‐633 conjugated purified IgG from SLE patients, that FcgRIIa, TLR9, and IgG colocalized within the cell prior to the induction of IFN‐a. Although there have been no genetic polymorphisms associated with DC function in SLE patients, polymorphisms of FcgRII, which is located within the SLE‐linked 1q23 region, have been associated with SLE (Moser et al., 1998; Salmon et al., 1990, 1992, 1996). The potential roles of FcgR in SLE pathogenesis will be discussed in more detail later (see Section V).

2.5. Cytokines Literature suggests a burgeoning number of cytokines are involved in the pathogenesis of SLE. Some of the cytokines that are associated with SLE undoubtedly contribute to disease pathogenesis, while others may reflect compensatory mechanisms that are failing to inhibit the developing autoimmunity. Table 2 lists some of the major cytokines which have been reported to be implicated in the pathogenesis of SLE. These cytokines are discussed individually in the following sections. 2.5.1. IL‐1b The importance of IL‐1b in the pathogenesis of autoimmune diseases, particularly rheumatoid arthritis (RA), is well documented (Kay and Calabrese, 2004). However, the data for SLE is less clear. Inhibition of IL‐1b in cultured SLE PBMCs decreases spontaneous IgG production, suggesting its spontaneous production is promoted by IL‐1b (Jandl et al., 1987). In addition, there is an increase in IL‐1b mRNA in PBMCs when cultured with SLE sera (Means et al., 2005) and an increase in serum IL‐1b has been reported in two studies (Alcocer‐Varela et al., 1992; Nagahama et al., 2001). A variety of polymorphisms has been identified in the IL‐1b gene. Most notably, a polymorphism at þ3954T conferred an increase in LPS‐induced IL‐1b production (Pociot et al., 1992). Parks et al. also found a significant correlation between the –511T allele and SLE in African Americans; however, analysis in Japanese and Chinese populations did not show any difference (Huang et al., 2002; Muraki et al., 2004; Parks et al., 2004). This contradictory data points to the necessity of further studies, together with a meta‐analysis of the data. The levels of IL‐1ra, which is the natural antagonist of IL‐1 receptor,

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Table 2 Cytokine Associations in SLE Polymorphism (Y/N) Cytokine IL‐1b

IL‐6

IL‐10

Immunological association Inhibition suppresses spontaneous IgG production

IL‐6 drives proliferation and differentiation of B cells. SLE B cells spontaneously produce IL‐6 Increased serum levels, correlation with severity, increased spontaneous production. B cell stimulation

Detected

Location

Association

References

Y

511T

AA and Caucasian

Parks et al., 2004

Y

511T

Y Y Y Y

Promoter allelic polymorphisms 174 174 1082G

N Asian Y

Huang et al., 2002; Muraki et al., 2004 Linker‐Israeli et al., 1996, 1999 Hrycek et al., 2005 Schotte et al., 2001 Meta‐analysis Nath et al., 2005

Y

IL‐10.G total pop

Y N Y Asians Y

IFN‐a

Maturation of monocytes to DC, proliferation and differentiation of B cells when combined with IL‐6. T cell activation B, T, and myeloid cell stimulation

N

Y

308 A

MCP‐1

Serum levels correlate with severity and activity

Y

2518

IFN‐g

Increased or decreased levels depending on the stage of disease

Y Y

114 bp

TNF‐a

For immunological association, see text for details and references.

Y Total and European Y nephritis specific N Y

Meta‐analysis Lee et al., 2006 Aguilar et al., 2001; Kim et al., 2002; Tucci et al., 2004 Lee et al., 2001 Miyake et al., 2002

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are increased in SLE, but levels correlate negatively with severity of kidney disease, suggesting that the regulation of IL‐1b is complex (Chang, 1997; Sturfelt et al., 1997). 2.5.2. IL‐6 A number of reports have shown an increase in serum IL‐6 in SLE patients (Asanuma et al., 2006; Davas et al., 1999; Gabay et al., 1993; Spronk et al., 1992; Suh et al., 2006). Moreover, B cells from SLE patients spontaneously produce IL‐6 and SLE sera added to normal PBMCs induces an increase in IL‐6 mRNA (Means et al., 2005; Nagafuchi et al., 1993). Several studies have assessed the association of polymorphisms in IL‐6 with SLE susceptibility. Analysis of minisatellites in the IL‐6 gene have shown that alleles in the AT‐ rich region of the 30 flanking region are associated with SLE and that this is correlated with a shorter allelic size and an increase in IL‐6 mRNA stability and IL‐6 positive monocytes (Linker‐Israeli et al., 1996, 1999). Analysis of a single polymorphism located at 174 within the promoter region has shown conflicting results, one study demonstrating no association and other significant association (Hrycek et al., 2005; Schotte et al., 2001). Overall, these results support a potential mechanistic role for IL‐6 in SLE pathogenesis and suggest that polymorphisms of this gene may be a factor in susceptibility, although more studies are required to determine the relative role of IL‐6 polymorphisms in SLE disease. 2.5.3. IL‐10 The importance of IL‐10 in SLE has also been demonstrated in a number of studies (Beebe et al., 2002). Serum IL‐10 is increased and correlates with the SLEDAI score (Park et al., 1998). SLE patients exhibit increased numbers of PBMCs that spontaneously produce more IL‐10, and stimulation with phytohemagglutinin (PHA) results in greater numbers of IL‐10 producers compared to controls (Grondal et al., 2000). IL‐10 has also been shown to correlate with levels of anti‐dsDNA Abs in the serum of a number of patients with SLE (Ronnelid et al., 2003). Consistent with this, exogenous IL‐10 can increase the production of ssDNA and dsDNA antibodies in PBMCs in inactive SLE patients (Tyrrell‐Price et al., 2001). However, in active patients it appears that it has the opposite effect and depresses autoantibody production. IL‐10 augments the proliferation of human B cells induced by CD40, IgM, or Staphyloccocus aureus Cowan I (SAC) (Rousset et al., 1992; Saeland et al., 1993) and induces differentiation into plasma cells with CD40 stimulation (Rousset et al., 1995). In addition, this cytokine may play a role in the induction of apoptosis of T cells in SLE (Georgescu et al., 1997; Grondal et al., 2002).

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Evidence has suggested that both environmental and genetic factors may result in an alteration of IL‐10 (Grondal et al., 2002; Nath et al., 2005). Analysis of the production of IL‐10 and apoptosis of PBMCs is also increased in spouses of SLE patients implicating environmental influences (Grondal et al., 2002). However, a number of studies have analyzed polymorphisms in the IL‐10 gene, which is located on chromosome 1 at 1q31‐q32 in a region associated with susceptibility to SLE (Gray‐McGuire et al., 2000; Tsao et al., 1997). A meta‐analysis of 15 studies has shown that a polymorphism located in the promoter region of IL‐10 at –1082G is associated with SLE in Asians (Nath et al., 2005). In addition, an association with the microsatellite IL‐10.G was found with SLE in the whole population. 2.5.4. TNF‐a TNF‐a which is a central immune modulator has already been discussed in relation to apoptotic pathways and phagocytic capacity. TNF‐a will induce its own synthesis as well as the production of many other cytokines and inflammatory mediators, including, IL‐1, 6, 8, and GM‐CSF. In addition, TNF‐a induces transmigration and chemotaxis, increases metabolism and actively prevents monocytic differentiation and proliferation (Zhang and Tracey, 1998). In lymphocytes, TNF‐a induces T cell colony formation, superoxide generation in B cells, and apoptosis in mature T cells and in neutrophils, TNF‐a primes neutrophils and potentiates CR3, but not FcgR‐mediated superoxide generation (Forsberg et al., 2001) in addition to promoting the transudation of neutrophils from the intravascular space to the sites of inflammation (Beutler and Cerami, 1989). The gene for TNF is located on chromosome 6, at 6p21.3, in the class III region of MHC (Dunham et al., 1987). A polymorphism has been located within the promoter region at position 308 where an arginine (A) replaces a guanine (G). The arginine residue reportedly confers a recessive phenotype characterized by an increase in the transcription rate of TNF‐a (Allen, 1999). There have been conflicting data on the association of this polymorphism with SLE (Danis et al., 1995; Rood et al., 2000; Sullivan et al., 1997; van der Linden et al., 2001). Lee et al. (2006) performed a meta‐analysis of 21 studies to determine the association of the TNF‐a promoter 308 A/G polymorphism with SLE. They determined that the A allele is a risk factor for SLE in the whole population analyzed and that this allele confers a gene‐dosage effect on susceptibility. Further dissection of the data determined that the risk is significant in European populations but not in either Asian or African populations. The role of TNF‐a in SLE appears to be complex. Although there is an upregulation of soluble TNF in patients with SLE, downregulation, or inhibition

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of the cytokine has been associated with the development of SLE in both human and murine disease. First evidence in humans came from RA patients who were treated with infliximab, an inhibitory monoclonal antibody to TNF. Some of these patients developed autoantibodies, including anti‐dsDNA and ANA (Charles et al., 2000; De et al., 2003; Debandt et al., 2003; Eriksson et al., 2005; Shakoor et al., 2002). This is supported by murine studies primarily from the New Zealand Black/White (NZB/W) and its NZM2410 derivative which demonstrate depressed TNF signaling (Blenman et al., 2004; Jacob and McDevitt, 1988). In addition, restoration of TNF with a recombinant antibody delays the onset of nephritis in these mice (Jacob and McDevitt, 1988). SLE patients also have increased levels of the soluble TNFR I and II (Davas et al., 1999; Gattorno et al., 1998; Studnicka‐Benke et al., 1996). One of the mechanism by which TNF‐a is regulated is through downregulation of its receptors. During cell activation, both TNFR I and TNFR II are shed from the cell surface by sheddase, or TNF alpha converting enzyme (TACE) (Peschon et al., 1998; Reddy et al., 2000). These receptors may bind free circulating TNF and therefore act as competitive inhibitors of membrane bound TNFRs and prevent TNFR stimulation. This adds to the hypothesis that a decrease in TNF signaling is associated with SLE. An increase in TACE would also result in the increase in circulating TNF, since it cleaves active TNF from its precursor, pro‐TNF (Black et al., 1997; Moss et al., 1997). An increase in serum TNF in SLE patients has been documented in a number of studies (Aringer et al., 2002; Davas et al., 1999; Studnicka‐ Benke et al., 1996). It is not clear whether the excess of receptors block the effects of the circulating TNF. Certainly in one study they demonstrated that exogenous TNF could mediate an upregulation of CD54 and apoptosis, but this was with concentrations of TNF of 1 ng/ml and higher. Reported levels in SLE sera are between at an average of 12.3 pg/ml and between 10 and 222 pg/ml in the other, suggesting that the high level of receptors may prevent receptor ligation and stimulation (Davas et al., 1999; Studnicka‐Benke et al., 1996). 2.5.5. MCP‐1 Increases in serum chemoattractant cytokines, including monocyte chemoattractive protein‐1 (MCP‐1) have been documented in a number of studies on SLE (Asanuma et al., 2006; Lit et al., 2006; Tucci et al., 2005). In addition, MCP‐1 has been detected in the urine (Rovin et al., 2005; Wada et al., 1996). Both serum and urine levels appear to correlate with SLEDAI and therefore levels are probably characteristic of the increasing inflammation and disease activity, rather than the initiation of the disease itself (Lit et al., 2006; Tucci et al., 2005). Chemokines, such as MCP‐1, are secreted by cells at the site of inflammation. They can recruit and activate other cells to the inflammatory

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site thereby accelerating the disease process. MCP‐1 has also been associated with crescents and interstitial lesions in nephritis, indicating its role in the later phases of disease (Wada et al., 1999). Consistent with this data, a polymorphism in MCP‐1 at position 2518 upstream from the transcription site in the 50 flanking region has been associated with nephritis and disease severity in SLE, although an association with susceptibility to SLE has not been shown (Aguilar et al., 2001; Kim et al., 2002; Tucci et al., 2004). 2.5.6. IFN‐g Inconclusive evidence exists on the levels of IFN‐g in sera from patients with SLE. There has been evidence for both increases and decreases in the cytokine, and this may reflect the stage of disease and renal involvement (Miyake et al., 2002; Suh et al., 2006). Analysis of polymorphisms in both the gene for the cytokine and the receptor is limited. Lee and colleagues found no significant associations with SLE (Lee et al., 2001) and Miyake et al. (2002) suggest that a polymorphism in a 114‐bp allele is associated with decreased cytokine production and increased severity of nephritis. Analysis of the IFN gamma receptor (IFN‐gR) gene by Tanaka (1999) revealed a genetic polymorphism at Val14Met located at the COOH terminal of the signal peptide of the IFN‐gR. 2.5.7. IFN‐a A role for the proinflammatory cytokine, IFN‐a in lupus pathogenesis was first indicated in 1979 with the discovery of circulating IFN in autoimmune patients (Hooks, 1979). Subsequent to this finding, Preble et al. (1982, 1983) in the early 1980s, found this to be specific to SLE. A decade later it was found that a small subset of patients receiving high dose IFN‐a for malignancies or chronic viral infections developed lupus‐like symptoms, including anti‐nuclear antibodies (ANAs), cerebritis, anemia, and lymphopenia. In many instances, these features decreased with the cessation of IFN‐a treatment (Mehta et al., 1992; Ronnblom et al., 1991a,b; Schilling et al., 1991; Wandl et al., 1992). Investigations by Ronnblom and colleagues then showed that sera from SLE patients stimulated the production of IFN‐a from PBMCs (Vallin et al., 1999a,b). In addition, the combination of anti‐dsDNA antibodies and immunostimulatory plasmid DNA could replicate this response, suggesting that anti‐DNA antibody–DNA complexes were responsible and providing a role for TLR9. The studies by both the Pascual and Devis groups demonstrate the importance of IFN in SLE sera in promoting DC maturation (Blanco et al., 2001;

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Dall’era et al., 2005). Using a luciferase reporter assay to detect IFN‐a and eliminating the possibility of RF interfering with an Ig‐based ELISA system, Dall’era and colleagues also demonstrated that the serum IFN‐a level also correlates with rash, SLEDAI, and anti‐dsDNA antibodies, and it is higher in African Americans compared to Hispanics and Caucasians. The use of microarrays has also identified a set of genes that are significantly upregulated in the peripheral blood cells of SLE patients which are inducible by IFN‐a (termed an IFN gene signature) supporting its role in this disease (Baechler et al., 2003, 2004; Bennett et al., 2003; Crow and Wohlgemuth, 2003; Han et al., 2003; Kirou et al., 2004, 2005). This signature is also apparent in systemic onset juvenile idiopathic arthritis (SONIA) patients receiving anti‐TNF therapy, suggesting an autoregulatory loop between the two cytokines (Palucka et al., 2005). This is also supported by the observation that in a number of RA patients receiving infliximab, an inhibitory monoclonal antibody to TNF develop lupus‐like symptoms, including autoantibody production with autoantibodies, including anti‐dsDNA and ANA (Charles et al., 2000; De et al., 2003; Debandt et al., 2003; Eriksson et al., 2005; Shakoor et al., 2002). 2.5.8. Additional Polymorphic Associations in Cytokines with SLE With the increasing published data on cytokines in SLE, there is an increase in the focused research of specific cytokines. Data is limited, and from the experience from well‐documented cytokines, such as IL‐10 and TNF, extremely large sample sizes in studies or meta‐analyses are required to examine a real association. In addition to the cytokines mentioned earlier polymorphisms in IL‐4 are associated with clinical manifestations of SLE and IL‐8, a neutrophil chemoattractant, has been associated with disease severity (Lit et al., 2006; Rovin et al., 2002; Wu et al., 2003).

2.6. Toll‐Like Receptors in SLE The Toll‐like receptor family provides one of the first lines in host defense against invading pathogenic microorganisms. They identify specific pathogen associated molecular patterns (PAMPs), triggering a wide variety of immune and inflammatory responses to ultimately control the invading pathogen (Medzhitov and Janeway, Jr., 1997a,b). Receptors have been classified into two different types; receptors which preferentially recognize bacterial pathogens on the surface of the cell and receptors which are localized intracellularly and therefore are specialized to detect viral pathogens (Iwasaki and Medzhitov,

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2004). The latter includes TLR3, TLR7, and TLR9, which recognize dsRNA, ssRNA, and dsDNA, respectively. 2.6.1. TLR9 TLR9 was first described as a receptor essential for recognition of bacterial DNA containing unmethylated CpG dinucleotides (Hemmi et al., 2000). Since the discovery of TLR9, there has been a burgeoning interest in the role of TLR9 in autoimmune diseases, particularly in SLE (Anders, 2005), due to the presence of autoantibodies with specificity to dsDNA, particularly chromatin (Cervera et al., 2003). Patients with active disease have an increase in the levels of circulating hypomethylated DNA and a decrease in the activity of DNA methyltransferases (Richardson, 2003; Richardson et al., 1992; Yung and Richardson, 1994). The dsDNA autoantibodies may form ICs with the circulating hypomethylated DNA and may be responsible for some of the inflammatory phenotype associated with the disease (Vallin et al., 1999b). In support of this, it has also been shown that autoreactive B cells may internalize soluble Ig antigen associated with self‐DNA, possibly with anti‐DNA antibodies in the form of an IC (Leadbetter et al., 2003) is thought to costimulate activation of B cells encoding anti‐DNA antibodies, possibly perpetuating these responses. Consistent with this, activation of TLR9 with CPG‐ODN in vivo promotes kidney disease in both the NZB/W and MRLlpr/lpr mouse models of SLE, increasing DNA antibodies, cellular infiltration into the kidney and accelerating nephritis (Anders et al., 2004; Hasegawa and Hayashi, 2003). In addition, TLR9 deficient mice on an autoimmune background have specifically lower titers of autoantibodies to dsDNA and chromatin (Christensen et al., 2005). However, TLR9 deletion has no effect on the development of kidney disease in these mice, suggesting a limited role in the progression of nephritis. Means et al. (2005) showed that the IFN‐a response of PBMCs and pDCs by SLE sera is dependent on the presence of DNA. Furthermore, by using an inhibitory CpG, they showed a dose‐dependent reliance on TLR9 and that FcgRIIa, TLR9, and IgG colocalized within the cell prior to the induction of IFN‐a. Despite the accumulative evidence suggesting a role for TLR9 in SLE, evaluation of polymorphisms by several different groups have not determined any correlation with disease in Chinese, Korean, and European populations (De Jager et al., 2006; Hur et al., 2005; Ng et al., 2005). 2.6.2. TLR7 Viruses, including vesicular stomatitis virus (VSV), stimulate the production of IFN‐a through TLR7. This receptor specifically binds single‐stranded RNA. It has come to the forefront of SLE research due to the overwhelming

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presence of anti‐RNA antibodies in patients with lupus. During apoptosis, small macromolecules of RNA accumulate at the surface of membrane. These include chromatin and other small nuclear ribonuclear particles (snRNP). The addition of the snRNP, U1 can stimulate TLR7 directly to produce IFN‐a in pDCs and TNF from monocytes (Vollmer et al., 2005). In addition, U1snRNP complexed with anti‐Sm, an antibody often found in lupus, can stimulate IFN‐a and IL‐6 production in vivo and that this effect is dependent on TLR7 (Savarese et al., 2006). The receptor for dsRNA, TLR3 is not involved in this mechanism. This suggests that ICs in patients containing TLR7‐specific sequences can promote the disease process through innate pathway activation. Anti‐Sm/SM RNP ICs also stimulate proliferation of RF B cells (AM14 RF BCR) in a TLR7‐dependent manner (Lau et al., 2005). To date there have been no reports on TLR7 polymorphisms. However, a role of TLR7 in a genetic mouse model of disease has been identified. The BXSB mouse model of lupus has a strong male bias for nephritis, with mortality of males being far greater than females (5 months vs 15 months). This is due to a Y‐chromosome associated accelerator (yaa) of disease (Murphy and Roths, 1995). We have shown that the yaa locus results in a translocation event from the X‐chromosome to the Y‐chromosome, resulting in a twofold increase in messenger RNA expression and an increase response to TLR7 ligands (Subramanian, 2006). Further studies are needed to confirm the necessity of TLR7 for this lupus‐like phenotype. 2.6.3. TLR3 The recognition of dsRNA viral genomes is mediated through TLR3. This is the only TLR to be MyD88 independent, mediating signaling events through TRIF and RIG‐I (Yamamoto and Akira, 2004; Yamamoto et al., 2003). Patole et al. (2005) have shown TLR3 expression in infiltrating APCs and mesangial cells of the kidney in the MRLlpr strain. In addition, administration of Poly I: C, a potent TLR3 inducer, in these mice augments kidney disease independent of B cell activation and IgG autoantibody effects (Patole et al., 2005). Although TLR3 polymorphisms have been investigated in other diseases, there have been no reports suggesting this occurrence in SLE. Further research is necessary to elucidate the requirement of TLR3 the progression of SLE. 3. Genetics of SLE Susceptibility in Humans Genetic predisposition is a key element in susceptibility to SLE in humans. This was first established through measurements of the concordance rate for SLE within monozygotic twin pairs. Several independent studies observed

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significant increases in concordance in the diagnosis of SLE between members of a monozygote twin pair (reported concordances range from 16% to 56%) in comparison to the incidence of disease in the general population (1:800 to 1:2500, depending on the population assayed) (Block et al., 1975; Deapen et al., 1992; Hopkinson et al., 1993; Johnson et al., 1990, 1994; Naleway et al., 2005; Stahl‐Hallengren et al., 2000). These results not only established that genetic predisposition is a major factor in susceptibility to SLE but also established that environmental and/or stochastic processes also play a role in triggering disease development. Epidemiologic data can also provide a measurement of the strength of genetic predisposition via calculation of the l statistic, which is defined as the risk of recurrence in siblings of probands as compared to that in the general population. A l ¼ 1 indicates no genetic contribution, whereas the l of fully penetrant single gene Mendelian diseases, such as cystic fibrosis, are on the order of 500 (Landers and Schork, 1994). The l for SLE has been estimated to be in the range of 20–40 (Hochberg, 1985; Lawrence et al., 1987), which indicates that genetic predisposition is a key element and comparable or greater in magnitude than what is observed in other complex autoimmune disorders (Kotzin, 1996). 3.1. Linkage Analysis of SLE with Affected Sibpairs Current modeling indicates that multiple genes and environmental factors impact disease development. This mode of inheritance, classically termed multifactorial, is a common feature of most autoimmune diseases (Wandstrat and Wakeland, 2001). This complex mode of inheritance severely complicates the application of classical linkage approaches for disease gene discovery. The best approach for linkage analysis of multifactorial traits has been through the analysis of affected sibpairs in which allele sharing is analyzed using families with two affected siblings. Given the incidence of SLE, the collection of large cohorts of such families has been difficult and, as a result, many of the initial linkage studies had relatively limited sample sizes. Consequently, although linkage analyses have provided a variety of interesting insights into the genetics of SLE susceptibility, few disease alleles have been conclusively identified by this approach. Five major independent linkage studies of SLE have been performed in humans, which have produced data supporting the presence of susceptibility loci in over 20 linked genomic regions (Gaffney et al., 1998; Gray‐McGuire et al., 2000; Lindqvist et al., 2000; Moser et al., 1998; Tsao et al., 1997). A comparison of all of these independent studies reveals that most of the intervals identified are not the same from study to study, which is a common characteristic of linkage analyses with complex traits. This is thought to predominantly reflect the fact that most of the disease alleles involved are relatively weak and that these studies were

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underpowered for the detection of weak alleles. Thus, it is likely that many of the intervals putatively associated with susceptibility may be artifactual. However, several of the intervals were shared between two or more of the studies, and six of these linkage intervals with the strongest statistical linkage values are presented in Table 3. Fine mapping analysis of two of these intervals, 2q32 and 6p11, ultimately Table 3 Genetic Linkage and Association Studies of SLE Intervals identified in two or more independent linkage analyses Locus

Study design

Maximum LOD score

1q23 1q41‐q44 2q32‐q35

Extended pedigrees Extended pedigrees Extended pedigrees

3.37 3.50 2.09

2q37 4p16 6p11‐p21 12q24 16q12‐q13

Extended pedigrees Extended pedigrees Sib‐pairs, simplex Extended pedigrees Sib‐pairs, extended pedigrees

3.90 2.18 4.19 4.19 3.85

References Moser et al., 1998 Shai et al., 1999 Moser et al., 1998; Quintero‐Del‐Rio et al., 2004 Lindqvist et al., 2000 Gray‐McGuire et al., 2000 Gaffney et al., 1998, 2000 Nath et al., 2004 Gaffney et al., 1998 Gaffney et al., 2000 Nath et al., 2004

Genetic association studies of candidate SLE genes Gene

Associated alleles

PTPN

R620W

Fcgr2a Fcgr3a IL‐10 Ctla4 PDCD‐1 HLA‐DR3, ‐DR2 TNF‐a TNF‐b C4 IRF5

R131 F176 Multiple alleles þ49G PD1.3A DR2/DR3 TNF2 TNFB*2 AQ0 rs2004640T

MBL FASL FAS Bcl2

230A –844C 297C/416G Multiple alleles

Statistical significance

References

p ¼ 0.0007 (case/control) NS (TDT and PDT) OR ¼ 0.18 (0.05–0.69) p < 0.01 p ¼ 0.0001 p ¼ 0.003 OR ¼ 2.6 (1.6–4.4) p < 0.0005 p ¼ 0.04 p < 0.0001 p < 106 p ¼ 4.4  1016 (case/control) p ¼ 0.0006 (TDT) OR ¼ 1.6 (1.0–2.8) p ¼ 0.024 RR ¼ 5.0 p ¼ 0.0001

Reddy et al., 2005; Wu et al., 2005 Salmon et al., 1996 Wu et al., 1997 Mehrian et al., 1998 Ahmed et al., 2001 Prokunina et al., 2002 Hartung et al., 1992b Wilson et al., 1994 Kim et al., 1996 Hartung et al., 1992a Graham et al., 2006

OR, Odds ratio with 95% confidence interval; RR, relative risk.

Davies et al., 1995 Wu et al., 2003 Horiuchi et al., 1999 Mehrian et al., 1998

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led to the identification of disease alleles of PDC1 and HLA‐DR (Graham et al., 2002; Prokunina et al., 2002). Another linkage approach, which has the potential capacity to uncover genes with a more Mendelian inheritance pattern, has been to subdivide complex patient populations based on differences in phenotype, age of onset, family history, and severity of disease. By restricting the clinical criteria, investigators are able to study a subset of patients with a more homogeneous disease phenotype and therefore possibly a smaller pool of contributing genes that can be mapped more readily. Validity of this method lies in reports of clinical concordance of traits between SLE sibpairs and twins (Reichlin et al., 1992; Tsao et al., 2002). Differences in onset of disease between both parent–offspring and sibpairs suggest that shared genetic contribution and not just a shared environment are needed to account for these similarities. The use of this approach in studies by Harley and colleagues at the Oklahoma Medical Research Foundation (OMRF) suggests that it will be difficult, since the subdivision of patients into specific clinical groups is complex and requires large patient samples (Rao et al., 2001). This group undertook a genome scan against 101 sibpairs and subdivided analysis into dermatologic, renal, immunological, hematologic, neurologic, cardiopulmonary, and arthritic characteristics, in addition to race and age of onset. Using this method of analysis, they revealed several more candidate loci, including 1q41‐42, 2q34, 7p13, and 15q26. Stronger linkage to a principal component vs overall SLE was also detected at several loci, including 2q24, 4q36, 9q24, 9q22, 17p12, and 22q12 and may indicate the presence of specific modifier genes in these regions. Overall, the results of these linkage studies have provided some important insights into the characteristics of genetic predisposition to SLE. First, these results indicate that although most of the loci are quite weak (i.e., contributing an increase in relative risk of <2), a few loci appear to be commonly detected. For example, two loci on human chromosome 1 were detected with reasonable strength in three or more of the linkage studies. The syntenic region of the mouse genome is also associated with susceptibility to lupus, suggesting that two or more loci within this genomic segment may be commonly associated with susceptibility to disease. Second, several studies identified intervals that were only detected within specific ethnic groups, suggesting that genetic susceptibility may vary between different populations (Lindqvist et al., 2000; Moser et al., 1998). This is not surprising, given the multigenic nature of multifactorial disease, however, the results suggest that distinct genetic pathways may mediate disease in different population subsets. Finally, the clinical heterogeneity of SLE is likely to reflect at least in part, the presence of distinct genetic diseases within the broad classification of SLE. As a result, a focus on disease components may validate and/or delineate new disease subsets.

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3.2. Association Analysis The detection and cataloguing of SNPs thoroughout the human genome, coupled with the development of new technologies that allow high throughput SNP typing for >300,000 SNPs simultaneously, is leading to a fundamental change in strategy for the genetic analysis of complex traits (Carlson et al., 2004a,b; de Bakker et al., 2005; Matise et al., 2003; Pe’er et al., 2006; Reich et al., 2001; Sachidanandam et al., 2001; Stram et al., 2003; Stumpf et al., 2005). Theoretical considerations also support a transition to association analysis, based on the realization that many or most disease alleles for multifactorial diseases may be relatively common within the human population (Reich and Lander, 2001). Modeling clearly indicates that association analysis has much more power than linkage analysis for the detection of common alleles associated with disease, thus making association analysis a high priority for future studies of multifactorial traits. An extensive collection of SNPs, together with population demographic data, is being assembled by an international consortium, and it is anticipated that this resource will enable genome‐wide SNP typing strategies in the future (de Bakker et al., 2005). The use of high‐density SNP typing to identify genes associated with autoimmune disease has been a successful strategy (Laitinen et al., 2001; Rioux et al., 2001). As described throughout Section II, association analysis using candidate genes identified based on functional relevance to disease pathology has been a common strategy utilized by numerous investigators. Table 3 lists a collection of genes for which association analysis has supported a role in susceptibility to SLE. Many of these associations are based on relatively small sample sizes and, in some instances, have been inconsistently associated with disease. However, within this list, three of the genes or gene families, the Fc receptors, PTPN22, and IRF5, are supported by strong statistical analyses. As discussed in Section II, polymorphisms of the Fc receptor complex have been associated with susceptibility to SLE and nephritis in a variety of studies (Kimberly et al., 1995; Moser et al., 1998; Salmon et al., 1996). The association of polymorphisms with protein tyrosine phosphotase PTPN22 with autoimmunity was initially detected using a genome‐wide coding region screen with potentially functional SNPs, and subsequently identified as a factor in SLE (Carlton et al., 2005; Kyogoku et al., 2004). Finally, two separate groups have recently associated IRF5, a key signaling molecule in the innate immune system, with susceptibility to SLE (Graham et al., 2006; Sigurdsson et al., 2005). 3.3. Future Prospects for the Analysis of SLE Susceptibility Several technological and organizational advances within the field of SLE genetics indicate that the analysis of SLE susceptibility is on the verge of important new discoveries. First, a consortium has been formed that includes

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all of the major investigators in the field and this consortium has received funding to perform a whole genome association study for SLE susceptibility. The importance of this consortium is that it has assembled a collection of patient samples of sufficient size to allow meaningful statistical analysis via genome wide association. The results of this pending screen is expected to identify a plethora of new disease alleles, thus providing important new research directions for future biological characterizations of SLE pathogenesis. In addition, the development of this consortium and established strategies for pooling resources and patient populations will dramatically enhance the quality of genetic analyses performed in the future. Although the initial association analyses will be focused on the identification of candidate genes, the availability of a large (>5000 samples) collection of well‐characterized patients and matched controls will be a valuable resource for many future studies. Key among these will be the analysis of epistatic interactions among disease alleles and modifiers. The analysis of animal models has clearly demonstrated that epistatic interactions play key roles in the acceleration and suppression of disease, and similar interactions are undoubtedly impacting human disease susceptibility. The analysis of such interactions will require detailed genotyping of extremely large samples of patients and controls. The resources developed by the consortium participants will provide such a resource, and it is quite likely that important new insights into the genetics of disease susceptibility will be obtained through a detailed analysis of epistatic interactions. Additional elements that can be addressed in future studies will include disease subsetting, variations in disease alleles among ethnic groups, and the association of disease alleles with specific disease components. Overall, the future for genetic analysis of SLE susceptibility is very promising. The impact of environmental factors on disease susceptibility is a major missing piece in our growing comprehension of the elements of disease predisposition. The importance of some type of environmental trigger in disease development is well established, and it is reasonable to predict that microbial infection is a major element in this process. Findings by Harley and coworkers, which implicate infection with EBV as an environmental element in SLE susceptibility are consistent with a role for infections in disease initiation (Harley and James, 1999; James et al., 2001; McClain et al., 2005, 2006; Poole et al., 2006). Given the importance of dysregulation of innate immune effector mechanisms, such as type I interferon and the Toll‐like receptors in SLE, it is quite reasonable to predict that infectious disease will play a role in disease development. However, dissecting the interactions of genetic predisposition with environmental factors will be extremely complex and statistically demanding.

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4. Murine Models of SLE The analysis of lupus‐prone murine models of SLE has contributed significantly to unraveling the immunological processes that mediate SLE. Currently, three lupus‐prone mouse strains serve as the predominant models of human SLE. Each of these strains develops disease spontaneously and to a significant degree represents the rodent equivalent of human SLE. These models were all developed over 30 years ago, and each has yielded important insights into disease processes that are relevant to human SLE pathogenesis. There are many strengths in the analysis of murine models. Most notably, their genomes can be manipulated, large cohorts of genetically identical animals can be produced, and the immunological mechanisms involved in generating autoimmunity can be explored in detail. The weaknesses are that subtle clinical phenotypes are not as readily identifiable as in humans (mice cannot tell us how they feel), and many of the end organ manifestations of disease that are recognized in humans have not been detected in murine models. In general, kidney disease is the major targeted end organ in murine lupus. The genetic dissection of disease pathogenesis is another major strength of murine lupus models. Many investigators have identified the locations of loci predisposing lupus‐prone mice to disease and congenic dissection strategies have led to the creation of many new models. Furthermore, congenic strains have been of tremendous value for elucidation of the component phenotypes associated with the development of disease. One caveat to murine gene discovery has been that identification of disease‐causing alleles in an animal model may not translate directly into identifying disease‐causing mutations in human populations. However, there is a high degree of synteny between the human and mouse genomes (Nadeau, 1989), therefore it is not surprising that recent data from genomic analysis has shown lupus susceptibility loci in the human genome which is syntenic to the regions of murine loci (Gaffney et al., 1998; Moser et al., 1998; Shen and Tsao, 2004). 4.1. Commonly Utilized Lupus‐Prone Inbred Mouse Strains Table 4 lists three of the classic lupus‐prone mouse models together with the recently produced B6.Sle123 triple congenic model of lupus. Currently, all of these strains are being used extensively by investigators working on murine lupus. The classic lupus‐prone strains include: (1) the female F1 hybrid of the New Zealand Black (NZB) and New Zealand White (NZW) strains (NZB/W) and its congenic recombinant inbred derivatives; (2) the BXSB strain, which develops rapidly fatal disease due to the presence of the Y‐chromosome autoimmune accelerator (yaa) locus; and (3) the MRL and MRL/lpr strains

Table 4 Commonly Used Lupus‐Prone Inbred Strains SLE phenotypes (5 months) Strain

29

BXSB NZB/W MRL/lpr B6.Sle1Sle2Sle3

Candidate gene

a‐dsDNA

ANA

Spg

C. ICs

GN

Many Many Fas Many

þ þ þþ þþþ

þ þ þþþ þþþ

þþþ þþþ þþþ þþ

þ þ þþþ nd

þþ þþ þþþ þþþ

HGG, hypergammaglobinemia; Spg, splenomegaly; GN, glomerularnephritis; C. ICs, circulating IgG.

HGG þ þ þþ þ/–

Males

Females

5 12 5.5 7

15 9 5 6

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which develop rapidly fatal systemic autoimmunity due to a mutation that disrupts the fas gene. These strains carry the characteristic features of lupus, including increased circulating autoantibodies and IgG, IC deposition, and kidney disease (Andrews et al., 1978). They differ on sex bias, age of onset, and other affected organs. The immunological mechanisms that mediate disease manifestation are quite different, as demonstrated by their differing responses to immunological challenge and T cell depletion. Weekly injection of anti‐Thy1.2 reduces kidney disease in MRL/lpr mice, with no change in NZB/W mice and inducing a rapid anaphylactic death in BXSB mice (Wofsy, 1993). However, treatment with L3T4, a monoclonal antibody to CD4, reduced disease in both NZB/W and BXSB strains, suggesting that dysregulations in the immune system are quite distinct among the individual models (Wofsy and Seaman, 1986). 4.1.1. (NZB  NZW)F1 Hybrids and the NZM2410 Recombinant Congenic Collection The NZB/W F1 hybrid was the first lupus‐prone mouse model of SLE described and has been used for experimental studies for over 40 years (Andrews et al., 1978; Theofilopoulos and Dixon, 1985). These mice have the characteristic SLE phenotype of severe humoral autoimmunity to nuclear antigens and early onset immune complex–mediated GN. In addition, these mice develop splenomegaly, with an expansion and activation of the T and B cell compartments (Andrews et al., 1978; Theofilopoulos, 1996). NZB/W mice have a strong bias for female susceptibility, paralleling the human disease, with a mortality rate of 50% at 9 and 15 months for females and males, respectively (Andrews et al., 1978). Rudofsky et al. (1993) subsequently produced a collection of congenic recombinant strains by inbreeding (NZB  NZW)F1  NZW progeny. This collection of strains expressed variable levels of autoimmunity, ranging from essentially normal to severe disease. We predominantly utilized the NZM2410 strain from this collection, however, other investigators have used other strains (Jacob et al., 2003; Morel et al., 1994; Waters et al., 2004). The activation of polyreactive B cells, together with increased IgM production is a characteristic feature of the autoimmune disease of these mice (Cohen and Ziff, 1977; Izui et al., 1978; Moutsopoulos et al., 1977; Theofilopoulos and Dixon, 1985). Splenic B cells have higher levels of the activation markers CD80, CD86, and ICAM‐1 and have a heightened response to CD40 cross‐linking (Jongstra‐ Bilen et al., 1997; Wither et al., 2000a,b). NZB/W mice develop high titers of autoantibodies against dsDNA and glomerular antigens during end organ disease, consistent with the presence of a major defect in B cell tolerance to nuclear antigens and the presence of strong T cell help for systemic autoimmunity.

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The importance of activated CD4þ T cells in this model has recently been characterized using an adenovirus containing a plasmid to CTLA‐4Ig or CD40Ig (Ye et al., 2005). Blockade of T cell costimulation within this system led to decreased autoantibody production, CD4þ T cell activation, and reduced nephritis. Furthermore, activated clonally expanded T cells have been detected in the kidney during disease development, suggesting that T cells may play a role in inflammatory processes mediating end organ damage (Zhou et al., 2004). Phagocytosis of apoptotic cells by macrophages is also impaired in these mice, although it appears that this is not cell intrinsic and the deficiency lies within the serum (Licht et al., 2004). However, in vivo phagocytosis is also defective, suggesting its relevance to the disease pathology (Potter et al., 2003). Cytokines play a significant role in this lupus model, the individual immunological response paralleling data in human patients. Studies have indicated that IFN‐g is a primary cytokine involved in the progression of disease in NZB/W, since blockade with a monoclonal Ab to IFNg ameriorates disease (Jacob et al., 1987) and plasmids containing IFN‐g accelerate lupus phenotypes (Hasegawa et al., 2002). Treatment of IFN‐a via an adenoviral vector system or with the recombinant protein, also accelerates IC deposition and nephritis in the NZB/W (Adam et al., 1980; Mathian et al., 2005). IL‐10 production also appears to be excessive and deleterious since antibodies IL‐10 also reduce the morbidity of the disease and recombinant IL‐10 promotes nephritis (Ishida et al., 1994). The damaging affect of IL‐10 appears to be, in part from a lack of TNF‐a, since inhibition of TNF with anti‐IL‐10 therapy accelerates disease. 4.1.2. The BXSB Strain The BXSB mouse strain is a recombinant inbred strain derivative of the hybrid of the SB/Le and C57B6J strains (Andrews et al., 1978; Murphy et al., 1980). Mortality of males is far greater than females (5 months vs 15 months), and this is due to the presence of a yaa of disease (Murphy and Roths, 1995). The yaa gene, which has been shown to result from the abnormal expression of TLR7 due to a translocation event, is a unique feature of this strain (Subramanian et al., 2006). Investigators have demonstrated that yaa is dysregulated in the B cell lineage and that CD4 Tcells were necessary for disease but need not carry the yaa mutation (Fossati et al., 1995; Lawson et al., 2001; Merino et al., 1991). BSXB male mice develop high levels of circulating IgG autoantibodies to a variety of nuclear antigens leading to IC deposition in the kidney and fatal (GN). In addition, splenomegaly and lymphadenopathy, indicative of severe chronic activation of the immune system, are typical of this strain (Andrews et al., 1978; Theofilopoulos and Dixon, 1985). BSXB male mice also develop a dramatic expansion in circulating monocytes, also termed monocytosis (Wofsy et al., 1984).

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Potent B cell hyper‐reactivity and upregulation of costimulatory molecules are a major feature of BXSB disease, consistent with the dominant role of yaa in disease. IL‐21, a cytokine which accelerates plasma differentiation, has been demonstrated to be tremendously upregulated in the serum of male BXSB mice (Ozaki et al., 2004). 4.1.3. MRL and MRL/lpr The MRL/MpJ strain was derived from a complex cross involving several inbred strains. As a result, the MRL genome contains a combination of LG/J (75%), AKR/J (12.6%), C3H/HeDi (12.1%), and C57BL/6 (0.3%) segments. A spontaneous mutation occurred near the twelfth generation of breeding that resulted in lymphoproliferation (lpr) and an extreme acceleration of the autoimmune phenotype (Andrews et al., 1978; Theofilopoulos and Dixon, 1985). This mutation was subsequently shown to be a mutation in fas, which encodes a receptor of the TNF receptor superfamily (Takahashi et al., 1994; Watanabe‐ Fukunaga et al., 1992). Fas has subsequently been shown to be involved in immunoreceptor‐mediated apoptosis of activated T and B lymphocytes. MRL/ MpJ/lpr (MRL/lpr) mice manifest a high titer of ANA within 2–3 months of age with hypergammaglobinemia, and extremely high levels of IC in the sera beginning at 4–5 months (Andrews et al., 1978). Mortality approaches 100% by 9 months, with 50% mortality occurring at around 5–5.5 months. Introgression of the lpr gene on the B6 background has a much milder phenotype, indicating that this mutation accelerates the disease initiated by other alleles within the MRL/MpJ background (Theofilopoulos and Dixon, 1985). The MRL/lpr strain has unique phenotypes, not observed in either the NZB/W or BXSB strains, which include a dramatic expansion of the double negative (DN; CD4–CD8–) T cell population (Theofilopoulos and Dixon, 1985). In addition, serum IgG and levels of circulating ICs are significantly higher, indicative the extreme hypergammaglobulinemia exhibited by this strain (Andrews et al., 1978). 4.1.4. B6.Sle123 The B6.Sle1Sle2Sle3 triple congenic strain is a newly derived lupus‐prone strain that was produced by introgressing the three predominant lupus susceptibility loci from NZM2410 onto the C57Bl/6 background (Morel et al., 2000). This strain develops highly penetrant systemic autoimmunity, leading to the production of high titers of anti‐dsDNA autoantibodies, extensive T and B cell activation, and IC‐mediated severe GN culminating in fatal disease with a penetrance of ~80% in both genders by 12 months of age. Abnormal activation phenotypes are observed in T, B, and monocyte lineages during the development of severe disease, indicative of chronic immune activation.

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This strain develops severe disease as a consequence of a defined series of susceptibility loci that have been bred onto the B6 background. One advantage of this system is that the B6 parental strain can be used as a ‘‘normal’’ when assessing disease‐related phenotype. This comparison is significantly superior to comparing between a lupus‐prone strain with a control strain, such as BALB/c, for which the entire genome is variable. In addition, a large array of targeted gene knockouts, transgenics, and spontaneous mutants are maintained on the B6 background, which facilitates the introgression of other genes into the lupus‐prone genome. 4.2. Linkage Analysis and Congenic Dissection of Lupus‐Prone Strains The genetic basis for susceptibility to fatal lupus has been assessed by linkage analysis for all of the standard lupus‐prone strains (Drake et al., 1994; Haywood et al., 2000; Kono et al., 1994; Morel et al., 1994, 1999a; Vidal et al., 1998; Watson et al., 1992). These studies have associated systemic autoimmunity with a variety of genetic intervals scattered throughout the genome. However, three distinct genomic regions, namely (1) telomeric chromosome 1, (2) the central segment of chromosome 4, and (3) the centromeric segment of chromosome 7, have been associated with susceptibility in multiple strains. This suggests that these segments each contain one or more susceptibility loci that are involved in disease development in multiple lupus‐prone strains. Detailed analyses of these intervals has been performed with congenic strains produced in a variety of fashions by several laboratories, leading to a significant body of information about their properties. We have overviewed the positions of all loci detected in murine linkage crosses previously (Wakeland et al., 2001). Consequently, this review will focus on the loci within these three common genomic intervals. A key experimental procedure that has been utilized by mouse geneticist to characterize specific susceptibility loci has been the construction of congenic strains. This strategy, originally developed by George Snell for the analysis of histocompatibility (Snell, 1948), converts the polygenic system responsible for disease susceptibility into a series of monogenic systems via the production of individual congenic strains, each carrying a single susceptibility interval on the resistant genetic background. The component phenotypes mediated by each individual susceptibility gene can then be analyzed separately via the phenotypic analysis of individual congenic strains. In addition, component phenotypes detected in individual congenic strains are amenable to genetic and functional analysis as Mendelian traits, thus making traditional fine mapping and subsequent positional cloning strategies feasible. This strategy has been used extensively by investigators working on lupus‐prone strains and has led to a variety of important insights, both into the component phenotypes associated

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with individual susceptibility loci and the genetic complexity underlying individual susceptibility loci identified by linkage analysis. 4.2.1. Chromosome 1 (Sle1, Nba2, Lbw7, Sbw1, Bxs1, Cgnz1) The telomeric segment of chromosome 1 has been associated with susceptibility to lupus in linkage studies utilizing every lupus‐prone mouse strain (Haywood et al., 2000; Kono et al., 1994; Morel et al., 1994; Vyse et al., 1997; Waters et al., 2004; Zhang et al., 2006). The production of congenic strains has uniformly revealed that this genomic segment predominantly impacts autoantibody production (Haywood et al., 2004; Mohan et al., 1998; Morel et al., 1997; Rozzo et al., 2001; Waters et al., 2004). This segment of murine chromosome 1 is syntenic with two regions of human chromosome 1 that have been associated with SLE susceptibility in multiple linkage crosses (Moser et al., 1998, 1999; Tsao et al., 1997). As a result, this susceptibility locus has been investigated intensively. 4.2.1.1. Sle1 B6.Sle1 was produced by introgressing the telomeric segment of chromosome 1 from the lupus‐prone NZM2410 recombinant congenic strain onto B6 (Morel et al., 1997). This genomic segment, which was derived from the NZW parental strain, confers a loss of tolerance to nuclear antigens, with a high specificity for the H2A/H2B/DNA subnucleosomes (Mohan et al., 1998; Morel et al., 1997). Anti‐nuclear antibodies develop at around 3 months of age, together with hypergammaglobinemia, mild splenomegaly, and an expansion of the CD4 T and B cell populations in the spleen. In addition, there are greater numbers of activated CD4þ cells producing IL‐2, IL‐4, and IFN‐g compared to B6 controls (Chen et al., 2005a). There is activation of B cells, as demonstrated by the upregulation of CD86 and CD69, an expanded population of histone‐reactive T cells, together with an increase in activated and memory CD4þ T cells (Mohan et al., 1998; Sobel et al., 2002b; Subramanian et al., 2005). Autoimmunity of B6.Sle1 mice is strongly gender biased, with female mice producing IgG ANA with a penetrance of ~80% by 7–9 months of age. Although these mice are strongly ANA positive, they live a normal life span and have predominantly normal kidney histology. The Sle1 congenic interval is highly epistatic with a variety of other susceptibility loci. Thus, if yaa, lpr, Sle2, or Sle3 are introgressed onto B6.Sle1 to produce bicongenic strains, severe disease will develop (Mohan et al., 1999a; Morel et al., 2000; Shi et al., 2002; Subramanian and Wakeland, 2005). Each of these bicongenic combinations expresses some unique autoimmune features, although all of these combinations transition to the production of high‐titered IgG autoantibodies that recognize dsDNA and a variety of glomerular antigens.

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Fatality in these various combinations varies from about 15% (B6.Sle1Sle2) to more than 70% (B6.Sle1yaa and B6.Sle1lpr). Fine mapping analysis revealed that the Sle1 congenic interval contains at least four loci capable of independently mediating autoimmune phenotypes (Morel et al., 2001). Sle1a, Sle1b, and Sle1c were each capable of causing a breach in immune tolerance to nuclear antigens. The Sle1c locus, which is localized to an extremely telomeric segment of chromosome 1 (101.6–106.6 cM), mediates an increase in circulating IgG antibodies without specificity to nuclear antigen (Morel et al., 2001). Recently, it has been shown that Sle1c confers an alteration in T cells (Chen et al., 2005a). Sle1c‐containing T cells can provide help to expressing chromatin‐specific B cells only in Sle1‐containing B cells (Chen et al., 2005a). Cr2 has been proposed as a candidate genes of Sle1c (Boackle et al., 2001). These investigations identified a point mutation that caused a change in glycosylation and impacted a critical ligand‐binding domain of the CR1/CR2 receptor. This structural variation was shown to cause an alteration in the B cell response and GC formation (Boackle et al., 2001; Chen et al., 2005b). The Sle1a locus conferred a moderate increase in anti‐chromatin autoantibodies, with a penetrance of 30% (Morel et al., 2001). Further analyses suggested that the Sle1a region resulted in primarily T cell activation (Boackle et al., 2001). In adoptive transfer experiments, autoreactive T cells from Sle1a mice provide help to autoreactive chromatin‐specific B cells from both Sle1‐containing and non‐Sle1 B cells. This occurs after a decrease in T cells with a regulatory phenotype (CD4þCD25þ; Chen et al., 2005a). The Sle1a locus has been localized to a ~4 mBase interval bounded by D1MIT400 and D1MIT148 (Morel et al., 2001). The Sle1b locus is the most potent susceptibility locus capable of mediating a breach in tolerance to nuclear antigens. These mice spontaneously produce high levels of antichromatin autoantibodies with a penetrance approaching 80%. Both the T and B cell compartments exhibit a variety of activation phenotypes (Wandstrat et al., 2004). The phenotypes of Sle1b are highly female biased, and this locus can interact with yaa or the lpr mutation to mediate fatal lupus (Croker et al., 2003). Fine mapping analysis has produced the B6.Sle1b mouse, which contains Sle1b within a ~960‐kb genomic interval. A detailed genomic analysis of Sle1b has identified the SLAM/CD2 gene cluster as primary candidates for mediating autoimmunity (Wandstrat et al., 2004). 4.2.1.2. Nba2 The NZB autoimmunity 2 locus, or Nba2, is a lupus susceptibility allele linked with GN and death, that was mapped to distal chromosome 1 in an (NZB  SM/J)F1  NZW and B6.H2Z  NZB)F1  NZB backcrosses (Drake et al., 1995; Rozzo et al., 1996). The congenic interval of B6.Nba2 mice

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is derived from NZB and is generally located in the same genomic segment as Sle1. Nba2 is associated with hypergammaglobinemia and the development of a wide variety of autoantibodies (Vyse et al., 1997). B cells express a spontaneous activation phenotype, characterized by the upregulation of costimulatory molecules (Atencio et al., 2004; Wither et al., 2000a). The combination of this region with the Y‐chromosome autoimmune accelerator leads to increased anti‐ nuclear autoantibody production, and enhanced autoimmune hemolytic anemia, gp70‐anti‐gp70 immune complex (gp70 IC) formation, and GN (Kikuchi et al., 2005a,b). Overall, the autoimmune phenotypes of B6.Nba2 mice are similar to those of B6.Sle1. B6.Nba2 mice carry a large (>20 cM) congenic interval derived from NZB and consequently contains a variety of potential candidate genes. These include the Fc receptor gene cluster, SLAM/CD2 family, interferon inducible genes (IFI) 202 and 203, C‐reactive protein (Crp) and serum amyloid P component (SAP). Polymorphisms between B6 and NZB lead to a dramatic variation in the expression of Ifi202 in B and non‐B/non‐T cells, together with an associated downregulation in the expression of Ifi203 (Rozzo et al., 2001). The parental NZB strain also expresses this variation in expression, while NZW has levels similar to the B6. In addition, elimination of the type I interferon receptors in the NZB strain reduces the parental autoimmunity but has no effect on the upregulated expression of the ifi202 (Santiago‐Raber et al., 2003). Thus, although ifi202 remains a candidate gene for Nba2, it is also possible that this polymorphism has no impact on autoimmunity. 4.2.1.3. Sbw1, Lbw7, Cgnz1, Bxs1, and Bxs2 Theofilopoulos and colleagues identified Sbw1 and Lbw7 during their original linkage analysis of (NZB  NZW) F2 progeny (Kono et al., 1994). Sbw1 defines a locus associated with splenomegaly while Lbw7 defines a locus associated with anti‐chromatin autoantibodies. No further studies have been performed on these loci. Cgnz1 was detected in a cross of NZM2338 X C57/L (Waters et al., 2004). Although this locus maps in proximity to Sle1, it has been reported to have a distinct phenotype. Congenic dissection of the BXSB mouse model determined a unique susceptibility locus to this lupus‐prone strain; Bxs1 (Hogarth et al., 1998; Haywood et al., 2000). Initial linkage suggested an association with splenomegaly and GN. When combined with the yaa allele, Bxs1 accelerates the development of kidney disease (Haywood et al., 2004). Bxs2 lies at the same region of chromosome 1 as Sbw1 (Hogarth et al., 1998; Kono et al., 1994). In the initial linkage analysis, these loci were linked to ANA production and increased circulating IgG3. In addition, this region was associated with splenomegaly and nephritis. Bxs3 lies within the same region as Sle1, Nba2, and Lbw (Hogarth et al., 1998). Similar to these susceptibility loci, initial dissection suggested a linkage with anti‐nuclear

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antibodies and GN. When Bxs2 is combined with yaa and Bxs3, it confers a phenotype indistinguishable from the full BXSB strain (Haywood et al., 2004). 4.2.2. Chromosome 4 (Sle2, Nba1, Lmb1, and Lbw2) Several susceptibility loci were mapped into a similar region of chromosome 4 by the original linkage studies of the NZM, NZW/B, and MRL/lpr strains (Drake et al., 1995; Kono et al., 1994; Morel et al., 1994; Vidal et al., 1998). This region of chromosome 4 contains a variety of potentially interesting candidate genes, including the type 1 interferon gene cluster. Subsequent congenic dissection analysis has delineated a variety of novel phenotypes mediated by genes within this region. The Sle2 susceptibility locus from NZM2410 and the Lmb1 locus from MRL/lpr predominantly influenced B cell activation and humoral autoimmunity, while the Nba1 locus from NZB and the Lbw2 susceptibility locus from NZB/W were associated with kidney disease. 4.2.2.1. Sle2 The B6.Sle2 congenic strain does not develop spontaneous autoimmunity, but the B cell compartment has a lowered activation threshold, leading to polyclonal activation together with an expansion of the B1a compartment (Mohan et al., 1997; Morel et al., 1997). This congenic interval has been dissected into three smaller gene regions, named Sle2a–c. Sle2a and Sle2b promote kidney disease and lymphatic expansion when combined with Sle1 and Sle3 on a B6 background (Xu et al., 2005). Sle2c promotes the expansion of the B1a sublineage. This ongoing analysis clearly indicates that the Sle2 congenic interval contains at least three loci that contribute to the autoimmune phenotypes associated with chromosome 4. 4.2.2.2. Nba1/Lbw2/Lmb1 Nba1 was originally detected in the linkage analysis of the NZB/W strain (Drake et al., 1994; Kono et al., 1994; Vyse et al., 1996). This region is not associated with the production of autoantibodies, but instead is linked with susceptibility to kidney disease (Kono et al., 1994; Vyse et al., 1996). This locus is derived from the parental NZB strain and linkage with nephritis appears to be independent of MHC polymorphisms (Rozzo et al., 1996). Linkage analysis in NZB/W by Kono et al. (1994) suggested that two linked susceptibility loci were located on chromosome 4 in proximity to the location of Sle2 and Nba1 (Kono et al., 1994). Lbw2 was associated with kidney disease and death, while Sbw2 mapped to exactly the same region but was associated with splenomegaly. Theofilopoulos’ group has also mapped a susceptibility locus in MRL/lpr mice within this same region. Lmb1 conferred splenomegaly, lympadenopathy, and anti‐dsDNA autoantibody production (Vidal et al., 1998).

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4.2.3. Chromosome 7 (Sle3, Sle5, Lmb3, Nba5, and Aia3) Susceptibility loci in the centromeric segment of chromosome 7 were detected in linkage analysis of NZB/W, NZM2410, and MRL/lpr. Sle3 was associated with susceptibility to kidney disease and Sle5 with humoral autoimmunity in two linkage analyses with NZM2410 (Morel et al., 1994, 1999a). The Lmb3 susceptibility locus was associated lymphoadenopathy, splenomegaly, and hurmoral autoimmunity in a linkage cross with MRL/lpr (Vidal et al., 1998). The Nba5 susceptibility locus was associated with higher titers of anti‐GP70 autoantibodies (Kikuchi et al., 2005b) and Aia3 with autoimmune hemolytic autoimmunity in a linkage analysis of NZB (Kikuchi et al., 2005a). Overall, these data indicate that this segment of chromosome 7 contains several susceptibility loci containing disease alleles with distinct functional properties. 4.2.3.1. Sle3/5 B6.Sle3/5 congenic mice develop low‐titered autoantibodies to a variety of cytoplasmic and nuclear antigens at low penetrance (Morel et al., 1997). Initial characterization of the B6.Sle3/5 strain revealed a variety of phenotypic changes in CD4 T cells, including an increase in the CD4:CD8 ratio in the spleen, an increase in activated CD4þ T cells, and an increased tolerance to activation‐induced apoptosis (Mohan et al., 1999b). Subsequent analysis of this region has revealed the presence of two loci; Sle3 in the telomeric portion and Sle5 in the centromeric portion of the original congenic interval (Morel et al., 1999a). These analyses associated susceptibility to kidney disease with Sle3 and enhanced humoral autoimmunity with Sle5. The Sle3 region also has been reported to alter IgH VDJ recombination in the periphery, altering the junctional diversity in VHDJH and the mutational diversity in VH (Wakui et al., 2004). Mixed bone marrow chimeric studies indicated that Sle3/5 is functionally expressed in non‐T/non‐B cells and is capable of dysregulating T lymphocytes derived from both B6 and B6.Sle3 donors (Sobel et al., 2002a). More recently, Sle3 was shown to be expressed intrinsically in the myeloid cell lineage (Zhu et al., 2005). Bone marrow derived monocytes and dendritic cells from B6.Sle3 mice have decreased thresholds for activation by LPS and upregulated effector mechanisms in comparison to B6. These altered phenotypes in the myeloid lineage are likely to account for the altered in vivo phenotypes of CD4þ T cells in B6.Sle3 mice. 4.2.3.2. Lmb3/Nba5/Aia3 All of these susceptibility loci were identified in linkage crosses with NZB or MRL/lpr lupus‐prone strains and have not yet been characterized in congenic strains.

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4.3. Genetic Interactions and Disease Development The importance of genetic interactions in the development of severe systemic autoimmunity was apparent early in the analysis of lupus‐prone mouse models. Although NZB and NZW have minimal autoimmune phenotypes, the (NZB  NZW)F1 hybrid is severely autoimmune. Thus, genetic interactions between alleles in NZB and NZW cause the expression of a phenotype (severe systemic autoimmunity), which is absent in both parental strains. Similarly, the lpr spontaneous mutation of Fas lost its autoimmune phenotype when introgressed onto other genetic backgrounds (Fossati et al., 1993; Warren et al., 1984). Similar findings have been made with the yaa gene in BXSB. These relationships illustrate that the autoimmune potential of specific susceptibility genes is exquisitely dependent upon the presence of a ‘‘permissive’’ genome to potentiate the expression of an autoimmune phenotype. The spontaneous autoimmune phenotypes of the congenic B6.Sle1, B6.yaa, and bicongenic B6.Sle1/yaa strains provide a clear illustration of synergism between susceptibility alleles for lupus (Subramanian and Wakeland, 2005). B6.Sle1 and B6.yaa spontaneously produce nonpathogenic autoantibodies to nuclear antigens but fail to develop severe autoimmunity. However, when these two susceptibility alleles are combined in the B6.Sle1/yaa bicongenic strain, a severe systemic autoimmunity develops, culminating in fatal GN with an incidence of 70% by 9 months of age (Morel et al., 2000). This illustrates an epistatic interaction between two susceptibility alleles that causes a greater increase in disease severity than would be predicted by simply adding their individual phenotypes together. A second type of epistasis, in which epistatic modifiers suppress the autoimmune phenotypes of susceptibility alleles, has also been detected using the B6.Sle congenic strains (Morel et al., 1999b). Genes capable of suppressing autoimmunity were detected via the analysis of the disease phenotype mediated by Sle1, Sle2, and Sle3 when introgressed onto different genetic backgrounds. As discussed earlier, B6.Sle123 triple congenic mice develop fatal lupus nephritis with a penetrance approaching 90% in both genders by 9 months of age (Morel et al., 2000). All three of these susceptibility alleles are derived from the NZW genome, and yet, as discussed earlier, NZW exhibits only very benign autoimmune phenotypes in females greater than 12 months of age (Kelley and Winkelstein, 1980). Thus, the phenotypic expression of Sle1/Sle2/Sle3 is significantly suppressed in NZW. A linkage analysis found four separate loci that accounted for the suppression of lupus susceptibility in NZW (Morel et al., 1999b). These results indicate that the disease mediated by susceptibility genes can be fully suppressed by other ‘‘modifying’’ genes in the genome. Sles1, which has been fine mapped into a

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960‐kb congenic interval on murine chromosome 17, specifically suppresses the autoimmune phenotype of Sle1. Clearly, the identification of molecular pathways mediating the suppression of autoimmunity by Sles1 will provide important insights into the mechanisms by which the immune system maintains a regulatory balance. 5. Modeling Disease Development We have previously described a three‐step hypothetical model of the roles that various genetic pathways play in the initiation and progression of SLE (Wakeland et al., 1999, 2001). An updated version of this model is presented in Fig. 1, and potential disease alleles and processes involved in each stage of the model system are discussed in detail later. 5.1. Pathway I: Breaking Immune Tolerance to Nuclear Antigens The first stage in the development of systemic autoimmunity and SLE is a loss in immune tolerance to nuclear antigens and the production of IgG autoantibodies. Current estimates indicate that close to 5% of the adult human population is ANA

Figure 1 Model demonstrating the three hypothetical genetic pathways that lead from benign autoimmunity to fatal disease in SLE.

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positive, with IgG autoantibody titers greater than 1:120 (Shmerling, 2003). ANA positivity is not strongly associated with any specific pathologic consequences; however, this population does have a significant increase in relative risk for the development of rheumatic disease. As recently demonstrated by Arbuckle et al. (2003) 70% of all individuals diagnosed with SLE are ANA positive several years prior to the development of clinical pathology. These results indicate that the transition to being ANA positive is commonly the first step toward the development of SLE. The nature of the genes responsible for this phenotype in humans is unclear, however, some information is available in murine models. The Sle1 gene cluster is a collection of genes capable of converting B6 mice to an ANA positive phenotype. This transition is consistent with the expectations of Arbuckle et al., in that the addition of a second susceptibility locus (see later) will convert this benign autoimmune phenotype into severe disease. Polymorphic genes that regulate the activation of the immune system, such as the SLAM/CD2 gene family, are excellent candidates for mediating this phenotype. Recent work by Nussenzweig and coworkers suggest that individuals with systemic autoimmunity have defects in key B cell checkpoints (Yurasov et al., 2005). Given the frequency of an ANA positive phenotype in the general population, it is possible that the genotypes of many individuals are prone to this form of autoimmunity. 5.2. Pathway II: Dysregulation of the Innate/Adaptive Immune Systems The benign autoimmunity elicited by disease alleles in pathway I is a relatively common phenotype in the human population and is not associated with significant pathology. It is estimated that only about 1–2% of ANA positive individuals will ultimately transition to more pathologic manifestations of autoimmunity, indicating that a normal immune system is capable of controlling the development of disease manifestations as a result of a breach in immune tolerance to nuclear antigens by the adaptive immune system. Individuals that also have disease alleles in pathway II, however, are unable to control this benign autoimmunity, and as a result, develop severe autoimmune pathologies. Analysis of the B6‐congenic series indicates that individual susceptibility loci, such as Sle2, Sle3, or Sle5, which do not mediate high ANAs or aggressive lupus in isolation, will mediate a transition to fatal disease when combined with pathway I genes such as Sle1. We propose that genes impacting the regulation of both the innate and adaptive immune systems are within this pathway. 5.2.1. Dysregulation of the Innate Immune System The identification of the genetic lesion underlying yaa as a dysregulated copy of TLR7 provides an important insight into this version of an interaction between pathways I and II. Thus, a twofold upregulation in the expression of

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TLR7 lowers the activation threshold for signaling by TLR7 ligands, causing increases in the proliferation of B cells and the secretion of a variety of cytokines. Although this modified TLR7‐phenotype has minimal autoimmune consequences in isolation on the normal B6 background, it will drive the rapid development of fatal autoimmunity when bred in combination with Sle1. Thus, this interaction would appear to involve a synergistic dysregulation of the innate immune system by yaa together with a dysregulation of the adaptive immune system by Sle1. This combination results in a dysregulated immune system that becomes chronically activated and rapidly spirals into severe systemic autoimmunity. 5.2.2. Dysregulation of T Lymphocytes Mutations or ablations of a variety of genes involved in the regulation of the T lymphocytes also have been shown to potentiate the development of systemic autoimmunity. For example, disruption of fas, which is a member of the TNFR superfamily and is important in the regulation of cell death, is the underlying genetic lesion of the lpr mutation. When lpr is introgressed onto an autoimmune genome, such as MRL or B6.Sle1, the combination promotes a dysregulated lymphoproliferation and drives the T cell dependent production of autoantibodies, culminating in fatal disease (Shi et al., 2002; Takahashi et al., 1994; Watanabe‐Fukunaga et al., 1992). Another example would be the programmed cell death‐1 (PD‐1) gene, which normally functions to inhibit the activation of autoreactive T cells, thereby controlling lymphoproliferation. Mutation or deletion of PD‐1 results in mild GN, which is amplified by addition of the fas gene (Nishimura et al., 1999). As discussed earlier, the human equivalent of murine PD‐1, called PDCD‐1 has been identified as a susceptibility gene in human studies (Prokunina et al., 2002). A final example is CTLA‐4, which is similar to PD‐1, both structurally and functionally. CTLA‐4 is expressed on T cells and binds to B7.2 on DCs, preventing costimulation through CD28–B7.1 interactions (Chen, 2004). Deficiency for CTLA‐4 confers hyperproliferative T cells and high levels of circulating ANAs (Waterhouse et al., 1995). 5.2.3. Dysregulation of B Cells Several genes that predominantly regulate B cells are also included within this pathway. Bcl2 is an antiapoptotic oncogene which when overexpressed as a transgene can prolong B cell life and cause an increase in B cell proliferation and ANAs with some penetrance to GN (Strasser et al., 1991). B‐lymphocyte stimulator (BLyS), which is a member of the TNF‐ligand superfamily, drives B cell proliferation and SLE‐like phenotypes when overexpressed as a transgene

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(Gross et al., 2000; Khare et al., 2000; Mackay et al., 1999). In addition, antagonists of BLyS significantly reduce but do not eliminate disease in both the MRL/lpr and NZB/W lupus‐prone strains (Gross et al., 2000; Kayagaki et al., 2002). 5.3. Pathway III: End Organ Targeting Factors that specifically affect the disease pathogenesis of the end organ disease are classified into pathway III. This includes the receptors for ICs, factors that enhance inflammatory processes, and adhesion molecules. 5.3.1. Receptors for Immune Complexes Immune complexes can activate an inflammatory response by two overlapping pathways: (1) through the soluble proteins of the complement (C) system and (2) through interaction with one of three possible receptors for the Fc constant region (Fanger et al., 1989; Ravetch and Clynes, 1998; Robinson et al., 1992). Cross‐linking of three or more of the Fcg receptors may induce a variety of cellular responses such as degranulation, phagocytosis, the generation of ROS, and antigen presentation, all of which are important aspects of inflammation (Deo et al., 1997; Ravetch, 1997). FcgRI recognizes with high‐affinity monomeric IgG, while FcgRII and III are moderate to low‐affinity receptors, which bind best with complexed IgG. FcgRII (CD32) is the most widely expressed group within the IgG Fc family. FcgRIIa has two allotypic forms, defined as R131 or H131, according to whether there is an arginine (R) or a histidine (H) at position 131 in the extracellular domain of the receptor (Parren et al., 1992). The lower affinity A131 receptor is associated with a decreased phagocytic response in PMNs (Salmon et al., 1996). The association of the R131 alleles with susceptibility to SLE‐mediated kidney disease has been somewhat variable (Duits et al., 1995; Villarreal et al., 2001). A meta‐analysis of a number of inconsistent studies has shown that overall there is an increase in incidence in the homozygous low‐ affinity A131/A131 genotype in SLE patients (Karassa et al., 2002). In addition, FcgRIIa expression on monocytes is reduced when SLE patients are compared with healthy controls, which may reflect a downregulation as a consequence of circulating ICs (Hepburn et al., 2004). Means et al. (2005) have also demonstrated the necessity of FcgRIIa in IC‐mediated IFN‐a production of pDCs, together with TLR9, providing a mechanism of IFN production in human disease. There are two alleles of FcgRIIIa which differ at position 158 in the extracellular domain, designated V158 and F158, reflecting a valine (V) or

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phenylalanine (F), respectively (Karassa et al., 2004). Association of these alleles with SLE has been controversial; however, a meta‐analysis has demonstrated an association with increased risk of nephritis in patients with SLE (Karassa et al., 2003). Murine studies have demonstrated that deletion of FcgRIIIa in the NZB/W stain does not alter levels of ANAs or IC deposition but does protect from nephritis (Clynes et al., 1998). Taken together, these findings support a role for FcgRIIIa in the modulation of IC‐mediated inflammatory processes. FcgRIIIb is a GPI‐anchored Fc receptor expressed in human resting neutrophils and on IFN‐g–treated eosinophils (Ravetch and Perussia, 1989; Selvaraj et al., 1989; van de Winkel and Capel, 1993). The soluble form of FcgRIIIb is increased in patients with SLE (Hutin et al., 1994). sFcgRIIIb retains the IgG‐binding activity and can coprecipitate with IgG from plasma, suggesting a role in the regulation of immune complex–activation of phagocytes (Fleit et al., 1992; Huizinga et al., 1990). FcgRIIIb has three alleles (Bux, 2002), however, there is no evidence that these polymorphisms play a role in the development or pathogenesis of SLE. 5.3.2. Factors That Enhance Inflammatory Processes The complement system is responsible for generating key effector molecules in the inflammatory response to IC and several complement components are known to play a role in susceptibility to SLE and in disease progression. The significant disease risks associated with deficiencies of C1q, C2, and C4 are well established. There is also evidence that deficiency of other complement factors can impose a risk factor for SLE (Morgan and Walport, 1991; Pickering et al., 2000; Sontheimer et al., 2005). Some studies have reported decreased serum levels of C1q in SLE, which correlate with increased numbers of apoptotic cells and decreased phagocytosis (Bengtsson et al., 2004; Bijl et al., 2006; Klint et al., 2000). CR1 is also increased on monocytes from patients with active SLE or active RA (Hepburn et al., 2004) and a meta‐analysis of 17 different cohorts from 15 studies by Harley and colleagues has shown that polymorphisms in CR1 are associated with the disease (Nath et al., 2005). In murine studies there is strong evidence that all three complement pathways play a role in the MRL/lpr strain (Trouw et al., 2005). Data from the NZB/W have also suggested that complement proteins have a role in the progression of disease (Ault and Colten, 1994; Miura‐Shimura et al., 2002). Furthermore, CR2 is a key candidate gene Sle1c, which has been shown to have functional polymorphisms that are consistent with a role in disease progression (Boackle et al., 2001). Both human and mouse studies demonstrate that IFN can drive inflammatory processes, including B cell activation and differentiation and DC maturation.

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We and others have shown that the addition of IFN‐a in a number of different parental strains, drives end organ pathogenesis. We propose that by increasing the inflammation locally, it drives the existing pathology to an augmented state. Since this does not occur on nonlupus‐prone backgrounds, such as the BALBc and B6, existing susceptibility regions are required for this amplifying effect. 5.3.3. Adhesion Molecules and End Organ Targeting Adhesion molecules play a crucial role in recruiting and directing inflammatory cells to the target organ. The level of soluble l‐selectin, which is shed on activation during the initial stages of leukocyte emigration to an inflammatory site, is higher in patients with SLE (Russell et al., 2005). However, there is no evidence to suggest that this arises from any genetic abnormalities, but rather that it is a consequence of the systemic inflammatory response that accompanies severe disease flares. Intracellular adhesion molecule‐1 (ICAM‐1) is a member of the immunoglobulin superfamily which binds to the B2 integrin LFA‐1 on lymphocytes. ICAM‐1 expression is reduced on monocytes from patients with SLE; however, this may be due inpart to therapy (Hepburn et al., 2004). MRL/lpr and NZB/W mice have increased levels of ICAM‐1 in the kidneys when compared to nonautoimmune prone strain (Wuthrich et al., 1990). In addition, deficiency of ICAM‐1 in the MRL/lpr strain results in decreased vasculitis in the kidney, lung, skin, and salivary glands (Lewis and D’Cruz, 2005). References Adam, C., Thoua, Y., Ronco, P., Verroust, P., Tovey, M., and Morel‐Maroger, L. (1980). The effect of exogenous interferon: Acceleration of autoimmune and renal diseases in (NZB/W) F1 mice. Clin. Exp. Immunol. 40, 373–382. Aguilar, F., Gonzalez‐Escribano, M. F., Sanchez‐Roman, J., and Nunez‐Roldan, A. (2001). MCP‐1 promoter polymorphism in Spanish patients with systemic lupus erythematosus. Tissue Antigens 58, 335–338. Aguilar, F., Torres, B., Sanchez‐Roman, J., Nunez‐Roldan, A., and Gonzalez‐Escribano, M. F. (2003). CTLA4 polymorphism in Spanish patients with systemic lupus erythematosus. Hum. Immunol. 64, 936–940. Ahmed, S., Ihara, K., Kanemitsu, S., Nakashima, H., Otsuka, T., Tsuzaka, K., Takeuchi, T., and Hara, T. (2001). Association of CTLA‐4 but not CD28 gene polymorphisms with systemic lupus erythematosus in the Japanese population. Rheumatology (Oxford) 40, 662–667. Alcocer‐Varela, J., Eman‐Hoey, D., and Alarcon‐Segovia, D. (1992). Interleukin‐1 and interleukin‐6 activities are increased in the cerebrospinal fluid of patients with CNS lupus erythematosus and correlate with local late T‐cell activation markers. Lupus 1, 111–117. Allen, R. D. (1999). Polymorphism of the human TNF‐alpha promoter: Random variation or functional diversity? Mol. Immunol. 36, 1017–1027. Anders, H. J. (2005). A Toll for lupus. Lupus 14, 417–422.

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Association analysis of the R620W polymorphism of protein tyrosine phosphatase PTPN22 in systemic lupus erythematosus families: Increased T allele frequency in systemic lupus erythematosus patients with autoimmune thyroid disease. Arthritis Rheum. 52, 2396–2402. Wu, J., Edberg, J. C., Redecha, P. B., Bansal, V., Guyre, P. M., Coleman, K., Salmon, J. E., and Kimberly, R. P. (1997). A novel polymorphism of FcgammaRIIIa (CD16) alters receptor function and predisposes to autoimmune disease. J. Clin. Invest. 100, 1059–1070. Wu, J., Edberg, J. C., Gibson, A. W., Tsao, B., and Kimberly, R. P. (1999). Single‐nucleotide polymorphisms of T cell receptor zeta chain in patients with systemic lupus erythematosus. Arthritis Rheum. 42, 2601–2605. Wu, M. C., Huang, C. M., Tsai, J. J., Chen, H. Y., and Tsai, F. J. (2003). Polymorphisms of the interleukin‐ 4 gene in Chinese patients with systemic lupus erythematosus in Taiwan. Lupus 12, 21–25. Wuthrich, R. P., Jevnikar, A. M., Takei, F., Glimcher, L. H., and Kelley, V. E. (1990). Intercellular adhesion molecule‐1 (ICAM‐1) expression is upregulated in autoimmune murine lupus nephritis. Am. J. Pathol. 136, 441–450. Xu, Z., Duan, B., Croker, B. P., Wakeland, E. K., and Morel, L. (2005). Genetic dissection of the murine lupus susceptibility locus Sle2: Contributions to increased peritoneal B‐1a cells and lupus nephritis map to different loci. J. Immunol. 175, 936–943. Yamamoto, M., and Akira, S. (2004). [TIR domain‐containing adaptors regulate TLR‐mediated signaling pathways]. Nippon Rinsho. 62, 2197–2203. Yamamoto, M., Sato, S., Hemmi, H., Hoshino, K., Kaisho, T., Sanjo, H., Takeuchi, O., Sugiyama, M., Okabe, M., Takeda, K., and Akira, S. (2003). Role of adaptor TRIF in the MyD88‐independent toll‐like receptor signaling pathway. Science 301, 640–643. Ye, X., Zhu, T., Bastacky, S., McHale, T., Li, J., and Xiao, X. (2005). Prevention and reversal of lupus in NZB/NZW mice by costimulatory blockade with adeno‐associated virus‐mediated gene transfer. Arthritis Rheum. 52, 3975–3986. Yung, R. L., and Richardson, B. C. (1994). Role of T cell DNA methylation in lupus syndromes. Lupus 3, 487–491. Yurasov, S., Wardemann, H., Hammersen, J., Tsuiji, M., Meffre, E., Pascual, V., and Nussenzweig, M. C. (2005). Defective B cell tolerance checkpoints in systemic lupus erythematosus. J. Exp. Med. 201, 703–711. Zhang, M., and Tracey, K. J. (1998).Tumor Necrosis Factor. In ‘‘The Cytokine Handbook,’’ pp. 517–548. Academic Press, Pittsburgh, USA. Zhang, M. C., Misu, N., Furukawa, H., Watanabe, Y., Terada, M., Komori, H., Miyazaki, T., Nose, M., and Ono, M. (2006). An epistatic effect of the female‐specific loci on the development of autoimmune vasculitis and anti‐nuclear autoantibody in murine lupus. Ann. Rheum. Dis. 65, 495–500. Zhou, G., Fujio, K., Sadakata, A., Okamoto, A., Yu, R., and Yamamoto, K. (2004). Identification of systemically expanded activated T cell clones in MRL/lpr and NZB/W F1 lupus model mice. Clin. Exp. Immunol. 136, 448–455. Zhu, J., Liu, X., Xie, C., Yan, M., Yu, Y., Sobel, E. S., Wakeland, E. K., and Mohan, C. (2005). T cell hyperactivity in lupus as a consequence of hyperstimulatory antigen‐presenting cells. J. Clin. Invest. 115, 1869–1878. Zhuang, H., Narain, S., Sobel, E., Lee, P. Y., Nacionales, D. C., Kelly, K. M., Richards, H. B., Segal, M., Stewart, C., Satoh, M., and Reeves, W. H. (2005). Association of anti‐nucleoprotein autoantibodies with upregulation of Type I interferon‐inducible gene transcripts and dendritic cell maturation in systemic lupus erythematosus. Clin. Immunol. 117, 238–250.

Avian Models with Spontaneous Autoimmune Diseases Georg Wick,* Leif Andersson,† Karel Hala,*,‡ M. Eric Gershwin,§ Carlo Selmi,§ Gisela F. Erf,|| Susan J. Lamont,** and Roswitha Sgonc* *Division of Experimental Pathophysiology and Immunology, Biocenter, Innsbruck Medical University, A‐6020 Innsbruck, Austria † Department of Medical Biochemistry and Microbiology, Uppsala Biomedical Center, Uppsala University, SE 75124 Uppsala, Sweden; and Department of Animal Breeding and Genetics, Swedish University of Agriculture Sciences, SE 75124 Uppsala, Sweden ‡ Faculty of Agriculture, University of South Bohemia, Cˇeske´ Budeˇjovice, Czech Republic § Department of Internal Medicine, Division of Rheumatology, Allergy and Clinical Immunology, Genome and Biomedical Sciences Facility, University of California, Davis, California k Center of Excellence for Poultry Science, University of Arkansas, Fayetteville, Arkansas **Department of Animal Science, Iowa State University, Ames, Iowa

Abstract............................................................................................................. 1. Introduction ....................................................................................................... 2. Chicken Genomics and Its Application to the Genetic Dissection of Autoimmune Disorders ...................................................................... 3. The OS Chicken: Model for Human Hashimoto Disease ............................................ 4. The UCD‐200 Line of Chickens: A Model for Human Systemic Sclerosis ...................... 5. The SL Chicken Model for Human Autoimmune Vitiligo............................................ 6. Conclusions and Outlook ...................................................................................... References .........................................................................................................

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Abstract Autoimmune diseases in human patients only become clinically manifest when the disease process has developed to a stage where functional compensation by the afflicted organ or system is not possible anymore. In order to understand the initial etiologic and pathogenic events that are generally not yet accessible in humans, appropriate animal models are required. In this respect, spontaneously developing models—albeit rare—reflect the situation in humans much more closely than experimentally induced models, including knockout and transgenic mice. The present chapter describes three spontaneous chicken models for human autoimmune diseases, the Obese strain (OS) with a Hashimoto‐like autoimmune thyroiditis, the University of California at Davis lines 200 and 206 (UCD‐200 and ‐206) with a scleroderma‐like disease, and the amelanotic

71 advances in immunology, vol. 92 # 2006 Elsevier Inc. All rights reserved.

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Smyth line with a vitiligo‐like syndrome (SLV). Special emphasis is given to the new opportunities to unravel the genetic basis of these diseases in view of the recently completed sequencing of the chicken genome. 1. Introduction Avian species in general and the chicken in particular have proven to be both an extremely valuable tool for scientific research and a major economic factor (Muir and Aggrey, 2003). With respect to the latter, it is important to note that the chicken is the largest and most efficient source of animal protein worldwide since it can produce the highest amount of protein in the shortest period of time with the least amount of food (Havenstein et al., 1994). This is the reason why research on various aspects of the lifecycle of chickens in order to improve its economic usefulness is conducted with high speed and efficiency in many laboratories. On the other hand, the chicken as an animal model for the elucidation of many important questions in basic research has lost ground over the past 30 years as compared to other species, notably rodents, especially with the advent of transgenic and knockout murine models. This situation has, unfortunately, led to the extinction of many irretrievable chicken lines since institutional budgets and research grants for maintaining and using these breeds have been severely curtailed (Fulton and Delany, 2003). However, the successful completion of the sequencing of the chicken genome in 2004 (International Chicken Genome Sequencing Consortium, 2004) has dramatically changed this situation and will again assign a prime position to this species in many fields of research that take advantage of its unique features and possibilities. Therefore, this chapter begins with a review on chicken genetics with special emphasis on the new horizons that have now been opened for the identification of genetic factors underlying the etiology and pathogenesis of autoimmune diseases. This seems to be a good time to review data on three unique lines of chickens that are afflicted with genetically determined, spontaneously occurring organ‐specific or systemic autoimmune disease:
the Obese strain (OS) of chickens that develops a spontaneous autoimmune thyroiditis perfectly mimicking human Hashimoto disease
the University of California at Davis 200 and 206 (UCD‐200 and ‐206) lines that serve as a model for human systemic sclerosis (scleroderma)
the amelanotic Smyth line that develops a vitiligo‐like syndrome (Smyth line with vitiligo, SLV).

It also seems a right moment to recall some of the unique advantages and characteristics of chickens with special emphasis on their usefulness for immunologic research.

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Thus, avian Rous sarcoma virus was the first tumor virus identified and the same is true for the discovery of endogenous virus loci (ev) (Crittenden, 1981; Svoboda, 1986). It should be remembered that the formulation of the B‐ and T‐lymphocyte concept heavily depended on the discovery of the immunologic role of the bursa of Fabricius and thus bursa‐derived lymphocytes (B cells). It was just a fortunate etymologic coincidence that the bone marrow turned out to be the mammalian correlate as the cradle of B cells. Phylogenetically, the birds stand between fish and mammals (albeit closer to the latter) in the tree of life and provide evidence that the latter branched of the realm of birds about 310 Mio years ago. A major asset of the chicken is the possibility to easily observe and manipulate the embryo throughout its development, and the most important knowledge in embryology stems from studies in this species and its ontogenetic similarities with mammalian development. In this context, it is also appropriate to mention the exciting discoveries on the ontogenetic development of the central nervous system and the immune system using chicken–quail chimeras (Le Douarin et al., 2000). Apart from the phylogenetic and ontogenetic aspects, chickens are optimal subjects for genetic studies since many offspring can be produced from one pair of parents, reproduction is fast, phenotypic characteristic can easily be assessed, and many valuable mutants still exist, in spite of the loss of a considerable number of these (Somes, 1988). Chickens were the first species where vaccination against an oncogenic virus (Marek’s disease virus, MDV) was developed on an appropriate immunogenetic background and is now successfully applied on a large industrial scale (Bacon and Witter, 1994). Also, the chicken egg has been and still is used as an ‘‘incubator’’ for the development of human vaccines, for example, against influenza. The chicken has also proved to be a good source for the production of viral vectors for molecular biological purposes. The chicken displays a very interesting solution to the generation of antibody diversity where a very limited original immunoglobulin repertoire is altered by gene conversion events with many pseudo‐VH and pseudo‐VL segments, respectively (Reynaud et al., 1994). Antibodies produced in hens are transferred via the yolk into the eggs and can easily be harvested in large amounts from this source. Recently, monoclonal antibodies have also been successfully produced from chicken lymphoid cells. Decisive studies leading to the discovery of immune tolerance were the chicken parabiosis experiment by Hasek (1953) and parallel investigations in the murine system by Medawar’s group (Billingham et al., 1953), who also provided the right explanation for the observed immunologic phenomenon and was awarded the Nobel Prize for this work.

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The graft‐versus‐host reaction (GvHR) has been described by Simonsen using the chicken embryonic spleen chorioallantoic membrane (CAM) assay (Simonsen, 1962). In addition to the above‐mentioned spontaneously occurring chicken models afflicted with autoimmune diseases, there are many other examples of spontaneously developing pathologic conditions in this species on a genetic basis. Chickens also serve as important models for experimentally induced diseases, for example, in tumor research (Fulton and Delany, 2003). In chickens, the genetic basis for susceptibility and resistance against viral disease has first been demonstrated (Longenecker et al., 1976). Here, it is also important to remember that the first example of a possible role of infections for the development of atherosclerosis stems from work in chickens (Fabricant et al., 1983). Chicken erythrocytes are nucleated, and DNA can thus be easily prepared from these nuclei after hemolysis. Chicken erythrocytes also express major histocompatibility complex (MHC, B locus in chickens) class I antigens (B‐F antigens) on their surface that can be identified by simple hemagglutination techniques (Plachy and Hala, 1997). The chicken major histocompatibility (B) complex, identified by Briles, was the second MHC recognized in animals (Miller et al., 2004). The economic impact of chickens has already been mentioned, but it is important to emphasize that old lines with potential economic value are endangered, too. This loss of genetic diversity in poultry livestock breeds and the endeavors to conserve animal genetic resources for global agriculture is a focus of the Food and Agriculture Organization (FAO) of the United Nations. Thus, between 1984 and 1998 over 230 poultry stocks were eliminated, presenting 40% of the US stocks and over 60% of Canadian stocks. Further major losses since that time have occurred. In a small country like Austria, three endogenous lines are endangered, but even for this small number no support is available from governmental sources. If one considers the possible impact of special chicken lines that are able to survive under various adverse conditions and thus could play a crucial role in alleviating the lack of food protein in the Third World, these losses are even more deplorable. However, from a scientific standpoint one also has to realize that chickens have certain disadvantages as laboratory animals. Thus, most research institutions are not equipped for keeping chickens. Chickens have a relatively long generation time, that is 1 year. Furthermore, the raising and housing of chickens are expensive; they are noisy and special ornithologic, epidemiologic, and virologic expertise is required for their sustainment. Chickens are carriers of diseases that may be pathogenic for humans such as the avian flu. Finally, to date there are fewer immunologic reagents available for this species as compared to mice and rats.

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However, an interesting and unorthodox view has been put forward in favor of chickens by the late Morten Simonsen, namely that no religion has ever raised reservations that may preclude the use of chickens as a source of animal proteins. 2. Chicken Genomics and Its Application to the Genetic Dissection of Autoimmune Disorders 2.1. Introduction This chapter concerns three unique chicken models for autoimmune disorders in humans. An extensive amount of data on the immunology and pathology of these models has been described but their genetics are still poorly understood. A genetic dissection of the genes and mutations causing these disorders is well justified since genetics can reveal the primary cause(s) of the disease and thereby give a deeper understanding of the pathologic process leading to an autoimmune disorder. The rapid progress in chicken genomics during recent years facilitates such genetic studies in several ways:
the access to millions of genetic markers makes high‐resolution linkage mapping of loci controlling phenotypic traits possible
the access to a high‐quality draft sequence of the chicken genome means that once a trait locus has been mapped to a chromosomal region, it is easy to generate a complete or near‐complete list of all genes in the interval
the access to a genome sequence facilitates the resequencing of a chromosome region in the search for mutations underlying phenotypic traits
high‐throughput expression analysis can now be carried out using high‐ density cDNA or oligonucleotide arrays.

Thus, genetic dissections of thyroiditis in the OS line, vitiligo in the Smyth line, and scleroderma in the UCD‐200 line are now both realistic and highly desirable research goals. This section will describe the strategies that may be employed to successfully accomplish the positional identification of the genetic factors required for the development of these autoimmune disorders. A better understanding of the genetics behind these disorders will increase their value as models for human disease. 2.2. The Chicken Genome A high‐quality draft sequence of the chicken genome was released in 2004 (International Chicken Genome Sequencing Consortium, 2004). A 6.6 sequence coverage was generated by sequencing a single inbred red jungle

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fowl (RJF) female. The total size of the chicken genome is 1050 million base pairs (Mb) and the assembled genome sequence that was ordered and assigned to a specific chromosome constituted 907 Mb (86%). The major part of the remaining sequences occurs as unassigned sequence contigs. The genome sequence can be accessed through the major genome browsers (http://genome.ucsc.edu; http://www.ensembl.org; http://www.ncbi.nlm.nih.gov). This is a high‐quality draft sequence which means that the major part of the genome is well assembled but there are regions which are still poorly covered. The assembly of the two sex chromosomes (Z and W) is still far from complete. The reason for this is that a female bird (Z/ W) was sequenced and thus there was only one copy of each of these chromosomes while two copies of each autosome were sequenced. Furthermore, GC‐rich regions and regions containing gene duplications caused problems in the assembly, and in particular the MHC region on chromosome 16, which harbors clusters of duplicated genes, is poorly covered in the current assembly. The great majority of these problematic regions will be resolved within the next few years when a finished sequence will be completed. Chickens have 38 pairs of autosomes, 5 macrochromosomes, 5 intermediate chromosomes, and 28 microchromosomes, which differ widely in size from about 2 Mb for the smallest microchromosome to about 200 Mb for the largest macrochromosome, chromosome 1. The sequence analysis of the chicken genome revealed many striking differences between the macrochromosomes and microchromosomes. Microchromosomes have in comparison with macrochromosomes a higher G þ C content, a higher density of CpG islands, a higher gene density, shorter introns, a lower frequency of repetitive sequences, and a higher recombination rate. It is still unclear why the chicken genome and many other bird genomes show this variation in the size of chromosomes. The size of the chicken genome is only 30–40% of an average mammalian genome that usually contains 3000 Mb. A major reason for this size difference is a lower proportion of repetitive sequence in the chicken genome, 11% vs 40–50% in mammalian genomes (International Chicken Genome Sequencing Consortium, 2004). The chicken genome also contains fewer pseudogenes and segmental duplications. About 2.5% of the human genome (70 Mb) can be aligned with chicken genome sequences and basically all of these sequences are expected to be conserved because of their functional significance. Only 44% of the conserved sequence represents protein‐coding sequences. Many of the conserved noncoding sequences are located far from well‐defined genes and may have important regulatory functions. Another important progress in chicken genomics has been the development of a rich collection of expressed sequence tags (ESTs) (Abdrakhmanov et al., 2000; Boardman et al., 2002; Savolainen et al., 2005; Tirunagaru et al., 2000).

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Two of these studies have involved transcripts from cells of immunologic importance, Abdrakhmanov et al. (2000) sequenced clones from bursal cells while Tirunagaru et al. (2000) sequenced splenocytes enriched for activated T cells. These EST projects have been important for the annotation of the genome sequence, in particular for those transcripts that are novel to birds or show a high sequence divergence to their mammalian homologues. In fact, genes associated with the immune response appear to be the group of proteins that has evolved most rapidly during avian and mammalian evolution (International Chicken Genome Sequencing Consortium, 2004). Thanks to the development of these resources and the generation of the genome sequence, it is now possible to carry out genome‐wide expression analysis using cDNA or oligonucleotide arrays. This approach can be used to further characterize autoimmune disorders in chicken. However, this is not an alternative to a genetic study but rather a complement. The strength of a genetic study is that it can link a specific gene and a specific mutation to a phenotype through segregation analysis, whereas a large‐scale expression analysis is an excellent tool to study the consequences of such mutations. Another feature of the chicken genome is that chickens have a fairly high recombination rate, and the rate varies as a function of the size of the chromosomes (International Chicken Genome Sequencing Consortium, 2004). The recombination rates have been estimated at 2.8 and 6.4 cM/Mb for macrochromosomes and microchromosomes, respectively; 1 centiMorgan (cM) corresponds to a recombination rate of 1% per gamete. This is significantly higher than the corresponding estimates for humans (1 cM/Mb) and for mouse (0.5 cM/Mb). A high recombination rate is initially a disadvantage in a gene‐mapping project since more genetic markers are required to detect linkage to the trait locus. However, it is a major advantage in the final stage of a mapping project since it is the identification of recombination events that defines the borders of a chromosomal region harboring the causative gene, and it is crucial that this region is as narrow as possible to facilitate the identification of the causative mutation (see in a later section). Thus, a mouse pedigree comprising 5000–10,000 animals is required in order to achieve the same mapping resolution as can be obtained using 1000 chickens. 2.3. Chicken Is a Highly Polymorphic Species As part of the chicken genome project the level of sequence polymorphism was examined by generating partial genome sequences (0.25 coverage) from three domestic chickens, one White Leghorn, one broiler, and one Silkie (International Chicken Polymorphism Map Consortium, 2004). These sequence data were aligned with the near‐complete genome sequence that was

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generated from an RJF, the wild ancestor of the domestic chicken. The analysis revealed as many as 2.8 million single nucleotide polymorphisms (SNPs) in total. The nucleotide diversity was estimated at 5 sequence differences per 1000 bp in comparisons between domestic lines and RJF, between domestic lines as well as within some domestic lines; this figure dropped to 4 sequence differences per 1000 bp for two of the domestic lines, which have been maintained as closed populations. These estimates are about fivefold higher than those found in humans even when comparing humans from different ethnic groups and it is on the same level as observed between mouse subspecies (International Chicken Polymorphism Map Consortium, 2004). Thus, the domestic chicken is a highly polymorphic species, and there is a considerable genetic diversity both within and between lines. The results show that chicken domestication has not involved a severe population bottleneck leading to a drastic loss of genetic diversity and the long‐term effective population size must have been much larger for the domestic chicken and its wild ancestor than it has been for humans. This extensive study revealed on average one SNP every 350 bp throughout the chicken genome. The crucial question is how many of these are true SNPs and how many of them are segregating within a particular population. An initial evaluation indicated that a very high proportion (>90%) represents true SNPs and not sequence artifacts and a surprisingly high proportion (70%) are common SNPs that are polymorphic in many chicken populations (International Chicken Polymorphism Map Consortium, 2004). Subsequent studies have confirmed this (Andersson, L. et al., unpublished data). Thus, the established database comprising 2.8 million SNPs is an outstanding resource for high‐resolution genetics in the chicken. The data can be accessed through the major genome browsers (see in an earlier section) or through the chicken variation database (http://chicken.genomics.org.cn/index.jsp). 2.4. Linkage Mapping: A Powerful Approach for Unraveling the Genetic Basis for Phenotypic Traits The vertebrate genome contains on the order of 20,000 genes, and the functional roles of many genes are still poorly understood. Thus, for any phenotypic trait there are many potential candidate genes, and in the worst case scenario the causative gene(s) may not be identified as a candidate gene simply because it has not yet been studied in any detail. This is a major reason why a genetic investigation is a highly relevant approach for understanding the molecular basis of phenotypic traits, like the three autoimmune disorders that are the subjects of this study. Linkage mapping allows us to define a chromosomal region that harbors one or more genes affecting the manifestation of the phenotype. It is essential that such a region is as narrow as possible to reduce

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the work required to identify the causative gene(s). In fact, linkage mapping can be seen as a method to exclude candidate genes since all genes that do not show cosegregation with the trait locus can be excluded. It is much easier to exclude a candidate gene by a genetic analysis than by functional studies. A linkage‐mapping experiment starts with the collection or generation of an informative pedigree material in which the locus/loci controlling the phenotypic traits is segregating. The OS, the UCD‐200, and the Smyth lines (SLs) are kept as closed populations in which alleles predisposing to disease are expected to be fixed or close to fixation. In this case the best strategy is to cross the autoimmune line with a line that does not express the disorder. The choice of line used for the intercross will determine how many susceptibility loci will be segregating in the intercross pedigree. For instance, the SL appears to share some susceptibility factors for the development of vitiligo with the Brown line but not with the Light Brown Leghorn line. All animals in the first intercross generation (F1) will be heterozygous at the trait locus/loci if the two lines are fixed for alternative alleles (Fig. 1). One can then choose to generate an F2 generation by intercrossing F1 animals or a

Figure 1 Schematic illustration of the segregation of a recessive allele a predisposing to disease in an F2 intercross design and a backcross design. The cosegregation with the flanking genetic markers M1 with alleles 1 and 2, and M2 with alleles 1 and 2, is indicated. The genotype at the disease locus can easily be deduced using these flanking markers.

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backcross population by backcrossing F1 animals to the susceptible line. An F2 design is generally more powerful because both parents are expected to segregate at the trait loci. However, a backcross design may be preferable if the expression of a disorder is dependent on the interaction between several loci. For instance, let us assume that the expression of an autoimmune disorder is caused by homozygosity for recessive alleles at three unlinked loci and that the lines used in the intercross are fixed for different alleles at all loci. In this case the number of progeny showing disease in an F2 design will be (1/4)3 ¼ 1/64 whereas (1/2)3 ¼ 1/8 will express the disease in a backcross design. After the collection of DNA samples and phenotypic data from the entire pedigree, a genome scan with genetic markers is carried out. About one informative marker per 20 cM throughout the genome is required to carry out a complete genome scan. Thus, about 200 evenly spaced markers are required in total; the total map distance in chicken is about 4000 cM. To date highly polymorphic microsatellites have been used for this type of studies (Kerje et al., 2003) but high‐throughput analysis of a set of SNPs is today an attractive alternative. The next step in a linkage‐mapping experiment is a statistical analysis with the aim to identify which markers cosegregate with the trait locus/loci. Linkage mapping of traits showing a simple monogenic inheritance, one gene with full penetrance, is straightforward. This is because there is a direct relationship between phenotype and genotype for such traits, which makes it possible to directly score recombination events between genetic markers and the trait locus. Thus, the chromosomal position can be determined with high accuracy. For instance, with an F2 design comprising 800 progeny there will be 1600 informative meioses (since each F2 animal receives one gamete from each parent). This makes it possible to map a monogenic trait locus to a fraction of a centiMorgan, which is expected to correspond to not more than a few hundred kilobase pairs. The region harboring a causative mutation is defined as the region between the two closest flanking markers showing at least one recombination event to the trait locus. Previous data indicate that vitiligo in the SL, thyroiditis in the OS line, and scleroderma in the UCD‐200 line are all caused by a limited number of genes predisposing to disease. This assumption is based on the observation that lines with a high incidence of disease were established by a limited number of generations of selective breeding and because a fairly high proportion of affected birds have been observed in the limited backcross or intercross experiments that have been carried out. However, the likely presence of multiple susceptibility loci makes the linkage analysis more complicated since there is no more a simple one‐to‐one relationship between genotype and phenotype. For instance, a bird may be homozygous for a susceptibility allele at one locus but does not express the disorder since it is not homozygous at other loci affecting the disorder. In this

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case it will be essential to carry out a linkage analysis that does not assume full penetrance at the susceptibility loci or alternatively analyze the data using a quantitative trait locus (QTL) model (Andersson and Georges, 2004). A QTL is defined as a chromosomal region harboring one or more genes affecting a complex trait and is a relevant concept for classical quantitative traits, like weight, or for all‐or‐none traits, like an autoimmune disorder, determined by multiple genes. It may also be possible to quantify the severity of an autoimmune disorder and use that as the trait in a QTL analysis. A QTL analysis does not require any prior knowledge about the number of loci controlling the trait of interest or the mode of inheritance. It may also be highly relevant to analyze the data using a statistical model that searches for epistatic interaction between loci (Carlborg and Haley, 2004) because it is quite likely that an autoimmune disorder is only manifested in those birds that carry a certain genotype combination at two or more loci. The major challenge in a QTL analysis of complex traits is not the detection of QTLs but the subsequent identification of the underlying genes and mutations (Andersson and Georges, 2004). This is because of the poor precision in the initial mapping experiments even if hundreds of F2 progeny are used. However, several strategies can be used to achieve high‐resolution mapping of QTLs, in particular if the number of loci controlling the trait is limited. For instance, let us assume that there are three loci controlling one of the autoimmune disorders described in this chapter. There should be no problem to identify the approximate chromosomal position of the three loci in an initial mapping experiment. One can then use marker data to select animals that are homozygous at two loci but heterozygous at the third locus (Fig. 1) and then use such birds for further breeding experiments. By this approach it may be possible to transform a trait with complex inheritance to a simple monogenic trait that can easily be subjected to high‐resolution mapping. An alternative approach is to determine the QTL genotype with high confidence by progeny testing. In this case one selects birds that carry recombinant chromosomes for the QTL interval of interest, and these birds are then backcrossed to a line with known QTL status, if dominance occurs one should backcross to birds that are homozygous for the recessive allele. This exercise makes it possible to establish a collection of chromosomes with known QTL status which are then sequenced. For instance, this approach was successfully used for the identification of a single point mutation underlying a major QTL in pigs affecting muscle growth (Van Laere et al., 2003). 2.5. Identification of Causative Mutations Once a chromosomal region harboring a causative gene has been defined the search for the gene itself and the causative mutation is initiated. This phase of a mapping project can be the most laborious one, and it is therefore wise to

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exploit genetics as far as possible so that the confidence interval for the trait locus becomes tiny. First, one of the web browsers that displays the chicken genome sequence is used to download a list of all genes that are present in the actual chromosome region. For most parts of the chicken genome, there is a high accuracy regarding gene content and gene order. However, as described earlier, there are some parts of the genome, like the sex chromosomes, for which the genome assembly is not of high quality yet. Caution is also required since the current annotation of the genome most certainly has missed some genes; the gene sequences may be there but they have not yet been recognized as coding sequences for various reasons. The first question to ask is, of course, if there are any obvious candidate genes in the interval. If one of the regions showing linkage for an autoimmune disorder turns out to include the MHC region, it is quite likely that there are one or several mutations in MHC genes that are causing the disorder. Expression analysis may also be used to evaluate the genes present in the defined interval. For instance, it is very unlikely that a gene that is only expressed in the brain is associated with the development of vitiligo, which involves the destruction of melanocytes. DNA sequence analysis is used to search for candidate mutations. This is now greatly facilitated in chicken by the access to a high‐quality draft genome sequence, which can be used to design primers for PCR amplifications. Ideally one should resequence the entire chromosome region defined by linkage analysis from individuals with different genotypes at the trait locus. However, if the region is too large for a complete resequencing experiment, the analysis has to be restricted to the top candidate genes but then it is essential to sequence both coding sequences and regulatory regions because the causative may be noncoding and control gene expression. The sequencing of potential regulatory regions for a candidate gene is challenging because such regions may be located hundreds of kilobases from the coding sequence. The outcome of a resequencing experiment is a list of sequence differences between haplotypes associated with different alleles at the trait locus. Ideally this list should be restricted to a single mutation, a result which provides genetic evidence that the causative mutation has been identified. This favorable outcome is possible if one can identify the ancestral haplotype on which the causative mutation occurred. This is not completely unrealistic since it may be known in which line of chicken a disease‐causing mutation first occurred and the ancestral haplotype may still be present in that line. In fact, the identification of a noncoding mutation underlying a major QTL in pigs was greatly facilitated by the access to the ancestral haplotypes, which only differed from the mutant haplotype by a single base substitution (Van Laere et al., 2003). The final stage of a gene‐mapping experiment is to prove that a candidate mutation is causing the phenotypic effect. The strategy for this depends very

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much on the nature of the candidate mutation, if it is a coding or noncoding mutation, and the nature of the phenotypic effect, for instance if it can be manifested in a cultured cell or not. The ultimate proof for the causative nature of a candidate mutation may be to replicate the phenotypic effect in a transgenic model. Taking advantages of the new opportunities that have arisen with the successful sequencing of the chicken genome we are now jointly attempting to identify the genes underlying the three avian autoimmune disease models described in the following section of this chapter. 3. The OS Chicken: Model for Human Hashimoto Disease 3.1. Introduction The OS was originally developed by R. K. Cole at Cornell Veterinary College, Ithaca, NY, in the late 1950s (Cole, 1966) when he observed symptoms of hypothyroidism in less than 1% of female birds of the Cornell special C‐strain (CS). By selective breeding of such dams with essentially normal appearing CS roosters, the frequency of spontaneous autoimmune thyroiditis (SAT) increased in male chickens, too (Wick et al., 1981). Following a selective breeding program, the OS was then developed and since 1968, male and female birds are afflicted to about the same percentage up to the present day (Wick et al., 1989, 1994). The phenotypical symptoms of SAT‐based hypothyroidism consists in small body size with relatively high body weight (hence the name OS), lipemia, long silky feathers, small combs, low fertility, and poor hatchability (Fig. 2). These symptoms can be prevented or reversed by supplementation of the diet with thyroxine. As a matter of fact, the OS can only be bred when appropriate thyroid hormone substitution is given (Dietrich, 1989). The reason for these clinical symptoms is a severe mononuclear cell (MNC) infiltration of the thyroid gland resulting in the complete destruction of its architecture (Fig. 2 inset). After many decades of selective breeding, 100% of OS chicks show severe thyroid infiltration and the first signs of infiltration already appear at 1 week of age (Dietrich et al., 1997). The natural history of the OS with an extensive documentation of the breeding program has been summarized earlier (Dietrich et al., 1999). 3.2. Histologic and Immunologic Hallmarks of the OS Histologically, SAT significantly differs from experimentally induced autoimmune thyroiditis (EAT—using adjuvants) by the presence of numerous germinal centers similar to the situation in human Hashimoto thyroiditis (Wick and Graf, 1972). The severity of SAT is either classified arbitrarily or planimetrically

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Figure 2 Five‐month‐old male normal White Leghorn (NWL) (left) and Obese strain (OS) (right) chickens. The latter shows the typical hypothyroid phenotype, that is, small body size, silky feathers (especially visible over the legs), small comb. The feathers are ruffled due to cold sensitivity even at normal ambient temperature. Inset: Severely infiltrated thyroid gland with prominent germinal centers (dark round structures) and only small thyroid follicles (clear empty structures) remaining. Original magnification 100.

according to a standard scoring schedule where 0 ¼ no infiltration, þ ¼ up to 25% of the entire thyroid cross section occupied by infiltrating cells, þþ ¼ 25–50% infiltrated, þþþ ¼ 50–75% infiltrated, and þþþþ ¼ 75% total infiltration (Wick et al., 1994). The very first cells infiltrating the thyroid are MHC class II (B‐L)þ, interleukin‐2 receptor (IL‐2R)þ, CD4þ T cells expressing the T cell receptor a/b (TCRa/b) (Cihak et al., 1995; Wick et al., 1984). Neonatal thymectomy with subsequent depletion of peripheral T cells using specific turkey anti‐chicken T cell antibodies prevents the development of SAT (de Carvalho et al., 1981). Injection of monoclonal mouse antibodies against T cell receptor 2 (TCRa/Vb1) and T cell receptor 3 (TCRa/Vb2) supports the notion that most of the infiltrating T cells carry the TCR2 (Cihak et al., 1995). To date, the role of the small number of infiltrating TCRg/d (TCR1)þ cells in the development of SAT is still elusive. Early in the selective breeding program, circulating autoantibodies against thyroglobulin (TgAAb) were demonstrated in a high percentage of these chickens (Cole et al., 1968). Later, autoantibodies against the second colloid antigen (CA2), microsomal thyroid antigens, as well as thyroid hormones were

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also demonstrated in lower frequency (Khoury et al., 1982). Also, autoantibodies against nonthyroid antigens are found in some OS birds, for example, against proventricular parietal cells, exocrine and endocrine components of the pancreas, antigens of the adrenal cortex and the parathyroid glands (Aichinger et al., 1984). However, no clinically or histopathologically sizeable symptoms are associated with these latter autoantibodies. TgAAb are only produced upon stimulation with autologous Tg as demonstrated in experiments where OS chicks were first thyroidectomized on the day of hatching and then turned out to be devoid of TgAAb. However, such antibodies did develop after injection of autologous Tg (de Carvalho et al., 1982). It is also important to note, that the immune system of the OS chicken shows in general hyperreactivity against both exogenous and autologous antigens as well as against Tcell mitogens (Schauenstein et al., 1987). This seems to be partly due to a dominantly encoded hyperproduction of IL‐2 and a hyperexpression of IL‐2 receptors (Kroemer and Wick, 1989). More recently, increased levels of IL‐15, another proinflammatory cytokine, has also been shown to be associated with the onset of SAT (Kaiser et al., 2002). Detailed analyses of the thymic cellular make up revealed a deficit of so‐called thymic nurse cells (TNC) in the OS. TNCs are large complexes consisting of thymic epithelial cells (TEC) that contain many T cells in membrane‐coated vacuoles. This is important, since TNCs have been identified as sites for positive T cell selection that thus may also be disturbed in the OS (Boyd et al., 1984). 3.3. Effector Mechanisms and Immunoregulation Several forms of virus infections were excluded as possible causes for the development of SAT. These included Newcastle disease virus, infectious laryngotracheitis virus, reoviruses, infectious bursitis virus, and avian encephalomyelitis virus. In addition, serum samples from our OS chicken colony were tested negative for Mycoplasma gallisepticum and Mycoplasma synoviae. Avian leucosis virus (ALV) was also considered and again our colony proved to be leucosis free. Furthermore, injection of Rous‐associated virus type I and type II did not change the timing and severity of thyroid infiltration. Also, experiments under germ‐free conditions did not substantiate the possible role of microbial infection on the course of SAT (Hala et al., 1996; Malin et al., 1994). As mentioned earlier, activated T cells seem to be the first effector cells arriving in the thyroid gland. Adoptive transfer experiments have shown that this can most efficiently be achieved by intravenously injecting Tcells harvested from infiltrated donor OS thyroid glands into CS recipients (Kroemer et al., 1985). However, neonatal and in ovo bursectomy has been shown to significantly delay the development of SAT (Wick et al., 1970a), supporting the concept that

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TgAAb accelerate disease development. It has been shown by Kofler et al. (1983) that complement binding TgAAb are transferred from the mother hen via the egg yolk into the newly hatched chickens where they are deposited in the target organ. In a series of classical experiments it was shown that neonatal thymectomy of OS chickens entails the development of most severe SAT (Wick et al., 1970b). At that time, this result was unexpected and the author of this part of the present chapter (G.W.) was forced by his supervisor, Ernest Witebsky, to repeat the experiments about 10 times before he was allowed to publish this effect on spontaneously occurring autoimmune thyroiditis, because experimentally, that is, with adjuvant, induced autoimmune diseases, such as EAT and experimental autoimmune encephalomyelitis (EAE) (Wick and Steiner, 1972), thymectomized normal animals became resistant against the induction of disease. We, therefore, hypothesized that neonatal thymectomy of OS chicken resulted in the depletion of what we then called ‘‘self‐recognition‐controlling cells’’ that reside within the thymus for a longer time than effector T cells. Neonatal thymectomy, therefore, seemed to result in the depletion of these cells that were later, when similar experiments were conducted in rodents (Penhale and Ahmed, 1982; Sakaguchi and Sakaguchi, 1989), called T suppressor cells or now T regulatory cells (Tregs) (Sakaguchi and Sakaguchi, 2005). Thymectomy thus apparently allowed previously emigrated T effector cells to exert their autodestructive potential without being suppressed by the ‘‘controlling cells’’ that still had not left the thymus in sufficient numbers. The existence of intrathymic suppressor T cells was proven in subsequent experiments that also showed that the kinetics of thymocyte emigration of these cells was severely disturbed in OS chickens (Boyd et al., 1985). Definite proof that this concept was true derived from experiments, where neonatal thymectomy was combined with peripheral T cell depletion resulting in a complete inhibition of SAT development (de Carvalho et al., 1981). More recently, the idea that T cells initiate SAT has come under renewed scrutiny since immunohistological experiments have shown that macrophages in the OS thyroid gland may not only act as normally functioning antigen‐presenting cells but also play a decisive early role as effector cells (Hala et al., 1996). Immune reactivity in general and autoreactivity in particular are known to be regulated by various mechanisms on different levels. On one hand, there are mechanisms intrinsic to the immune system itself, such as Tregs, and the idiotypic network, etc. On the other hand, the potency of the immune reaction is also influenced by regulatory factors such as hormonal effects. In the OS, it has been shown that the effect of Tregs as, for example, reflected by hyperproduction of TgAAb and the increased expression of IL‐2R is encoded by dominant genes (Kroemer et al., 1989). In addition to these essential

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genetic effects, the OS also displays an altered immunoendocrine communication that exacerbates its immunologic hyperreactivity (Wick et al., 1993). Although, basic glucocorticoid (in birds corticosterone) serum levels are equal in OS and normal White Leghorn (NWL) chickens, the former show significantly increased serum concentrations of corticosterone binding globulin (CBG) entailing decreased free metabolically active corticosterone levels independent of age and sex. Furthermore, in the OS, pathologically altered immunoendocrine feedback regulation via the hypothalamo‐pituitary‐adrenal (HPA) axis was first demonstrated in an autoimmune animal model. Similar to the situation in normal rats and mice (Besedovsky and Del Rey, 1996), immunization with a foreign antigen leads to an increased corticosterone serum concentration that in turn downregulates the immune response. However, in the OS, it was for the first time shown that this postimmunization glucocorticoid surge is severely blunted and that this represents an additional factor for the overshooting autoimmune reactivity. This phenomenon has not only been shown on immunization with foreign antigens but also after injection of so‐ called glucocorticoid‐increasing factors (GIF) contained, for example, in conditioned medium of mitogen‐activated spleen cells or peripheral blood lymphocytes (PBL) or applied in the form of recombinant cytokines such as IL‐1, IL‐6, or tumor necrosis factor (TNF)‐a. Finally, OS thymocytes have been shown to be resistant against the apoptosis‐inducing effect of glucocorticoids. Thus, in summary, in addition to the defect of intrinsic T regulatory mechanisms, the OS also suffers from a severely altered dialogue between the immune and the endocrine systems (Wick et al., 1993). 3.4. Endocrinology Hypothyroid symptoms in the OS are due to a deficiency of triiodothyronine (T3) and thyroxine (tetraiodothyronine, T4) and they can be prevented or reversed by hormonal substitution. OS chickens also display a thyroid‐ stimulating hormone (TSH)‐independent autonomous hyperfunction of thyroid epithelial cells and a defect of iodine organification, that is, the enzymatically catalyzed process that finally results in the iodination of Tg‐associated tyrosine and the formation of thyroid hormones, preceding the actual development of SAT (Rose et al., 2002; Sundick et al., 1991). Depletion of dietary iodine leads to a significant delay in the onset of SAT as well as an attenuation of SAT. Conversely, resupplementation of iodine entails a rapid exacerbation of SAT. As will be discussed later, a genetically determined susceptibility of the target organ is an absolute prerequisite for the development of SAT in the OS. To date, the nature of this thyroiditis susceptibility gene has not yet been clarified, but it

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seems to be associated with the pathologically altered tyrosine iodination process. Recent data by Vasicek et al. (2001) point to the possibility that thyrotrophic endogenous viruses play a role in the pathologic iodine metabolism that predisposes the OS to SAT. The altered immunoendocrine communication via the HPA axis in the OS as compared to chickens of normal strains has already been discussed earlier. This phenomenon correlates with the presence of an endogenous virus locus (ev‐22) that is unique for the OS (Ziemiecki et al., 1988), but seems to have a modulatory role only rather then being the candidate thyroid susceptibility gene: chicks of an F2 generation developed from (OS  NWL)F1 crossings can develop severe thyroiditis without being carriers of ev‐22 (Kroemer et al., 1989). Treatment of OS chicks with glucocorticoids or with specially designed androgen analogues that retain their immunosuppressive potential, but no longer carry their endocrinological side effects, leads to a prevention of the development of SAT or reversal of already manifest disease (Schuurs et al., 1992).

3.5. Immunogenetics with Special Emphasis of Genetically Determined Target Organ Susceptibility 3.5.1. Minor Modulatory Genes As mentioned earlier, the original OS colony was established by R. K. Cole in 1957 after beginning selective breeding of CS parental birds for hypothyroid ‘‘obese’’ symptoms in 1955. A colony derived from this OS flock kept at Cornell Veterinary College, Ithaca, NY was then established in Vienna, Austria, in 1970, and transferred to Innsbruck, Austria, in 1975 where it is still maintained and shows a nearly 100% incidence of SAT independent of sex (Dietrich, 1989). Immunogenetic analyses of OS by Bacon et al. (1974) led to the identification of MHC (B locus in chickens) genes as important factors for the development of the disease. Of the three B‐haplotypes (B1, B3, and B4, later renamed as B13, B15, and B5, respectively) (Briles et al., 1982), B13 and B15 were associated with severe disease while B5 positive birds were low responders with only mild SAT. At this stage of development, the modulatory effect of the MHC could still be recognized (Wick et al., 1979). In later generations, however, the influence of the B‐haplotypes was not evident anymore, indicating that the MHC haplotype has only a modulatory role and is not a prerequisite for the development of the disease (Hala, 1988). The OS has been close bred over many generations for the hypothyroid phenotype and the three sublines are only homozygous for the B locus but on purpose not inbred (Dietrich et al., 1996).

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3.5.2. Major Genes Several theories have been put forward to explain the genetic basis of SAT. The first of these was formulated by Cole who—based on classical genetic analyses—concluded that the trait is under the control of more than one gene (Cole, 1966). Subsequently, Rose et al. (1976) and then Wick et al. (1979) proposed that at least three genetic loci are involved in the natural history of SAT. This three‐locus model included: immune response genes associated with MHC genes, non‐MHC immune response genes, and gene(s) coding for a primary target organ defect that emerged from previous genetic analyses. In our experiments, aimed at determining the genetic background of the disease, we resorted to crosses between OS and chickens of an inbred healthy normal line (CB line) (Hala and Plachy, 1997) unrelated to OS and serving as a homogenous genetic background for crossbreeding experiments. Table 1 summarizes the findings in the F1 generation in a simplified version. From these experiments and further studies of these backcrosses and the F2 generation it was concluded that about three to four genes regulate the full development of SAT. Two to three of these are dominant and responsible for the hyperreactivity of the immune system as already mentioned earlier. One of them is recessive and encodes susceptibility of the target organ to the autoimmune attack of the immune system (Neu et al., 1985, 1986). On the basis of these results, a new theory with respect to the development of SAT in the OS in particular but also other autoimmune diseases in general has been formulated (Hala, 1988). This theory postulates the existence of two essential sets of genes that must be present in order that an autoimmune disease develops, viz., genes coding for a hyperreactivity of the immune system on one hand and genes coding for target organ/structure susceptibility to the attack of the immune system, on the other hand. The incidence and severity of a given autoimmune disease based on the presence of these two sets of essential genes can then be fine tuned by

Table 1 Genetic Basis for Target Organ Susceptibility in Spontaneous Autoimmune Thyroiditis Disease phenotype Chicken strain OS NWL (OS  NWL)F1

Spontaneous autoimmune thyroiditis þþþþ – –

OS, Obese strain; NWL, normal White Leghorn.

Anti‐thyroglobulin autoimmunity þþþþ – þþþþ

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Figure 3 Schematic representation of ‘‘two essential gene family’’ concept for the development of autoimmune disease. Arrows represent modulatory factors affecting either the immune system or the target organ.

modulatory factors that affect either the immune system (such as an altered immunoendocrine feedback regulation via the HPA axis) or the target organ (such as the iodine content of food) in the OS (Fig. 3). The issue of target organ susceptibility as a prerequisite for the development of autoimmune diseases has now preoccupied our group for many years (Wick et al., 1987) and the two‐gene family concept has been successfully applied to the study of many other autoimmune diseases in our laboratory, notably scleroderma (Sgonc and Wick, 1999) and atherosclerosis (Wick et al., 2004). 3.6. Attempts to Identify Genes Responsible for SAT Development A primary intrinsic expression of MHC class II molecules on the surface of thyroid epithelial cells as originally proposed by Bottazzo et al. (1983) for human Graves’ disease and Hashimoto thyroiditis has been excluded in the OS. Aberrant MHC class II (B‐L) expression was only observed in the neighborhood of preexisting MNC infiltrations, that is, the presence of interferon‐g (IFN‐g) (Kuehr et al., 1994; Wick et al., 1984). This phenomenon may, therefore, play a major role in the perpetuation rather than the initiation of SAT. We did, however, observe that OS thyroid epithelial cells have a lower threshold for IFN‐g‐induced MHC class II antigen expression as compared to those of

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Table 2 Sequenced Clones from SSH of OS/NWL Thyroid cDNA (3 Days Old) Specificity of hybridization

Clone

Length (bp)

1.2.1

385

No

1.2.2 1.1.M3 1.1.V5 2.2.6E

378 400 505 904

Thyroid specific No Thyroid specific OS specific

3.2.2.A

514

OS specific

6.2.1.E2 6.2.2.C3 CB8G

854 620 400

Thyroid specific OS specific (quantitative) Thyroid specific

Significant alignment

Accession number

Chicken EST Human coatomer protein None None Human thyroglobulin ALV ev–6 ALV ev–3 ALV ev–1 ALV strain NTRE–2 ALV ev–3 ALV ev–1 Unknown gene None Human thyroglobulin

AF370360 U24105 AJ414704 AJ414705 X05615 AY013305.1 AY013304.1 AY013303.1 MI4897.1 AY013304.1 AY013303.1 AJ414706 AJ414707 X05615

Suppression subtractive hybridization of RNA prepared from thyroid glands of OS and normal control (inbred B12 B12 line) chicks at the age of 3 days, that is, before onset of lymphoid infiltration. Sequences were analyzed by BLAST search (National Center for Biotechnology Information). EST, expressed sequence tag; ALV, avian leucosis virus; ev, endogenous virus (adapted from Vasicek et al., 2001).

normal chickens (Wick et al., 1987). In the thyroid glands of OS but not of CB chickens, infiltrating macrophages and follicular epithelial cells are positive for nonspecific esterase. This esterase expression can already be detected on the first day after hatching, that is, before lymphoid effector cell infiltration (Hala et al., 2000). To date, it is not yet known, if this phenomenon is based on thyroid specific features that reflect the primary target organ susceptibility. Inflicting mechanical injury on one thyroid gland does not precipitate autoimmune thyroiditis in the injured as well as the contralateral thyroid. Most probably, the target organ susceptibility has something to do with the tyrosine iodination process as mentioned earlier (Brown et al., 1998). In a recent attempt to identify disease‐specific transcripts responsible for the initiation of SAT, suppression subtractive hybridization (SSH) of RNA prepared from OS and CB thyroid lobes, respectively, obtained from 3‐day‐old chicks (i.e., before the beginning of infiltration), were performed (Vasicek et al., 2001). Final screening and analyses by Northern blot and sequencing revealed nine clones to be of potential interest (Table 2). Three of these were OS specific, and four thyroid gland specific. We are at present in the process of determining a possible function of these genes in the course of SAT. As a different approach, taking advantage of the now known sequence of the whole chicken genome,

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parallel microsatellite typing experiments are underway in order to identify the recessive target organ susceptibility gene as well as the dominant genes determining immunologic hyperreactivity in the OS. 3.7. Breeding and Maintenance of the OS Chickens OS chickens, similar to the other chicken lines described in this chapter, are difficult to breed and maintain. In the special case of the OS, sufficient supplementation of the diet with thyroid hormones is necessary to ascertain sufficient fertility and hatchability. This supplementation should be started only at the age of 10 weeks, that is, a time point when assessment of the clinical symptoms of hypothyroidism is possible. At that age, normal looking roosters and dams as well as excessive dwarfs are eliminated from a further breeding program and only birds with an intermediate size displaying a hypothyroid phenotype are retained as future breeders and put on a thyroid hormone supplementation diet. The final selection of breeders is then performed at the age of 20 weeks when the phenotypic symptoms of hypothyroidism are reassessed, B‐locus typing is performed, and all chickens are tested for the presence and titers of TgAAb and a virus‐free state. The breeding program is then performed with artificial insemination. Especially designed cages for housing of single birds by far exceeding the size of commercially available cages are used in our animal unit in order to facilitate the macroscopic observation and the handling for semen collection and artificial insemination (for details see Dietrich, 1989, 1999; Wick et al., 1994). 4. The UCD‐200 Line of Chickens: A Model for Human Systemic Sclerosis 4.1. Clinical Features and Pathogenesis: The Enigma of Systemic Sclerosis Systemic sclerosis (SSc) is a female predominant autoimmune connective tissue disease that is characterized by microvascular alterations, perivascular inflammatory infiltrates, and ultimately fibrosis of the skin and several internal organs, and by the presence of multiple autoantibodies (Jimenez and Derk, 2004). Clinically, SSc can manifest in a wide range of forms ranging from a limited skin involvement with minimal systemic alterations (limited cutaneous SSc) to severe forms (diffuse cutaneous SSc), fulminant in some cases (LeRoy et al., 1988). Nevertheless, a progressive thickening and fibrosis of the skin is universally found in patients with SSc, while internal organs are commonly involved often only subclinically at presentation. In a significant number of cases, however, the cutaneous involvement is confined to the digits and the

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dorsum of the extremities (acrosclerosis) and slowly progresses to generalized sclerosis; this particular form of SSc was long included in the CREST syndrome (i.e., calcinosis, long‐standing Raynaud’s phenomenon, esophageal dysmotility, sclerodactyly, and telangiectasia) (Fritzler and Kinsella, 1980). The Raynaud’s phenomenon is the second most frequent sign in SSc being referred by the majority of patients (Block and Sequeira, 2001), while musculoskeletal involvement is also common (Pope, 2003). As mentioned earlier, internal organs are often affected by fibrosis in SSc, including the esophagus (Rose et al., 1998), the lungs (producing severe respiratory failure as a frequent consequence) (Co et al., 2000), the heart and the pericardium (Deswal and Follansbee, 1996), the kidneys (producing the scleroderma renal crisis characterized by the acute onset of hypertension and renal failure) (Steen, 2003), the thyroid (hypothyroidism) (Gordon et al., 1981), and the male reproductive system (causing impotency) (Lally and Jimenez, 1981). The main pathologic features of SSc include an abnormal deposition of collagen in the skin and several internal organs, inflammatory alterations of both the cellular and humoral compartments of acquired immunity, and typical alterations in the microvasculature (Fleischmajer et al., 1977; Herrick, 2000; White, 1996). In advanced stages, progression of the vascular and fibrotic changes is observed alongside with a decrease in inflammation. Similar to other multifactorial diseases, the pathogenesis of SSc appears to be extremely complex. Genetic predisposition is considered as necessary, yet not sufficient, to determine SSc onset, as indicated by the concordance rates among monozygotic twins that are similar for the disease onset and higher for the presence of SSc‐associated autoantibody patterns when compared to concordance rates in dizygotic twins (Feghali‐Bostwick et al., 2003). As indicated by pathohistological findings, fibroblasts, endothelial cells, and lymphocytes are the key players in the pathogenesis of the three main observed phenomena, that is, cutaneous and visceral fibrosis, obliteration of small arteries and arterioles, and immunologic alterations, such as the production of serum autoantibodies, the chronic MNC infiltration of affected tissues, and the dysregulation of cytokine and growth factor production (Jimenez and Derk, 2004). Although some progress has been made in the elucidation of the pathogenesis, there are still many open questions, and the etiology of SSc remains enigmatic. The search for the ultimate etiology requires animal models. Several models have been proposed that can be subdivided into two main categories based on whether the disease is induced by exogenous compounds or transmitted genetically as a stable trait. Examples of the former group are represented by the induction of SSc‐like disease by the administration of bleomycin, glycosaminoglycans (GAG) derived from patients’ urine, and organic solvents, or the sclerodermatous GvHD. On the other hand, genetically

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transmitted animal models of SSc include the tight skin (Tsk) mice. The Tsk1/þ mouse was obtained by a dominant mutation in the fibrillin‐1 gene on chromosome 2 of the B10.D2 (58N)/SN inbred strain, the Tsk2/þ mouse is a mutant that appeared in the offspring of a 101/H mouse after the administration of the mutagenic agent ethylnitrosourea (for review of the murine models see Jimenez and Christner, 2002). Importantly, all these models display only some of the pathologic features of SSc, for example, Tsk1/þ mice lack MNC infiltration and microvascular damage, and Tsk2/þ also do not show any microvascular alterations. Thus neither model is an ideal mimic for human SSc, and they are not useful to understand the etiology of the disease (Sgonc et al., 1999). In contrast, the avian SSc observed in the UCD‐200 line of chickens appears as a better model for the human disease since it manifests similar inflammatory, immunologic, vascular, digestive, and articular involvement. In particular, these chickens develop the whole spectrum of human SSc, including vascular occlusion, lymphocyte infiltrate, and fibrosis of the skin and internal organs, distal polyarthritis, and serum abnormalities including the appearance of autoantibodies.

4.2. The UCD‐200 and ‐206 Chickens The first chickens showing signs of a genetically determined fibrotic disease were discovered in 1942 by P. Bernier at the Department of Poultry Husbandry, Oregon State University, Corvallis. In 1977, hatching eggs were brought to the University of California at Davis where the line UCD‐200 was developed and first described by Gershwin et al. (1981). Some years later, the UCD‐206 subline was established, which is homozygous for MHC (B locus) B15, thus being histocompatible to the NWL lines UCD‐058 and H.B 15 FIN, chicken strains that serve as healthy controls. In 1988, a UCD‐200 colony was established in the Experimental Animal Facilities of the Innsbruck Medical University, followed by a colony of UCD‐206 chickens in 1993. After the loss of these valuable chicken lines at the University of California at Davis, the colonies at the Innsbruck Medical University are the only still available for research. The natural history of SSc‐like disease in this avian model is relatively consistent in all chickens (Sgonc and Wick, 1999; Van de Water et al., 1995). A comparison of the features observed in human and avian SSc is illustrated in Table 3. One to two weeks after hatching UCD‐200 and ‐206 manifest typical comb lesions with a current incidence of 97.5% and 92%, respectively. It starts with swelling and erythema leading to necrosis and the loss of the comb (‘‘self‐dubbing’’) in the majority of cases (Fig. 4). By the age of 3–4 weeks 20–40% of UCD‐206 chickens develop dermal lesions at the neck followed by necrotic lesion of the toes (Fig. 5), and articular involvement in 10% of the birds. UCD‐200

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Table 3 Comparison of Clinical, Biochemical, Immunological, and Pathologic Features of Human and Avian SSc

Clinical features Disease presentation Skin fibrosis Esophageal fibrosis Lung fibrosis Kidney involvement Heart involvement Polyarthritis Eye involvement Autoimmune features Autoantibodies ANA Anti‐Scl‐70 Anticentromere Anticytoplasmic Autoreactive T cells Etiology Genetic susceptibility Environmental factors Pathology Fibroblast alterations Endothelial alterations Smooth muscle alterations

Human SSc

Avian SSc

Subtle, middle age Present Present Present Present Present Present Debated

Acute, early in life Present Present Present Present Present Present Absent

Present Present Present Present Present

Present Absent Present Present Present

Necessary, not sufficient Hypothesized

Necessary and sufficient Not important

Present Present Present

Present Present Present

animals show dermal lesions of the neck to a lesser degree. Skin inflammation appears early in the natural history of UCD‐200 and is later replaced by fibrosis of the dermis, subcutaneous fat and muscle. Involvement of internal organs also occurs in the esophagus and small intestine (wall thickening by collagen deposition), lungs (lymphocyte infiltration and fibrosis is found in 50% of 6‐week‐old chickens), heart (pericardial effusion is detected in 40% of 6‐month‐ old animals), kidney (with alterations of renal arterioles in nearly all animals), and testicles. Fibrosis of the reproductive organs, which seems to be more severe in UCD‐206 than UCD‐200, makes breeding very difficult and special care has to be taken not to lose the strains. 4.2.1. Genetics The genetic basis of UCD‐200 chickens has been investigated by means of crossed strains and using the comb abnormalities at 4 weeks of age as phenotype

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Figure 4 One‐week‐old UCD‐200 chickens with early inflammatory SSc showing sequence of erythema, edema, and necrosis of the comb.

Figure 5 Eight‐week‐old UCD‐206 chickens with chronic fibrotic SSc. Note the loss of feathers, and the extremely thickened skin on the neck (left chicken), the necrotic lesion of the toe, which resembles the consequence of Raynaud’s phenomenon (right chicken), and the loss of the combs (‘‘self‐dubbing’’).

determinant (Abplanalp et al., 1990). UCD‐200 chickens (in which 100% of male and 60% of female chickens manifested the SSc‐like disease at the time of this genetic study) were crossed with partially inbred strains and the obtained chickens (F1) did not show signs of disease. When F1 cocks were backcrossed

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with UCD‐200 hens, the offspring manifested a prevalence of disease varying between 42% and 88%. Importantly, signs of SSc‐like disease were consistently observed more frequently in male compared to female chickens in all strains. This is particularly intriguing if one considers that, unlike mammals, male birds are homozygous for sex chromosomes while females are heterozygous, thus possibly explaining the original discrepancy in sex ratios between the human (Whitacre, 2001) and avian SSc. However, due to continuous selective breeding male and female chickens are now affected equally. As in human SSc, the MHC (B locus) haplotype also has some influence on disease development in the avian model. Thus, backcrossing of UCD‐200, which are homozygous for MHC‐B17, with chickens with different MHC haplotypes led to lower disease penetrance, except MHC‐B15, which is carried by UCD‐206 (Abplanalp et al., 1990). 4.2.2. Immunobiology Several studies investigated the T cell development and differentiation within the thymus of UCD‐200 chickens. The use of monoclonal antibodies recognizing specific epithelial areas of the thymus allowed the definition of abnormalities of the thymic ontogenic development in affected chickens at different stages of the disease natural history. The rationale of these studies was based on the critical function of the thymus in the process of T cell negative selection that is key to immunologic tolerance. With respect to this, the inappropriate presentation of potentially autoreactive (self) peptides or the disruption of the normal microenvironment of the stromal architecture might ultimately lead to survival of autoreactive T cells and predispose to autoimmunity. UCD‐200 chickens have significant and highly specific abnormalities in the thymus subcapsular regions and in the MHC class II expression in the cortex (Boyd et al., 1991; Wilson et al., 1992); these observations led to the hypothesis that T cell maturation might be impaired in affected chickens. Importantly, these alterations were specific to strains manifesting the SSc‐like disease and were consistent at all ages, being detectable already prior to the disease onset. The use of monoclonal antibodies further made it possible to determine that the regions most affected in UCD‐200 chickens were the subcapsular area and the medulla. Moreover, significantly fewer apoptotic thymocytes are found in UCD‐200 chickens compared to healthy controls, strongly suggesting a disturbed negative selection during thymic T cell maturation that might result in an insufficient deletion of autoreactive cells (Sgonc and Wick, 1999). Most studies on the cellular infiltrates in UCD‐200 chickens were performed in the skin, although several lines of evidence were also confirmed in other affected tissues. The prominent cellular infiltrate is composed of lymphocytes while monocytes and macrophages are found in significantly

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lower proportions. Both types of T cells (i.e., helper, cytotoxic) are detected with CD4þ cells being numerically predominant over CD8þ by 44% at 4 weeks of age. The vast majority of skin‐infiltrating cells in the dermis and subcutaneous tissue in affected chickens are TCRa/bþ/CD3þ/CD4þ/MHC class IIþ cells with 10% of these also being IL‐2R (CD25) positive (Tregs) while the papillary dermis mainly contains TCRg/dþ/MHC class II T cells (Gruschwitz et al., 1991; Van de Water et al., 1989). At the same time diseased chickens show significantly reduced percentages and numbers of T cells expressing TCR1, TCR2, CD3, CD4, or IL‐2‐receptor in the periphery, probably owing to an increased influx into affected tissues (Gruschwitz et al., 1991). In vitro, UCD‐200 peripheral blood T cells show a significantly decreased mitogen‐induced proliferation rate associated with a decreased capacity to produce IL‐2 and to express IL‐2 receptors compared with healthy control chickens. In contrast to the deficient in vitro IL‐2 production, the sera of UCD‐200 chickens contain significantly higher levels of IL‐2 bioactivity (Gruschwitz et al., 1991; Wilson et al., 1992). The in vitro vs in vivo discrepancy might be explained by a state of preactivation of peripheral T lymphocytes, either by autoantigens or by nonspecific signals resulting in a transient exhaustion of IL‐2 secretion that becomes effective in vitro. The increase of MHC class IIþ cells in the circulation also points to such an endogenous prestimulation (Gruschwitz et al., 1991). The reduced in vitro T cell response might also derive from a reduced calcium influx following stimulation with mitogens, with or without IL‐2 (Wilson et al., 1992). Similar changes in T cell phenotype and function are observed during the course of human SSc, and it is widely accepted that T cells play an important role in the pathogenesis of SSc. There is also some evidence pointing to an antigen‐driven T cell activation (Sakkas and Platsoucas, 2004). However, the antigen(s) activating T cells is (are) still unknown in human and chicken SSc. Several autoantibodies characterize human SSc with antinuclear antibodies (ANAs) found in over 90% of cases. The highly disease‐specific antitopoisomerase‐I (anti‐Scl‐70) antibodies are found in 20% of SSc‐sera and are associated with diffuse SSc. Anticentromere antibodies, which are also rarely detected in other connective tissue diseases, are present in SSc with an overall frequency of 20–30%, and are mainly seen in patients with limited SSc and CREST (Cepeda and Reveille, 2004). Similarly, circulating autoantibodies are also found in UCD‐200 and ‐206 chickens. Most frequently the ANA display a speckled or nucleolar immunofluorescence pattern on Hep‐2 cells. Centromeric staining is found especially with sera from UCD‐206 (Sgonc and Wick, 1999). The ANA‐subset autoantibody profile shows a chronological increase of antibodies against ssDNA,

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poly(I), and poly(G), as well as an increase of anticardiolipin antibodies. Early in life, the majority of UCD‐200 and ‐206 sera are positive for anti‐ cytoplasmic antibody staining on HEp‐2 cells, and 60% have detectable rheumatoid factor by the age of 6 month. No reactivity has been observed against Scl‐70, RNA, SS‐A/Ro, SS‐B/La, or Sm using test kits destined for diagnostic use in humans (Gruschwitz et al., 1993; Haynes and Gershwin, 1984). It is not known if these autoantibodies have any pathophysiological function or are merely epiphenomena. In contrast, anti‐endothelial cell antibodies (AECA) that are present in UCD‐200 and ‐206 chickens already before disease onset, and in human SSc mainly in the early inflammatory disease stage, are centrally involved in the induction of primary endothelial cell injury (Sgonc et al., 1996, 2000; Worda et al., 2003). 4.2.3. Vascular Alterations Microvascular damage is found in all involved organs and leads to underperfusion and chronic ischemia, which may play an important role in organ dysfunction. The typical vascular lesions consist of intimal proliferation leading to luminal narrowing of arterioles and capillaries, duplication of the basement membrane, perivascular edema, and MNC infiltration. With disease progression, there is an accumulation of intravascular platelets at progressively damaged endothelial sites, the latter reflected by release of von Willebrand factor (vWF) into the circulation. Lesions progress with increased perivascular collagen deposition leading to fibrosis, obliteration of many capillaries, and dilatation of the remaining ones. Further evidences of endothelial cell dysfunction include changes in prostacyclin, thrombomodulin, and angiotensin‐converting enzyme (ACE). Platelet activation, which can be found in the presence of endothelial damage, is demonstrated by increased levels of thromboxan, b‐thromboglobulin, and circulating platelet aggregates (Sgonc, 1999). It has long been unclear which of the three salient pathologic features, that is, vascular abnormalities, perivascular MNC infiltration, and increased collagen deposition, is the primary pathogenic event in SSc. In looking for the initiating factors in such a complex disease, it is of great value to study animal models sharing as many as possible of the hallmarks of the human disease. The UCD‐200 and ‐206 chicken lines are the only animal model displaying all key symptoms, that is, endothelial lesions, severe perivascular lymphocytic infiltration of skin and viscera, fibrosis of skin and internal organs, ANA, and AECA. A comparative study of skin lesion biopsies from UCD‐200 and ‐206 chickens and human SSc patients clearly showed that endothelial cells are the primary target of the autoimmune attack, subsequently undergoing apoptosis (Sgonc et al., 1996).

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This endothelial cell apoptosis is not a localized phenomenon in the skin, but also the first pathogenic event demonstrable in affected internal organs, thus supporting the hypothesis that endothelial cell apoptosis plays an important role in the initiation of SSc followed by accumulation of MNCs and finally fibrosis (Nguyen et al., 2000). Follow‐up studies further revealed that endothelial cell apoptosis in SSc is induced by AECA‐dependent cellular cytotoxicity (ADCC) via the Fas/Fas ligand pathway (Sgonc et al., 2000). NK cells, the effector cells in ADCC, recognize IgG‐AECA‐coated target cells by binding of the antibodies to their Fcg receptor. Ligation of the Fcg receptor results in up‐regulation of FasL expression, and NK cells can then kill targets that bear Fas (Eischen et al., 1996). As in human SSc, AECA are the causative principle for the first pathogenic event in avian scleroderma, viz., vascular endothelial cell apoptosis. This was shown by in vivo studies using AECA‐positive UCD‐200 serum samples for application onto the CAM of various healthy control lines on embryonic day (ED) 10 or for intravenous injection into normal CC‐chicken embryos on ED 13. The results revealed that AECA bind to small vessels and that apoptosis of endothelial cells is significantly increased after transfer of AECA‐positive sera in comparison to controls (Worda et al., 2003). However, the involved antigen(s) has (have) not yet been identified. The identification of this (these) endothelial cell antigen(s) is a main goal of current research and should help to elucidate the etiology of this enigmatic disease. It also might lead to the development of new tests for early diagnosis and to a rational endothelial cell–directed therapy. 4.2.4. Fibrosis Fibroblasts from UCD‐200 and ‐206 fibrotic skin display an activated phenotype producing elevated quantities of collagen, mainly types I, III, and VI, and GAG compared to skin fibroblasts derived from NWL (Duncan et al., 1992). Similar to the human disease, restriction fragment length polymorphism (RFLP) studies did not show any gross alteration of collagen genes (Sgonc et al., 1995). Cytokines produced by tissue infiltrating immune cells are critical to human SSc onset since they act on growth, migration, and collagen synthesis by smooth muscle cells, endothelial cells, and fibroblasts (Ihn, 2005). Using supernatants from MNCs isolated from lesional UCD‐206 skin, a link between infiltrating cells and fibroblast activation has also been shown in the avian model. Supernatants from cultured MNCs isolated from developing fibrotic skin lesions, normal appearing skin, and peripheral blood of UCD‐206 chickens were added to normal chicken skin fibroblasts. The results revealed that

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only MNCs from lesional skin secrete fibroblast‐activating cytokines leading to increased collagen and GAG production (Duncan et al., 1995; Van de Water et al., 1994). Numerous studies have suggested various pro‐ and antifibrotic cytokines to be involved in the pathogenesis of SSc (Kissin and Korn, 2003), In particular, transforming growth factor‐beta (TGF‐b) emerged as an important mediator in the development of fibrosis, possibly acting locally in a paracrine fashion (Falanga et al., 1987; Kikuchi et al., 1992). The TGF‐b superfamily represents a large family of closely related proteins with TGF‐b1, 2, and 3 being the most common members. It has been shown in vitro and in vivo that TGF‐b is a potent stimulator of collagen production (Cotton et al., 1998). But although much effort has been made to elucidate the pathogenic role of the TGF‐b isoforms in SSc, their specific functions remain elusive since some results are contradictory. A recent study on chicken embryonic fibroblasts (CEF) from UCD‐200 and NWL helped to elucidate the contradictory results on TGF‐b2 in the pathogenesis of SSc (Prelog et al., 2005). As in human SSc, TGF‐b1 has a profibrotic activity on UCD‐200 fibroblasts reflected by enhanced proliferation, altered interaction with the surrounding extracellular matrix, increased expression of a profibrotic proa2(I) mRNA variant, and decreased expression of the classic proa2(I) mRNA transcript. These proa2(I) mRNA variants have been detected in an earlier study by RNase protection assays (RPA) where the smaller variant, which is significantly increased in inflammatory lesions of UCD‐200 skin and esophagus, is represented by a 115 bp band, and the classic proa2(I) mRNA transcript by a 180 bp band (Ausserlechner et al., 1997). The smaller proa2(I) mRNA variant seems to play a crucial role in the development of fibrosis, and the 115/180 bp ratio, which is strongly elevated early in fibrosis, probably is a good marker of fibrosis onset. TGF‐b2 and TGF‐b3 both reduced the expression of the profibrotic proa2(I) mRNA in UCD‐200‐CEF to the same levels seen in healthy control NWL‐CEF. Analysis of cell culture supernatants revealed that NWL‐CEF produced 4.1 times more TGF‐b2 than UCD‐CEF. The constitutive overproduction of profibrotic proa2(I) mRNA variant and diminished TGF‐b2 synthesis found in untreated UCD‐200‐CEF suggest that TGF‐b2—in contrast to general belief—can act as an antifibrotic cytokine and might be a key player during fibrosis onset (Prelog et al., 2005).

4.3. Scientific Value of UCD‐200 and ‐206 Chicken Lines The strength of the UCD‐200 model lies in the spontaneous development of a disease closely resembling human SSc. It is the only model that manifests the whole clinical, histopathological, and serological spectrum of human SSc and,

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thus, can up to date still be regarded as the best animal model for SSc. The studies on UCD‐200 and ‐206 chickens have provided substantial and valuable information on the pathogenesis of the human disease. Thus, only the comparative study of UCD‐200 and human SSc made it possible to identify endothelial cell apoptosis as a primary pathogenic event. Although, some progress has been made during the last years, the etiology and pathogenesis of human SSc remain poorly understood and current medical treatments are often unsatisfactory. The striking immunologic and pathologic similarities found between the avian and human forms of SSc make this model an ideal tool to further investigate the initial pathomechanisms of SSc and to test novel approaches of evidence‐based therapies. 5. The SL Chicken Model for Human Autoimmune Vitiligo 5.1. Introduction to Vitiligo Vitiligo is a common dermatological disorder in humans, affecting at least 1–2% of the world’s population. It is characterized by destruction of pigment cells (melanocytes) in the skin, generally resulting in patches of depigmentation and, in some individuals, complete depigmentation of the skin. The cosmetic disfiguration resulting from vitiligo leads to psychosocial effects that are particularly severe in the young and in people with darker skin pigmentation. In most cases of vitiligo, the loss of pigmentation is due to an autoimmune, primarily cell mediated, destruction of melanocytes, and there is a recognized association between vitiligo and a variety of other autoimmune diseases (Mason, 1997; Nordlund and Lerner, 1982; Ortonne and Bose, 1993; Passeron and Ortonne, 2005; Spritz, 2006). The mutant SL of chicken (Fig. 6), previously known as the delayed amelanosis (DAM) chicken, is the only animal model for autoimmune vitiligo that recapitulates the entire spectrum of clinical and biological manifestations of the human disease. The onset and incidence of SLV are predictable, and the autoimmune lesion is easily accessible (located in the feather). Because the feather regenerates, it provides the opportunity to study the evolving lesion prior to and throughout the development of SLV in the same individuals. As in most autoimmune diseases, several factors are involved in the expression of SLV, including a genetic susceptibility, an immune system component, and an environmental component. The origin of the SL chicken and the establishment of this animal model for human vitiligo have been reviewed extensively by Smyth (1989). The goal of this chapter is to briefly summarize key aspects of autoimmune SLV and its relationship to human vitiligo.

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Figure 6 Smyth line animal model for vitiligo. The rooster on the left demonstrates normal pigmentation; the rooster on the right is amelanotic, there is no pigmented new feather growth.

5.2. The SL Animal Model of Vitiligo The mutant SL chicken was developed by Dr. J. Robert Smyth, Jr., a poultry geneticists at the University of Massachusetts, Amherst, MA. The SL chicken is characterized by a spontaneous, vitiligo‐like, posthatch loss of melanin producing melanocytes in feather and choroidal tissue (Fig. 6). Vitiligo occurs in 70–95% of hatchmates, with about 70% of those affected expressing complete depigmentation (amelanosis) in adulthood (Smyth, 1989). The SL of chickens maintained at the University of Arkansas (U of A) originated from the SL101 subline (B101/101 MHC haplotype). This line has been studied most extensively due to its early expression of vitiligo (6–10 weeks of age) (Smyth and McNeil, 1999). Additionally, the U of A maintains two MHC‐matched control lines of chickens. One line, the parental Brown line (BL101) has a low incidence of vitiligo (<2%) and is considered genetically susceptible to vitiligo, based on a high incidence of vitiligo (71%) following intravenous treatment with the DNA methylation inhibitor 5‐azacytidine; the other line, the Light Brown Leghorn (LBL101) has no incidence of vitiligo even with 5‐azacytidine treatment (Sreekumar et al., 1996). SL101, BL101, and LBL101 lines of chickens (hereafter referred to as SL, BL, and LBL), therefore, offer a unique opportunity to study the factors and mechanisms involved in the susceptibility and expression of autoimmune vitiligo. There are many similarities between SL and human vitiligo. Both are characterized by a destruction of melanocytes, usually first seen during adolescence

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and early adulthood. In both SL chickens and humans, pigmentation loss may be either partial or complete. Remelanization of amelanotic tissue occurs, although severe pigment loss and remelanization are more frequent in the chicken (Boissy and Lamoreux, 1988). In addition to vitiligo, SL chickens exhibit uveitis (40–45%), often resulting in blindness, and have associated autoimmune diseases such as hypothyroidism (4–5%) and an alopecia areata‐like feathering defect (2–3%) (Smyth and McNeil, 1999). Similarly, in humans it is not uncommon to find thyroidal and other autoimmune diseases associated with vitiligo (Nordlund and Lerner, 1982; Spritz, 2006). Like in human vitiligo, melanocyte loss in SL chickens is accompanied by lymphocyte infiltration into the affected area/target tissue and melanocyte death appears to be mediated by melanocyte‐ specific cell‐mediated immune processes (Passeron and Ortonne, 2005; Wang and Erf, 2003, 2004). Additionally, SL chickens have altered antioxidant capacity, heightened lipid peroxidation, and increased production of reactive oxygen species in feathers and other tissues (Erf et al., 2005; Wijesekera, 2004), phenomena also reported in vitiligo patients (Agrawal et al., 2004). The genetic basis of amelanosis, and the other line‐associated traits, in SL chickens has long been recognized to be under the complex control of multiple autosomal genes (Smyth et al., 1981). Although multiple MHC types originally existed in the SL and related lines, to eliminate that gene complex as a variable in studies of SL vitiligo, MHC‐matched sublines of the SL, BL, and LBL were bred to near‐homozygosity for different MHC types. Most studies were based on the B101 sublines, and those are the only sublines currently in existence. The exact equivalent of B101 with standard MHC types is not defined. The presence of endogenous viral (ev) genes is related to expression of SLV, which is in agreement with the reported role of ev genes in autoimmune diseases (Nakagawa and Harrison, 1996) as well as the effect of Turkey herpesvirus (HVT) vaccination in potentiating the expression of depigmentation in SL chickens (Section III.C). A greater number of ev genes were expressed in SL and BL birds than in control line LBL birds (Sreekumar et al., 2000). In an F2 resource population (SL  BL), there was linkage disequilibrium between the SLV phenotype and the ev fragments: 16.2‐kb SacI and 19‐kb HindIII (Sreekumar et al., 2000). To date, however, the exact identity of most of the genes controlling SLV expression remains undefined. Molecular characterization of the SL sublines and their parental controls demonstrated high levels of inbreeding within lines and very high levels of genetic similarity between the SL sublines and their MHC‐matched parental control lines (Sreekumar et al., 2001), suggesting that the genes responsible for the SL phenotype reside in a relatively small proportion of the chicken genome. The recent availability of a dense map of single‐nucleotide variation in the chicken genome (International Chicken Polymorphism Map Consortium, 2004)

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combined with the defined SL subline will facilitate mapping the genomic regions and identifying the specific genes responsible for the depigmentation and other abnormalities seen in SL chickens, as described in this chapter. 5.3. Multifactorial Nature of SL Vitiligo 5.3.1. Melanocyte Defect Previous studies by J. Robert Smyth, Jr., and coworkers describe the presence of a competent pigment system in SL chicks at hatch. One of the earliest manifestations of SLV, detectable at the light microscope level, was the appearance of histologically abnormal melanocytes in the feather epithelial barb ridge, where melanocytes are located. These melanocytes had thickened, partially retracted dendrites, and an irregular shape. Pigment transfer from melanocyte dendrites to keratinocytes was reduced at this stage. More advanced stages were represented by marked clumping or the absence of melanocytes and further reduction in pigment transfer. The earliest abnormalities within SL melanocytes, prior to visible onset of SLV, were irregularly shaped melanosomes containing pigmented membrane extensions, hyperactive melanization, and selective autophagocytosis of melanosomes. These observations suggested that the synthesis of abnormal melanin granules with pigmented extensions was related to a hyperactive process of melanization, and that this aberrant process, in turn, stimulated the selective autophagocytosis of melanosomes (Boissy et al., 1983, 1985). Similar degenerative processes were also observed in vitro in neural crest‐derived melanocytes from embryos of SL chickens (Boissy et al., 1986) and were found to include heightened lipid peroxidation and catalase activity (Lockhart, 2004). The occurrence of these melanocyte malfunctions ex vivo provides strong evidence for an inherent melanocyte defect in SLV. However, as shown through immunosuppression studies, the inherent melanocyte defect alone is not sufficient to cause SLV without a functioning immune system, but appears to be involved in provoking a melanocyte‐specific autoimmune response (Boissy et al., 1984; Fite et al., 1986; Lamont and Smyth, 1981; Pardue et al., 1987). 5.3.2. Immune System Involvement Several studies provide evidence supporting a role of the immune system in the pathology of SLV. Phenotypic cell population analysis based on immunohistochemical staining of growing feathers, as well as on flow cytometric analysis of feather pulp cell suspensions, revealed that the majority of feather‐ infiltrating lymphocytes were T cells (most with ab1 T cell receptors) and

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included both T helper cells and cytotoxic T cells, whereby cytotoxic T cells predominated during active SLV (Erf et al., 1995; Shresta et al., 1997). Melanocyte death was shown to occur by apoptosis, apparently induced by cytotoxic T cells (Wang and Erf, 2004). Vitiliginous feathers also exhibited increased numbers of MHC class II–expressing cells (including macrophages, activated T cells and B cells) and heightened levels of MHC class II expression compared to controls, suggesting the presence of inflammatory mediators such as IFN‐g in affected feathers (Wang et al., 1998). The presence of IFN‐g in feathers with active SLV, but not in normally pigmented or completely amelanotic feathers, was later demonstrated by Northern and Western blots and by quantitative reverse transcription‐polymerase chain reaction (RT‐PCR) (Plumlee et al., 2006). Vitiligo was not associated with altered numbers and proportions of circulating lymphocytes, although the development of SLV was accompanied by substantial increases in inflammatory blood leukocytes 1–2 weeks prior to, and at first observation of, SLV (Erf and Smyth, 1996). Similarly, lymphocyte infiltration was not observed in the skin where the undifferentiated melanocyte precursor pool is located (Bowers, 1988; Erf et al., 1995). However, dermal lymphoid aggregates (DLA) contained similar numbers of B and T cells per mm2, but were larger, more abundant, and differed greatly in the proportions among T cell subsets in vitiliginous SL birds compared to controls (Erf et al., 1997). The function of DLA is not established; however, they may serve as local sites for the activation and expansion of melanocyte‐specific lymphocytes in SLV. Overall, the immunopathology of SLV described earlier is very similar to observations in affected skin of vitiligo patients and supports cell‐mediated immune mechanisms in melanocyte destruction. In SL chickens, moreover, direct evidence for a role of cell‐mediated immunity in the development of SLV was provided by immunosuppression studies (Fite et al., 1986; Pardue et al., 1987) and by in vivo demonstration of anti‐melanocyte cell‐mediated immunity in vitiliginous SL, but not in non‐vitiliginous SL and control, chickens (Wang and Erf, 2003). In both human vitiligo and SLV, anti‐melanocyte autoantibodies have been described (Harning et al., 1991; Park et al., 1996). Although their contribution to the onset and progression of vitiligo is not understood, the observation that bursectomy reduced the incidence of SLV (Lamont and Smyth, 1981) suggests a role of humoral immunity in the chicken model. SL autoantibodies first appear in the peripheral circulation 1–2 weeks before the onset of SLV, cross‐react with mouse and human melanocytes, bind to pigment cells within tissues, and recognize antigens expressed in the cytoplasm and on the surface of melanocytes and melanoblasts (Austin and Boissy, 1995; Searle et al., 1993). Specifically, SL autoantibodies recognize mammalian

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tyrosinase‐related protein‐1 and, based on molecular studies, its avian homologue (Austin and Boissy, 1995). 5.3.3. Environmental Components In addition to an inherent melanocyte defect and an autoimmune component, Erf et al. (2001a) reported a role of environmental factors, specifically vaccination with live HVT at hatch, in the expression of SLV. Without HVT, the incidence of SLV is <20%, but with HVT, the incidence is 70–95% (Erf et al., 2001a). HVT is an alpha herpesvirus commonly used in commercial chicken production as a vaccine to protect chickens from Marek’s disease caused by serotype 1 Marek’s disease viruses (MDV‐1). HVT is a nononcogenic serotype 3 MDV isolated from turkeys that causes only minor inflammatory lesions, but, like other MDV, exhibits strong tropism for feather follicles (Holland et al., 1998). Additional studies on the role of HVT in SLV revealed that killed HVT neither had any effect on the expression of SLV nor on other live vaccine viruses that do not exhibit feather follicle tropism, such as Newcastle disease virus and infectious bronchitis virus (Erf, 2002). Moreover, SL chickens appear to have heightened cell‐mediated immune activity to live HVT compared to MHC‐matched BL chickens (Erf et al., 2001b). It is likely that the local immune response brought to the feather due to HVT presence and, potentially, the direct infection of melanocytes by HVT (Wang et al., 2000), may alter the local and internal melanocyte environment in such a way that an already inherently defective, potentially immunologically active melanocyte, would now become visible to the immune system, thus provoking a melanocyte‐ specific immune response. The report by Grimes et al. (1996) on the presence of cytomegalovirus DNA in depigmented and normally pigmented skin from some patients with vitiligo and the absence of this herpesvirus DNA in control subjects suggests that vitiligo may be triggered by a viral infection in selected patients. The establishment of HVT as an environmental trigger of vitiligo in vitiligo‐susceptible individuals further underlines the value of SL chickens as a model for human vitiligo and other autoimmune diseases. 5.4. Scientific Value of SL Chickens In summary, the SL chicken offers unique opportunities to study the interplay between genetic susceptibility, environmental factors, and the immune system that lead to the development of anti‐melanocyte autoimmune activity. The similarities of the clinical manifestations and pathologic progression between human and SL vitiligo, together with the easy, nonterminal, repeatable access to the autoimmune lesion, the predictability of the disease, the ability to

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visually monitor the disease development, and the availability of MHC‐matched parental and non‐vitiligo‐susceptible controls, make the SL animal model an excellent model to study spontaneously developing autoimmune vitiligo. 6. Conclusions and Outlook Chickens have many advantages as compared to other species as experimental animals for genetic and immunologic research. In the three models described in this chapter these advantages fortuitously coincide with the spontaneous, genetically determined occurrence of autoimmune diseases with still unknown etiology and pathogenesis. In addition, these models represent both two organ‐ specific (OS and SLV) and one systemic (UCD‐200 and ‐206) autoimmune diseases. With the availability of the whole sequence of the chicken genome a resurrection of avian models for these and other human diseases can take place provided that these valuable endangered lines are not lost like so many others in the past. The genetic basis of these crippling autoimmune diseases will soon be defined hopefully opening new diagnostic, preventive, and therapeutic horizons for their human counterparts. Acknowledgments We would like to devote this chapter to the late R. K. Cole, DVM, Cornell Veterinary College, Ithaca, NY, who not only discovered and developed the OS but also continued to work and share his friendship, generosity, and expertise with us until his death at the age of 93 during the writing of this chapter. We would also like to acknowledge the original work of Dr. P. Bernier, Corvalis, OR, who developed the UCD‐200 line. The excellent scientific insight and tireless work of Dr. J. Robert Smyth, Jr., in recognizing the value of the SLV model, developing the genetic resources for study, conducting the initial research to validate the model, and generously mentoring other scientists using the SL, are all gratefully acknowledged. We also thank Barbara Gschirr for secretarial help and Ilona Lengenfelder for artwork during preparation of this manuscript. The work on the OS and UCD‐200 and ‐206 lines has been continuously supported by the Austrian Research Fund (FWF) most recently projects no. 14466 (to GW) and no. 18726‐B05 (to R.S.).

References Abdrakhmanov, I., Lodygin, D., Geroth, P., Arakawa, H., Law, A., Plachy, J., Korn, B., and Buerstedde, J. M. (2000). A large database of chicken bursal ESTs as a resource for the analysis of vertebrate gene function. Genome Res. 10, 2062–2069. Abplanalp, H., Gershwin, M. E., Johnston, E., and Reid, J. (1990). Genetic control of avian scleroderma. Immunogenetics 31, 291–295. Aichinger, G., Kofler, H., Diaz‐Merida, O., and Wick, G. (1984). Non‐thyroid autoantibodies in obese strain (OS) chickens. Clin. Immunol. Immunopathol. 32, 57–69.

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Sgonc, R., Gruschwitz, M. S., Dietrich, H., Recheis, H., Gershwin, M. E., and Wick, G. (1996). Endothelial cell apoptosis is a primary pathogenetic event underlying skin lesions in avian and human scleroderma. J. Clin. Invest. 98, 785. Sgonc, R., Dietrich, H., Sieberer, C., Wick, G., Christner, P. J., and Jimenez, S. A. (1999). Lack of endothelial cell apoptosis in the dermis of tight skin 1 and tight skin 2 mice. Arthritis Rheum. 42, 581–584. Sgonc, R., Gruschwitz, MS, Boeck, G., Sepp, N., Gruber, J., and Wick, G. (2000). Endothelial cell apoptosis in systemic sclerosis is induced by antibody‐dependent cell‐mediated cytotoxicity via CD95. Arthritis Rheum. 43, 2550–2562. Shresta, S., Smyth, J. R., Jr., and Erf, G. F. (1997). Profiles of pulp infiltrating lymphocytes at various times throughout feather regeneration in Smyth line chickens with vitiligo. Autoimmunity 25, 193–201. Simonsen, M. (1962). Graft‐versus‐host reactions. Their natural history and applicability as tools of research. Progr. Allergy 6, 349–467. Smyth, J. R., Jr. (1989). The Smyth chicken: A model for autoimmune amelanosis. Poult. Biol. 2, 1–19. Smyth, J. R., Jr., and McNeil, M. (1999). Alopecia areata and universalis in the Smyth chicken model for spontaneous autoimmune vitiligo. J. Investig. Dermatol. Symp. Proc. 4, 211–215. Smyth, J. R., Jr., Boissy, R. E., and Fite, K. V. (1981). The DAM chicken: A model for spontaneous postnatal cutaneous and ocular amelanosis. J. Hered. 72, 150–156. Somes, R. G. (1988). International registry of poultry genetics stocks. In ‘‘Storrs Agricultural Experiment Station,’’ Bulletin 476, p. 98. The University of Connecticut Storrs. Spritz, R. A. (2006). The genetics of generalized vitiligo and associated autoimmune diseases. J. Dermatol. Sci. 41, 3–10. Sreekumar, G. P., Erf, G. F., and Smyth, J. R., Jr. (1996). 5‐Azacytidine treatment induces autoimmune vitiligo in the parental control strains of the Smyth line chicken model for autoimmune vitiligo. Clin. Immun. Immunopathol. 81, 136–144. Sreekumar, G. P., Smyth, J. R., Jr., Ambady, S., and Ponce de Leon, F. A. (2000). Analysis of the effect of endogenous viral genes in the Smyth line chicken model for autoimmune vitiligo. Am. J. Pathol. 156, 1099–1107. Sreekumar, G. P., Smyth, J. R., Jr., and Ponce de Leon, F. A. (2001). Molecular characterization of the Smyth chicken sublines and their parental controls by RFLP and DNA fingerprint analysis. Poult. Sci. 80, 1–5. Steen, V. D. (2003). Scleroderma renal crisis. Rheum. Dis. Clin. North Am. 29, 315–333. Sundick, R. S., Herdegen, D., Brown, T. R., Dhar, A., and Bagchi, N. (1991). Thyroidial iodine metabolism in obese strain chickens prior to immune‐mediated damage. J. Endocrinol. 128, 239–244. Svoboda, J. (1986). Rous sarcoma virus. Intervirology 26, 1–60. Tirunagaru, V. G., Sofer, L., Cui, J., and Burnside, J. (2000). An expressed sequence tag database of T‐cell enriched activated chicken splenocytes: Sequence analysis of 5251 clones. Genomics 66, 144–151. Van de Water, J., Haapanen, L., Boyd, R., Abplanalp, H., and Gershwin, M. E. (1989). Identification of T cells in early dermal lymphocytic infiltrates in avian scleroderma. Arthritis Rheum. 32, 1031–1040. Van de Water, J., Boyd, R., Wick, G., and Gershwin, M. E. (1994). The immunologic and genetic basis of avian scleroderma, an inherited fibrotic disease of line 200 chickens. Int. Rev. Immunol. 11, 273–282.

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Van de Water, J., Jimenez, S. A., and Gershwin, M. E. (1995). Animal models of scleroderma: Contrasts and comparisons. Int. Rev. Immunol. 12, 201–216. Van Laere, A. S., Nguyen, M., Braunschweig, M., Nezer, C., Collette, C., Moreau, L., Archibald, A. L., Haley, C. S., Buys, N., Andersson, G., Georges, M., and Andersson, L. (2003). Positional identification of a regulatory mutation in IGF2 causing a major QTL effect on muscle growth in the pig. Nature 425, 832–836. Vasicek, D., Vasickova, K., Kaiser, P., Drozenova, R., Citek, J., and Hala, K. (2001). Analysis of genetic regulation of chicken spontaneous autoimmune thyroiditis, an animal model of human Hashimoto’s thyroiditis. Immunogenetics 53, 776–785. Wang, X., and Erf, G. F. (2003). Melanocyte‐specific cell mediated immune response in vitiliginous Smyth line chickens. J. Autoimmun. 21, 149–160. Wang, X., and Erf, G. F. (2004). Apoptosis in feathers of Smyth line chickens with autoimmune vitiligo. J. Autoimmun. 22, 21–30. Wang, X., Smyth, J. R., Jr., Bersi, T. K., and Erf, G. F. (1998). MHC class II expression by T cells present in growing feathers from Smyth line chickens with vitiligo. Poult. Sci. 77(Suppl. 1), 26. Wang, X., Parcells, M. S., and Erf, G. F. (2000). Marek’s disease virus infection of cultured chicken melanocytes. Pigment Cell Res. 13, 217. Whitacre, C. C. (2001). Sex differences in autoimmune disease. Nat. Immunol. 2, 777–780. White, B. (1996). Immunopathogenesis of systemic sclerosis. Rheum. Dis. Clin. North Am. 22, 695–708. Wick, G., and Graf, J. (1972). Electron microscopic studies in chickens of the obese strain with spontaneous hereditary autoimmune thyroiditis. Lab. Invest. 27, 400–411. Wick, G., and Steiner, R. (1972). Bursectomy and thymectomy of obese strain (OS) chickens with spontaneous autoimmune thyroiditis and simultaneous experimental and allergic encephalomyelitis. J. Immunol. 109, 1031–1035. Wick, G., Kite, J. H., Cole, R. K., and Witebsky, E. (1970a). Spontaneous thyroiditis in the obese strain of chickens. III. The effect of bursectomy on the development of the disease. J. Immunol. 104, 45–54. Wick, G., Kite, J. H., and Witebsky, E. (1970b). Spontaneous thyroiditis in the obese strain of chickens. IV. The effect of thymectomy and thymo‐bursectomy on the development of the disease. J. Immunol. 104, 54–62. Wick, G., Gundolf, R., and Hala, K. (1979). Genetic factors in spontaneous autoimmune thyroiditis in OS chickens. J. Immunogenet. 6, 177–183. Wick, G., Boyd, R., Hala, K., de Carvalho, L., Kofler, R., Mueller, P. U., and Cole, R. K. (1981). The obese strain (OS) of chickens with spontaneous autoimmune thyroiditis: Review of recent data. Curr. Top. Microbiol. Immunol. 91, 109–128. Wick, G., Hala, K., Wolf, H., Boyd, R. L., and Schauenstein, K. (1984). Distribution and functional analysis of B‐L/Ia positive cells in the chicken: Expression of B‐L/Ia antigens on thyroid epithelial cells in spontaneous autoimmune thyroiditis. Mol. Immunol. 21, 1259–1265. Wick, G., Kroemer, G., Neu, N., Faessler, R., Ziemiecki, A., Mueller, R. G., Ginzel, M., Beladi, I., Kuehr, T., and Hala, K. (1987). The multi‐factorial pathogenesis of autoimmune disease. Immunol. Lett. 16, 249–257. Wick, G., Brezinschek, H.‐P., Hala, K., Dietrich, H., Wolf, H., and Kroemer, G. (1989). The obese strain (OS) of chickens. An animal model with spontaneous autoimmune thyroiditis. Adv. Immunol. 47, 433–500. Wick, G., Hu, Y., Schwarz, S., and Kroemer, G. (1993). Immunoendocrine communication via the hypothalamo‐pituitary‐adrenal axis in autoimmune diseases. Endocr. Rev. 14, 539–563. Wick, G., Cole, R. L., Dietrich, H., Maczek, C., Mu¨ller, P.‐U., and Hala, K. (1994). The Obese strain of chickens with spontaneous autoimmune thyroiditis as a model for Hashimoto disease.

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Functional Dynamics of Naturally Occurring Regulatory T Cells in Health and Autoimmunity Megan K. Levings,*,1 Sarah Allan,*,1 Eva d’Hennezel,†,1 and Ciriaco A. Piccirillo,†,1,2 *Department of Surgery, University of British Columbia and Immunity and Infection Research Centre, Vancouver Coastal Health Research Institute, Vancouver V6H 3Z6, Canada † Department of Microbiology and Immunology, McGill University Health Center, Montreal, Quebec, Canada H3A 2B4; and McGill Centre for the Study of Host Resistance, McGill University Health Center, Montreal, Quebec, Canada H3A 2B4 Abstract............................................................................................................. Introduction ....................................................................................................... Phenotype of CD4þCD25þ nTreg Cells .................................................................. Factors Regulating the Expansion and Specificity of nTreg Cells .................................. Innate and Adaptive Inflammatory Signals Dictating the Function of nTreg Cells................................................................................... 5. Growth Factor–Mediated Control of nTreg Cell Development, Function, and Homeostasis ................................................................................... 6. Control of Autoimmune Responses by nTreg Cells .................................................... 7. Summary and Conclusions .................................................................................... References .........................................................................................................

1. 2. 3. 4.

119 120 122 126 129 134 138 145 146

Abstract A network of regulatory T (Treg) cells exists to downregulate immune responses in various inflammatory circumstances and ultimately assure peripheral T cell tolerance. Naturally occurring CD4þCD25þ Treg cell represents a major lymphocyte population engaged in the dominant control of self‐reactive T responses and maintenance of tolerance within this network. CD4þCD25þ Treg cells differentiate in the normal thymus as a functionally distinct subpopulation of T cells bearing a broad T cell receptor repertoire endowing these cells with the capacity to recognize a wide spectrum of self‐Ag and non‐self‐Ag specificities. The development of CD4þCD25þ Treg cells is genetically determined, influenced by Ag‐specific and nonspecific signals, costimulation, and cytokines that control their activation, expansion, and suppressive activity. Functional abrogation of these cells in vivo, or genetic defects that affect their development or function, unequivocally predisposes animals and humans to the onset of autoimmune and other inflammatory diseases. Studies have 1

All authors contributed equally. Corresponding author.

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119 advances in immunology, vol. 92 # 2006 Elsevier Inc. All rights reserved.

0065-2776/06 $35.00 DOI: 10.1016/S0065-2776(06)92003-3

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shed light in our understanding of the cellular and molecular basis of CD4þCD25þ Treg cell–mediated immune regulation. In this chapter, we discuss the contribution of naturally occurring CD4þCD25þ Treg cells in the induction of immunologic self‐tolerance in animal models and humans and attempt to provide a comprehensive overview of recent findings regarding the phenotype, functional dynamics, and effector mechanism of these cells in autoimmune diseases. 1. Introduction 1.1. Regulation of Peripheral T Cell Tolerance: A Multilayered Process Self/nonself discrimination requires a finely controlled balance between maintaining peripheral tolerance to auto‐Ags (Ags) while preserving the ability to generate protective immune responses against a variety of invading pathogens (McHugh et al., 2002; Singh et al., 2001). In order to achieve a fine balance between these two drastically different immunological outcomes, a network of induced (i) and naturally occurring (n) CD4þ regulatory T (Treg) cells exists to maintain tolerance in homeostatic vs inflammatory settings (Fig. 1). Remarkably, Treg cells can simultaneously suppress autoreactive T cells that escape thymic negative selection, maintain normal intestinal immunity toward enteric bacteria, and dampen the antipathogen effector mechanisms from inducing immune pathology (Fehervari and Sakaguchi, 2004). A great deal of confusion exists in the literature regarding the relative roles of i vs nTreg cells since in many cases it is not possible to trace the origins of regulatory activity due to the lack of unique lineage markers. In addition, growing evidence indicates that n and iTreg work togther, and in many cases in vivo suppression may be dependent on both cell types (Dieckmann et al., 2002; Stassen et al., 2004). Here we will focus our attention of the role of nTreg cells in autoimmunity and only briefly discuss iTreg. The reader is also referred to several reviews on the role of iTreg cells in autoimmune and other diseases (Mills and McGuirk, 2004; Roncarolo et al., 2006). 1.2. Naturally Occurring CD4þCD25þ Regulatory T Cells In 1995, the laboratory of Shimon Sakaguchi made seminal observation demonstrating that a unique subset of CD4þ T cells expressing the IL‐2 receptor (R) alpha (a) chain, termed CD25, in normal animals display potent immunoregulatory functions in vitro and in vivo (Sakaguchi et al., 1995). nTreg cells develop during the normal process of T cell maturation in the thymus, survive in the periphery poised for normal surveillance of self‐Ags, and prevent potential autoimmune responses by an as of yet undefined mechanism. nTreg cells represent 1–10% of total CD4þ T cells in thymus, peripheral blood, and lymphoid tissues, and at least in vitro, are a hyporesponsive lymphocyte

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Figure 1 Control of immune responses by naturally occurring and induced CD4þ regulatory T cells. Naturally occurring (red) and induced (blue) CD4þ regulatory T cell subsets downregulate the function of activated effector T cells in several immunological settings. While FoxP3‐expressing CD4þCD25þ nTreg cells differentiate in the thymus and are found in the normal, naı¨ve CD4þ T cell repertoire, multiple iTreg cell subsets, possibly expressing CD25, GITR, CTLA‐4, or FoxP3, emerge from conventional CD4þ T cells, which are activated and differentiated in the periphery under unique stimulatory conditions including IL‐10 or TGF‐b1. The relative contribution of each population in the overall regulation of immune responses is unclear but both conceivably can cooperate to achieve this goal.

population that fail to produce most T cell–derived cytokines. CD25 continues to be the most useful cell‐surface marker for nTreg cells in the normal T cell repertoire, although several other markers may allow more accurate identification and/or sorting of specific subsets of nTreg (see Sections 2.1 and 2.2). In particular, the FoxP3 transcription factor is likely a more specific marker than CD25 (Fontenot and Rudensky, 2005; Ziegler, 2006), and it is now generally accepted that both these markers should be followed in parallel. Experiments involving depletion or functional abrogation of nTreg cells from the periphery, conclusively demonstrated that these cells are critically important for the regulation of organ‐specific autoimmunity, antitumor immunity, graft rejection, and pathogen clearance (Hori et al., 2003a; Piccirillo and Thornton, 2004; Sakaguchi, 2004).

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1.3. Induced Regulatory T Cells While nTreg cells differentiate intrathymically and are fully functional upon exit from the thymus, the development of iTreg cells occurs in the periphery upon Ag activation under a variety of unique ‘‘tolerogenic’’ immunological settings. The phenotype of the resulting iTreg cells can be diverse, with some cells taking on the characteristics of nTreg cells, and others acquiring cytokine‐mediated suppressive function. For example, stimulation with CD40L blockade, in the presence of nondepleting anti‐CD4 and anti‐CD8 treatment, bivalent nonmitogenic anti‐CD3 therapy (Belghith et al., 2003; Foussat et al., 2003; Graca et al., 2000; van Maurik et al., 2002; Waldmann et al., 1998), or with TGF‐b (Horwitz et al., 2004; Rao et al., 2005) results in the development of iTreg cells which are thus far indistinguishable from nTreg cells. TGF‐b may be a key factor in the development of nTreg‐like cells regardless of the inducing stimuli via its capacity to induce and maintain FoxP3 expression (Marie et al., 2005) (see Section 5.3). In humans, development of iTreg cells that are similar to nTreg cells can also occur stochastically, with a certain fraction of activated cells remaining CD25bright, beginning to express FOXP3, and acquiring suppressive capacity (Gavin et al., 2006; Walker et al., 2003, 2005). Based on evidence that FOXP3þ cells occur within the CD25– subset, however, it is possible that ‘‘induction’’ of CD4þCD25þ iTreg cells from CD25
T cells actually represents expansion of a preexisting subset (Fontenot et al., 2005c). In contrast, iTreg cells generated in the presence of IL‐10, vitamin D3 and dexamethasone, or immature DC populations develop into cytokine‐producing cells such as the IL‐10–producing type 1 T regulatory (Tr1) cells, or the TGF‐b‐producing Th3 cells (Barrat et al., 2002; Levings and Roncarolo, 2005; Weiner, 2001). It is unknown whether nTreg and iTreg cells preferentially function alone or in synchrony in suppression of noninflammatory or inflammatory T cell responses to self/nonself proteins (Piccirillo and Shevach, 2004). 2. Phenotype of CD4þCD25þ nTreg Cells 2.1. Cell‐Surface Biomarkers in Mice In addition to CD25, cell‐surface markers, such as CD45RB, CD62L, CD38, and DX5, have been used to define CD4þ Treg cells in some models, and can all partition, to varying degrees, suppressor function in vitro and in vivo (Gonzalez et al., 2001; Read et al., 1998; Singh et al., 2001; You et al., 2004). Studies using multiparametric flow cytometry and gene microarray analysis have attempted to further define the phenotype of CD4þCD25þ nTreg cells. In contrast to nonregulatory CD4þCD25– cells, nTreg cells not only constitutively express CD25,

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but also preferentially express other surface markers such as CD103, Ly6, galectin‐1, OX‐40, 4–1BB, CTLA‐4, glucocorticoid‐induced TNF receptor SF18 (GITR), PD‐1, TNFR2, TGF‐bR1, neuropilin‐1, and LAG‐3 (Bruder et al., 2004; Huang et al., 2004; Gavin and Rudensky, 2003; McHugh et al., 2002). The functional significance of these molecules in nTreg cell–mediated suppression remains elusive. Thus, the generally accepted phenotype of reshly isolated, unstimulated murine nTreg cells generally appears to be CD4þCD25þ CD45RBlowCD62LhighCTLA‐4highGITRhigh, although some suppressive activity has been documented outside of this definition. For example, CD4þCD25þCD62Llow and CD4þCD25–CD45RBlow cell subsets have shown some regulatory activity in vitro and in vivo (Ermann et al., 2005; Read et al., 2000). CD4þCD25þ nTreg cells also express high levels of other accessory molecules such as CD5, CD44, LFA‐1, and ICAM‐1 (Kuniyasu et al., 2000; unpublished results). It is noteworthy that surface phenotype of nTreg cells resembles that of activated T cells, suggesting that nTreg cells may be continuously stimulated by self‐Ags in the normal repertoire. Furthermore, selective expression of CCR4, CCR5, CCR6, and CCR8, chemokine receptors may enable nTreg cells to be preferentially guided to secondary lymphoid tissues or sites of inflammation to control immune responses (Bystry et al., 2001; Iellem et al., 2001). 2.2. Cell‐Surface Biomarkers in Humans Similarly, human CD4þCD25þ nTreg cells express a variety of markers of activated T cells. In addition to CD25, nTreg cells isolated from peripheral blood constitutively express high levels of CTLA‐4, GITR, CD71, HLA‐DR, CD45RO, IL‐2Rb (CD122), IL‐2Rg (CD132), PD‐L1, and ICOS (Baecher‐ Allan et al., 2004). In contrast to murine cells, human nTreg cells do not express high levels of the integrin CD103 (Stassen et al., 2004; our unpublished data), but high expression of the chemokine receptors CCR4 and CCR8 may be functionally relevant (Iellem et al., 2001; unpublished observation). Unfortunately, none of these markers have proven to be truly specific for nTreg cells, and their expression is merely indicative of constitutive T cell activation. nTreg cells have short telomeres, suggesting that these cells have experienced repeated episodes of Ag‐specific stimulation in vivo (Taams et al., 2002; Wolf et al., 2006). Further evidence of in vivo activation comes from analysis of T receptor excision circles (TRECs), a measure of peripheral expansion following thymic export, which also suggested that nTreg cells undergo high levels of peripheral expansion (Kasow et al., 2004). Thus, the use of any of these surface proteins as markers of nTreg cells, especially CD25, in a situation in which the immune system has been perturbed experimentally, treatment or

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disease may render the results difficult to interpret. The identification of specific markers, which are preferentially and stably expressed on nTreg cells before and after Ag activation, is urgently needed. 2.3. FoxP3 as a Biomarker and ‘‘Master Regulator’’ of nTreg Cells Studies have shown that the FoxP3 forkhead transcription factor is key for the development of nTreg cells, and that they continue to express this molecule throughout their lifespan (Allan et al., 2005; Fontenot et al., 2003; Hori and Sakaguchi, 2004; Hori et al., 2003a; Khattri et al., 2003; Yagi et al., 2004). In mice, the majority of FoxP3þ cells are CD4þCD25þ, and a genetic deficiency in FoxP3 results in an autoimmune pathology secondary to a loss of CD4þCD25þ nTreg cells (Fontenot et al., 2003; Hori and Sakaguchi, 2004; Hori et al., 2003b; Khattri et al., 2003). FoxP3‐overexpressing mice have more CD4þCD25þ T cells, and these cells can potently suppress the development of autoimmune disease (Fontenot et al., 2003; Hori and Sakaguchi, 2004; Hori et al., 2003b; Khattri et al., 2003; Ochs et al., 2005; Sakaguchi, 2003; Yagi et al., 2004). Overexpression of FoxP3 in nonregulatory murine CD4þCD25– or CD8þ T cells induces a phenotype similar to CD4þCD25þ nTreg cells, and CD4þCD25– T cells transduced with FoxP3 acquire potent suppressive functions in vitro and in vivo (Fontenot et al., 2003; Hori et al., 2003b). This remarkable finding leads to the concept that FoxP3 is a ‘‘master’’ regulator of nTreg cells (Table 1) (Sakaguchi, 2005). Human nTreg cells also express high levels of FOXP3, and the devastating autoimmunity that results in patients with mutations in FoxP3 demonstrates its fundamental importance in immune homeostasis (see Section 6.2). In contrast to mice, humans express two different splice variants of FOXP3, with the smaller form lacking exon 2 (FOXP3D2) (Allan et al., 2005; Manavalan et al., 2004; Yagi et al., 2004). It remains to be determined whether the two variants are similtaneously coexpressed at the single cell level, and/or if they may cooperate at the molecular level. We have shown that both variants independently possess an abililty to reprogram CD4þ T cells toward an anergic phenotype (Allan et al., 2005). However, retroviral‐mediated overexpression of either isoform alone, or together, is not sufficient to induce potent suppressive activity (Allan et al., 2005). We are investigating whether only certain maturation states and/or activation environments may be permissive to allow expression of FOXP3 alone to induce regulatory activity in human CD4þ T cells. As discussed earlier, one important consideration is that, in contrast to murine cells, human CD4þCD25
T cells upregulate expression of FOXP3 upon activation (Allan et al., 2005; Gavin et al., 2006; Mantel et al., 2006; Walker et al., 2003), and nonsuppressive, but FOXP3þ, T cell clones can be isolated

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Table 1 Comparison of FoxP3 Function in Murine and Human CD4þ T Cells Mouse cells High constitutive expression in CD25þ Treg cells Expression of FOXP3D2 splice isoform Genetic mutation in FoxP3 result in to lethal lymphoproliferative autoimmune diseases Inducible expression in CD4þCD25– non‐Treg cells

Human cells

Yes

Yes

No

Yes

Yes

Yes

Yes: TCR stimulation þ TGF‐b1 induces FOXP3 and results in iTreg cell populations

Yes: TCR activation leads to low and transient expression; TCR stimulation þ TGF‐b1 induces FOXP3 and results in iTreg cell populations Yes: CD8þ T cells (CD8þCD28– iTreg cells or transient, activation‐induced expression) Yes/No

Expression outside the CD4þ T cell subset

Yes: Thymic stromal cells

Gene transfer into non‐Treg cells leads to acquisition of suppressor functions

Yes

(Roncador et al., 2005). Thus, in humans, expression of FOXP3 may not be exclusively linked to suppression, and this molecule may have an important biological role outside the nTreg cell subset. Although it is clear that FoxP3 is highly expressed in both mouse and human nTreg cells, and is likely key for their development, its molecular mechanism of action and role in their suppressive function remain unknown. Overexpression, knock‐out, and molecular studies indicate that FoxP3 can directly influence a number of T cell characteristics, including the capacity to proliferate, produce cytokines, and express cell‐surface markers (Allan et al., 2005; Fontenot et al., 2003; Hori et al., 2003a; Khattri et al., 2003; Yagi et al., 2004). Like other members of the forkhead family, FoxP3 binds DNA through its conserved forkhead domain, and possibly also via other unique regions of the protein (Ziegler, 2006). The presence of classical protein–protein interaction domains, such as leucine zipper and zinc‐finger regions, suggest that FoxP3 may act as homo‐ or heterodimer to repress transcription of genes including IL‐2 and

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other NFAT‐regulated cytokines (Schubert et al., 2001). Data suggest that there may also be a direct interation between FoxP3, NFAT, and/or NF‐kB, strengthening the hypothesis that FoxP3 negatively regulate the effects of these two key transcription factors (Bettelli et al., 2005). 3. Factors Regulating the Expansion and Specificity of nTreg Cells 3.1. Regulation of Expansion and Survival The principles that direct the proliferation of nTreg cells differ from their nonregulatory CD4þCD25
counterparts. Although nTreg cells do require specific TCR triggering for their functional activation, they themselves are hypoproliferative (anergic) to in vitro antigenic stimulation (Thornton and Shevach, 1998). In mice, nTreg cells are refractory to TCR‐induced proliferation even in the presence of anti‐CD28 agonistic antibodies but retain their proliferative potential when activated by PMA/ionomycin, suggesting a possible defect in the capacity of the TCR to activate PKC (Thornton and Shevach, 1998). Despite this hypoproliferative response to TCR triggering, however, physiologically relevant signaling is initiated upon Ag recognition since studies using TCR transgenic T cells show that CD4þCD25þ nTreg cells can acquire suppressor function at peptide concentrations from 10‐ to 100‐fold lower than those needed to trigger proliferation in responding CD4þCD25– T cells (Takahashi and Sakaguchi, 2003). This inability to proliferate upon TCR engagement is largely attributed to a failure to initiate IL‐2 transcription in these cells since addition of exogenous IL‐2 or IL‐15 reverses this deficit (Pace et al., 2006; Thornton and Shevach, 1998; Thornton et al., 2004). Based on their profound hyporesponsiveness, and by analogy with classical anergic T cells (Schwartz, 2003), it has been hypothesized that nTreg cells may have unique TCR and/or IL‐2 signaling pathways. Besinger et al. (2004) demonstrated that murine nTregs have a defect in their capacity to activate the phosphatidylinositol 3‐kinase (PI3K) pathway downstream of IL‐2 signaling, while the JAK‐STAT5 pathway is intact (Bensinger et al., 2004). In addition, nTreg cell lines derived from cord blood have reduced TCR‐ mediated activation of the ERK MAP kinases (Li et al., 2005). More recently, our own experiments in ex vivo human nTreg cells revealed a defect in TCR‐ mediated activation of AKT, a key kinase downstream of PI3K (Crellin et al., submitted for publication). The exact relationship between the anergic state of nTreg cells and their suppressive activity is unknown. Initial studies showed that abrogation of their anergic state by in vitro TCR stimulation in the presence of a high dose of IL‐2 or CD28 ligation results in simultaneous loss of suppressive activity (Takahashi and

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Sakaguchi, 2003). Moreover, these studies showed that this hyporesponsiveness is an intrinsic characteristic, as nTreg cells spontaneously revert to the original anergic state and regain suppressive activity once IL‐2 or IL‐2 inducing signals (i.e., anti‐CD28) are removed (Thornton and Shevach, 2000). However, studies have clearly shown that nTreg cells can still mediate active suppression of IL‐2 mRNA in responding T cells even in the presence of exogenous IL‐2 or agonistic antibodies to CD28 (Thornton, 2004). nTreg cells lacking CD25, and therefore the capacity to respond to IL‐2 are fully suppressive, suggesting that other cytokines that signal via the gc may compensate in vivo (Fontenot et al., 2005b). In contrast to their in vitro resistance to proliferation upon TCR stimulation, nTreg can actively proliferate in vivo in response to antigenic stimulation (Hori et al., 2002, 2003a). A seemingly small fraction of nTreg cells in normal naı¨ve mice proliferates even without exogenous antigenic stimulation, presumably in response to recognition of self‐Ags in the periphery (Hori et al., 2003b). Thus, nTreg cells clearly have the capacity to expand in vivo and presumably mediate long‐term suppression of immune responses. 3.2. Repertoire and Ag Specificity The antigenic determinants, responsible for thymic selection, differentiation, and peripheral activation of nTreg cells are not fully understood. Recent data indicate that their development is delayed compared to effector T cells and that they arise in response to signals from the thymic medulla (Fontenot et al., 2005a). Although nTreg cells have a polyclonal TCR repertoire based on diverse gene expression of various TCR a/b elements (Hori et al., 2002, 2003b; Taguchi, 1987), there is evidence that a large proportion recognize self‐Ags (Hsieh et al., 2004) and that their primary function is regulation of autoreactive T cells. Of interest is their apparent capacity to expand and mediate Ag‐non‐specific suppression in inflammatory settings. This may be due to copresentation of self‐Ags, their capacity to cross‐react with foreign Ags, and/or the fact that they can also be specific for foreign Ags. There is increasing evidence in the literature that the specificity of nTreg cells may be important for their in vivo efficacy. Several animal models of autoimmunity in which the initiating auto‐Ags have been characterized have made it possible to address this issue. For example, data from diabetes models suggest that nTreg cells specific for islet Ags are several orders of magnitude more effective at preventing disease initiation or progression than polyclonal populations of nTreg cells (Fisson et al., 2006; Jaeckel et al., 2005; Tang et al., 2004). Likewise, nTreg cells have been shown to be protective in models of experimental allergic encephalomyelitis in an Ag‐specific manner (Hori et al., 2002; Yu et al., 2005). Evidence for Ag‐specific suppression by nTreg cells has

128 Figure 2 Functional significance of cell‐surface molecules in Treg‐mediated suppression. Inhibitory roles have been postulated for molecules including CTLA‐4 and TGF‐b1, while signaling through CD28 and CD40 (not shown) has been linked to

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also been demonstrated for viral Ags (Aandahl et al., 2004), allo‐Ags (Albert et al., 2005; Bushell et al., 2005; Sawitzki et al., 2005), and for foreign Ags in transgenic mouse models (Tanchot et al., 2004). Thus, although it has been firmly established by in vitro studies that nTreg cells can mediate nonspecific suppression once activated by their cognate Ag, the key to their in vivo efficacy may be their activation at specific sites, a property only measurable in animal models. This is consistent with the fact that many systems require Ag persistence for maintaining long‐term tolerance, thereby providing a continuous specific activation signal for nTreg cells. 4. Innate and Adaptive Inflammatory Signals Dictating the Function of nTreg Cells The complete spectrum of signals needed for the induction and maintenance of nTreg cell activity is not understood. While FoxP3 appears to be pivotal in nTreg development, its role in the suppressive function of nTreg cells has yet to be elucidated. A number of studies have explored the functional role of many other molecules associated with regulatory function and shown that signals in addition to TCR engagement contribute to the activation and proliferation of nTreg cells and hence fine‐tune suppression. In some cases, ligation of these molecules may trigger signals that activate suppressor activity, while others may serve as a means of communication between nTreg cells and their targets of suppression (Fig. 2). 4.1. The Role of the B7/CD28 Pathway in nTreg Cell Development and Function The role of B7/CD28 signals in the induction of suppressor activity has been extensively investigated. Classically, upon engagement of the TCR, interactions between B7 molecules and CD28 deliver a costimulatory signal to naı¨ve T cells and promote IL‐2 production, T cell expansion, and survival (Salomon and Bluestone, 2001). The activation of nTreg cell function is CD28 independent because nTreg cells from CD28
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or wild‐type (WT) mice exhibit comparable in vitro suppressive activity (Thornton and Shevach, 1998). development and/or survival of Treg cells in vitro. Several TLRs are selectively expressed on Treg cells and may be involved in fine‐tuning suppressor function. The regulatory network is further complexed by factors affecting susceptibility of responder cells to suppression, such as GITR binding and inflammatory cytokines such as IL‐6. Other molecules used to sort and track T cells enriched for regulatory function include CD45RB and CD103 (mouse), CD45RO, CCR4, and HLA‐DR (human), in addition to a growing list of several other markers (see text).

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Furthermore, activation of nTreg cells with either WT or B7.1/B7.2 double deficient APC, or in the presence of CTLA‐4‐Ig, a known antagonist of B7/ CD28 interactions, retains potent suppressive function (Thornton et al., 2004). Thus, B7/CD28 interactions are seemingly not required for the in vitro activation of suppressor function in peripheral Treg cells (Piccirillo and Thornton, 2004). The contribution of the B7/CD28 costimulatory pathway in thymic development and peripheral homeostasis of the nTreg cells has also recently been studied (Tang et al., 2003). In this context, signaling via CD28 was found to be essential for their development since CD28
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mice have a reduced number of nTreg cells in the thymus and periphery (Salomon et al., 2000; Tang et al., 2003). CD28 may therefore promote their survival via induction of antiapoptotic molecules, and/or cytokines that function as growth, survival, or suppressor activity maintenance factors (Bluestone and Abbas, 2003; Malek and Bayer, 2004; Piccirillo and Shevach, 2004). In addition, induction of FoxP3 expression in double positive thymocytes in vitro requires simultaneous stimulation via the TCR and CD28 (Tai et al., 2005). As a result of a deficit in nTreg cells, mice with a functional abrogation of the B7/CD28 costimulatory pathway have autoimmune disease, for example, CD28–/– and B7.1‐B7.2
/– NOD mice develop an accelerated form of diabetes compared to WT NOD controls (Salomon et al., 2000). Tai et al. (2005) investigated whether the reduction of nTreg cell numbers in CD28‐deficient mice may be secondary to the loss in IL‐2 production from effector cells. Although nTreg cell development and production of IL‐2 required an identical Lck‐binding motif in the CD28 cytosolic domain, the two processes were independent. Overall, it can be concluded that although engagement of the B7/CD28 pathway is required for thymic development and peripheral survival, the capacity of nTreg cells to suppress immune responses is CD28 independent. 4.2. The Role of CTLA‐4 in nTreg Cell Development and Function Several lines of evidence indicate that CTLA‐4, a negative costimulatory molecule, could potentially play a critical role in nTreg cell–mediated suppression. The lethal lymphoproliferative autoimmune syndrome that spontaneously develops in CTLA‐4
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mice is not T cell autonomous and can be inhibited by transfer of WT T cells, indicating that CTLA‐4‐deficiency may lead to impaired dominant regulation (Bachmann et al., 1999). Moreover, Read et al. (2000) reported that, in contrast to naı¨ve T cells, resting nTreg cells express high levels of intracellular CTLA‐4, and antibody‐mediated blockade studies concluded that CTLA‐4 signaling is required for nTreg cell function in vitro and in vivo.

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Similarly, Read et al. (2000) showed that anti‐CTLA‐4 mAb treatment abrogated the protection conferred by nTreg in the murine IBD model (Read et al., 2000). Blockade of CTLA‐4 has also been shown to prevent the capacity of nTreg cells to stimulate IDO synthesis and tryptophan catabolism in DC cells (Fallarino et al., 2003). In contrast to this evidence for a functional role of CTLA‐4, we and others found no evidence for a role of this pathway in suppression mediated by human or murine nTregs (Levings et al., 2001; Thornton and Shevach, 1998). These findings are further substantiated by the observation that nTreg cells from CTLA‐4–/– mice display potent suppressive activity in vitro (Tang et al., 2004). Thus, the capacity of CTLA‐4 blockade to exacerbate autoimmunity, enhance rejection of transplanted organs, or provoke tumor immunity is more likely attributed to the downregulatory role of CTLA‐4 on all T cells, rather than specifically on nTreg cells (Salomon and Bluestone, 2001). Nevertheless, it remains to be examined how the balance between signals through CTLA4 and CD28 contribute to the tuning of the regulatory activity of nTreg cells.

4.3. The Role of GITR/GITR‐L Interactions A role for GITR has also been reported in the modulation of nTreg cell function. nTreg cells in normal naı¨ve mice express high levels of GITR, as revealed by DNA microarray and flow cytometric analyses (McHugh et al., 2002; Shimizu et al., 2002). However, a number of immune cells, including other T cell subsets, B cells, DCs, and macrophages, also express GITR, and activation can further increase expression levels (McHugh et al., 2002; Shimizu et al., 2002). The finding that Ab‐mediated cross‐linking of GITR reverses nTreg‐mediated suppression, although blockade with Fab fragments fails to neutralize suppression, leads to the conclusion that GITR could play a functional role (McHugh et al., 2002; Shimizu et al., 2002). In support of this concept, administration of anti‐ GITR mAbs elicited autoimmune disease in normal mice (Shimizu et al., 2002). Analysis of cells from GITR
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mice, and experiments with agonistic antibodies and GITR‐L, however, ultimately demonstrated that the role of this molecule in suppression is actually due to its affects on the CD4þCD25
responding T cells (Stephens et al., 2004), since stimulation of GITR on effector T cells renders them resistant to suppression by nTreg cells. The capacity of various APC to downregulate GITR‐L during inflammation may therefore play a role in reestablishment of immune homeostasis via enhancing the susceptibility of effector T cells to suppressor cell activity. Although GITR is clearly not necessary for the suppressive function of nTregs, its capacity to reverse suppression

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may nevertheless be useful therapeutically based on evidence that GITR activation can enhance antitumor responses (Ko et al., 2005). 4.4. Toll‐Like Receptors–Dependent Signals Modulate nTreg Expansion and Function Toll‐like receptors (TLRs) are germ line‐encoded receptors that recognize pathogen‐associated molecular patterns (PAMPs) expressed by large groups of microbes, or certain endogenous molecules released during inflammation. Classically, these receptors were thought to be exclusively involved in stimulating innate immune response, but several recent reports have revealed that nTreg cells can respond to TLR ligands, indicating that they may also have direct control of adaptive immunity. For example, Caramalho et al. (2003) found that murine nTreg cells express TLR4 and that high concentrations of lipopolysaccharide (LPS or endotoxin) induce their proliferation and augment their in vitro suppressive activity. Similarly, TLR2 has been implicated in modulation of murine nTregs since TLR2‐deficient mice have diminished numbers of nTreg cells in circulation, and TLR2 ligands can promote their expansion in vitro in an APC‐free system (Netea et al., 2004). Remarkably, TLR2‐mediated signaling in nTreg cells has been found to be key in the control of Candida albicans infection (Sutmuller et al., 2006). In addition, we found that in humans, TLR5 is highly expressed on nTreg cells, and that its ligand, flagellin, augmented their suppressive capacity, possibly due to enhanced FOXP3 expression (Crellin et al., 2005). Similarly, murine nTreg cells also express TLR5 and exposure to flagellin can significantly promote their in vitro expansion independently of DC. TLR5 triggering on murine nTreg cells with flagellin can stimulate expansion and potent suppressor activity in the absence of antigenic stimulation via the TCR, suggesting that innate signals may potentially activate nTreg cell function and promote Ag‐non‐specific suppression (Sgouroudis, E., and Piccirillo, C. A., unpublished data). Collectively, these results show that inflammatory innate signals can promote the induction and expansion of Treg function and ultimately modulate immune responses. While direct ligation of TLR2, TLR4, or TLR5 on nTreg cells can have a prosuppressive effect in the absence of APCs, exposure to these TLRs in the presence of APCs, or to other TLRs, may also negatively affect suppression (Crellin and Levings, 2006). For example, stimulation of TLR8 abrogates nTreg cell‐mediated suppression in an APC‐independent manner (Peng et al., 2005). In addition, cytokines, such as IL‐1b and IL‐6, produced by TLR‐activated APCs render T effector cells resistant to the suppressive action of nTregs (Pasare and Medzhitov, 2003). Similarly, administration of TLR2

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ligands in vivo abrogates nTreg cell–mediated suppression in a model of C. albicans infection (Sutmuller et al., 2006). We hypothesize that in the context of the early inflammatory response to invading microorganisms, TLR‐ mediated activation of APCs results in cytokine release and CD4þ T cell responses which are resistant to suppression. Concurrently, the same TLR ligands act directly on nTreg cells to preserve FoxP3 expression and their suppressive capacity. With time, or in the presence of noninvasive commensal bacteria, the cytokine milieu would shift toward a tolerogenic environment, where nTreg cells suppress CD4þ effector T cells more efficiently. Whether other endogenous molecules, such as heat shock proteins, also have a modulatory role in TLR‐regulated control of nTreg cells, remains to be investigated. 4.5. Chemokine Receptor‐Mediated Control of nTreg Cell Homing A topic that has recently been explored is the relative capacity of nTreg vs effector cells to home to lymphoid organs and sites of inflammation. Lymphocyte homing and trafficking in inflamed lymphoid and nonlymphoid tissues is controlled by the expression of distinct sets of chemokine receptors, which provide directional cues for the migration and recruitment of T cells into sites of inflammation. Currently, the chemokines involved in directing nTreg cells to sites of inflammation are not clearly understood. In vitro studies have shown that a large fraction of nTreg cells from human peripheral blood selectively express CCR4 and CCR8 and show a strong chemotactic response to CCR4 ligands (D’Ambrosio et al., 2003; Iellem et al., 2001). Another study showed that nTreg cells in prediabetic NOD mice express high levels of CCR7, giving them the capacity to migrating toward the lymphoid‐derived chemokines CCL19 and CCL21 (D’Ambrosio, 2006). Kleinewietfeld et al. (2005) found that CCR6 is expressed on a distinct subset of mouse and human nTreg cells and that these CCR6þ nTreg cells are enriched in the peripheral blood, have a high turnover rate, rapidly produce IL‐10 following Ag stimulation, and accumulate in the central nervous system after induction of EAE (Kleinewietfeld et al., 2005). Bystry et al. (2001) demonstrated that murine nTreg cells express CCR5 and respond in vitro to CCR5‐activating chemokines, including MIP‐ 1a, MIP‐1b, or RANTES. In addition, other studies have indicated that CCR5 may modulate nTreg cell activity in graft‐versus‐host disease and human tumors (Wysocki et al., 2005). More recently, we demonstrated in a mouse model of Leishmania major infection that CCR5 directs the homing of IL‐10 producing nTreg cells into sites of infection where they suppress effector T cell expansion and IFN‐g production, thus promoting the establishment of infection and ensuring the long‐term survival of the parasite in the immune host (Yurchenko and Piccirillo, submitted for publication; Yurchenko et al., 2006).

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Overall, there is substantial evidence indicating that expression of a variety of chemokine receptors on nTreg cells may endow them with a competitive advantage over effector T cells and allow them to respond to inflammatory signals, migrate more efficiently to inflammatory sites, and prevent immune responses.

5. Growth Factor–Mediated Control of nTreg Cell Development, Function, and Homeostasis 5.1. The Key Role of IL‐2 The role of cytokines in the development and function of nTreg cells has also been extensively examined (Fig. 3). Most studies have focused on the role of IL‐2 based on the profound inability of nTreg cells to produce this critical T cell growth factor, despite high expression of all three chains of the IL‐2R. Mice deficient for IL‐2, IL‐2Ra, or IL‐2Rb have few or no peripheral

IL-2, IL-4, IL-7, IL-15 • Promote nTreg cell proliferation • Maintain of FOXP3 expression

gc

Other cytokine receptors?

IL-2Ra IL-2Rb

STAT3? STAT5? Peptide-MHC binding • Sustains FoxP3 and promotes suppressor function in an ab TCR Ag-specific manner

?

?

FOXP3 gene

TGF-b R

TGF-b • Upregulates/maintains FOXP3 • Communication within regulatory networks

Figure 3 Possible links between cytokines, FOXP3, and suppressor function. gc cytokines and other signal transduction pathways that activate STAT5 may promote, stabilize, or enhance FoxP3 expression and Treg cell function. TCR activation causes transient FoxP3 upregulation in human CD4þCD25– T cells and maintains high levels in Treg cells, while TCR stimulation in combination with TGF‐b1 leads to stable upregulation and corresponding iTreg cell activity in both human and murine cells.

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nTreg cells and die prematurely from a severe lymphoproliferative and autoimmune syndrome (Nelson, 2004; Schimpl et al., 2002). The adoptive transfer of WT nTreg cells prevents autoimmunity in mice lacking a functional IL‐2R, but not those lacking IL‐2 itself, implying that IL‐2 is a critical growth and activation factor in vivo (Malek, 2003; Wolf et al., 2001). Furthermore, stat5
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mice have very few nTreg cells, and mice transgenic for the active form of STAT5 possess a greater frequency of these cells (Burchill, 2003; Kagami, 2001; Snow, 2003). This requirement for IL‐2 signaling, however, is not simply related to nTreg survival, since the absence of nTreg cells in stat5
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mice cannot be rescued by overexpressing the antiapoptotic Bcl‐2 protein (Antov et al., 2003). In addition, nTreg cell numbers are restored when an IL‐2Rb transgene is expressed solely in the thymus of IL‐2Rb
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mice, suggesting that an intact IL‐2/IL‐2R pathway is required for thymic generation of nTreg cells (Malek et al., 2002). As a consequence of this requirement for IL‐2, systemic neutralization of IL‐2 induces autoimmune gastritis in BALB/c mice, provokes spontaneous autoimmune neuropathy and exacerbates diabetes in NOD mice (Setoguchi et al., 2005). In addition, administration of IL‐2 to patients with chemotherapy‐induced lymphopenia results in peripheral expansion of Treg cells. In contrast, two studies have documented that IL‐2 is in fact dispensable for generation of nTreg cells in the thymus but essential for their peripheral expansion, maintenance, and ‘‘metabolic fitness’’ (D’Cruz and Klein, 2005; Fontenot et al., 2005b) (reviewed Toda and Piccirillo, 2006). Given that nTreg cells suppress T cell responses by downregulating IL‐2 synthesis, yet require IL‐2 for expansion and suppression, the dynamics of IL‐2 production during suppression remain enigmatic in vivo. Classically, IL‐2 originates from activated T cells, but DC‐derived IL‐2 can also play a critical role in nTreg cell activation (Guiducci et al., 2005), leaving open the possibility that nTreg selectively suppress T cell–derived IL‐2. Our own unpublished data suggests that DC‐derived IL‐2 may in part be responsible for sustaining the activation and expansion of nTreg cells in sites of autoimmune inflammation (Sgouroudis, E., and Piccirillo, C. A., unpublished data). 5.2. The Role of Other gc Cytokines In addition to IL‐2, other cytokines capable of signaling through the gc chain have also been shown to be important for in the survival and function of nTreg cells (Fig. 3). For example, in the mouse, IL‐4 can replace IL‐2 for full induction of suppressive activity in vitro (Thornton, 2004), and similar to IL‐2, simultaneously makes T effector cells resistant to their suppressive effects (Pace et al., 2006). Fontenot et al. (2005b) found that signaling through

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the gc receptor was critical for FoxP3 expression, while IL‐2 was dispensable for generation of nTreg cells in the thymus. Initiation of signaling from gc activates Stat5, which as discussed earlier, appears to be important for nTreg cell development and/or survival (Burchill, 2003; Kagami, 2001; Snow, 2003). Thus, there may be redundancy among cytokines that trigger this pathway, induce FoxP3 expression, and promote nTreg cell survival. Receptors for other gc cytokines, such as IL‐7, are also expressed on the surface of nTreg cells (Cupedo et al., 2005; and our unpublished data), and IL‐7 has a modest ability to induce nTreg cell proliferation upon TCR stimulation in vitro (Thornton et al., 2004; and our unpublished data). Moreover, murine nTreg cells express IL‐21R mRNA, and although IL‐21 does not reverse anergy, it appears to compromise their suppressive activity (Comes et al., 2006). Our own unpublished observations suggest that a subset of gc cytokines, including IL‐2, IL‐15, and IL‐7, have a unique ability to support FOXP3 expression and viability of human nTreg cells in vitro. It is tempting to speculate that a connection between FoxP3 and Stat5 is the key molecular mechanism in control of nTreg cell survival and/or function. 5.3. The Controversial Role of TGF‐b TGF‐b1 is another cytokine with a putative role in the development, expansion/survival, and/or effector function of nTreg cells (Fig. 3). TGF‐b1 has a critical role in the downregulation of immune responses, as highlighted by the development of a severe autoimmune‐like syndrome in TGF‐b1
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mice. (Li et al., 2006). This is further corroborated by the observation that genetic disruption of TGF‐b1 signaling in T cells by overexpression of a dominant‐ negative TGF‐b type II receptor, conditional deletion of the TGF‐b type II receptor in hematopoetic progenitors, or inactivation of the smad3 gene, abolishes the sensitivity of T cells to TGF‐b inhibition and leads to aberrant T cell responses (Li et al., 2006). These observations have fueled the view that TGF‐b1 may have a possible effector role in nTreg cell–mediated suppression. Nakamura et al. (2001, 2004) reported that activated CD4þCD25þ nTreg cells express an inactive form of TGF‐b1 complexed to its latency associated peptide (LAP), which is required for their suppressive capacity in vitro and in vivo. Other studies have also found a correlation between Treg effector function and the apparent expression of a latent, membrane‐bound form of TGF‐b1 on nTreg cells isolated from diabetes‐prone and tumor‐bearing mice (Chen et al., 2005; Green et al., 2003). In contrast, most murine and human studies conducted in vitro conclude that neither secreted nor membrane‐ bound forms of active or latent TGF‐b, are required for contact‐dependent

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suppression mediated by resting or activated nTreg cells (reviewed in (Iellem et al., 2001; Piccirillo and Shevach, 2004; Piccirillo and Thornton, 2004). More importantly, nTreg cells isolated from neonatal TGF‐b1
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mice display comparable suppressive activity to WT nTreg cells in vitro (Piccirillo et al., 2002). Finally, in Kullberg et al. we showed that nTreg cells from neonatal TGF‐b1
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mice suppress the incidence and severity of T cell–induced IBD normally (Kullberg et al., 2005). Overall, we favor the view that TGF‐b, whether it be membrane bound on or secreted by nTreg, is not a direct effector essential for suppression of T cells. Evidence indicates that the requirement for TGF‐b1 may actually be related to its effects on the nTreg cells themselves, possibly by maintaining their survival, expansion, or suppressive function. For example, nTreg cells from mice expressing a dominant negative form of TGFbRII fail to expand in vivo and suppress dextran sulfate sodium–induced colitis, suggesting that abrogating the capacity of nTreg cells to respond to TGF‐b1 affects their function. This hypothesis is consistent with studies showing that TGF‐b1 signaling in nTreg cells promotes Foxp3 expression and subsequent nTreg function in vitro and in vivo (Chen et al., 2003; Peng et al., 2004). Evidence to the contrary comes from analysis of TGF‐b‐resistant Smad3
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nTreg cells which are equivalent to WT nTreg cells in their capacity to suppress colitis in vivo (Kullberg et al., 2005). Our own unpublished results indicate that the growth and suppressor potency are not affected when nTreg cells are activated in the presence of exogenous TGF‐b1, or anti‐TGF‐b1–blocking mAbs, suggesting that autocrine or paracrine sources of TGF‐b1 do not modulate nTreg cell expansion or effector function in vitro (Pyzik and Piccirillo, 2006). We did confirm, however, that the levels of Foxp3 gene expression in nTreg cells are significantly increased following TGF‐b1 priming, suggesting that via inducing and/or stabilizing Foxp3 expression, this cytokine has a role maintaining nTreg cell peripheral homeostasis. We demonstrated that TGF‐b1 can selectively promote the differentiation of IL‐10 secreting, Foxp3þ CD4þ Treg cells from Ag‐experienced CD4þCD45RBlowCD25
T cell precursors (Pyzik and Piccirillo, 2006). Thus, although TGF‐b1 might be dispensable for nTreg cell suppressive activity, long‐term TGF‐b1 signaling may be required to sustain regulatory networks by promoting the de novo development of Foxp3þ Treg cells, possibly from the memory pool, and bolstering Foxp3 expression in preexisting nTreg cells. The source of TGF‐b1 in this context remains to be explored. A variety of immune and nonimmune cells can produce TGF‐b1 and possible sources include nTreg cells themselves, activated effector T cells, or nonlymphoid cells in tissues that are inflammed or in the process of healing.

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6. Control of Autoimmune Responses by nTreg Cells The onset of autoimmune disease in a given host is determined by a complex array of environmental, genetic, and immune factors, which can synergize to varying degrees to impact the incidence and severity of disease. As nTreg cells play a central role in the induction and maintenance of self‐tolerance in many instances, a major question is whether the robustness and/or persistence of nTreg cell activity is a major factor during the onset of clinical autoimmunity. In considering the role of nTreg cells in the development of spontaneous autoimmunity, there are at least two nonmutually exclusive possibilities: (1) The onset of disease may result from the overriding of normal nTreg cell–mediated regulation by an uncontrollable activation of autoreactive T cells and/or (2) developmental/functional deficiencies in nTreg cells may promote the occurrence of a dysregulated immune system and consequently tip the balance toward the activation of autoreactive T cells and disease onset. 6.1. Intrinsic and Extrinsic Drivers of Autoimmunity The etiology of any given autoimmune disease is multifactorial and may differ between diseases (Bach, 2003; Sakaguchi, 2004). The nature and ‘‘dose’’ of the etiologic agent may also vary between individuals and diseases. One etiologic agent affecting nTreg cells may lead to the occurrence of different autoimmune diseases in one host. Alternatively, different causal agents may produce the same autoimmune disease in genetically susceptible individuals through a common mechanism. It is possible that most autoimmune diseases have a common mechanism, and not necessarily a specific etiology for each autoimmune disease (Sakaguchi, 2004). Genetic or environmental insults can alter the delicate balance between nTreg/self‐reactive T cells, provoke a developmental or functional deficiency in nTreg cells, and ultimately provoke autoimmunity (Fig. 4). In various rodent systems, it has been shown that physical, chemical, and biological agents or genetic abrogation can cause autoimmune disease by either affecting thymic T cell output, thymic selection (Anderson et al., 2005), or the size of the peripheral nTreg pool. For example, administration of cyclosporin A, viral infection, low‐dose irradiation, or changes in TCR gene expression lead to autoimmunity in mice, with clinical outcomes similar to those induced by depletion of nTreg cells or day 3 thymectomy (Sakaguchi, 2004). As the majority of nTreg cells likely cycle continuously in response to self‐Ag recognition in the periphery, it remains possible that they are inherently more sensitive than conventional T cells to certain environmental/genetic triggers throughout life, particularly during gestational life, which in turn may affect developing nTreg cells and

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Figure 4 Immunological self‐tolerance mediated by naturally occurring CD4þ regulatory T cells. CD4þ nTreg cells represent a central master‐switch of peripheral T cell tolerance and play a determining role in the balance between tolerance and autoimmunity. Functional alterations in their development or function provoked by various genetic, environmental modifiers, infection, physical, and chemical triggers, may represent a determining variable in disease resistance or susceptible.

thereby trigger autoimmunity. The degree of the nTreg cell deficiency may also influence the manifestation of a given autoimmune disease. It is noteworthy that a functional deficiency in nTreg cells might not be evident as a reduction in the cellular frequency in peripheral tissues, and might rather result from selective gaps in Ag‐specific TCR specificities or various genetic polymorphisms potentially modulating various aspects of nTreg cell function, including activation, effector function, survival, or trafficking (Piccirillo and Shevach, 2004). In addition to the degree of nTreg cell deficiency, an array of MHC and non‐ MHC genes in the host determines the onset, specificity, severity, and duration of autoimmune responses. For example, genetic alterations in critical non‐MHC immune genes such as Foxp3, Aire, CTLA‐4, TGF‐b1, CD40, IL‐2, CD25 (IL‐2Ra), and CD122 (IL‐2Rb) elicit fulminant autoimmune diseases (Anderson, 2002; Gorelik, 2002; Hunig, 1997; Kulkarni, 1993; Schimpl et al., 2002). Thus, inherited polymorphisms of certain genes critical for nTreg cell development undoubtedly contribute to determining the overall genetic susceptibility to autoimmune disease. However, a mere

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deficiency of nTreg cell number or function alone cannot determine which tissues will be targeted by autoreactive T cells, as illustrated by the day 3 thymectomy and CD25‐depletion studies where the incidence, onset, and severity of many autoimmune diseases varies according to the age, sex, and genetic background of the host (Chen et al., 2005a; Dejaco et al., 2006; Reddy et al., 2005). Although a reduction in nTreg cellular frequency or function is clearly a critical predisposing factor to autoimmunity, it may be insufficient. The nature and magnitude of the inflammatory context that accompanies a deficiency in nTreg cells is also a critical parameter. This concept is corroborated in a study by McHugh et al. (2002) showing that a mere deficiency of nTreg cells on its own is insufficient to cause autoimmune gastritis unless this deficiency is accompanied by inflammatory signals (McHugh et al., 2002). Thus, it is most likely that factors affecting both regulatory and effector populations play a role in triggering autoimmune diseases. 6.2. Autoimmunity as a Result of Mutations in FoxP3 Direct evidence for the capacity of nTreg cells to prevent autoimmunity can be found in studies of the scurfy mouse, a natural mutant that lacks a functional Foxp3 protein. Scurfy mice lack nTreg cells, and as a result, suffer from various spontaneous and early onset organ‐specific autoimmune pathologies due to hyperactivation of CD4þ T cells (Brunkow et al., 2001; Clark et al., 1999). This phenotype is similar to that of mice depleted of nTreg cells, but overall is more severe, possibly due to the loss of Foxp3þ cells outside of the CD25þ compartment and/or effects on effector T cells. Based on evidence that adoptive transfer of WT CD4þCD25þ T cells (Fontenot et al., 2003) can rescue the fatal disease in scurfy mice, it has been concluded that FoxP3þ nTreg cells are critical for preventing autoimmunity in mice. A phenotypically similar syndrome, known as Immune dysregulation, Polyendocrinopathy, Enteropathy, X‐linked (IPEX) develops in humans with mutations in FOXP3. Clinical symptoms of the disease vary widely, but commonly include T1D, enteropathy, and eczema (Ochs et al., 2005) that develop very soon after birth. Unlike the defined genotype and phenotype of the scurfy mouse, IPEX patients have a wide range of possible mutations (more than 20 have been characterized to date) and correspondingly variable phenotypes (Ochs et al., 2005; Ziegler, 2006). This variability is due to the fact that whereas scurfy mice fail to translate any protein, IPEX patients may have either a null or a point mutation that results in a fully translated, but functionally impaired, FOXP3 protein. The most severe disease typically arises in IPEX patients who have a null mutation or a point mutation in the DNA‐binding forkhead

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domain. In contrast, children with mutations outside of the forkhead domain often present with mild or late‐onset disease (Ochs et al., 2005). Remarkably, although patients with IPEX have autoimmunity that resembles scurfy mice, CD4þCD25þ T cells with suppressive activity are detectable in the circulation of children with point mutations in FOXP3 (Bacchetta et al., 2006). When tested in vitro for suppressive activity, however, these cells were found to have less potent suppressor activity in comparison to cells from age‐matched controls. A patient with a mutation in the initiating codon of FOXP3 and consequential loss in FOXP3 expression had severely reduced numbers of CD4þCD25þ cells that were nonfunctional in vitro (Bacchetta et al., 2006). These data indicate that the type of mutation, and its location in the FOXP3 gene, may dictate the relative functionality of nTreg cells and therefore be predictive of the clinical severity of IPEX. In addition, evidence that brothers with the same mutation can have remarkably different phenotypes, suggests that, as for other autoimmune diseases, other genetic and environmental factors ultimately influence self‐tolerance. A particularly interesting finding from these studies of children with IPEX is that they also have a major defect in effector T cell function (Bacchetta et al., 2006). Thus, unlike the hyperresponsive cells in the scurfy mouse, in humans, mutations in FOXP3 lead to a significant defect in the capacity of non‐Treg cells to proliferate and produce cytokines. As discussed earlier, these data support our hypothesis that in humans FOXP3 also has a role outside of the nTreg compartment. Overall, although analysis of the scurfy mouse has provided invaluable insight into the link between nTreg cells and autoimmunity, further studies are required to unravel the exact mechanistic basis of autoimmune pathology believed to arise due to aberrant nTreg development and/or function in humans. 6.3. nTreg‐Mediated Control of Autoimmunity in the Mouse: The NOD Mouse as a Prototypic Model The nonobese diabetic (NOD) mouse represents a prototypic model of immune dysregulation since it spontaneously develops several autoimmune diseases, including type 1 diabetes (T1D) (Delovitch and Singh, 1997). The extended time lag between the initial immune cell infiltration of b‐islets, termed insulitis (checkpoint 1), and the onset of overt T1D (checkpoint 2), suggests that regulatory mechanisms in the periphery control self‐reactivity and disease progression in prediabetic NOD mice (Bach, 2003). An array of different types of Treg cells, but particularly Foxp3þ nTreg cells, has been found to represent a central control point in tolerance induction in NOD mice (Bach, 2003). An unresolved question is whether the onset of spontaneous

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T1D in NOD mice results from a simple decline in nTreg cell function over time, consequently tipping the balance toward the activation, expansion, and recruitment of diabetogenic T cells, and clinical T1D. If this is the case, therapeutic potentiation of their numbers and/or function could shift disease progression toward tolerance and protection from T1D (Bach, 2003; Belghith et al., 2003; Delovitch, 1997). Some of the first evidence implicating nTreg cells in T1D disease prevention came from studies where adoptive transfer of CD25‐depleted NOD splenocytes into immunodeficient NOD.scid recipient mice lead to accelerated onset of diabetes compared to transfer of total splenocytes (Salomon et al., 2000). Moreover, disruption of the B7/CD28 pathway in NOD mice, which blocks normal nTreg cell development, leads to an accelerated diabetic phenotype, which can be prevented by infusion of WT nTreg cells (Salomon et al., 2000). Other studies have also reported a significant expansion of CD25‐expressing CD4þ T cells with apparent regulatory activity, sometimes correlating with the expression of a membrane‐bound form of TGF‐b1, in the inflamed pancreatic lymph nodes of diabetes‐free NOD mice (Belghith et al., 2003; Green et al., 2003; Herman et al., 2004; Salomon et al., 2000). It remains unclear from the above studies whether these CD4þCD25þ T cells are induced Treg cells arising from CD4þCD25– progenitors during pancreatic inflammation, or whether they migrate from the thymus derived, nTreg cell pool. Studies from our laboratory indicate that thymic and peripheral nTreg cells from prediabetic, neonatal NOD mice are fully functional in vitro and dramatically halt the onset of primary and established T1D in vivo, indicating there is no defect in these cells at birth (Tritt et al., submitted for publication). The protective role of nTreg cells in vivo has been well established in a variety of autoimmune diseases, and may involve induction of alternate migration, activation, differentiation, and/or clonal expansion of effector T cells in lymphoid and nonlymphoid tissues. Studies in BDC2.5 mice have shown that the initial activation of effector T cells in draining lymph nodes is unaffected in the presence of nTreg cells suggesting that Ag presentation, and initial TCR signals are not inhibited by nTreg cells (Chen et al., 2005b; Tritt et al., submitted for publication). This is also in agreement with the observation that nTreg cells suppress disease transfer mediated by primed T cells from diabetic mice, which likely traffic directly to islets, and circumventing priming in the pancreatic lymph node (pLN) (Tritt et al., submitted for publication). In addition, the frequency of proliferating diabetogenic CD4þ T cells in the pLN, either in the presence or absence of nTreg cells remains unchanged, suggesting that Ag‐induced cell division of autoreactive T cells is not directly affected by nTreg cells (Chen et al., 2005b; Tritt et al., submitted for publication). In contrast, our results indicate that the frequency of effector T cells is dramatically increased in the absence of nTreg cell function, suggesting that

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regulation may effect later stages of T cell clonal expansion, survival and/or homing (Tritt et al., submitted for publication). These results do not exclude the possibility that nTregs alter effector T cells to become less pathogenic only when localized in islets. A similar observation was also made in studies involving the transfer of BDC2.5 T cells into thymectomized NOD.B7–2
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recipients, in conjunction with in vivo depletion of CD25þ T cells, which resulted in an increased accumulation of BDC2.5 T cells in the pLN compared to control mice (Bour‐Jordan et al., 2004). These data are in stark contrast with previous reports in other T1D models that suggest that nTreg cells do suppress the homing, activation, and expansion within pLN sites. Furthermore, there is also evidence suggesting that suppression of APC functions may also be, in part, responsible for nTreg cell‐mediated protection. For example, Serra et al. (2003) found that nTreg cells control the pathogenicity of islet specific, CD8þ effector T cells by inhibiting DC maturation in the pLN (Serra et al., 2003). Similarly, in vitro expanded CD25þ Treg cells disrupt the interaction between BDC2.5 CD4þ T effector cells and DCs in the pLN (Tang et al., 2006). It is possible, however, that the in vitro culture of nTreg cells altered their normal physiological role. The location where nTreg cells mediate tolerance induction occurs in vivo is currently unknown. In terms of evidence from T1D models, nTreg cells have been found to preferentially home to and expand within inflammed pLN and islets of T1D‐protected mice, suggesting that these local tissues are the sites of control (Herman et al., 2004). The preferential accumulation of nTreg cells in the inflammed pancreas also suggests that nTreg cells only become activated or mediate their suppressor function once inflammation has been initiated. There are also instances where the majority of nTreg cells actively suppressing in the pancreas, rather than the pLN where the initial priming of the autoreactive T cells response is presumably occurring (Herman et al., 2004; Tritt et al., submitted for publication). A recent study from Chen et al. (2005b) found that the gene expression profile of nTreg cells within the insulitic infiltrate differs from nTreg cells residing in the pLN, suggesting that stimuli in the target tissue initiate a unique transcriptional program, possibly thereby increasing their ability to regulate immune responses in these sites (Chen et al., 2005b). It remains to be determined, however, whether these distinct nTreg cell gene signatures occur as a result of their tissue localization, or are merely the consequence of their own suppression. Overall, these studies in NOD mice indicate that nTreg cells are functionally and phenotypically heterogeneous in nature and that each regulatory cell type can potentially control autoreactive T cells via various mechanisms, and in tissue‐specific fashions (Alyanakian et al., 2003). The reasons behind this context‐dependent mode of T cell regulation are unknown, but target organs may confer unique regulatory pressures on infiltrating effector T cells and may shape the type of regulation needed for disease resolution.

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A study by Chen et al. (2005b) used NOD mice harboring the scurfy mutation (FoxP3sf) to examine the functional contribution of this defined subset of nTreg cells to tolerance induction. NOD.FoxP3sf displayed significantly advanced onset of T1D compared to normal NOD mice, implying that Foxp3þ nTreg cells control T1D pathogenesis (Chen et al., 2005b). This study, however, did not explore whether the capacity wild‐type, Ag‐specific nTreg cells to rescue NOD.FoxP3sf from the early onset of T1D, was related to restoration of the simple deficit in nTreg cells, or whether the wt nTreg cells also suppressed the global inflammation that arises in FoxP3
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mice. Furthermore, this study did not exclude the possibility that NOD.FoxP3sf mice abnormally present auto‐Ags, and in conjunction with increased costimulation in these mice, may reduce activation thresholds for effector T cells. Chang et al. (2005) recently demonstrated that a T cell extrinsic defect may contribute to the development of the scurfy disease and IPEX, since the Foxp3sf mutation in thymic stromal cells leads to defective thymopoiesis. In most of studies of T1D models, the CD25 surface marker is used to monitor nTreg cell frequencies in prediabetic and adult NOD mice. As discussed earlier, CD25 is only a specific marker for resting CD4þCD25þ nTreg cells in neonatal lymphoid environments and becomes unreliable during immune responses as conventional CD4þ T cells upregulate CD25 upon activation. To circumvent this caveat, we evaluated the frequency of Foxp3‐expressing CD4þ T cells and observed that nTreg cells, irrespective of CD25 expression, represent a stable pool within the total CD4þ T cell compartment from thymocytes, LN, or spleen in neonatal and adult NOD mice (Tritt et al., submitted for publication). The proportion of Foxp3þ cells in NOD mice is comparable to C57BL/6 mice, refuting the widespread view that NOD mice have a developmental defect in nTreg cells. Our unpublished results also indicate that the cellular potency of nTreg cells is fully operative in neonatal mice, but declines with age, despite a stable cellular frequency of Foxp3þ nTreg cells in primary and secondary lymphoid tissues (Tritt et al., submitted for publication). Our results, however, do not exclude the possibility that a functional deficiency in nTregs in NOD mice may not be manifested as a sudden decline in the cellular frequency of these cells in peripheral tissues, but rather may due to subtle gaps in the TCR repertoire and/or polymorphisms in genes modulating effector and regulatory functions (Piccirillo and Shevach, 2004). 6.4. Spontaneous Autoimmunity in Humans: A Role for CD4þCD25þ nTreg Cells? The triggers for autoimmune diseases and the factors that exacerbate their progression have remained largly elusive, despite extensive study. While it is

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difficult to establish a direct causative link between human disease states and nTreg cells, recent research indicates that a decrease in number and/or function of these cells is associated with autoimmunity in many instances, and strongly suggests that these abnormalities may be a determining factor in disease development. For example, a study of systemic lupus erythrematosus (SLE) patients demonstrated that patients have a reduced proportion of peripheral nTregs, although the functional suppressor activity of these cells was found to be intact when tested in vitro (Miyara et al., 2005). Lee et al. (2006) also found a correlation between a decrease in nTreg cell numbers and active SLE and incresased autoantibody levels. In contrast, Liu et al. (2004) failed to find such a connection. Studies of patients with multiple sclerosis have revealed that in this disease patients have normal numbers of circulating nTreg cells, but a functional defect in their suppressive capacity was detected in vitro (Haas et al., 2005; Viglietta et al., 2004). A similar trend was also observed in a study of patients with myasthenia gravis in which thymic nTreg cells showed a striking defect in suppressive capacity in comparison to healthy controls (Balandina et al., 2005). Defects in nTreg cell function were also detected in patients with psoriasis (Sugiyama et al., 2005), diabetes (Lindley et al., 2005), and autoimmune polyglandular syndromes (Kriegel et al., 2004). In the case of rhuematoid arthritis, evidence suggests that the immunological defect involves changes to both T effector cells and nTreg cells within affected joints, and that inflammation and pathology results from an imbalance of the two populations (Cao et al., 2003; de Kleer et al., 2004; Valencia et al., 2006). While the evidence for decreased nTreg number or function as a factor in autoimmunity is increasing, the reasons for why and how these abnormalities arise remain elusive. It is also puzzling that the reported global defects in peripheral nTreg cell function do not lead to widespread autoimmunity, suggesting that a tissue‐specific imbalance may also predispose individuals to a given disease. There is also the possibility that nTreg cell defects are indicative, but not causative of autoimmunity. A limited number of remarkable studies, however, have shown that some clinically effective treatments actually reverse nTreg cell functional defects (Ehrenstein et al., 2004; Valencia et al., 2006), supporting the concept that nTreg cell dysfunction is invovled in disease pathology and that therapeutic manipulation of these cells will be an effective treatment for a variety of autoimmune diseases. 7. Summary and Conclusions Although multiple immune tolerance pathways regulate adaptive immune responses, nTreg cells have a pivotal role in determining role in the balance between tolerance and immunity. Alterations in their development or function,

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which may be provoked by environmental or genetic triggers, may therefore represent a major factor underlying resistance or susceptibility to autoimmune disease. This hypothesis is supported by evidence from several mouse models of disease. The ultimate goal in the treatment of autoimmune disease is therefore to reestablish tolerance to the relevant self‐Ags by potentiating nTreg cell numbers and/or function. Major outstanding questions limiting widespread clinical application include the fact that the mechanisms underlying nTreg cell–mediated suppression remain unknown and that there appear to be some major differences between findings in in vitro vs in vivo studies and those conducted in mice vs humans. In addition, the unreliable nature of CD25 as a stable and exclusive marker of nTreg cells in inflammatory contexts often complicates the interpretation of many studies. The discovery of more specific biomarkers for nTreg cells is imperative, as this will undeniably facilitate our ability to monitor nTreg cellular frequency and function in the context of a given disease and will serve to determine the clinical effectiveness of novel therapeutic strategies destined to modulate nTreg function in vivo. Acknowledgments M. K. L. and S. A. are supported by grants from the Canadian Institutes for Health Research and the BC Transplant Society. M. K. L. holds a Canada Research Chair in Transplantation and is a Michael Smith Foundation for Health Research Scholar. S. A. holds a Canada Graduate Scholarship Doctoral Award and a Michael Smith Foundation for Health Research Junior Graduate Studentship Award. C. A. P. and E. H. acknowledge the financial support of the Canadian Institutes for Health Research (CIHR MOP 67211), Canadian Diabetes Association (CDA #GA‐3–05–1898‐CP), and Canadian Foundation for Innovation. E. H. is recipients of the fellowship from the CIHR Training Grant in Neuroinflammation. C. A. P is the recipient of the Canada Research Chair in Regulatory Lymphocytes of the Immune System.

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BTLA and HVEM Cross Talk Regulates Inhibition and Costimulation Maya Gavrieli, John Sedy, Christopher A. Nelson, and Kenneth M. Murphy Department of Pathology and Center for Immunology, Howard Hughes Medical Institute, Washington University School of Medicine, St. Louis, Missouri

1. 2. 3. 4. 5. 6. 7. 8.

Abstract............................................................................................................. Overview of BTL A and HVEM Ligand Discovery..................................................... Structural Characterization of BTLA Bound to HVEM .............................................. Viral Modulators of the HVEM–BTLA Pathway........................................................ Expression and Regulation of BTLA, LIGHT, and HVEM on T Cells and APCs............................................................................................ Mechanisms of the CD28 Family Inhibitory Receptors .............................................. Consequences of HVEM Ligation .......................................................................... BTLA and HVEM in Models of Disease ................................................................. Conclusions........................................................................................................ References .........................................................................................................

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Abstract Recently a new inhibitory immunoglobulin domain–containing lymphocyte receptor was identified on the basis of its T helper 1 (TH1)‐selective expression in murine T cell lines, which was named B and T lymphocyte attenuator (BTLA). Several groups have confirmed the initial characterization of BTLA as an inhibitory receptor, which was initially inferred from the mild increases in several parameters of BTLA‐deficient mice. The initial expectation that BTLA would interact with a B7 family ligand, such as the B7x protein, was surprisingly overturned with the functional cloning of the actual BTLA ligand as herpesvirus entry mediator (HVEM). This was unexpected largely due to the fact that this interaction represents the convergence of two very different, although each quite extensive, families of receptors and ligands. The interaction of BTLA, which belongs to the CD28 family of the immunoglobulin superfamily, and HVEM, a costimulatory tumor‐necrosis factor (TNF) receptor (TNFR), is quite unique in that it is the only receptor–ligand interaction that directly bridges these two families of receptors. This interaction has raised many questions about how receptors from two different families could interact and which are the signaling events downstream of receptor ligation. As we discuss here and recently demonstrated, HVEM interaction with BTLA serves to negatively regulate T cell responses, in contrast to the strong activation observed when

157 advances in immunology, vol. 92 # 2006 Elsevier Inc. All rights reserved.

0065-2776/06 $35.00 DOI: 10.1016/S0065-2776(06)92004-5

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HVEM engages its endogenous ligand from the TNF family. Finally, as studies of BTLA are just now beginning to extend beyond the initial characterizations, it is becoming clear that there are many complex issues remaining to be resolved, particularly potential polymorphisms that may engender disease susceptibility in the human. 1. Overview of BTL A and HVEM Ligand Discovery 1.1. Introduction Activation of lymphocytes involves signals that are delivered through primary antigen receptors, which may be the T cell receptor (TCR) or B cell receptor, as well as secondary signals that can be delivered through an array of various costimulatory and inhibitory receptors. These costimulatory receptors can regulate the extent, the quality, and the duration of lymphocyte activation. In the case of T cells, the costimulatory and inhibitory cell‐surface receptors can include CD28, cytotoxic T lymphocyte antigen‐4 (CTLA‐4), and their homologues (Greenwald et al., 2005; Sharpe and Freeman, 2002) as well as several members of the tumor‐necrosis factor (TNF) receptor (TNFR) family (Croft, 2003; Watts, 2005). These costimulatory receptors of the immunoglobulin superfamily are composed of CD28 and inducible costimulator (ICOS), and the inhibitory receptors that include CTLA‐4, programmed death‐1 (PD‐1), and the recently identified molecule B and T lymphocyte attenuator (BTLA). Recently, new data has altered our assumption that all of these immunoglobulin superfamily members only interacted with members that belong to the B7 family of cell‐surface receptors. Expression of the B7‐family members by antigen‐ presenting cells (APCs) and peripheral tissues is able to regulate T cell activation in the context of inflammatory stimuli (Chen, 2004; Sharpe and Freeman, 2002). Likewise, until recently, all TNFR family members were thought to interact only with TNF family ligands, with the exception of the nerve growth factor receptor (NGFR) binding neurotropins, and herpesvirus entry mediator (HVEM) binding herpesvirus‐1 glycoprotein D (HSV1 gD), an immunoglobulin domain–containing viral protein. A subset of TNFRs, including 4‐1BB, CD27, CD30, HVEM, and OX40 are important in costimulating T cells during the late phase of activation (Croft, 2003). These receptors are induced during T cell activation and are not detectable in naı¨ve T cells, excluding HVEM and CD27. Expression of their TNF ligands is also induced on APCs by inflammatory stimuli (Croft, 2003; Watts, 2005) (Table 1). Recently, we found that cross talk between the immunoglobulin superfamily and the TNFR family of costimulatory molecules is possible. This is based on the unexpected discovery that the immunoglobulin domain–containing receptor BTLA binds the TNFR family member HVEM in both mouse and human (Fig. 1).

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Table 1 Costimulatory and Inhibitory Receptors of the Immunoglobulin Superfamily and TNFR Families Molecule

Expression

Ligand

Expression

Immunoglobulin domain–containing receptors Costimulatory CD28 Constitutively expressed by B71, B72 B cells, Monos, DCs, T cells T cells, inducible in some somatic tissues ICOS Activated T cells, ICOSL B cells, Monos, DCs, activated DCs T cells, inducible in some somatic tissues Inhibitory CTLA‐4 Rapidly expressed by B71, B72 B cells, Monos, DCs, activated T cells T cells, inducible in some somatic tissues PD‐1 Activated T cells, activated PDL1, PDL2 B cells, T cells, some B cells, activated DCs somatic tissues, inducible in Monos and DCs Some somatic tissues, inducible in Monos and DCs BTLA T cells, B cells, DCs, HVEM T cells, B cells, NK cells, myeloid cells DCs, myeloid cells, inducible in somatic tissues 41BB

Costimulatory TNFRs Activated T cells, activated 41BBL B cells, activated DCs

OX40

Activated T cells, activated B cells, activated DCs

OX40L

CD27

T cells, activated B cells

CD70

CD30

Activated T cells, activated B cells, activated DCs B cells, DCs

CD30L

CD40 HVEM

T cells, B cells, NK cells, DCs, myeloid cells, inducible in somatic tissues

CD40L LIGHT, BTLA

Activated T cells, activated B cells, activated DCs, activated Monos Activated T cells, activated B cells, activated DCs, activated Monos Activated T cells, activated B cells, activated DCs, activated Monos Activated T cells, activated B cells, activated Monos Activated T cells, activated DCs Immature DCs, Monos, activated T cells T cells, B cells, DCs, myeloid cells

BTLA, B and T lymphocyte attenuator; CTLA‐4, cytotoxic lymphocyte antigen‐4; DC, dendritic cell; HVEM, herpesvirus entry mediator; ICOS, inducible costimulator; L, ligand; Mono, monocyte; PD‐1, programmed death‐1.

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Figure 1 Summary of the interactions between TNFRs, TNFs, and immunoglobulin domain– containing receptors. TNF receptors (TNFR), such as TNFR1, TNFR2, lymphotoxin b receptor (LTbR), DcR3, and herpesvirus entry mediator (HVEM), can interact with one or more soluble or cell surface TNF ligands such as lymphotoxin a (LTa) or LIGHT. HVEM binds to LTa and LIGHT, while LIGHT also binds by LTbR and DcR3. HVEM binds the immunoglobulin domain–containing proteins B and T lymphocyte attenuator (BTLA) and herpesvirus‐1 glycoprotein D (HSV1 gD), and its viral homologue human cytomegalovirus (CMV) UL144 binds human BTLA.

This interaction is all the more unusual in that BTLA has been described to inhibit T cell responses, in contrast to HVEM that has been described to activate T cell responses. Several studies have recently explored this interaction, both from a structural and a biological perspective in vitro and in vivo. This chapter surveys the current state of affairs for the role of BTLA and HVEM in

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the immune response and attempts to indicate the many missing pieces still left to be filled in. 1.2. Identification of HVEM as the Ligand for BTLA BTLA was initially identified as a transcript highly specific to T helper 1 (TH1) cells (Watanabe et al., 2003) but later was shown to be expressed in thymocytes (Han et al., 2004). Since BTLA was initially recognized to have inhibitory actions in vivo (Watanabe et al., 2003), suggesting functional similarity to CTLA‐4 and PD‐1, the initial search for its ligand centered around candidates among the B7 family of receptors. B7x was initially considered as a candidate ligand for BTLA because the binding of a B7x Fc fusion protein to BTLA‐deficient TH1 cells was decreased compared to wild‐type TH1 cells (Watanabe et al., 2003). However, direct interactions between BTLA and B7x could not be verified in studies that used B7x‐ or BTLA‐staining reagents to analyze transfected cells expressing either B7x or BTLA (Sedy et al., 2005). We identified HVEM as a ligand for mouse BTLA using a functional cloning approach (Sedy et al., 2005), and this was confirmed almost simultaneously for human BTLA (Gonzalez et al., 2005). In our effort, tetramers of the extracellular domain of BTLA were used to purify ligand‐expressing cells that had been transduced with a retroviral cDNA library produced from mouse spleen (Sedy et al., 2005). The other approach used a candidate‐based screen, based on surface plasmon resonance, for BTLA‐binding partners in a recombinant protein library expressing over 2000 secreted proteins (Gonzalez et al., 2005). These complementary approaches provide solid evidence for direct protein–protein interactions between BTLA and HVEM (Fig. 1). A human cDNA encoding HVEM was initially identified in a screen for proteins that would allow HSV1 entry into Chinese hamster ovary (CHO) cells (Montgomery et al., 1996). Additional murine and human cDNAs for HVEM were quickly identified by three other groups using homology‐based searches for novel TNFRs (Hsu et al., 1997; Kwon et al., 1997; Marsters et al., 1997).

1.3. Identification of TNF Ligands for HVEM Prior to the identification of BTLA as a ligand for HVEM, precipitation studies using an HVEM Fc fusion protein identified lymphotoxin a and a previously unknown protein as binding partners for HVEM. A candidate‐based search of novel TNF ligands identified the second ligand as a molecule homologous to lymphotoxin, LIGHT (Mauri et al., 1998). Lymphotoxin is a soluble homotrimer (Smith et al., 1994) that also interacts with TNFR1, TNFR2,

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A HVEM

N-terminal

HSV1 gD

N-terminal

HVEM

C-terminal

N-terminal

BTLA

N-terminal C-terminal C-terminal C-terminal

B

CRD1

gD

BTLA

LIGHT

BTLA

CRD2 CRD3

HVEM

CRD4

Figure 2 Modeling of herpesvirus entry mediator (HVEM) and its various ligands. (A) Comparison of the structures of HVEM HSV1 gD and BTLA complexes. Shown at left is a model of human HVEM bound to herpesvirus‐1 glycoprotein D (HSV1 gD) (Ca‐ribbon in cyan) and on the right is human HVEM bound to human B and T lymphocyte attenuator (BTLA) (Ca‐ribbon in magenta). In both complexes HVEM is represented as a space‐filled model spanning CRD1 through 3. Both HSV1 gD and BTLA use a structurally similar short b‐strand to bind HVEM (gold). The residues on HVEM having atoms within 4 A˚ of the interface are colored as follows: contacts

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and lymphotoxin b receptor (LTbR), whereas LIGHT is a membrane‐bound homotrimer (Rooney et al., 2000) that also has been shown to interact with LTbR and with the soluble TNFR decoy receptor 3 (DcR3; Mauri et al., 1998; Yu et al., 1999) (Fig. 1).

1.4. Structural Characterization of the Interaction Between HVEM and TNF Ligands TNFR family members are thought to trimerize on binding with their trimeric TNF ligands, as originally observed in the crystal structure of TNFR1 and lymphotoxin a (Banner et al., 1993; Bodmer et al., 2002). This binding is mediated by cysteine‐rich domains (CRDs) of TNFRs, and lymphotoxin binds TNFR1 through interactions with CRD2 and CRD3. HVEM shares homology with TNFRs, including the sequences containing these CRDs. Antibody‐blocking and peptide‐mapping studies have shown that HVEM binds both LIGHT and lymphotoxin through CRD2 and CRD3 (Sarrias et al., 2000) (Fig. 2). Presumably, LIGHT and lymphotoxin bind HVEM in a manner similar to how lymphotoxin binds TNFR1, although currently there is no cocrystal of HVEM and its TNF family ligands to confirm these studies. Although HSV1 gD and LIGHT were initially reported to compete with each other for binding to HVEM (Mauri et al., 1998), subsequent binding studies produced contradictory results, and the structure of the cocrystal of HSV1 gD and HVEM confirmed that HSV1 gD binds HVEM at a region involving CRD1 and CRD2 that is a distinct surface than that which binds LIGHT (Carfi et al., 2001; Whitbeck et al., 2001). Specifically, a loop structure at the N‐terminus of the immunoglobulin domain of HSV1 gD, and not the immunoglobulin domain itself, was found to mediate direct binding to HVEM. Furthermore, the HSV1 gD–binding site is localized on the exterior of a trimolecular HVEM complex that is distinct from the binding site of trimolecular LIGHTwithin the interior of the trimolecular configuration of HVEM (Fig. 2). shared between HSV1 gD and BTLA are red, HSV1 gD‐specific contacts residues are cyan, and BTLA‐specific contacts are magenta. Structural data is from Carfi et al. (2001). (B) Proposed model of HVEM complexes at the cell surface. LIGHT self‐associates into a noncovalent homotrimer (green) similar to other TNF family members. It is unknown how HVEM binds TNF ligands, but by analogy with the structure of TNFR1 bound to TNFb (Banner et al., 1993), it is expected that LIGHT contacts HVEM (gray) primarily through an elongated surface spanning CRD2 and CRD3. The contact region on HVEM shared between HSV1 gD and BTLA (red) is exposed in this model. The surface of HVEM contacting HSV1 gD or BTLA is color coded as in (A). It remains to be determined whether BTLA interacts with HVEM in trans on an adjacent cell or in cis on the same cell.

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2. Structural Characterization of BTLA Bound to HVEM Analysis of BTLA binding to various HVEM mutants and to mouse–human HVEM chimeras demonstrated that the interaction requires at least the CRD1 region of HVEM (Sedy et al., 2005). Also, BTLA binding to HVEM could be blocked by addition of soluble HSV1 gD proteins, which interact with CRD1 and CRD2 of HVEM (Cheung et al., 2005; Gonzalez et al., 2005). However, the structure of the BTLA immunoglobulin domain (Nelson et al., submitted for publication) indicates that it does not contain a loop region that mediates binding to HVEM similar to the N‐terminal loop of HSV1 gD (Carfi et al., 2001). The recently solved cocrystal between human BTLA and human HVEM (Compaan et al., 2005) resolves how two proteins with relatively different structures can interact with the same protein surface. Specifically, it shows that BTLA binds to a largely overlapping, but distinct, surface of HVEM from HSV1 gD, and this is in agreement with functional mapping and competitive binding studies (Compaan et al., 2005) (Fig. 2A, B). The N‐terminal loop of HSV1 gD and eight b‐strand of the BTLA immunoglobulin domain each interact with the same surface of HVEM predominantly using intermolecular b‐strand interactions and not using side chain interactions. The cocrystal of BTLA and HVEM also suggests that it might be possible for HVEM to interact with BTLA and LIGHT (or lymphotoxin) simultaneously, since LIGHT binds HVEM at a distinct surface (Fig. 2B). It has been reported that BTLA and LIGHT interact noncompetitively with HVEM (Cheung et al., 2005; Gonzalez et al., 2005) and that BTLA, LIGHT, and HVEM could be isolated in a complex by size exclusion chromatography (Compaan, D. M., and Hymowitz, S. G., unpublished observation). However, these results have been achieved only with purified recombinant proteins, and it remains to be seen whether such a complex can be isolated in vivo.

3. Viral Modulators of the HVEM–BTLA Pathway 3.1. HSV1 gD as a T Cell Inhibitor and Mediator of Viral Fusion Interactions between HSV1 gD and HVEM are important in mediating HSV1 entry into T cells expressing HVEM (Campadelli‐Fiume et al., 2000; Spear and Longnecker, 2003). The observation that HSV1 gD competes with LIGHT binding to HVEM (Mauri et al., 1998) implied that HSV1 gD might block LIGHT expressed on APC from costimulating HVEM expressed on T cells. Purified HSV1 gD extracellular domains, as well as cells expressing HSV1 gD are sufficient to block CD3‐specific antibody‐induced T cell proliferation and HVEM‐induced activation of NF‐kB (La et al., 2002). However, it is unclear

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whether HSV1 gD functions to block HVEM costimulation in vivo or whether it competes with BTLA for HVEM binding during HSV1 infection (Cheung et al., 2005; Gonzalez et al., 2005). The crystal structure of the entire HSV1 gD extracellular domain alone has recently been solved, with the finding that the C‐terminal domain of the protein, which is distinct from the main immunoglobulin domain structure, interacts with the N‐terminal loop (Krummenacher et al., 2005) and would be predicted to block the interaction of the N‐terminal loop with HVEM (Fig. 1A). Some HSV1 gD mutants with changes in the C‐terminal region have higher affinity for HVEM than other receptors for HSV1 gD, but show much less activity at mediating viral fusion. Therefore, it is possible that the HVEM‐interacting loop of HSV1 gD is blocked in the native virion, but becomes exposed during interaction of the virion with receptor‐expressing cells through displacement of the HSV1 gD C‐terminus, thereby allowing membrane fusion (Krummenacher et al., 2005). In summary, HSV1 gD might have a dual role in HSV1 infection, mediating both viral entry and modulating immune responses. 3.2. Human BTLA Interacts with Human CMV UL144 The first two CRDs of HVEM share sequence homology with UL144, a viral TNFR homologue derived from human cytomegalovirus (CMV) (Benedict et al., 1999). The UL144 protein consists of only two CRD domains but lacks the third CRD required for binding TNF family members. Several allelic isoforms of UL144 from different CMV strains bind human BTLA with weaker affinity than human HVEM (Cheung et al., 2005). Despite this lower affinity, UL144 competes with HVEM for BTLA binding, implying that BTLA might interact with the CRD1 of UL144. UL144 Fc fusion proteins have also been shown to potently inhibit T cell activation to a greater extent than HVEM fusion proteins (Cheung et al., 2005). UL144 competes with HVEM for BTLA binding, but does not interact with LIGHT (Benedict et al., 1999) (Fig. 1), and so might provide a more selective targeting of BTLA signaling and perhaps greater inhibition than HVEM (Cheung et al., 2005). Formally, it is still unknown whether UL144 induces BTLA phosphorylation and SHP1 and/or SHP2 recruitment as HVEM does (Sedy et al., 2005). 4. Expression and Regulation of BTLA, LIGHT, and HVEM on T Cells and APCs The analysis of BTLA, LIGHT, and HVEM is complicated by their wide cellular distribution. In fact T cells, dendritic cells (DCs), and most other

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Figure 3 HVEM, BTLA, and LIGHT expression is regulated on T cells, B cells, and APCs. Naı¨ve Tcells express high levels of herpesvirus entry mediator (HVEM) and low levels of B and T lymphocyte attenuator (BTLA). On activation BTLA is rapidly expressed within hours, while LIGHT is expressed within days. HVEM expression is decreased with approximately the same kinetics as LIGHT is expressed. On return to quiescence, T cells restore high levels of HVEM expression and express low levels of LIGHT. BTLA becomes highly expressed on TH1‐polarized cells, but is absent on TH2‐ polarized cells. B cells express low levels of HVEM in mice but higher levels of HVEM in humans. Resting B cells express high levels of BTLA and may reduce BTLA levels with LPS activation. In humans, HVEM is not expressed in germinal center B cells but is restored to high levels in memory B cells. B cells do not express LIGHT at any stage in vivo but express LIGHT under certain conditions in vitro. Immature dendritic cells (DCs) express high levels of both HVEM and LIGHT and lower levels of BTLA. With maturation, DCs decrease expression of both HVEM and LIGHT. In contrast, bone marrow–derived DCs increase BTLA expression with maturation.

cells within the lymphoid compartment express BTLA, LIGHT, and HVEM at some point during activation or quiescence (Table 1, Fig. 3). BTLA is expressed by lymphoid and myeloid cells, with particularly high expression by B cells and lower expression by CD11cþ DCs and naı¨ve T cells (Han et al., 2004; Hurchla et al., 2005). TCR ligation increases expression of BTLA, as does lipopolysaccharide (LPS) stimulation of bone marrow–derived DCs (BMDCs). By contrast, in vitro LPS stimulation of B cells from C57BL/6 mice, but not from BALB/c mice, decreases BTLA expression (Han et al., 2004; Hurchla et al., 2005) (Fig. 3). LIGHT was originally described to be expressed by T cells following activation, but not before, and at low levels on resting T cells (Mauri et al., 1998;

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Morel et al., 2000, 2001; Shi et al., 2002). LIGHT is also highly expressed by immature DCs but is downregulated on maturation with LPS or CD40 (Tamada et al., 2000a). Although B cells do not seem to express LIGHT in vivo, stimulation of B cells with LIGHT and CD40L together can synergize to induce LIGHT expression on B cells (Duhen et al., 2004; Sedy et al., 2005; Watts, 2005) (Fig. 3). HVEM is highly expressed on naı¨ve T cells, and this expression is decreased during activation, with high levels being reexpressed at the end of the activation phase (Harrop et al., 1998b; Morel et al., 2000; Sedy et al., 2005; Wang et al., 2005a). Blocking LIGHT during T cell activation using antibodies or HVEM Fc fusion proteins prevents HVEM downregulation, indicating that LIGHT interactions with HVEM directly signal to decrease in HVEM expression. However, sustained LIGHT expression by T cells does not seem to be sufficient for a continued decrease in HVEM expression, since LIGHT transgenic T cells maintain high levels of HVEM (Shaikh et al., 2001). Naı¨ve mouse B cells express low levels of HVEM, while naı¨ve and memory human B cells express high levels of HVEM (Duhen et al., 2004; Harrop et al., 1998b; Wang et al., 2005a). HVEM expression by human B cells is reduced after stimulation with LIGHT and is undetectable on germinal center B cells (Duhen et al., 2004). Finally, HVEM expression on immature DCs is decreased after activation by LIGHT through HVEM and LTbR or CD40L (Morel et al., 2001). Thus generally, naı¨ve or resting cells seem to express high levels of HVEM, whereas activated cells express no or low HVEM, as a result of direct LIGHT interactions with HVEM. The identity of the cell types presenting LIGHT in vivo to these activated T cells, B cells, and DCs is not clear (Fig. 3). 5. Mechanisms of the CD28 Family Inhibitory Receptors 5.1. Background Due to its inhibitory function in T cells, BTLA joins two other inhibitory receptors expressed by T cells, CTLA‐4 and PD‐1. In contrast to the costimulatory receptors CD28 and ICOS, coligation of these inhibitory receptors with the TCR blocks T cell proliferation and effector function. There are differences in the signaling pathways and the pattern of expression of these receptors, reflecting their differential use in T cells during different stages of the immune response. CTLA‐4 regulates naı¨ve T cell activation, PD‐1 becomes expressed on activated T cells, and BTLA is expressed on both naı¨ve and activated T cells, potentially regulating all phases of T cell activation. Cross‐ linking of BTLA on T cells using BTLA‐specific antibodies inhibited T cell proliferation, although inhibition was less effective on simultaneous CD28 costimulation (Han et al., 2004; Krieg et al., 2005; Watanabe et al., 2003).

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Figure 4 CTLA‐4, PD‐1, and BTLA regulate T cell activation. (A) CTLA‐4, PD‐1, and BTLA inhibit T cells following activation. T cell activation induces cell surface expression of PD‐1 and phosphorylation of all inhibitory receptors. Phosphorylation of CTLA‐4 dissociates AP2 from Y201 and allows CTLA‐4 to be expressed at the cell surface. Phosphorylated Y201 also releases PP2A, allowing it to inhibit the activity of protein kinase B (PKB/Akt). Activated CTLA‐4, BTLA, and PD‐1 have all been reported to associate with Src homology 2 domain containing phosphatase 1 (SHP1) and SHP2, although a direct association has only been shown for BTLA and PD‐1. These phosphatases have been implicated both in directly downregulating proximal T cell receptor (TCR) signaling and in inhibiting PI3K activation and subsequent Akt activation. The BTLA residues Y274 and Y299 are both necessary for SHP2 association, while only Y248 of PD‐1 is necessary for SHP2 association. Phosphorylated Y245 of BTLA may also associate with growth receptor bound 2 (grb2) and the p85 subunit of phosphatidylinositol 3‐kinase (p85 PI3K) under certain conditions. Yellow boxes within signaling proteins indicate SH2 domains. Abbreviations: IL, interleukin; MHC, major histocompatibility complex; P, Phosphorylated tyrosine. (B) BTLA sequence

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In agreement with antibody cross‐linking data, analysis in an antigen‐specific priming system using CHO cells expressing HVEM confirmed that ligation of BTLA on T cells reduced T cell proliferation. Furthermore, coexpression of CD80 by these CHO cells caused the inhibition of T cell proliferation mediated by ligation of BTLA by HVEM to be seen only at low antigen doses (Sedy et al., 2005), but this inhibition remained dependent on BTLA expression on T cells. In other studies, an agonistic BTLA‐specific antibody was identified and treatment of T cells with this antibody inhibited T cell expression of the activation marker CD25, in part owing to a block in interleukin‐2 (IL‐2) secretion by the T cells (Krieg et al., 2005) but did not reduce expression of another activation marker, CD69, or cause apoptosis. Together, these data show that BTLA ligation by HVEM reduces T cell activation, but its function on other cell types is currently less well documented.

5.2. Mechanisms of Inhibition by CTL A‐4 and PD‐1 The mechanism by which CTLA‐4 inhibits T cell activation is still not entirely resolved but most likely involves both the sequestration of B7 molecules away from CD28, as well as the induction of specific signaling pathways (Teft et al., 2006). Ligation of CTLA‐4 by dimeric CD80 or CD86 induces CTLA‐4 oligomerization (Schwartz et al., 2001; Stamper et al., 2001) and phosphorylation of both cytoplasmic tyrosine residues (Chuang et al., 1999; Miyatake et al., 1998; Shiratori et al., 1997). Previously, it was thought that phosphorylated CTLA‐4 recruits Src homology 2 (SH2) domain containing phosphatase 1 (SHP1) and SHP2 which then dephosphorylate signaling intermediates and tyrosine kinases downstream of TCR activation, such as TCRz, linker for activation of T cells (LAT) and TCRz associated protein of 70 kD (ZAP70) (Guntermann and Alexander, 2002; Lee et al., 1998; Marengere et al., 1996) (Fig. 4A). However, direct interactions between CTLA‐4 and SHP2 have been questioned, due to the difficulty to precipitate a complex containing only CTLA‐4 and SHP2 (Nakaseko et al., 1999; Schneider and Rudd, 2000; Schneider et al., 2001). Phosphatase 2A (PP2A) associates with the unphosphorylated membrane‐proximal tyrosine in the cytoplasmic domain of CTLA‐4 (Y201); on TCR ligation, PP2A becomes phosphorylated and disassociates from CTLA‐4 (Baroja et al., 2002; Chuang et al., 2000). alignment. Shown is the alignment of BTLA partial sequences from human, chimp, dog, mouse, and rats. Spaces have been introduced for optimal comparison. Boxed sequences are tyrosine‐based signaling motifs conserved between all the known sequences of BTLA. The three conserved tyrosine‐based signaling motifs containing tyrosine 245, tyrosine 274, and tyrosine 299 relative to the mouse sequence are indicated.

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It has been argued that release of PP2A from CTLA‐4 allows it to inhibit protein kinase B (PKB)/AKT pathway and block cell‐survival signals (Alegre et al., 2001; Frauwirth and Thompson, 2004) (Fig. 4A). However, these conclusions are based on the inhibition of PP2A using okadaic acid which may also inhibit protein phosphatase 1 at the doses used to treat whole cells (Boudreau and Hoskin, 2005), and at this time genetic studies to specifically block PP2A function are lacking. Phosphorylation of Y201 also prevents the association of CTLA‐4 with adaptor protein‐2 (AP2), which mediates endocytosis, causing CTLA‐4 to accumulate at the cell surface (Bradshaw et al., 1997; Shiratori et al., 1997; Zhang and Allison, 1997), and contributes to blocking CD28 activation by sequestration of B7 ligands (Fig. 4A). Mice expressing CTLA‐4 with a Y201V mutation develop lymphoproliferation, although this is delayed compared to CTLA‐4‐deficient mice (Yi et al., 2004), showing that the inhibitory activity of CTLA‐4 is at least partly mediated by Y201. PD‐1 is largely monomeric on the cell surface (Zhang et al., 2004) and on ligand binding its cytoplasmic tyrosine residue is phosphorylated and recruits SHP1 and SHP2 (Chemnitz et al., 2004; Latchman et al., 2001; Okazaki et al., 2001) (Fig. 4A). The targets of these protein tyrosine phosphatases have been proposed to include signaling proteins in the TCR‐signaling complex such as TCRz and ZAP70 (Okazaki et al., 2001; Sheppard et al., 2004), similar to the targets of CTLA‐4 inhibition. PD‐1–recruited SHP1 and SHP2 have also been shown to target phosphatidylinositol 3‐kinase (PI3K) upstream of Akt (Parry et al., 2005) and in this way may inhibit signals initiated from CD28 ligation. Thus, the current model of inhibition by CTLA‐4 and PD‐1 involves the blockade of proximal TCR signals and AKT‐derived proliferative and cell‐survival signals (Fig. 4). 5.3. Early Events in BTLA Signaling The cytoplasmic domain of BTLA contains three tyrosine‐containing motifs that are conserved among all known species expressing this gene: mouse, rat, dog, chimp, and human (Gavrieli and Murphy, 2006) (Fig. 4B). Each tyrosine in these motifs can be phosphorylated after BTLA cross‐linking (Watanabe et al., 2003). The most membrane‐proximal tyrosine (Y245 in the BALB/c mouse strain) is a predicted recruitment site for growth factor receptor bound 2 (Grb‐2), while the second two tyrosines (274 and 299 in the BALB/C mouse strain) are within peptide sequences that are identifiable as immunotyrosine inhibitory motifs (ITIMs) or an inhibitory tyrosine‐based switch motif (ITSM) (Gavrieli et al., 2003) (Fig. 4B). We have documented that the second and third tyrosine motifs show inducible phosphorylation and association with the tyrosine phosphatases SHP1

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and SHP2 and that this association requires that both ITIM and ITSM motifs be phosphorylated and participate in the interaction (Gavrieli et al., 2003; Han et al., 2004; Watanabe et al., 2003) (Fig. 4A). The requirement for two phosphorylated tyrosine motifs to mediate association with SHP1/SHP2 is also seen in the associations of at least two other receptors, platelet‐derived growth factor (PDGF) and platelet endothelial cell adhesion molecule‐1 (PECAM‐1) receptors (Jackson et al., 1997; Ronnstrand et al., 1999). Notably, by a cell–cell interaction, HVEM expression in the BJAB (B cell) tumor line could induce BTLA phosphorylation and SHP2 recruitment in the EL4 (thymoma) tumor line (Sedy et al., 2005). At present, the targets downstream of SHP1 and SHP2 on BTLA cross‐ linking are unknown, although it is possible that these phophatases play a role in dephosphorylating signaling intermediates downstream of antigen receptors in lymphocytes (Gavrieli et al., 2003; Lee et al., 1998; Okazaki et al., 2001; Watanabe et al., 2003) or in specifically targeting the PI3K–AKT pathway, as proposed for PD‐1. Despite the prediction that Y245 of BTLA may recruit GRB2, direct evidence of protein recruitment to this tyrosine‐containing motif has been elusive. Nevertheless, this motif YXN is similar to the YMNM motif in CD28 that recruits GRB2 and PI3K (Alegre et al., 2001). PI3K activation induced by CD28 ligation activates the PI3K–AKT pathway leading to the production of IL‐2 and cell‐survival signals (Alegre et al., 2001). Recent findings confirm that a phosphorylated peptide containing the N‐terminal tyrosine motif of BTLA can interact with GRB2 and the p85 subunit of phosphatidylinositol 3‐kinase (p85 PI3K) in vitro (Gavrieli and Murphy, 2006) (Fig. 4A). While this sequence motif from BTLA was a recognized Grb‐2 recruitment site, it was unexpected that it interacts with the p85 PI3K, since the sequence within BTLA within this peptide has not been reported as a consensus motif for p85 recruitment (Okkenhaug and Vanhaesebroeck, 2003; Rudd and Schneider, 2003). It is conceivable that the recruitment of p85 PI3K to the phosphopeptide is indirect. An association between proline‐rich domain of p85 PI3K with the Grb‐2 SH3 region has been previously reported (Wang et al., 1995), Grb‐2 may be an adaptor for recruiting the p85 PI3K subunit to BTLA. In either case, such an association could provide a prosurvival signal on engagement of BTLA, similar to engagement of CD28 and ICOS and other receptors that recruit p85 PI3K. We have not yet analyzed native BTLA for phosphorylation on each of the individual tyrosine‐based motifs in cell lines or cells ex vivo, due to the lack of specific antibodies recognizing these phosphotyrosine peptide sequences. We are therefore unable to identify conditions for cellular activation which may lead to the selective phosphorylation of either the membrane‐proximal tyrosine motif within BTLA, or alternatively, the selective phosphorylation of the second and third ITIM and ITSM motifs.

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Further, we have been unable to directly demonstrate an association of either Grb‐2 or p85 PI3K subunit with the full‐length BTLA in IP Western analysis from activated cell lines or primary T or B lymphocytes ex vivo isolated from mice, but association of BTLA with either Grb‐2 or PI3K may require the isolation of lymphocytes from in vivo conditions harvested at the appropriate time within an ongoing immune response. 5.4. Differences Between Human and Murine BTLA Signaling The initial description of BTLA included the identification of both the human and the mouse forms, the indication that this product acted as an inhibitor and showed an association of the phosphorylated cytoplasmic domain with the phosphatases SHP1 and SHP2 (Watanabe et al., 2003). The mouse BTLA gene interacts with SHP1 and SHP2 via two tyrosine motifs that are conserved between the human and the mouse genes (Gavrieli et al., 2003). These studies did not claim that the association with SHP1 or SHP2 was required for the inhibitory actions of BTLA, since no mutations of BTLA in the ITIM domains were analyzed by function. Recently, a study analyzing the human BTLA gene was reported (Riley and June, 2005). This study demonstrated that the ITIMs that are conserved between human and mouse BTLA are not required for inhibitory activity of BTLA, at least as assayed by the inhibition of IL‐2 production when chimeras of CD28 and the cytoplasmic domains of human BTLA are cross‐linked at the cell surface. However, this study did confirm that phosphorylation of the BTLA cytoplasmic domains is likely to be involved in the inhibitory effect, since mutation of all tyrosines in the human BTLA tail did lead to loss of IL‐2 inhibition (Riley and June, 2005). This may indicate that the human BTLA contains redundant inhibitory motifs, which may use pathways that do not require recruitment of SHP1 and SHP2. Furthermore, another recent study of human BTLA regulation has suggested that BTLA sends a constitutive inhibitory signal to T cells (Otsuki et al., 2006). This study showed that like the mouse BTLA protein, that naı¨ve T cells express constitutive BTLA, which then is progressively decreased on activation. Both TH1 and TH2 cells later contained positive and negative populations for BTLA expression, suggesting that the TH1‐selective expression of BTLA seen in the mouse may not be preserved in the human system. In the mouse system, BTLA remains inducible on TH1 cells, although its expression is still detectible when cells become quiescent at the end of a cycle of in vitro activation. The differences reported for the induction and the maintenance of BTLA expression between human and mouse cells may still be a result of unrecognized difference in the conditions or protocols used. But it will be important to document real differences in BTLA signaling and expression that may exist between these

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species. There are apparently real expression polymorphisms for BTLA between various mouse strains, as well as sequence polymorphism that generate distinct proteins (Hurchla et al., 2005). Since the overall phenotype of the BTLA knockout is relatively subtle (Deppong et al., 2006; Han et al., 2004; Tao et al., 2005; Watanabe et al., 2003), it is possible that extensive polymorphisms in both structure and regulation are tolerated for BTLA, which may generate subtle variations in various aspects of its inhibitory regulation.

5.5. Role of BTLA and PD‐1 in Anergic T Cells PD‐1, which is highly expressed on lymphocytic choriomeningitis virus‐specific T cells that are chronically exposed to antigen, was recently shown to be critical in regulating the late phase of viral CD8þ T cell responses by inducing T cell exhaustion (Barber et al., 2006). Importantly, blockade of the PD‐1–PDL1 axis caused these cells to regain their capacity to proliferate, in essence reversing their ‘‘exhausted’’ phenotype. BTLA was also identified as a transcript that is highly expressed by CD4þ T cells exposed to chronic antigens (Hurchla et al., 2005). It is unclear what the function of elevated BTLA expression may be in anergic T cells, but it is possible that BTLA, like PD‐1, is involved in maintaining the anergic state of T cells.

6. Consequences of HVEM Ligation 6.1. HVEM Signaling in T Cells HVEM was grouped with the costimulatory TNFRs 4‐1BB, CD27, CD30, and OX40 based on similarity in its ability to activate downstream effector pathways following ligation (Croft, 2003; Watts, 2005). Similar to the other costimulatory TNFRs, the cytoplasmic domain of HVEM lacks a death domain but recruits the signaling molecules TNFR‐associated factor (TRAF) 1, 2, 3, and 5 (Hsu et al., 1997; Kwon et al., 1997; Marsters et al., 1997; Montgomery et al., 1996). HVEM signals the activation of NF‐kB and activator protein 1 (AP1) transcription factors, and this is thought to contribute to T cell survival and inflammation in vivo (Croft, 2003; Granger and Rickert, 2003; Harrop et al., 1998a; Hsu et al., 1997; Marsters et al., 1997). Treatment of T cells with recombinant LIGHT activates NF‐kB (Tamada et al., 2000a), and this is attributed to HVEM‐mediated signaling because T cells do not express LTbR, the other receptor for LIGHT (Force et al., 1995; Murphy et al., 1998), although the relevance of these biochemical findings to costimulation mediated by HVEM has not been specifically tested.

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HVEM expressed by T cells was initially expected to have costimulatory activity based on the in vitro inhibition of human T cell proliferation in a mixed lymphocyte reaction (MLR) caused by HVEM Fc fusion proteins which were thought to block HVEM interactions with LIGHT (Kwon et al., 1997; Tamada et al., 2000a). However, in human T cells HVEM Fc fusion proteins have also been shown to directly costimulate T cell proliferation, indicating that human T cells may be activated through LIGHT receptors (Wan et al., 2002), while in mouse T cells HVEM Fc fusion proteins have been used to directly inhibit proliferation through the activation of BTLA (Cheung et al., 2005; Gonzalez et al., 2005). One potential explanation for these conflicting results may be that in humans HVEM has at least a tenfold greater affinity for LIGHT than for BTLA (Cheung et al., 2005), and assays using human cells might preferentially reveal the effects of costimulatory signals through LIGHT. In mouse cells HVEM Fc may have a predominantly inhibitory effect due to a greater affinity between HVEM and BTLA than between HVEM and LIGHT. However, direct comparisons of affinity between murine HVEM and murine LIGHT were not determined. HVEM‐specific antibodies were shown to partially inhibit T cell proliferation and cytokine production (Harrop et al., 1998b), implying that HVEM interactions with LIGHT were blocked and that HVEM otherwise delivers costimulatory signals to T cells. Costimulation of T cells through HVEM, independent of CD28, has been directly shown by treating MLRs and cultures of T cells stimulated with suboptimal doses of CD3‐specific antibodies with LIGHT (Harrop et al., 1998a; Sedy et al., 2005; Tamada et al., 2000a,b; Yu et al., 2004) (Fig. 5). This LIGHT‐mediated costimulation is thought to be mediated by a specific signal through HVEM, since the other receptor for LIGHT, LTbR, is not expressed by T cells (Force et al., 1995; Murphy et al., 1998).

6.2. Role of HVEM‐Mediated Activation in B Cells, DCs, and NK Cells HVEM is expressed by other cells in addition to T cells (Table 1, Fig. 3), including natural killer (NK) cells, DCs, B cells, peripheral blood monocytes and neutrophils, and HVEM expressed by these cells has the ability to costimulate the effector function of these cells (Fig. 5). In an in vitro model of DC maturation, a LIGHT‐expressing fibroblast cell line could induce partial maturation of DCs and could synergize with CD40 ligation for full maturation. Specifically, LIGHT‐induced expression of MHC class II, CD80 and CD86, reduced macropinocytotic activity and increased cytokine production (Morel et al., 2001) (Fig. 5).

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Figure 5 HVEM costimulates several cells in the lymphoid compartment. HVEM provides a second signal to costimulate the effector function of T cells, B cells, natural killer (NK) cells, dendritic cells (DCs), monocytes (Mono), and neutrophils (not shown). HVEM pairs with primary signals through the T cell receptor (TCR), CD40 on B cells and DCs, activating receptors on NK cells. However, HVEM stimulation alone is sufficient for immunoglobulin (Ig) production in B cells, and cytokine production and nitric oxide (NO) and reactive oxygen species (ROS) production in monocytes and neutrophils. Activated T cells or DCs have both been proposed to provide the source for the cellular expression of LIGHT, since these cells are present during active immune responses. Highlighted effects are inducible with stimulation by LIGHT alone.

These effects of LIGHT are thought to be mediated through HVEM, since naturally occurring human DCs do not express LTbR (Murphy et al., 1998). However, accumulation of DCs in lymphoid organs is LTbR dependent (Wu et al., 1999) and HVEM‐independent (Wang et al., 2005b), and BMDCs could be stimulated to proliferate using an agonistic antibody against LTbR (Wang et al., 2005b). Therefore, caution should be used in interpreting the specificity of LIGHT costimulation. Stimulation of B cells with LIGHT and CD40 ligand (CD40L) induces increased proliferation, while LIGHTalone was sufficient to induce immunoglobulin production (Duhen et al., 2004). In addition, LIGHTacts on peripheral blood monocytes and neutrophils to increase phagocytic and bactericidal activity, cytokine production, and nitric oxide and reactive oxygen species production. Although LTbR expression is absent on B cells, monocytes, and neutrophils (Murphy et al., 1998), it has not been shown whether these actions are mediated by HVEM or through an intermediate cell. However, LIGHT also activates NK cell proliferation and cytokine production in a predominantly HVEM‐dependent manner (Fan et al., 2006) (Fig. 5). All together, LIGHTexerts pleiotropic effects to augment immune responses against pathogens and tumor cells and might counterbalance inhibition exerted by HVEM ligation of BTLA expressed by T cells.

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7. BTLA and HVEM in Models of Disease 7.1. BTLA Inhibits T Cell–Dependent Inflammation One BTLA‐deficient mouse line has been reported (Watanabe et al., 2003), and a second group deleted exon 2 coding for the extracellular immunoglobulin domain with a second ‘‘ligand‐independent’’ form of BTLA remaining (Han et al., 2004). BTLA‐deficient T cells proliferate more than wild‐type T cells in response to CD3‐specific antibodies and peptide‐antigen pulsed APCs (Watanabe et al., 2003), with similar findings also reported for mice expressing only BTLAs (Han et al., 2004). Consistent with an inhibitory role for BTLA in T cells, BTLA‐deficient mice showed increased severity and duration of experimental autoimmune encephalomyelitis (EAE) (Watanabe et al., 2003). In addition, in a model of airway hypersensitivity, BTLA‐deficient mice showed increased duration of inflammation in the lung following airway challenge with antigen (Deppong et al., 2006), suggesting that BTLA does not only function during primary T cell activation (Sedy et al., 2005), but might also regulate the duration of inflammatory responses in the tissues, either by providing inhibition in the periphery, or by termination of activation when T cells circulate through the lymph node, where high levels of HVEM are expressed. An inhibitory role for BTLA on T cells is also supported by transplantation studies (Tao et al., 2005). Normally, wild‐type C57BL/6 mice tolerate MHC‐ class–II–mismatched cardiac allografts for more than 100 days. By contrast, BTLA‐deficient mice rapidly reject these partially mismatched allografts (Tao et al., 2005). There is greater expression of BTLA than of PD‐1 within these rejecting grafts, indicating that in this particular physiologic context, BTLA is the predominant inhibitory receptor. Notably, the same phenotype is found for HVEM‐deficient mice (Tao et al., 2005). In conclusion, these results indicate that BTLA and HVEM both diminish inflammatory responses and cardiac allograft rejection. 7.2. LIGHT Costimulates T Cells Through HVEM and Stimulates Inflammation Through LTbR The importance of BTLA, LIGHT, and HVEM has recently been highlighted in several studies using mice deficient in each of these molecules and using mice with constitutive T cell expression of LIGHT. It is clear from these studies that LIGHT is a potent modulator of immune responses, and deficiency in LIGHT results in significant defects in T cell activation. BTLA and HVEM deficiencies both result in similar phenotypes, in which T cell hyperactivation leads to inflammation.

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Transgenic expression of LIGHT in thymocytes and mature T cells caused massive thymocyte deletion (Wang et al., 2001a) and clonal expansion of surviving T cells in peripheral tissues (Wang et al., 2001b). In a second transgenic system in which LIGHT was expressed on peripheral T cells, similar proliferation occurred, in addition to multiple organ inflammation and immunoglobulin A (IgA) nephropathy (Shaikh et al., 2001; Wang et al., 2001b, 2004). The thymocyte deletion and hyper‐immunoglobulin A were dependent on LTbR expression and not HVEM (Wang and Fu, 2003; Wang et al., 2004). Analysis of LIGHT‐deficient mice indicates that LIGHT can costimulate T cell proliferation presumably through HVEM (Croft, 2003; Liu et al., 2003; Scheu et al., 2002; Tamada et al., 2002; Watts, 2005; Ye et al., 2002) (Fig. 5). LIGHT deficiency on T cells reduced T cell activation to CD3‐specific antibodies but not to Concanavalin A (ConA) (Liu et al., 2003; Scheu et al., 2002; Wang et al., 2005a), although this defect was not observed universally (Tamada et al., 2002). Absence of LIGHT did not reduce T cell activation in an MLR but did cause reduced IL‐2 production and proliferation in a secondary MLR response (Liu et al., 2003; Scheu et al., 2002; Tamada et al., 2002), suggesting that LIGHT costimulates T cells through HVEM. However, others have suggested that LIGHT itself is a costimulatory receptor expressed by T cells, activated by ligation with HVEM (Shi et al., 2002; Wan et al., 2002). Host expression of LIGHT seems to be critical in regulating tolerance to allografts. LIGHT‐deficient hosts tolerate fully MHC‐mismatched cardiac transplants longer than wild‐type hosts and have substantially longer engraftment with cyclosporin A treatment (Ye et al., 2002) and decreased expression within grafts of CXC ligand 10, CXC receptor 3, and IFN‐g. Similarly, hosts that are deficient for LIGHT and CD28 tolerated allogeneic skin grafts significantly longer than wild‐type mice or single deficient mice (Scheu et al., 2002). Together, these results indicate that LIGHT stimulates inflammatory responses and cardiac allograft rejection.

7.3. Deficiency in HVEM Augments T Cell–Dependent Inflammation Prior to the recognition of the interaction between BTLA and HVEM, it was expected that HVEM functioned strictly to costimulate T cell activation. However, in contrast to initial expectations, HVEM‐deficient T cells show increased proliferation and cytokine production compared to wild‐type T cells following stimulation with CD3‐specific antibodies or ConA (Wang et al., 2005a). HVEM‐deficient mice challenged with myelin oligodendrocyte glycoprotein peptide showed increased EAE severity similar to BTLA‐deficient mice (Wang et al., 2005a). Also, HVEM‐deficient mice were more sensitive to

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ConA‐challenge (Wang et al., 2005a), which was inconsistent with the expected result that HVEM deficiency would lead to decreased T cell responses. Since LIGHT‐deficient T cells did not show increased proliferation or cytokine production in response to ConA (Liu et al., 2003; Scheu et al., 2002; Wang et al., 2005a), these results suggested that HVEM was acting with an unknown partner and are consistent with HVEM acting through BTLA to inhibit T cell responses. Consistent with this interpretation, BTLA‐deficient mice did show a similar increased sensitivity to ConA‐challenge (Hurchla, M. A., and Murphy, K. M., unpublished observations) as was observed in HVEM‐deficient mice. However, it remains to be proven that the BTLA–HVEM interaction underlies this similarity. In summary, both BTLA‐deficient and HVEM‐ deficient mice show similar phenotypes of T cell hyperactivation under certain circumstances. Although this was somewhat expected for BTLA, it was rather surprising for HVEM before the recognition that this TNFR family member was the ligand for BTLA. 8. Conclusions Costimulatory signaling through HVEM is dependent on LIGHT binding and is likely due to the trimerization of the HVEM receptor (Fig. 2B). HVEM is also a ligand for LIGHT ‘‘reverse signaling,’’ although it is not clear which pathway is more relevant in vivo. ‘‘Reverse signaling’’ occurs through other TNF ligands such as through 41BBL (Langstein et al., 1998), CD30L (Wiley et al., 1996), CD40L (van Essen et al., 1995), FASL (Suzuki and Fink, 1998), OX40L (Stuber et al., 1995), and membrane TNF (Kirchner et al., 2004). It is unclear whether HVEM receives signals through BTLA, although we have not observed APC expressed BTLA to costimulate T cell proliferation (Sedy et al., 2005), and BTLA is unlikely to induce trimerization and canonical TNFR signaling of HVEM similar to LIGHT (Fig. 2B). The widespread expression of BTLA and HVEM suggests that these receptors regulate many cell types. In addition to its costimulating activity, HVEM cooperates with LTbR to induce apoptosis of tumor cell lines (Zhai et al., 1998). It is still necessary to evaluate the actions of BTLA on cells besides T cells. B cells express the highest levels of BTLA, yet we still do not understand the purpose of this expression. In the context of immune regulation, loss of inhibition is generally associated with autoimmune diseases (Gough et al., 2005; Greenwald et al., 2005; Okazaki and Wang, 2005). Ongoing unpublished work is currently directed at this aspect of BTLA. In addition, there are presently preliminary findings to suggest that a search for linkage of BTLA polymorphisms to human autoimmunity will result in a positive correlation (Watanbe, N., unpublished observations) and that BTLA polymorphisms contribute

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to autoimmune activation. A recent study excluded an association of systemic lupus erythematosus with predominant alleles of HVEM in European Caucasians (Chadha et al., 2006), but did not sufficiently evaluate rare alleles. At present, one can confidently say that the interaction of BTLA and HVEM is conserved across species and can exert inhibitory regulation for initiation and maintenance of ongoing T cell responses. Much additional work is still required to fully understand the importance of this unusual ligand–receptor pair. References Alegre, M. L., Frauwirth, K. A., and Thompson, C. B. (2001). T‐cell regulation by CD28 and CTLA‐4. Nat. Rev. Immunol. 1, 220–228. Banner, D. W., D’Arcy, A., Janes, W., Gentz, R., Schoenfeld, H. J., Broger, C., Loetscher, H., and Lesslauer, W. (1993). Crystal structure of the soluble human 55 kd TNF receptor‐human TNF beta complex: Implications for TNF receptor activation. Cell 73, 431–445. Barber, D. L., Wherry, E. J., Masopust, D., Zhu, B., Allison, J. P., Sharpe, A. H., Freeman, G. J., and Ahmed, R. (2006). Restoring function in exhausted CD8 T cells during chronic viral infection. Nature 439, 682–687. Baroja, M. L., Vijayakrishnan, L., Bettelli, E., Darlington, P. J., Chau, T. A., Ling, V., Collins, M., Carreno, B. M., Madrenas, J., and Kuchroo, V. K. (2002). Inhibition of CTLA‐4 function by the regulatory subunit of serine/threonine phosphatase 2A. J. Immunol. 168, 5070–5078. Benedict, C. A., Butrovich, K. D., Lurain, N. S., Corbeil, J., Rooney, I., Schneider, P., Tschopp, J., and Ware, C. F. (1999). Cutting edge: A novel viral TNF receptor superfamily member in virulent strains of human cytomegalovirus. J. Immunol. 162, 6967–6970. Bodmer, J. L., Schneider, P., and Tschopp, J. (2002). The molecular architecture of the TNF superfamily. Trends Biochem. Sci. 27, 19–26. Boudreau, R. T., and Hoskin, D. W. (2005). The use of okadaic acid to elucidate the intracellular role(s) of protein phosphatase 2A: Lessons from the mast cell model system. Int. Immunopharmacol. 5, 1507–1518. Bradshaw, J. D., Lu, P., Leytze, G., Rodgers, J., Schieven, G. L., Bennett, K. L., Linsley, P. S., and Kurtz, S. E. (1997). Interaction of the cytoplasmic tail of CTLA‐4 (CD152) with a clathrin‐ associated protein is negatively regulated by tyrosine phosphorylation. Biochemistry 36, 15975–15982. Campadelli‐Fiume, G., Cocchi, F., Menotti, L., and Lopez, M. (2000). The novel receptors that mediate the entry of herpes simplex viruses and animal alphaherpesviruses into cells. Rev. Med. Virol. 10, 305–319. Carfi, A., Willis, S. H., Whitbeck, J. C., Krummenacher, C., Cohen, G. H., Eisenberg, R. J., and Wiley, D. C. (2001). Herpes simplex virus glycoprotein D bound to the human receptor HveA. Mol. Cell 8, 169–179. Chadha, S., Miller, K., Farwell, L., Sacks, S., Daly, M. J., Rioux, J. D., and Vyse, T. J. (2006). Haplotype analysis of tumour necrosis factor receptor genes in 1p36: No evidence for association with systemic lupus erythematosus. Eur. J. Hum. Genet. 14, 69–78. Chemnitz, J. M., Parry, R. V., Nichols, K. E., June, C. H., and Riley, J. L. (2004). SHP‐1 and SHP‐2 associate with immunoreceptor tyrosine‐based switch motif of programmed death 1 upon primary human T cell stimulation, but only receptor ligation prevents T cell activation. J. Immunol. 173, 945–954.

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The Human T Cell Response to Melanoma Antigens Pedro Romero, Jean‐Charles Cerottini, and Daniel E. Speiser Division of Clinical Onco‐Immunology, Ludwig Institute for Cancer Research, Lausanne Branch, University Hospital (CHUV), Lausanne, Switzerland; and National Center for Competence in Research (NCCR), Molecular Oncology, Epalinges, Switzerland

1. 2. 3. 4. 5. 6. 7.

Abstract............................................................................................................. Introduction ....................................................................................................... Melanoma Antigens ............................................................................................. Measurement of Antigen‐Specific T Cell Responses................................................... Naturally Acquired Tumor Antigen‐Specific T Cell Responses ..................................... Vaccine‐Induced T Cell Responses ......................................................................... Regulation of Tumor Antigen‐Specific T Cell Responses............................................. Conclusions........................................................................................................ References .........................................................................................................

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Abstract The cornerstone of the concept of immunosurveillance in cancer should be the experimental demonstration of immune responses able to alter the course of in vivo spontaneous tumor progression. Elegant genetic manipulation of the mouse immune system has proved this tenet. In parallel, progress in understanding human T cell mediated immunity has allowed to document the existence in cancer patients of naturally acquired T cell responses to molecularly defined tumor antigens. Various attributes of cutaneous melanoma tumors, notably their adaptability to in vitro tissue culture conditions, have contributed to convert this tumor in the prototype for studies of human antitumor immune responses. As a consequence, the first human cytolytic T lymphocyte (CTL)‐defined tumor antigen and numerous others have been identified using lymphocyte material from patients bearing this tumor, detailed analyses of specific T cell responses have been reported and a relatively large number of clinical trials of vaccination have been performed in the last 15 years. Thus, the ‘‘melanoma model’’ continues to provide valuable insights to guide the development of clinically effective cancer therapies based on the recruitment of the immune system. This chapter reviews recent knowledge on human CD8 and CD4 T cell responses to melanoma antigens.

187 advances in immunology, vol. 92 # 2006 Elsevier Inc. All rights reserved.

0065-2776/06 $35.00 DOI: 10.1016/S0065-2776(06)92005-7

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1. Introduction Although cutaneous melanoma represents <5% of skin cancers, it has become a major public health problem in many countries due to a steady increase in its incidence. While melanoma is curable if diagnosed early and surgically excised, little progress has been made in medical treatment of metastatic melanoma because of its poor response to current systemic therapies. Melanoma cells are derived from normal melanocytes, which are confined to the basal layer of the epidermis where they are interspersed among many more numerous keratinocytes. Substantial advances have been made recently in understanding the molecular pathogenesis of melanoma development and progression, but the potential of the identified pathways as therapeutic targets remains to be assessed. In contrast, immunotherapeutic approaches have been initiated long time ago. Occasional spontaneous regression of melanomas, an increased risk for melanoma in transplant patients receiving immunosuppression as compared to the general population, as well as the good prognostic value of tumor infiltrating lymphocytes (TILs) within a primary melanoma in a vertical growth phase or regional lymph node metastases in all suggested that tumor recognition by the immune system of melanoma patients may occur spontaneously. Direct evidence for such a contention has been provided by fine specificity analysis of tumor‐reactive CD8þ or CD4þ T cell clones derived from melanoma patients. Following molecular definition of melanoma antigens, antigen‐specific vaccines have been developed and many clinical studies have been implemented over the last 15 years. Such studies have benefited from the great progress made recently in monitoring antigen‐specific T cell responses in humans. There are now a number of new techniques that allow dissection of the molecular features of human antigen‐specific T cell responses in great detail. Application of these techniques to longitudinal analysis of responses in individual melanoma patients has provided new essential information on the complexity of antigen‐specific T cell responses in humans. In this chapter, we discuss the results of various immunological studies regarding antitumoral T cell responses elicited in melanoma patients either spontaneously or following antigen‐specific vaccination, with some emphasis given on recent findings from our laboratory. Recent advances in adoptive transfer therapy with tumor antigen‐specific T cells in melanoma have been reviewed recently elsewhere (Mahnke et al., 2005; Wrzesinski and Restifo, 2005) and are not discussed in this chapter. 2. Melanoma Antigens A milestone in human tumor cell–mediated immunity was the first molecular characterization of a melanoma antigen recognized by autologous cloned CD8þ cytolytic T lymphocyte (CTL) that were derived from a melanoma patient

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(van der Bruggen et al., 1991). This finding opened the way to the identification of many T cell–defined tumor antigens, a listing of which can be found on the website http://www.cancerimmunity.org/peptidedatabase/Tcellepitopes.htm. The original autologous‐mixed lymphocyte‐tumor cell culture system, used to isolate tumor‐reactive CTL clones, continues to be a rich source of tumor antigen specific CD8 T cells for tumor antigen identification (Lennerz et al., 2005). Tumor antigens need to be processed and presented by HLA molecules at the surface of tumor cells in order to allow recognition and destruction by tumor‐reactive T cells. It should be emphasized that several of the proposed tumor antigens remain to be validated by tumor cell recognition assays using well‐defined T cell clones. Further studies are also necessary to elucidate mechanisms of tumor antigen processing and presentation by tumor cells and dendritic cells (DCs). Tumor antigens can be classified as (1) differentiation antigens, (2) shared tumor‐specific antigens, and (3) overexpressed antigens. Well‐known examples of differentiation antigens that are used to vaccinate melanoma patients are Melan‐A/MART‐1, gp100, and tyrosinase. As these antigens are self‐antigens, that is, they are expressed by normal melanocytes, it is noteworthy that they can elicit strong T cell responses under special circumstances, indicating that immunological tolerance against them is far from complete. Despite many studies on differentiation antigens, several issues regarding expression, processing and presentation by major histocompatibility complex (MHC) molecules remain to be clarified (Chapatte et al., 2004). The prototypes of shared tumor‐specific antigens included in current vaccines are MAGE‐A1, the first CTL‐defined human tumor antigen (van der Bruggen et al., 1991), MAGE‐A3, and NY‐ESO‐1. These antigens are expressed in many tumors and in male germ line cells, but not in other normal tissues (Simpson et al., 2005). Also designated cancer/testis (CT) antigens, they appear to be expressed at least in part as a result of promoter hypomethylation. A recent study indicates that the transcription factor BORIS (Brother of the Regulator of Imprinted Sites), a gene duplicated from the zinc‐finger protein gene CTCF (Klenova et al., 2002), may induce demethylation of CT antigen‐encoding genes (Hong et al., 2005; Vatolin et al., 2005). It should be stressed that male germ line cells do not express MHC molecules on their surface and, hence, are not recognized by CT antigen‐specific T cells. In contrast to CT antigens, which are strictly tumor specific, overexpressed antigens, such as survivin or telomerase, are also present in many normal cells. Controversies exist as to the expression of defined epitopes on tumor cells, and further studies are needed to evaluate their potential as tumor targets (Ayyoub et al., 2001; Parkhurst et al., 2004; Schmidt et al., 2003; Schmitz et al., 2000; Vonderheide et al., 1999, 2004).

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Tumor antigen expression varies with disease stage (Simpson et al., 2005). Such variation is due to tumor cell differentiation as well as selective pressure by antigen‐specific T cells that involves mechanisms mediating immune escape. Several CT antigens are increasingly expressed with disease progression, while melanoma differentiation antigens are expressed at very high frequencies that do not differ between early and late stage disease (Barrow et al., 2006; Hodi, 2006). It remains to be determined which tumor antigens are optimal targets for protective immunity. Studies in melanoma patients have contributed a lot to the understanding of human tumor immunology to a good part due to in‐depth studies of T cell responses to the differentiation antigens Melan‐A/MART‐1, gp100, and tyrosinase (Michielin et al., 2005; Nagorsen et al., 2003; Rosenberg, 2004). Focusing on understanding antigen‐specific T cell responses is supported by the generally accepted notion that the latter are central players of immunity against solid tumors. The complexity inherent to the understanding of protective T cell immunity and the development of efficacious human T cell vaccines requires to focus initial studies on a small number of target antigens, thus allowing detailed investigation of mechanisms of human tumor antigen‐specific immune responses. 3. Measurement of Antigen‐Specific T Cell Responses Several techniques to directly assess antigen‐specific T cell responses have been developed recently (Bakker and Schumacher, 2005; Maino and Maecker, 2004; Rufer, 2005; Speiser, 2005). However, there are still major limitations, particularly with respect to the understanding and definition of protective T cell immunity. How can one distinguish protective from nonprotective T cells? The currently used strategy is to investigate a series of T cell parameters and to carefully assess correlations with disease outcome both in animal models and in patients. Crucial parameters are (1) T cell frequency and proliferative potential, (2) T cell differentiation and function, and (3) homing and function in peripheral (diseased) tissues. Although difficult to assess, T cell receptors (TCRs) affinity/avidity to cognate peptide‐MHC are also important, emphasizing the need for (4) investigations at the single cell level or at least at the level of individual T cell clones. An important technical aspect is the handling of T cells between collection from the patient and subsequent analysis (Fig. 1). The impact of cryopreservation on T cell function has been studied in detail, and an optimized protocol has been proposed (Disis et al., 2006). During many years, T cells were cultured for several days to weeks, mostly with the purpose to amplify the number of antigen‐specific T cells such that they could be analyzed in detail. If carefully monitored, this approach allows some degree of T cell quantification (e.g., when

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Figure 1 Advantages of studying antigen‐specific T cells freshly isolated from patients (i.e., ‘‘ex vivo’’) are (1) precise quantification, for example, by ELISPOT and/or multimer analysis, and (2) detailed characterization of cellular and molecular characteristics as they prevail in the patient. By contrast, analyses after in vitro stimulation (IVS) do not permit T cell quantification, unless detailed limiting dilution studies are performed, which require large cell numbers. Cellular and molecular expression studies on cultured cells provide many irrelevant results since their relations to the in vivo situation remain unknown.

combined with limiting dilution analysis, use of multimers, or ELISPOT assays). However, due to the failure of some T cells to proliferate in vitro, this approach is error prone. Moreover, qualitative assessment at the level of gene expression, phenotype, differentiation, and function cannot be made because cultured T cells are always a selected population of cells that undergo numerous changes during culture and are not necessarily representative of the T cells present in the patient (Fig. 1). Therefore, the field is moving rapidly to ex vivo analysis, that is, to investigation of T cells freshly isolated from patients. 3.1. Measuring CD8 T Cells Fluorescent peptide‐MHC‐I (pMHC) multimers (Altman et al., 1996; Bakker and Schumacher, 2005) allow direct identification, enumeration, phenotyping,

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and isolation of antigen‐specific T cells by high‐speed multicolor flow cytometry. Phenotypic analysis is done with the aid of fluorescent antibodies to various cell surface molecules associated with Tcell differentiation. A combination of markers that is gaining wide recognition is CD45RA, CCR7, CD28, and CD27 (Appay and Rowland‐Jones, 2004; Rufer, 2005; Rufer et al., 2003; van Lier et al., 2003). Phenotyping has recently been coupled with intracellular cytokine staining (Maino and Maecker, 2004). The prime factor limiting the number of assessable parameters seems to be the number of discernible fluorochrome conjugates that can be included in the same analysis. For instance, one format successfully used in the context of T cell responses to vaccination with Hepatitis B virus (HBV) and tetanus antigens calls for 14‐parameters, 12‐color flow cytometry analysis. This survey allowed to distinguish as many as 32 discrete functional subpopulations of Tcells with different profiles of production of 5 cytokines (De Rosa et al., 2004). However, it remains unclear which of the functionally different T cell subpopulations identified in this manner would correlate with clinical outcome. In experimental tumor rejection models, IFN‐g has been singled out as essential for protection mediated by CD8 T cells (Qin et al., 2003; Schuler and Blankenstein, 2003), but further analyses of this kind are needed, including direct assays to assess lytic activity. Recently, highly sensitive flow cytometry–based techniques have been proposed to replace the standard 51Cr‐release assay for the evaluation of antigen‐specific cytotoxicity, including the flow cytometry CTL (FCC) assay (Liu et al., 2002), the fluorometric assessment of T lymphocyte antigen‐specific lysis (FATAL) assay (Sheehy et al., 2001), the fluorolysis assay (Kienzle et al., 2002), and the versatile fluorometric technique (VITAL) assay (Hermans et al., 2004). At present, there are no studies comparing the suitability of these assays for monitoring clinical studies. In any event, cytolytic assays only provide an indirect measurement of CTL effector function by examining the death of target cells and therefore do not provide information on the actual CTL frequency. The lysosomal‐associated membrane glycoproteins (LAMPs), CD107a (LAMP‐1) and CD107b (LAMP‐2), are transiently expressed on the T cell surface on degranulation of perforin‐ and granzymes‐containing lytic granules (Betts et al., 2003). This has been exploited to identify tumor antigen‐specific cytolytic T cells that bind pMHC multimers and kill in an antigen‐specific manner (Rubio et al., 2003). Nevertheless, CD107a is sometimes expressed by T cells that do not kill (Wolint et al., 2004). In turn, this assay may miss those CTLs that kill via granule‐ independent pathways, namely, in a death receptor‐dependent manner. In any case, and for any of these assays to be adopted in the clinical setting, exploration of their sensitivity, specificity, and reproducibility is required. Along this line, we have recently combined multimer sorting of low numbers of antigen‐ specific CD8 T cells with flow cytometry–based monitoring of lytic activity to assess directly their lytic potential (Devevre et al., 2006). Recently, functional

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microarrays incorporating pMHCs and anti‐cytokine antibodies have been proposed for the high‐throughput monitoring of melanoma antigen‐specific T cell responses in patients (Chen et al., 2005a). Another widely used assay is the IFN‐g ELISPOT assay. Like multimers, it also allows direct ex vivo enumeration of single antigen‐specific CD8 T cells. In contrast to multimers, it measures effector memory and effector CD8 T cells, which are able to rapidly secrete IFN‐g in response to challenge with their cognate antigen. Both multimer and IFN‐g ELISPOT assays can and should be standardized. Some reports generally coincide in showing that their sensitivity is identical. Both assays have the power to detect one specific T cell in 10,000 lymphocytes (0.01%). As the analysis of at least 300,000–500,000 CD8 T cells requires, about 10‐ml heparinized blood, it is essential to use carefully validated reagents and methods. Implementation of standardized protocols in experienced laboratories permits coefficients of variation (up to 30%) that are well within acceptable limits of good laboratory practice (Comin‐Anduix et al., 2006; Hobeika et al., 2005; Keilholz et al., 2002; Speiser et al., 2004). 3.2. Measuring CD4 T Cells Fluorescent MHC‐II/peptide multimers have also been successfully developed albeit with more technical difficulties (Nepom, 2005). Although in principle these reagents open the way to direct quantitation of antigen‐specific CD4 T cell responses, the frequencies of such cells are generally lower than those of single antigen‐specific CD8 T cell responses. The monitoring of cell divisions by flow cytometry combined with MHC‐II/peptide multimers after 5‐day antigen‐driven expansion has been used to calculate frequencies of T cells otherwise below or at the lower multimer detection limit (Danke and Kwok, 2003). Alternatively, MHC‐II/peptide multimers can be used as read out of classical limiting dilution analysis to calculate specific T cell precursor frequencies (Bioley et al., 2006b, submitted for publication). Additional specific features of antigen recognition by CD4 T cells limit the usefulness of the multimer approach to immunomonitoring. The polymorphism of MHC‐II genes is greater than that of MHC‐I genes. Moreover, antigenic peptides frequently bind to multiple MHC‐II alleles, and the overall number of class II–restricted epitopes per polypeptide is greater than MHC‐I–restricted peptides. For these reasons, a more comprehensive approach amenable to standardization for the ex vivo measurement of CD4 T cell responses to a given protein target is the use of pools of synthetic peptides with lengths close to 15 amino acids to trigger a cytokine response in short‐term cultures followed by intracellular cytokine staining (Kern et al., 2005; Maecker et al., 2005).

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A minor but distinct subset of naturally occurring CD4 T cells with regulatory activity (Treg) has come into sharp focus as it has been implicated in the inhibition of effector T cell responses in autoimmunity and tumors (Yamaguchi and Sakaguchi, 2006). However, the identity of this CD4 T cell subset remained controversial due to the lack of specific marker(s). The cloning of the transcription factor Foxp3 as the mutated gene in an X‐linked lymphoproliferative disease in both mice (Brunkow et al., 2001) and humans (Bennett et al., 2001) provided the first clue to the involvement of such transcription factor in Treg biology. It has been now established that Foxp3 plays a critical role in both the development and function of natural Tregs (Fontenot et al., 2003; Hori et al., 2003; Khattri et al., 2003). Moreover, Foxp3 specifies Treg cell fate and identifies Tregs independently of the expression of any known cell surface marker (Fontenot et al., 2005). Thus, monitoring including identification at the single cell level of human Tregs is now possible with the help of Foxp3‐specific monoclonal antibodies for flow cytometry (Cesana et al., 2006) or immunohistology (Roncador et al., 2005). 3.3. In‐Depth Molecular Monitoring of T Cell Responses A parameter that is essential for effective in vivo tumor antigen recognition is TCR affinity. Unfortunately, there are no direct methods to measure this parameter directly on live T cells. Substitutes include determination of the relative peptide‐MHC multimer intensity of labeling or peptide dose–response analysis as assessed in either chromium release or intracellular cytokine‐labeling assays. A more accurate method may be the use of multimer dissociation assays (Valmori et al., 2002a), but their interpretation remains a controversial issue (Wang and Altman, 2003). A useful simplification could be an assay that selectively identifies T cells expressing high‐affinity TCR. Such a possibility has been proposed for CD8 T cells based on the use of multimers containing MHC class I molecules bearing structural modifications that greatly reduce their ability to interact with the CD8 coreceptor (Choi et al., 2003; Pittet et al., 2003). Two recent studies (Price et al., 2005; Trautmann et al., 2005) have shown that clonal expansion of CD8 T cells specific for herpesviruses is particularly marked in acute disease and more pronounced in (highly) differentiated effector Tcells that recognize peptide‐MHC complexes with higher avidity and/or CD8 independently. These findings support the notion that T cell ‘‘avidity’’ shapes clonal dominance. However, it remains difficult to distinguish CD8 dependence from TCR affinity, leaving the possibility open that that the two parameters play distinct roles in T cell differentiation and protective immunity. Recent developments apply modern molecular biology techniques to assess gene expression by antigen‐specific T cells. These approaches are particularly

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informative when T cells are assessed ex vivo (Rufer, 2005; Speiser, 2005). While such strategies have already revealed differentiation to effector cells by some but not all tumor antigen‐specific T cells detected by multimers, they have high potential to further elucidate molecular mechanisms of responding T cells. Besides the analysis of T cell activation, differentiation, and function, studies of in vivo TCR usage are a central issue in the analysis of T cell responses. Not surprisingly, the more sophisticated the techniques become, the more likely they identify mono‐ or oligoclonal T cell populations that dominate responses to a particular epitope. Recent studies of CD8 T cell responses against epitopes from CMV and EBV revealed strong selection of particular T cell clones in vivo, which correlated with TCR avidity and was particularly pronounced in patients with increased viral activity (Price et al., 2005; Trautmann et al., 2005). In vivo clonal focusing can also be seen in human tumor antigen‐specific T cell responses. The dissection of T cell responses at the clonal level opens the opportunity to investigate many aspects of T cell biology. For example, after adoptive transfer, long‐term dominant clones were described to bear long telomers (Huang et al., 2005; Zhou et al., 2005). The ability to maintain reasonably long telomers appears to be necessary for long‐term persistence. However, we recently observed a dominant Melan‐A‐specific T cell clone persisting at >2% of circulating CD8 T cells for >4 years despite relative rapid telomer shortening (Speiser et al., 2006). Obviously, many more studies are necessary to identify the precise requirements for successful in vivo T cell survival and function. Finally, the postulated needs and advantages of heterologous prime/boost strategies (Palmowski et al., 2002; Robinson and Amara, 2005) may be elucidated through careful clonal analysis of responding T cells. 3.4. Assessing T Cells in Peripheral Tissues In situ multimer staining (Skinner et al., 2000) has been developed to reveal tumor antigen‐specific T cell infiltrations in tumor tissue and sentinel lymph nodes (Andersen et al., 2001), as well as in skin biopsies from the inoculation site following administration of a DC vaccine (Schrama et al., 2002). However, the widespread application of this technique remains difficult. Current approaches to in situ/in vivo monitoring of tumor antigen‐specific T cell responses call for sequential fine‐needle aspiration biopsies from the same tumor site(s) in combination with gene expression profiling and immunohistology (Nagorsen et al., 2002; Wang et al., 2005). Unfortunately, the limited number of lymphocytes that may be recovered from these specimens precludes any antigen‐specific T cell flow cytometry analyses. New noninvasive methods are emerging that enable spatiotemporal tracking of antigen‐specific T cells in their native environment (Bulte et al., 2001;

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Kircher et al., 2003; Koehne et al., 2003; Mempel et al., 2004). High‐resolution small animal imaging systems are now being used, while new imaging agents and a variety of imaging transgenes are available for experimental use (Blasberg and Tjuvajev, 2003; Gambhir, 2002; Gross and Piwnica‐Worms, 2005; Michalet et al., 2005; Ntziachristos et al., 2005; Shaner et al., 2005; Weissleder, 2002; Weissleder and Ntziachristos, 2003; Welsh and Kay, 2005). A number of different techniques have been validated for cell tracking that fall into two categories: (1) the use of genetically modified cells (e.g., expressing fluorescent proteins, luciferases, or other imaging transgenes such as HSV‐thymidine kinase) and (2) the use of chemical labels retained by target cells (e.g., ‘‘cell‐trackers’’ such as isotope‐labeled molecules or multimodal nanoparticles). Ultimately, cell trackers could be used clinically in cell‐based therapies to monitor administered cells using noninvasive and three‐dimensional technologies such as magnetic or nuclear (e.g., single photon emission computed tomography, SPECT) imaging systems. Magnetic resonance imaging (MRI) permits visualization with high spatial resolution of whole organisms over time and is therefore ideally suited to monitor cell trafficking in vivo. Novel biocompatible and physiologically inert magnetic nanoparticles allow for fast and highly efficient intracellular labeling of a variety of cell types and in vivo MRI tracking at near single cell resolution (de Vries et al., 2005; Kircher et al., 2003; Lewin et al., 2000; Morawski et al., 2005). SPECT imaging also permits quantitative measurements of isotope‐labeled cells in the whole body. The recent combination of SPECT with X‐ray computed tomography now allows fusion of molecular data with anatomic data for improved spatial mapping. SPECT‐CT imaging systems have recently become available for clinical and mouse imaging. A number of labeling approaches exist for nuclear imaging of cells (Blasberg and Tjuvajev, 2003; Gambhir, 2002); Indium‐ oxyquinoline (111In‐oxine) is an FDA‐approved clinical agent to label leukocytes for the scintigraphic diagnosis of occult infections. Thus both magnetic and nuclear imaging approaches seem uniquely suited for repetitive imaging of adoptively transferred cells and for objective clinical evaluation of cell‐based therapies. 4. Naturally Acquired Tumor Antigen‐Specific T Cell Responses 4.1. CD8 T Cell Responses Extensive use of multimers incorporating various categories of tumor antigens has allowed insights in naturally acquired T cell responses against tumors, in particular in malignant melanoma. From these studies it can be concluded that most patients develop T cell responses specific for tumor antigens. However, the frequencies of these T cells in circulating blood are relatively low such that

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they cannot be readily detected in the majority of cancer patients. Some exceptions to this have been reported over the years (Lee et al., 1999; Meidenbauer et al., 2004; Valmori et al., 2002b; Walter et al., 2005). In these cases, studies of the functional competence of these cells have provided contradictory results. In the first study, T cells against tyrosinase were found to be in a profound state of anergy (Lee et al., 1999). The same dysfunction was reported in another study with anticytokeratin18‐specific CD8 T cells (Walter et al., 2005). However, two other studies reported the presence of high levels of tumor antigen‐specific T cells which were fully functional and expressed many of the characteristics of effector T cells (Meidenbauer et al., 2004; Valmori et al., 2002b). Apart from these case reports, the general finding is that tumor antigen‐specific T cells are not detectable on labeling of peripheral blood lymphocytes with multimers. Since most tumor antigen‐specific T cells are found at frequencies lower than 0.01%, their detection is indirect and depends on in vitro expansion techniques. One round of in vitro stimulation (IVS) can be sufficient to bring the levels of such cells to frequencies detectable by flow cytometry analysis with multimers (Valmori et al., 1999, 2000, 2001). Methods for the optimal activation and/or expansion of these cells have been explored (Li et al., 2005; Maus et al., 2003; Montes et al., 2005; Oelke et al., 2003; Stone et al., 2005). For some antigens, such as MAGE‐A1 and MAGE‐A3, the frequency of specific Tcells was found to be quite low (Chaux et al., 1998; Hanagiri et al., 2006). The relative low frequency of circulating T cells against well‐defined antigens precludes their direct functional assessment. Successful T cell expansion suggests that at least a fraction of these cells is fit for survival and proliferation despite that they originate from circulating blood and from patients with advanced cancer. The HLA‐A*0201–restricted peptide EAAGIGILTV derived from Melan‐A/ MART‐1 is a unique exception, since T cells specific for this epitope are present at unusually high frequencies. With ex vivo multimer analysis, they are found at frequencies of 0.07
0.06% of circulating CD8þ T cells in healthy volunteers (Fig. 2). In contrast to all other known antigen specificities, the human thymus is particularly productive in selecting and releasing these Melan‐A‐specific T cells (Pittet et al., 1999). This situation provides the unique opportunity for systematic ex vivo investigation of a tumor antigen‐specific T cell population in humans. In healthy individuals, these cells have all phenotypic and functional properties, which are characteristic for naı¨ve T cells, and bear high levels of TRECs (T cell receptor recombination excision circles) and long telomeres (Zippelius et al., 2002). Thus, they have not been activated. By contrast, in the majority of patients with stage III/IV melanoma, a fraction of Melan‐A‐specific T cells are activated and thus non‐naı¨ve. Only a small minority of these patients have spontaneously (i.e., disease induced) increased frequencies of Melan‐A‐specific T cells. Thus, spontaneous activation of circulating tumor

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Figure 2 Four cohorts of stage III/IV melanoma patients were vaccinated with the Melan‐A/ MART‐1 peptide ELAGIGILTV mixed together with the indicated adjuvants. Vaccines were given in monthly intervals subcutaneously (except for vaccines with the adjuvant AS02 which were given intramuscularly according to the manufacturers instructions). Frequencies of Melan‐A‐specific T cells were analyzed ex vivo in circulating blood withdrawn 7–11 days after booster injection. Results indicate percentages of A2/Melan‐A multimer positive cells among CD8 T cells.

antigen‐specific T cells is seen quite often, whereas the frequencies of these cells remain low in most patients. Even in metastatic tissues it is much more the activation state than the frequency of Melan‐A‐specific cells that is enhanced due to tumor‐driven activation. However, while in circulation only about 10–20% of untreated patients have >0.5% of Melan‐A‐specific T cells, values above this level can be found in roughly 50% of melanoma metastases, indicating that local accumulation of tumor antigen‐specific T cells is a frequent phenomenon. Clonal analysis reveals that Melan‐A‐specific cells from healthy volunteers comprise a wide range of TCR affinities. In fact, only about one‐third of these cells express TCRs with relatively high avidity sufficient for tumor cell recognition (Dutoit et al., 2002). Metastatic melanoma patients have a mixture of naı¨ve and antigen‐experienced T cells, expressing TCRs of low and relatively high avidity, respectively (Zippelius et al., 2002). The experimental strategy to generate T cell clones after eliminating

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naı¨ve T cells through multiparameter flow cytometry sorting was necessary and sufficient to reveal that spontaneously activated T cells from melanoma patients express TCRs that all have relatively high avidity to A*0201/Melan‐A and are competent to recognize melanoma cells efficiently. Direct assessment of tumor antigen‐specific T cells in tumor masses would require the ability to use multimers in tissue sections. Unfortunately, however, formalin fixation abrogates TCR reactivity with its ligand. Lighter fixation or labeling of fresh thick tissue sections may preserve TCR–multimer interactions (Andersen et al., 2001; Skinner et al., 2000). Besides making the procedure more cumbersome, no useful surveys of tumor antigen‐specific T cell responses in situ have been reported thus far. Thus, in situ monitoring has been limited to analysis of specific T cell responses in freshly prepared single cell suspensions from tumor fragments. As mentioned earlier, about two‐third of melanoma metastases contain relatively high levels of A2/Melan‐A multimerþ CD8þ T cells. In our experience, frequencies ranged from 0.1% to 16% of CD8þ T lymphocytes recovered from the tumor fragment (Romero et al., 1998, 2002). Other studies have confirmed the high frequencies of responses to the Melan‐ A/MART‐1 antigen in TIL populations (Benlalam et al., 2001; Seiter et al., 2002). Although these frequencies appear very high, their numerical value is dwarfed when considering the global cellular composition of the tumor mass. The number of tumor cells ranges from 50% to 90% of the recovered single cell suspension. In contrast, the number of T cells is usually around 10% in the case of the metastatic melanoma lesions analyzed. Moreover, in most cases only 10% of these T cells are CD8 T cells. Thus, at the time of surgical removal of tumor metastasis, tumor antigen‐specific CD8 T cells are vastly outnumbered by tumor cells and even by CD4þ T cells. This minority status would apply even if one assumes that all CD8 T cells present inside tumors are directed against antigens expressed by the malignant cells. In addition to measuring the numbers of antigen‐specific T cells inside tumors, it is important to obtain more information on their functional competence. A few studies have investigated this aspect taking advantage of the possibility of direct assessment of A2/Melan‐A multimerþ CD8 T cells by flow cytometry in both circulating lymphocytes and tumor fragments obtained simultaneously from the same patients. The picture that emerged from this analysis is that within the same patient there are major differences in the functional competence of tumor antigen‐specific T cells depending on their localization. While circulating A2/ Melan‐A multimerþ CD8þ T cells express high levels of granzymes A/B and perforin, and swiftly release IFN‐g after challenge with the antigen; A2/Melan‐A multimerþ CD8 T cells found in tumor lesions express low levels of perforin, low or undetectable levels of granzymes A/B, and fail to produce significant amounts of IFN‐g in response to antigen challenge. This IFN‐g hyporesponsiveness is

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antigen‐specific since the cells can produce normal levels of IFN‐g if TCR signaling is by‐passed. Another study has recently shown selective TCR unresponsiveness in tumor‐associated lymphocytes in various types of human tumors including lung carcinomas (Broderick et al., 2006). Moreover, the study of a single progressive melanoma patient showed both tumor antigen‐specific CD8 T cell hyporesponsiveness at the tumor site and additional negative regulatory mechanisms limiting the effector phase on the antitumor immune response (Harlin et al., 2006). Thus, the conclusion from these analyses is that tumor antigen‐specific T cells are rendered functionally tolerant in the tumor milieu (Mortarini et al., 2003; Zippelius et al., 2004). This may, at least in part, explain the apparent failure of tumor‐specific T cells to contain tumor growth. In this scenario, the fate of such T cells is an important issue with regard to the role that specific immunotherapy may have in advanced metastatic disease. Studies from a large number of metastatic melanoma lesions indicate that a sizable fraction of specific CD8 T cells survive and go on to proliferate vigorously once cultured in vitro in the presence of low concentrations of recombinant IL‐2 (Zippelius et al., 2004). Tumor cells are eliminated and rapidly outnumbered by proliferating T cells. IL‐2 is critical for this outcome. Moreover, independently of cell division, perforin as well as granzyme contents rapidly increase. Thus, after a short period of in vitro culture these antigen‐specific cells recover full effector functions including the ability to secrete IFN‐g in response to antigen challenge. IL‐12 was also shown to reverse anergy to TCR triggering in human lung tumor‐ associated memory T cells (Broderick et al., 2006). Thus, functional local tolerance appears to be reversible. The type of intervention that may restore T cell function in vivo remains to be identified. It may be argued that these T cells, even if properly reactivated in a cancer patient, are by definition unable to protect against tumor. A recent series of experiments may speak against this contention. Adoptive transfer of rapidly expanded TIL from metastatic melanoma has shown that they can mediate regression of even large tumors (Dudley et al., 2005). The rate of objective clinical responses reached 51% in a series of 35 melanoma patients (Dudley et al., 2005). For tumor regression to occur some conditions have to be met. First, selection of high tumor‐reactive TILs that were amplified by rapid expansion methods in vitro. Second, induction of lymphopenia by combined conditioning of the patient with cyclophosphamide and fludarabine. And third, infusion of high doses of IL‐2. It is unclear whether all three conditions are indispensible for tumor rejection. 4.2. CD4 T Cell Responses The isolation of tumor‐reactive CD4 T cell clones from either TILs (Topalian et al., 1996) or peripheral blood mononuclear cells (PBMCs) of melanoma patients provided tools to identify genes encoding the target tumor antigens via

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transfection of tumor cDNA libraries. The vectors had to be modified to ensure routing of the library gene products into the MHC class II antigen–processing and –presentation pathway (Wang et al., 1999). Since these early findings, a large number of MHC class II–restricted antigens have been discovered. Using PBMC from cancer patients, CD4 T cell responses specific for three antigens have been well studied. 4.2.1. NY‐ESO‐1 In a recent study 27 cancer patients, which included 11 with melanoma, and 4 healthy donors were analyzed for NY‐ESO‐1‐specific CD4 T cells. None of the 18 individuals that were NY‐ESO‐1 seronegative had detectable NY‐ESO‐1‐ specific CD4 T cells. In contrast, 11 of 13 patients with serum antibodies to NY‐ESO‐1 had polyclonal CD4þ T cell responses directed against various known and previously undescribed NY‐ESO‐1 epitopes. These included five melanoma patients (Gnjatic et al., 2003). The NY‐ESO‐1 peptide 80–109 was the most immunogenic, since 10 of 11 patients responded to it. This and other studies have observed that patients with antibody and CD4 specific immune responses generally also have naturally acquired specific CD8 T cell responses. Although an early study suggested a correlation between expression of HLA‐DP4 and natural humoral response to NY‐ESO‐1 (Zeng et al., 2001), a larger study including 102 cancer patients could not reproduce this association (Huarte et al., 2004). 4.2.2. SSX‐2 and SSX‐4 A survey of 61 melanoma tumors showed that 25% expressed the gene SSX‐4. Stimulation with a cocktail of SSX‐4 peptides covering the entire gene product sequence successfully expanded specific CD4 T cells in a subset of four SSX‐4þ melanoma patients, suggesting that the naturally acquired CD4 T cell response is frequent in patients with positive tumors (Ayyoub et al., 2005). Dissection of the target specificity of responding CD4 T cells uncovered 7 distinct antigenic peptides, four of which are efficiently processed by antigen‐presenting cells (APCs). An immunodominant SSX‐2‐derived epitope recognized by melanoma patient–derived CD4 T cells in an HLA‐DR‐restricted manner was recently identified (Ayyoub et al., 2004). The antigenic peptide recognized by CD4 T cells encompasses a dominant HLA‐A2 restricted epitope (Ayyoub et al., 2002). 4.2.3. Melan‐A/MART‐1 Initial studies using algorithms to forecast T cell epitopes identified an HLA‐ DR4‐restricted epitope in the Melan‐A/MART‐1 protein (Zarour et al., 2000). We have recently examined the overall CD4 T cell response to this protein in a group of 16 melanoma patients using a set of overlapping peptides. Nine patients

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had Melan‐A‐specific CD4 T cells that could be readily expanded by short‐term peptide stimulation. Only one patient had a DR4‐restricted response to the previously identified epitope. In contrast, five of seven responding patients had responses against the peptide 21–40. This peptide contains the immunodominant HLA‐A2‐restricted CTL epitope described in the previous sections. In addition, we could identify four novel CD4 T cell antigenic peptides (Bioley et al., 2006a, in press). Similar to the CT antigen studies, we also found a good correlation of Melan‐A‐specific humoral and CD4 T cell responses. 5. Vaccine‐Induced T Cell Responses Not only for the treatment of tumors but also for infectious disease, the development of T cell vaccines remains a great challenge. During past centuries, great progress has been made in vaccines eliciting antibody responses. By contrast, T cell responses are still difficult to achieve, and current vaccines are still not capable to generate strong T cell responses of similar magnitude and efficiency as T cell responses naturally triggered by acute infections. 5.1. Peptide‐Based Vaccines in Melanoma A lesson learned from peptide vaccination, and which might have been anticipated from studies in mice, is that free peptides alone are poorly immunogenic (Boon et al., 2005; Deres et al., 1989; Kast et al., 1991; Lienard et al., 2004). This deficiency may also apply to other subunit vaccines such as long synthetic polypeptides or highly purified recombinant proteins. Hence, there is a need to combine these molecules with adjuvants. For the identification of optimal adjuvants, it is necessary to perform clinical vaccination studies with direct comparison between various vaccine formulations. Accordingly, we have treated stage III/IV melanoma patients with vaccines based on the HLA‐A*0201–restricted analog peptide ELAGIGILTV derived from Melan‐A/MART‐1, and measured Melan‐A‐specific CD8 T cell frequencies in fresh blood, that is, directly ex vivo (Fig. 2). After vaccination with peptide in saline, T cell frequencies were comparable to healthy donors and thus were not increased. After vaccination with peptide in AS02 adjuvant (QS‐21 and MPL), only 1 of 12 patients showed an increased frequency of T cells. In contrast, vaccination with peptide in Incomplete Freund’s Adjuvant (IFA; Montanidew) lead to increased frequencies in about half of vaccinated patients (Lienard et al., 2004). Much more strikingly, the addition of synthetic oligodeoxynucleotides containing CpG motifs, known to trigger Toll‐like receptor‐9 (TLR‐9) and activate plasmacytoid dendritic cells (pDCs), B lymphocytes, and natural killer (NK) cells, gave consistently increased frequencies in all patients, reaching a mean of >1% of Melan‐A‐specific cells among circulating

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CD8 T cells (Speiser et al., 2005). Overall, it appears that the best ‘‘conventional’’ and widely available adjuvant is IFA (Montanidew ISA‐51) and that the addition of CpG oligonucleotides leads to roughly tenfold higher T cell frequencies (Speiser et al., 2005), indicating that CpG oligonucleotides, in conjunction with antigen, are the so far best‐known stimulators for CTL induction. Various other vaccine types appear to induce lower T cell frequencies. This accounts for many different approaches such as peptide‐pulsed DC (Mackensen et al., 2000; Trakatelli et al., 2006; Tuettenberg et al., 2005), autologous heat shock protein gp96 together with GM‐CSF and IFN‐a (Pilla et al., 2005), recombinant vaccinia viruses expressing peptides and B7 (Oertli et al., 2002) or multiple T cell antigens (Smith et al., 2005), recombinant ALVAC expressing MAGE‐A1 and MAGE‐A3 minigenes (Hanagiri et al., 2006; van Baren et al., 2005), recombinant canary pox encoding gp100 (Spaner et al., 2006), peptide with IL‐12 (Cebon et al., 2003), or peptides with IFA and anti‐CTLA‐4 mAb (Sanderson et al., 2005). Comparable conclusions can be drawn from similar studies with gp100 peptides, albeit no results are yet reported using CpG in conjunction with gp100 antigen. Similar to the studies with Melan‐A/MART‐1, various studies also suggest that IFA leads to better T cell activation as opposed to other vaccine formulations (Powell and Rosenberg, 2004; Slingluff et al., 2003; Smith et al., 2003). In our studies, we used low doses of CpG, IFA, and peptide, as necessary for a first‐time application of this combination in humans. Besides triggering quite high T cell frequencies, vaccination with CpG, IFA, and peptide was efficient to promote effector cell differentiation, such that effector functions reached a high level comparable to what was found in T cells specific for viral epitopes (Appay et al., 2006a). Meanwhile, it became clear that one may apply higher doses. With regard to the peptide/IFA mix, higher doses have been shown to induce higher T cell frequencies (Powell and Rosenberg, 2004). Possibly, higher doses of CpG, IFA, and peptide will finally be capable to reach the high levels of CD8 T cell frequencies, and strong effector function, as observed in acute viral diseases, clearly representing major benchmarks in the development of synthetic T cell vaccines. How do CpG oligodeoxynucleotides promote CTL responses in vivo? It is likely that multiple mechanisms are involved: CpG oligodeoxynucleotides primarily trigger pDC which are inefficient in antigen presentation but release large amounts of type I interferons that activate NK cells and T cells (Curtsinger et al., 2005; Kalinski et al., 2005; Kolumam et al., 2005; Moretta, 2002). Besides pDCs and B cells, CpG oligodeoxynucleotides also directly activate NK cells, which respond by IFN‐g and TNF‐a secretion, promoting activation of myeloid DCs which then become efficient in CTL activation. Besides TLR‐9–mediated activation, triggering of other TLRs may be beneficial for the generation of CTL responses in humans (Akira et al., 2001; Kadowaki et al., 2001; Medzhitov

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and Janeway, 1997). Since preclinical data are promising, such new TLR ligands should be tested as vaccine adjuvants in humans. Possibly, other ligands of innate immunity receptors or costimulators of adaptive immunity may contribute additional synergisms. For instance, it has been shown in mice that various TLR agonists synergize with agonistic anti‐ CD40 antibodies, which activate DCs, in inducing T cell–mediated immunity (Ahonen et al., 2004; Toka et al., 2005). Various cytokines have been frequently used in the hope of augmenting vaccine‐induced T cell responses. Use of GM‐ CSF, initially acclaimed as a potent adjuvant by virtue of its ability to recruit DCs in vivo, has generated conflicting results (Dillman et al., 2003; Schaed et al., 2002; Scheibenbogen et al., 2003; Singh et al., 2003; Slingluff et al., 2003; Somani et al., 2002). A molecule signaling through both the GM‐CSF and Flt3 receptors may also have adjuvant activity (Pullarkat et al., 2003). In another study, massive mobilization of immature DC could also be achieved by administration of Flt3L but immunity was not detectably augmented. Addition of a TLR agonist may signal DC maturation and improve specific vaccine immunity (Shackleton et al., 2004). Because of the ability of IL‐2 to support T cell expansion in vitro, IL‐2 has been administered together with vaccination. However, IL‐2 at either high or low dose has the paradoxical effect of reducing the frequency of circulating specific T cells (Andersen et al., 2003; Lienard et al., 2004; Rosenberg et al., 1998; Slingluff et al., 2004). There is evidence that this is caused by T cell extravasation and migration to diseased tissues, but T cell deletion may also be involved (Blattman et al., 2003; Kammula et al., 1999). As mentioned earlier, sophisticated immune‐monitoring techniques allow to identify individual T cell clones that make up tumor‐ and/or vaccine‐induced immune responses. A PCR‐based approach has been developed to identify responding T cell clones even at frequencies that are much below the ex vivo detection limit of 0.01% (Lurquin et al., 2005). The TCR sequences expressed by responding clones can be used to trace the corresponding clonotypes from various metastases and multiple blood samples of a given patient. This can best be done with longitudinal studies whereby blood and tumor tissues are systematically collected allowing to quantify the content of responding T cells and to assess their capability to exert effector function. We recently identified dominant CD8 T cell clones specific for Melan‐A and NY‐ESO‐1 epitopes. Some of these clonotypes persisted over more than 4 years. Most of them were already detectable in early metastatic lesion, before initiation of immunotherapy (Speiser et al., 2006). Vaccination successfully expanded the existing clones, indicating that immunotherapy can enhance previously established T cell responses, and confirming our earlier demonstration that vaccine‐driven CD8 T cell responses occur more frequently in patients

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with tumor‐driven priming of CD8 T cells (Speiser et al., 2003). Nevertheless, vaccination was also capable to recruit and activate novel clonotypes. Our recent analysis of T cells responding to vaccination with CpG, IFA, and Melan‐A peptide demonstrated a similar focusing, with corresponding reduction of crossreactivity to altered peptide ligands, thus showing for the first time that a cancer vaccine broadly impacts on clonal T cell fine specificity (Appay et al., 2006b). A major challenge is now to identify the most powerful (i.e., protective) T cells among the various responding populations. 5.2. Sustaining Antigen‐Specific T Cell Response The most obvious way of achieving a sustained T cell response is repeated administration of the vaccine. Although large expansions were reported in one trial using 1 mg of synthetic peptide given weekly for 40 weeks (Powell and Rosenberg, 2004), most trials achieved a plateau response that was not overcome by further boosting. Optimizing expansion in vivo will require testing various costimulatory ligands such as OX40, 4‐1BB (Lee et al., 2004), or CD70 (Ochsenbein et al., 2004) as well as removing signals that may dampen the immune response such as PD‐1 (Barber et al., 2006), CTLA‐4 (Phan et al., 2003), and regulatory cytokines (IL‐4, IL‐10). Theoretically, small molecules that could interfere with activation induced cell death and/or cytokines providing lymphocyte survival signals (Marrack and Kappler, 2004) should also contribute to increase the number of responding cells as well as their longevity. 5.3. Recruiting CD4 T Cells Responses mediated by CD4 T cells have been implicated in the outcome of the antitumor response at least in two ways. Following differentiation to effector CD4 T cells, tumor antigen‐specific T cells may exert direct antitumor effects (Schultz et al., 2004; van der Bruggen et al., 2002; Wang, 2002). In this regard, their ability to produce IFN‐g has been recognized as one of the major mediators. It has been suggested that these cells can also become cytolytic (Appay, 2004). A probably more important role of CD4 T cells is their ability to support the generation and maintenance of cellular immune responses. Numerous mechanisms may come into play such as engagement of CD40 on DCs thus activating them to mature, local release of IL‐2 that will contribute to CD8 T cell proliferation, and programming of a large CD8 memory response (Masopust et al., 2004; van Stipdonk et al., 2003). However, CD4 T cells can also downregulate immune

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responses (Sakaguchi, 2004). In particular, the understanding of the mechanisms mediated by regulatory T cells is still sketchy and is the focus of important research efforts at the present time. How to favor the positive aspects of immune regulation by CD4 T cells and simultaneously neutralize their inhibitory regulatory function(s) is of major importance for future effective cancer vaccines. Recent progress has brought the possibility to investigate antigen‐specific CD4 cells with novel generations of MHC/peptide multimers (Bakker and Schumacher, 2005; Nepom, 2005). It is hoped that these valuable reagents allow progress in understanding of CD4 T cell biology similarly as this was the case for CD8þ T cells. 6. Regulation of Tumor Antigen‐Specific T Cell Responses What are the mechanisms rendering specific T cells in TILs unresponsive or hypofunctional while their counterparts in the circulation are apparently competent in terms of response to antigen challenge? The most simple explanation is chronic antigen exposure in a poorly stimulatory context. As mentioned earlier, tumor to T cell ratio is equal or higher to 10:1. Such mechanism has been documented in chronic viral infection models in experimental mice (Wherry et al., 2003) and may also account for impairment of T cell responses in HIV‐ infected individuals (Appay and Rowland‐Jones, 2004). Reduction of tumor load, for instance, following efficient chemotherapy should tip the balance in favor of T cell functional competence. Recently, it has been shown that uncoupling of the PD‐1 pathway may reverse T cell unresponsiveness in a model of chronic murine viral infection (Barber et al., 2006). Potentiation of cancer immunotherapy has also been reported by interfering with PD‐1‐B7‐H1 interaction in a murine model (Hirano et al., 2005). This certainly opens a pathway to immune intervention in advanced tumors. In this regard, it is interesting that many tumors constitutively express B7‐H1, the ligand for PD‐1, and that engagement of such ligand may induce T cell apoptosis (Dong et al., 2002). Other mechanisms may be responsible for T cell unresponsiveness in the tumor milieu. Tumors may express IL‐10, VEGF, or TGF‐b, factors which may dampen T cell immunity (Zou, 2005). Tumors or activated DCs within tumors may express the enzyme indoleamine deoxygenase (IDO), which depletes tryptophan, an amino acid essential for T cell function (Le Rond et al., 2005; Muller et al., 2005; Munn et al., 2005; Uyttenhove et al., 2003; Weber et al., 2006). Immature myeloid cells frequently infiltrate tumors (Almand et al., 2001; Serafini et al., 2004) and dampen specific T cell immunity through the production of free radicals due to active iNOS and/or arginase (Bronte and Zanovello, 2005). Another potent regulatory mechanism may be the presence of Tregs. These cells are potent inhibitors of effector T cells (Yamaguchi and Sakaguchi, 2006) by

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mechanisms that are not completely understood (Chen et al., 2005b). They have been shown to infiltrate tumors such as lung cancer (Woo et al., 2002), metastatic melanoma (Viguier et al., 2004), ovarian cancer (Curiel et al., 2004; Sato et al., 2005; Wolf et al., 2005), or head and neck carcinomas (Badoual et al., 2006). As for the mechanisms of recruitment of Tregs to tumors, it was recently proposed that tumors induce DCs to secrete TGF‐b that selectively promote the proliferation of Tregs in lymph nodes (Ghiringhelli et al., 2005). In in vitro assays, induced Tregs appeared more potent than natural Tregs at interfering with melanoma antigen‐specific CD8 T expansion (Chattopadhyay et al., 2006). Tumors expressing COX‐2 might directly contribute to the induction of CD4þ Foxp3þ Tregs via prostaglandin E2 (Baratelli et al., 2005). Understanding the specific weight of these various mechanisms in blunting T cell functions inside tumors is essential for the design of the next generation of tumor vaccines and combined immunotherapies. Various therapeutic interventions have already been suggested and tested in preclinical models or phase I clinical trials. These include attempts to deplete Tregs prior to vaccination (Attia et al., 2005a; Dannull et al., 2005), blockade of CTL‐4 alone (Attia et al., 2005b) or in combination with recombinant IL‐2 (Maker et al., 2005), intratumoral delivery of vectors encoding costimulatory ligands (Kaufman et al., 2006), combination of therapeutic vaccination with blockade of the B7‐H1‐PD‐1 interaction (Geng et al., 2006; Hirano et al., 2005) or with sustained administration of COX‐2 inhibitors (Haas et al., 2006). Clearly, many more trials testing these intervention modalities will provide valuable insights regarding local and/or systemic immunomodulation of tumor‐specific T cell responses in the near future. 7. Conclusions It is important to realize that improvement of T cell vaccination is still needed, since not only cancer vaccines but also vaccines against chronic viral diseases fail to protect the majority of treated patients. This is despite the ability of the currently used vaccines to trigger relatively strong T cell responses (Klausner et al., 2003). Explanations for this discrepancy have been identified both with regard to insufficient T cell activation and reduced target tissue susceptibility to immune effector mechanisms (Table 1). Although many questions remain open, one can postulate that protective T cell responses include T cells expressing high avidity TCRs, efficient effector functions, appropriate homing capability, and prolonged survival or persistence as functionally active TILs (Table 2). The evaluation of putatively protective T cell features should be done ex vivo and requires considerable investments and know‐how. There are now a number of highly sensitive techniques that facilitate this approach.

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Table 1 Some of the Perceived Hurdles to Achieve Effective Cancer Vaccines Central tolerance to self‐antigens Weak immunogenicity Incomplete T cell differentiation Immune escape Tumor‐mediated immune tolerance Lack of costimulation Inhibitory receptors Oxidative stress IDO Arginase Immune regulatory mechanisms, regulatory T cells

Colella et al., 2000; Theobald et al., 1997 Yu et al., 2004 Lienard et al., 2004; Monsurro et al., 2004 Seliger et al., 2002 Abken et al., 2002; Chambers and Allison, 1999 Barber et al., 2006; Chen, 2004; Sharpe and Freeman, 2002 Kusmartsev et al., 2004; Schmielau and Finn, 2001 Mellor and Munn, 2004 Rodriguez et al., 2004 Curiel et al., 2004; Hussain and Paterson, 2004; Viguier et al., 2004; Wang et al., 2004

Table 2 Putative Properties of Antigen‐Specific Protective T Cell Responses High functional avidity to cognate antigen/MHC and efficient target cell recognition Highly developed effector function, with in vivo ongoing cytotoxicity and cytokine production (revealed by ex vivo readout) Appropriate homing to diseased tissue High clonogenic expansion and long‐term survival (resistance to apoptosis) Efficient ‘‘helper’’ activity promoting all arms of protective immunity Low level or absence of suppressive activity (antigen specific)

Malherbe et al., 2004; McHeyzer‐Williams et al., 1999; Speiser et al., 1992 La Gruta et al., 2004; Pantaleo and Koup, 2004; Perez‐Diez et al., 2002; Qin et al., 2003; Schuler and Blankenstein, 2003 Mora et al., 2005; Mullins et al., 2004; Rot and von Andrian, 2004 Ochsenbein et al., 2004; Robbins et al., 2004 Appay, 2004; Masopust et al., 2004; Schultz et al., 2004; Wang, 2002 Chen et al., 2005b; Sakaguchi, 2004

It is evident that molecular and cellular characterization of human T cell responses goes well beyond the usual surveys of T cells performed in clinical studies and even in animal models (Speiser, 2005). Phase I studies aiming to determine whether putatively protective T cell features (Table 2) are induced by candidate vaccines have to be further developed. Based on their results, optimal

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vaccine formulations can then be selected for subsequent clinical efficacy studies in larger patient cohorts. This approach can accelerate the development of cancer immunotherapy and will allow to test whether the currently identified features of protective immunity are correct or require modifications. In addition, tumor escape mechanisms also require further attention and appropriate design of clinical studies. Ultimately, these approaches will provide explanations as to why clinical and immunological responses in clinical trials performed thus far do not correlate more tightly with each other (Rosenberg et al., 2004). Acknowledgments We acknowledge the work of many past and present colleagues at the Lausanne branch of the Ludwig Institute for Cancer Research and at the Multidisciplinary Oncology Center (CePO), University Hospital, Lausanne, which is partially summarized in this chapter. We also thank Dr. Nathalie Rufer, Swiss Institute for Experimental Cancer Research, Epalinges and Dr. Mikael Pittet, Center for Molecular Imaging, Mass General Hospital, Boston for their contribution to some of the sections of this chapter, and Martine Van Overloop for excellent secretarial assistance.

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Weissleder, R. (2002). Scaling down imaging: Molecular mapping of cancer in mice. Nat. Rev. Cancer 2, 11–18. Weissleder, R., and Ntziachristos, V. (2003). Shedding light onto live molecular targets. Nat. Med. 9, 123–128. Welsh, D. K., and Kay, S. A. (2005). Bioluminescence imaging in living organisms. Curr. Opin. Biotechnol. 16, 73–78. Wherry, E. J., Blattman, J. N., Murali‐Krishna, K., van der Most, R., and Ahmed, R. (2003). Viral persistence alters CD8 T‐cell immunodominance and tissue distribution and results in distinct stages of functional impairment. J. Virol. 77, 4911–4927. Wolf, D., Wolf, A. M., Rumpold, H., Fiegl, H., Zeimet, A. G., Muller‐Holzner, E., Deibl, M., Gastl, G., Gunsilius, E., and Marth, C. (2005). The expression of the regulatory T cell‐specific forkhead box transcription factor FoxP3 is associated with poor prognosis in ovarian cancer. Clin. Cancer Res. 11, 8326–8331. Wolint, P., Betts, M. R., Koup, R. A., and Oxenius, A. (2004). Immediate cytotoxicity but not degranulation distinguishes effector and memory subsets of CD8þ T cells. J. Exp. Med. 199, 925–936. Woo, E. Y., Yeh, H., Chu, C. S., Schlienger, K., Carroll, R. G., Riley, J. L., Kaiser, L. R., and June, C. H. (2002). Cutting edge: Regulatory T cells from lung cancer patients directly inhibit autologous T cell proliferation. J. Immunol. 168, 4272–4276. Wrzesinski, C., and Restifo, N. P. (2005). Less is more: Lymphodepletion followed by hematopoietic stem cell transplant augments adoptive T‐cell‐based anti‐tumor immunotherapy. Curr. Opin. Immunol. 17, 195–201. Yamaguchi, T., and Sakaguchi, S. (2006). Regulatory T cells in immune surveillance and treatment of cancer. Semin. Cancer Biol. 16, 115–123. Yu, Z., Theoret, M. R., Touloukian, C. E., Surman, D. R., Garman, S. C., Feigenbaum, L., Baxter, T. K., Baker, B. M., and Restifo, N. P. (2004). Poor immunogenicity of a self/tumor antigen derives from peptide‐MHC‐I instability and is independent of tolerance. J. Clin. Invest. 114, 551–559. Zarour, H. M., Kirkwood, J. M., Kierstead, L. S., Herr, W., Brusic, V., Slingluff, C. L., Jr., Sidney, J., Sette, A., and Storkus, W. J. (2000). Melan‐A/MART‐1(51–73) represents an immunogenic HLA‐DR4‐restricted epitope recognized by melanoma‐reactive CD4(þ) T cells. Proc. Natl. Acad. Sci. USA 97, 400–405. Zeng, G., Wang, X., Robbins, P. F., Rosenberg, S. A., and Wang, R. F. (2001). CD4(þ) T cell recognition of MHC class II‐restricted epitopes from NY‐ESO‐1 presented by a prevalent HLA DP4 allele: Association with NY‐ESO‐1 antibody production. Proc. Natl. Acad. Sci. USA 98, 3964–3969. Zhou, J., Shen, X., Huang, J., Hodes, R. J., Rosenberg, S. A., and Robbins, P. F. (2005). Telomere length of transferred lymphocytes correlates with in vivo persistence and tumor regression in melanoma patients receiving cell transfer therapy. J. Immunol. 175, 7046–7052. Zippelius, A., Pittet, M. J., Batard, P., Rufer, N., de Smedt, M., Guillaume, P., Ellefsen, K., Valmori, D., Lienard, D., Plum, J., MacDonald, H. R., Speiser, D. E., et al. (2002). Thymic selection generates a large T cell pool recognizing a self‐peptide in humans. J. Exp. Med. 195, 485–494. Zippelius, A., Batard, P., Rubio‐Godoy, V., Bioley, G., Lienard, D., Lejeune, F., Rimoldi, D., Guillaume, P., Meidenbauer, N., Mackensen, A., Rufer, N., Lubenow, N., et al. (2004). Effector function of human tumor‐specific CD8 T cells in melanoma lesions: A state of local functional tolerance. Cancer Res. 64, 2865–2873. Zou, W. (2005). Immunosuppressive networks in the tumour environment and their therapeutic relevance. Nat. Rev. Cancer 5, 263–274.

Antigen Presentation and the Ubiquitin‐Proteasome System in Host–Pathogen Interactions Joana Loureiro and Hidde L. Ploegh Whitehead Institute, 9 Cambridge Center, Cambridge, Massachusetts Abstract ........................................................................................................... 1. Host–Pathogen Interactions ................................................................................. 2. Manipulation of the Host Response by Pathogens: Some General Considerations....................................................................................... 3. Antigen Presentation .......................................................................................... 4. Class I MHC Antigen Presentation ....................................................................... 5. Pathogen Recognition by CD8þ T Cells and NK Cells.............................................. 6. Class II MHC Antigen Presentation ...................................................................... 7. Ubiquitin‐Proteasome System .............................................................................. 8. The Ubiquitin Conjugation Cascade ...................................................................... 9. Ubiquitin Ligases............................................................................................... 10. Ubiquitin Chains and Ubiquitin‐Like Modifiers (Ubls) ............................................. 11. Deubiquitinating Enzymes .................................................................................. 12. The Proteasome ................................................................................................ 13. ER Quality Control and Degradation .................................................................... 14. ERAD Substrate Recognition............................................................................... 15. ERAD E3 Ligases ............................................................................................. 16. Mammalian ERAD E3s ...................................................................................... 17. The Elusive Dislocon ......................................................................................... 18. Driving Dislocation and the Ub‐Binding Route to the Proteasome.............................. 19. Peptide N‐Glycanase .......................................................................................... 20. Viral Interference with Class I MHC Antigen Presentation........................................ 21. Human Cytomegalovirus ..................................................................................... 22. HCMV Interference with Class I MHC Antigen Presentation .................................... 23. Dislocation from the ER: HCMV US11 and US2..................................................... 24. Signal Peptide Peptidase Is Required for Dislocation from the ER ............................. 25. SPP and Generation of HLA‐E Epitopes ............................................................... 26. SPP and Processing of the Hepatitis C Virus Core Protein ........................................ 27. SPP and Calmodulin Signaling ............................................................................. 28. SPP Peptide Peptidase and Development............................................................... 29. SPP and ER Quality Control................................................................................ 30. Three Routes of Pathogen‐Mediated ER Protein Disposal......................................... 31. Pathogen Interference with Class II MHC Antigen Presentation ................................ 32. Inhibition of Recognition at the Surface of the APC................................................. 33. Class II MHC Downregulation from the Surface of the APC ..................................... 34. CD4 Downregulation from the Surface of the CD4þ T Cell ...................................... 35. Pathogen Manipulation of the Ubiquitin‐Proteasome System ..................................... 36. Interference with Proteasomal Proteolysis .............................................................. 37. Control of Infection ........................................................................................... 38. Virus Budding ...................................................................................................

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0065-2776/06 $35.00 DOI: 10.1016/S0065-2776(06)92006-9

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Bacterial Chromosome Integration........................................................................ ISGylation and deISGylation................................................................................ Control of Inflammation...................................................................................... Posttranscriptional Gene Silencing ........................................................................ Downregulation of Cell Surface Receptors by Pathogen‐Encoded E3s......................... Programmed Cell Death in Plants......................................................................... Cytokine Responses ........................................................................................... Pathogen‐Encoded DUBs ................................................................................... Conclusions and Future Directions ....................................................................... References .......................................................................................................

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Abstract Relatively small genomes and high replication rates allow viruses and bacteria to accumulate mutations. This continuously presents the host immune system with new challenges. On the other side of the trenches, an increasingly well‐ adjusted host immune response, shaped by coevolutionary history, makes a pathogen’s life a rather complicated endeavor. It is, therefore, no surprise that pathogens either escape detection or modulate the host immune response, often by redirecting normal cellular pathways to their advantage. For the purpose of this chapter, we focus mainly on the manipulation of the class I and class II major histocompatibility complex (MHC) antigen presentation pathways and the ubiquitin (Ub)‐proteasome system by both viral and bacterial pathogens. First, we describe the general features of antigen presentation pathways and the Ub‐proteasome system and then address how they are manipulated by pathogens. We discuss the many human cytomegalovirus (HCMV)‐encoded immunomodulatory genes that interfere with antigen presentation (immunoevasins) and focus on the HCMV immunoevasins US2 and US11, which induce the degradation of class I MHC heavy chains by the proteasome by catalyzing their export from the endoplasmic reticulum (ER)‐membrane into the cytosol, a process termed ER dislocation. US2‐ and US11‐mediated subversion of ER dislocation ensures proteasomal degradation of class I MHC molecules and presumably allows HCMV to avoid recognition by cytotoxic T cells, whilst providing insight into general aspects of ER‐associated degradation (ERAD) which is used by eukaryotic cells to purge their ER of defective proteins. We discuss the similarities and differences between the distinct pathways co‐ opted by US2 and US11 for dislocation and degradation of human class I MHC molecules and also a putatively distinct pathway utilized by the murine herpes virus (MHV)‐68 mK3 immunoevasin for ER dislocation of murine class I MHC. We speculate on the implications of the three pathogen‐exploited dislocation pathways to cellular ER quality control. Moreover, we discuss the ubiquitin (Ub)‐proteasome system and its position at the core of antigen presentation as

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proteolysis and intracellular trafficking rely heavily on Ub‐dependent processes. We add a few examples of manipulation of the Ub‐proteasome system by pathogens in the context of the immune system and such diverse aspects of the host–pathogen relationship as virus budding, bacterial chromosome integration, and programmed cell death, to name a few. Finally, we speculate on newly found pathogen‐encoded deubiquitinating enzymes (DUBs) and their putative roles in modulation of host–pathogen interactions. 1. Host–Pathogen Interactions The vertebrate immune system is equipped to deal with invading pathogens, whether by means of mechanical barriers such as the skin and other epithelial surfaces or by means of innate immunity. Innate immunity comprises the phagocytic and inflammatory systems, with phagocytes like macrophages and neutrophils, dendritic cells (DCs), and natural killer (NK) cells, as well as soluble mediators such as cytokines and complement. Phagocytes are the immune system’s first line of defense: they recognize, engulf, and clear the pathogen and are the main cellular component of the innate antibacterial response. NK cells can directly recognize and kill pathogen‐infected cells that fail to express MHC molecules and secrete cytokines that affect the immune response. NK cells are the main cellular effectors of the innate response against viruses. The complement system can lyse infected cells or simply coat the surface of the pathogen or pathogen‐derived material, resulting in its neutralization and opsonization. To counteract pathogen infection, host cells also have extracellular and intracellular pathogen recognition receptors to alert the immune system, such as toll‐like receptors (TLRs) at the cell surface, and protein kinase R (PKR) and nucleotide‐ binding oligomerization domain (NOD) proteins in the cytosol (Akira et al., 2006; Inohara et al., 2005) that can detect pathogen‐associated molecular patterns (PAMPs) such as bacterial peptidoglycan or viral dsRNA. These ‘‘danger’’ signals initiate the synthesis of cytokines like interferons to induce inflammation, a crucial component of the innate defense against pathogens. Because innate immunity is not always successful at recognizing or eliminating the infectious agents, a more sophisticated line of defense, adaptive immunity, is also in place. The adaptive immune system includes cells originated in the thymus, the T lymphocytes, and the bone marrow–derived B lymphocytes (B cells), DCs, and macrophages. The two subsets of T lymphocytes, CD8þ and CD4þ T cells, possess distinct T cell receptors (TCRs), CD8 and CD4, respectively, that interact with their coreceptors on the surface of the target cell, the polymorphic class I and class II MHC molecules (Ploegh, 1998). Class I MHC molecules are expressed by nearly all nucleated cells, whereas class II MHC molecules are constitutively expressed only by professional antigen‐presenting cells (APCs),

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such as macrophages, B cells, and DCs. Class II MHC expression however, can be induced in many cells, in particular by IFN‐g treatment. APCs can endocytose, process, and display antigen in the context of class II MHC products at their cell surface to activate CD4þ T cells. In the presence of antigen displayed by the APC and the appropriate lymphocyte costimulatory molecules, CD4þ T helper (TH) cells produce cytokines that ‘‘help’’ activate other cells: TH1 (or inflammatory T cells) activate macrophages to kill the phagocytosed pathogens; TH2 cells (or helper Tcells) trigger Tand B cell proliferation and activate the B cell differentiation program into antibody‐producing plasma cells. Furthermore, activation of CD4þ TH cells is carefully regulated by a small subset of T cells, the regulatory T cells (Tregs). Regulatory T cells play an important role in downregulation of the host immune response, limiting the immunopathology resultant from antipathogen reactions, and preventing autoimmune disease (Beissert et al., 2006; Mills, 2004). In addition to making antibodies, B cells are a special kind of APCs. Unlike DCs and macrophages, B cells are not actively phagocytic. However, stimulation of the membrane immunoglobulin (mIg) antigen‐recognition component of their B cell receptor (BCR) with cognate antigen triggers the B cell to capture and deliver the antigen to class II MHC compartments, culminating with antigen presentation for activation of T cells. Bone marrow–derived professional APCs include macrophages and DCs. Macrophages are phagocytic APCs with a low basal antigen‐presenting capacity—owing to low surface expression of class II MHC and costimulatory molecules—that is induced on macrophage activation, for instance, by IFN‐g. Macrophages reside in (or are recruited to) peripheral tissues, where they phagocytose and clear pathogens. Phagocytosis, in turn, induces release of proinflammatory cytokines like IFN‐g that turn macrophages into potent APCs, resulting in initiation of CD4þ T cell activation. Dendritic cells, the consummate professional APCs, travel through the periphery, sampling all tissues for prospective invaders. Immature DCs phagocytose pathogens and home to the nearest lymphoid organ to ‘‘educate’’ (prime) naı¨ve CD8þ T cells by cross‐presenting antigen in the context of class I MHC molecules—a process described in more detail later. Mature DCs can also prime naı¨ve CD4þ T cells. Like resting macrophages, immature DCs have very low antigen‐presenting capability, and only on exposure to maturation signals [such as lipopolysaccharide (LPS) on bacterial surfaces] does internalized antigen get loaded into class II MHC products and get displayed at the cell surface to CD4þ T cells (Bryant and Ploegh, 2004; Stockwin et al., 2000). ‘‘Educated’’ (antigen‐specific) CD8þ T lymphocytes survey all cells in the body, ready to destroy any that displays signs of the presence of cellular alterations (such as viral and tumor peptides) within their surface class I MHC molecules (Andersen et al., 2006; Castelli et al., 2000). Antigen‐specific CD4þ T

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lymphocytes can coordinate macrophage bactericidal properties, activation of T and B lymphocytes, and antibody production. Not only do the cells of the adaptive immune system provide a more elaborate defense, but also an increased level of protection from a subsequent reinfection with the same pathogen, the bedrock principle of vaccine strategies (Crotty and Ahmed, 2004; Pulendran and Ahmed, 2006). Adding to the complexity of the immune system is the cross talk between innate and adaptive immunity, which is crucial in eliciting an effective immune response (Zingoni et al., 2005). As mentioned, phagocytes release cytokines that stimulate the adaptive response. Conversely, on activation by antigen recognition, T cells synthesize and secrete cytokines that activate macrophages, increasing their ability to kill ingested microbes, an innate immune response (Munz et al., 2005; Salazar‐Mather and Hokeness, 2003). The vertebrate immune system, therefore, is the appropriate battleground for microbial pathogens, selecting for those that devise successful strategies to avoid detection and elimination (Hilleman, 2004; Ploegh, 1998). 2. Manipulation of the Host Response by Pathogens: Some General Considerations Intracellular pathogens have evolved sophisticated mechanisms to subvert host processes to ensure their own replication and transmission. The initial hurdle is entry into the host cell, which poses great challenges for avoiding immune detection before establishing infection. To promote entry into host cells without alerting the immune system, bacteria possess capsular surfaces that have evolved to minimize antibody and complement deposition while in circulation through the body. On the other hand, filamentous adhesins (like fimbriae and pili) that protrude through the bacteria’s capsule enable binding to host cell receptors, which enables secretion systems to deliver bacterial effectors to modulate uptake and invasion (Finlay and McFadden, 2006; Galan and Collmer, 1999). Virus particles are very often coated with highly variable capsid (nonenveloped viruses) or envelope (enveloped viruses) proteins to avoid detection and clearance by antibody‐mediated responses. These capsids or envelopes can also be studded with immunomodulatory molecules of viral or even host origin and promote attachment to the host cell membrane, fusion and delivery of the virus internal core. Alternatively, they may act as signaling devices and induce intracellular cascades required for virus uptake. Ultimately, intracellular release of the viral DNA or RNA occurs (Marsh and Helenius, 2006; Skehel and Wiley, 2000). The establishment of an infection critically depends on bacterial and viral genes dedicated to manipulation of host functions. A number of reviews have covered the bacterial and viral genes involved in manipulation of the host immune system, from control of apoptosis, cytokine signaling, to the antibody response, so the

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reader is referred to Alcami (2003); Alcami and Koszinowski (2000); Bowie et al. (2004); Finlay and McFadden (2006); Hengel et al. (2005); Hilleman (2004); and Tortorella et al. (2000). For the purpose of this chapter, we will focus on pathogen manipulation of antigen presentation pathways and the Ub‐proteasome system. 3. Antigen Presentation Antigen presentation involves the conversion of protein antigens into peptide ligands that can bind to MHC products that are displayed at the cell surface for recognition by T cells. In a simplified view of antigen presentation, the class I and class II MHC pathways have evolved to sample different sources of antigen to which they have access: the class I MHC pathway usually deals with cytosolic antigens and is crucial for activation of CD8þ T cells, whereas the class II MHC pathway deals with exogenous antigens and the activation of CD4þ T cells (Bryant and Ploegh, 2004; Cresswell et al., 2005; Pamer and Cresswell, 1998). Antigen presentation is, of course, not as simple and clear‐ cut, as we shall discuss later. There are, however, common principles that apply to the discrete steps of antigen processing and presentation by class I and class II MHC molecules: antigen must be acquired, it is subjected to proteolysis, delivered to MHCþ compartments, and properly assembled with the MHC product. The complex is then subject to sorting through the secretory pathway and delivered to the cell surface (Fig. 1). Because each of these steps affords a target for interference by pathogens, we shall survey them for each pathway. 4. Class I MHC Antigen Presentation The class I MHC is a trimeric complex composed of the class I MHC heavy chain (HC), the b2‐microglobulin (b2m) or light chain, and the antigenic peptide. The structure of the fully assembled complex and its interactions with antigen‐specific receptors on T cells have been extensively reviewed (Alam et al., 1996; Rudolph and Wilson, 2002; von Boehmer, 2006). The class I MHC HC is inserted into the ER membrane and N‐glycosylated and binds in its course of synthesis to the membrane‐associated chaperone calnexin (CNX), at which point folding and intrachain disulfide bond formation take place. Once dissociated from CNX, the HC binds its soluble partner subunit, b2m, and enters the peptide‐loading complex (PLC). The PLC is composed of two MHC‐encoded components, TAP and tapasin, and two ‘‘housekeeping’’ ER proteins, calreticulin and ERp57. The transporter associated with antigen presentation (TAP) is an ATP‐dependent pump with two subunits, TAP1 and TAP2 that transports peptides into the ER. Tapasin, a transmembrane glycoprotein, mediates the interaction between the TAP transporter and peptide‐free HC/b2m dimers. The soluble calreticulin and ERp57, a chaperone and a thiol oxidoreductase, respectively, normally involved in

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Figure 1 Common principles in antigen processing and presentation by class I and class II MHC molecules. In the class I pathway, endogenous antigens are derived from cytosolic proteolysis and delivered to the ER lumen, where loading onto class I MHC products takes place. The assembled complex is then sorted to the cell surface. In the class II pathway, exogenous material is internalized from the extracellular space and delivered to the lysosome, where processing and loading onto MHC products occur. Sorting through the secretory pathway then delivers the class II complex to the cell surface.

folding of nascent glycoproteins, promote assembly of the class I MHC complex. The peptide antigen cargo for class I MHC originates from proteasomal proteolysis in the cytosol. The array of proteasome‐generated peptides is subject to trimming by cytosolic endopeptidases and delivered to the ER lumen by the TAP transporter. Further trimming by ER‐resident endopeptidases can also occur to guarantee a custom‐fit of the peptide antigens, typically 8–10 amino acids long, into the peptide‐binding groove on the HC/b2m dimer associated with the PLC. Empty HC molecules are detained in the ER by virtue of interaction with tapasin, until assembly with b2m and peptide takes place, at which point the HC/b2m/peptide trimeric complex is released from the PLC and allowed to exit the ER and enter the secretory pathway. Once displayed at the cell surface, the antigen‐loaded class I MHC complex is ready for inspection by the T cell receptor (TCR) on circulating cytotoxic CD8þ T cells (Cresswell et al., 2005; Heemels and Ploegh, 1995; Rammensee, 2002, 2004).

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5. Pathogen Recognition by CD8þ T Cells and NK Cells Class I MHC products on most cells present exclusively ‘‘self’’ peptides, derived from the cell’s own proteins, the majority of which results from protein synthesis on free ribosomes in the cytoplasm. Because of the intrinsic error‐prone nature of protein synthesis and folding, a sizable fraction of translation products (estimated at up to
30%) may never result in a finished product. These defective ribosomal products (DRIPs) are destroyed within 30 min of their synthesis by the cytosolic proteasomal pathway and enter the class I MHC antigen presentation pathway (Yewdell et al., 2001). In tumor cells or cells infected by a virus, mutated forms of endogenous proteins or viral proteins will compete with the host’s own proteins for presentation by class I MHC products. As ‘‘non‐self’’ (tumor‐ or virus‐derived) peptides displayed in the context of class I MHC products accumulate at the cell surface, their chance of triggering activation of CD8þ T cells with a cognate receptor increases. The activated cytotoxic CD8þ T lymphocytes will then lyse the target cell by releasing perforin and granzymes or by Fas ligand engagement. Secretion of IFN‐g and tumor necrosis factor‐a (TNF‐a) also aids in elimination of infected and tumor cells by cytotoxic T lymphocytes (CTLs) (Andersen et al., 2006; Castelli et al., 2000). The selective pressure imposed by immune surveillance has made loss of class I MHC expression a hallmark of some tumors and virus‐infected cells, as this allows them to be invisible to CTLs. There is, however, a backup system for when lack of class I MHC expression impairs the CD8þ T cell cytotoxic response: NK cells. NK cells display both activating and inhibitory receptors at their surface, which recognize different ligands at the surface of target cells. NK cell activity is ultimately determined by the integration of signals that are perceived by the NK cell surface receptors (Lanier, 2005). All NK cells express at least one inhibitory receptor, which engages class I MHC molecules on the surface of the target cell, resulting in downregulation of NK cell effector functions. Low levels or absence of class I MHC products on the surface of the target cell relieve the inhibitory signals and lead to NK cell cytotoxicity, resulting in clearance of the virus‐infected or tumor cells. NK cell recognition has been extensively revised and the reader is referred to Backstrom et al. (2004); Kumar and McNerney (2005); and Lanier (2005). There is an exception to the rule that the class I MHC pathway is devoted to display of peptide antigens from endogenously generated proteins: the so‐called professional APCs, DCs, and macrophages can acquire and process exogenous material and present it at the cell surface in class I MHC products, a process called cross‐presentation. Cross‐presentation allows noninfected professional APCs to prime naı¨ve T cells with pathogen‐ or tumor‐derived peptides acquired through endocytosis of infected cells/cell remnants. This ‘‘cross‐priming’’

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is essential for development of CD8þ T cell immunity to viruses and tumors in vivo, since only professional APCs can present viral/tumor antigens in the context of class I MHC products without being themselves infected/tumorigenic. There is considerable controversy as to the exact nature of the antigen acquired and modes of antigen acquisition, as well as the intracellular mechanisms leading to cross‐presentation and the subsets of APCs endowed with this property (Cresswell et al., 2005; Groothuis and Neefjes, 2005; Guermonprez and Amigorena, 2005; Jutras and Desjardins, 2005; Shen and Rock, 2006). This controversy is, however, beyond the scope of this discussion. 6. Class II MHC Antigen Presentation The class II MHC antigen presentation pathway deals with antigens that reside in extracellular space and are internalized into the endolysosomal pathway. All mammalian cells internalize their own cell surface proteins by constitutive endocytosis. In class II MHCþ cells, this allows class II MHC access to self‐proteins as a source of peptides. Professional APCs, such as B cells, macrophages, and DCs, also engage in receptor‐mediated endocytosis to acquire extracellular antigen: the antigen from the extracellular milieu is bound by cell surface receptors, internalized, and delivered to the class II MHC antigen processing machinery. Antibodies, complement system factors, and common bacterial or viral components that coat the surface of pathogens or their toxic products bind receptors on APCs that allow them to recognize and internalize this foreign material. Of the many receptors used by professional APCs for this purpose, the mannose receptor, which recognizes mannose residues and glycoproteins on viral and bacterial products, and the scavenger receptor, which recognizes very promiscuously many different classes of macromolecules, are among the most important. Professional APCs also have complement receptors and receptors for the Fc region of antibodies, the Fc receptors, which can assist in the acquisition of opsonized antigen and in its delivery to the proper intracellular destination. B cells can also use their surface immunoglobulin or BCR, to acquire antigen (Bryant and Ploegh, 2004; Cresswell, 1994; Kim et al., 2006c). Class II MHC loading with antigenic peptides takes place mostly in the endocytic vesicles of professional APCs. Class II MHC ab dimers assemble in the ER and associate with the chaperone invariant chain (Ii), which inserts its class II MHC‐associated Ii peptide (CLIP) portion in the peptide‐binding groove of the ab dimer, preventing its premature (prelysosomal) loading. Ii is also important for correct assembly and transport of class II MHC in the endocytic pathway. Further class II MHC maturation and peptide loading takes place in acidified compartments of the endolysosomal pathway of APCs, since low pH favors an ‘‘open’’ conformation of the class II MHC molecule and

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hence peptide exchange, as well as the action of specific cysteine proteases that displace Ii from the class II MHC–Ii complex, and that of the class II MHC‐ like molecule HLA‐DM which facilitates peptide loading. The many hydrolase activities present in the endolysosomal compartments of APCs, such as the IFN‐g‐inducible lysosomal thiol reductase (GILT), numerous cysteine proteases of the cathepsin (Cat) family, like CatB, CatS, CatL, and asparaginyl endopeptidase (AEP), produce the peptide ligands that are loaded onto class II MHC products. Peptides bound by class II MHC molecules are usually 13–25 residues long. The end result is a mature class II MHC–peptide complex at the cell surface, consisting of a class II MHC ab dimer loaded with peptide, which interacts with the TCR on CD4þ T cells. The result of this interaction is dependent on class II MHC–TCR contacts and also on the context provided by lymphocyte costimulatory molecules at the immunological synapse. The CD4þ T cell response may be cytolytic, but generally these antigen‐specific CD4þ T lymphocytes function as helper cells, releasing cytokines to enhance the overall immune response by inducing macrophage activation, T and B cell proliferation, and B cell differentiation to produce antigen‐specific antibodies and different immunoglobulin isotypes with different effector functions (Bryant and Ploegh, 2004; Chapman, 2006; Cresswell, 1994; Honey and Rudensky, 2003; Hsing and Rudensky, 2005; Stern et al., 2006; Villadangos et al., 1999). 7. Ubiquitin‐Proteasome System All cellular proteins, regardless of their half‐life, are subject to turnover. The main pathway for degradation of short‐lived proteins in the cytoplasm of eukaryotic cells is the Ub‐proteasome system (Hershko and Ciechanover, 1992). Since the discovery of Ub and Ub‐dependent proteolysis in the late 1970s, it has become increasingly clear that the Ub‐proteasome system is pivotal to numerous cellular processes: cell cycle control, transcriptional regulation, signal transduction, antigen presentation and induction of the inflammatory response, degradation from the ER, membrane trafficking, receptor endocytosis and downregulation, apoptosis, and development (Hershko and Ciechanover, 1992; Pickart, 2001). 8. The Ubiquitin Conjugation Cascade Ubiquitin is a small 76‐amino acid protein, synthesized as a precursor that is processed by deubiquitinating enzymes (DUBs) to expose the glycine–glycine sequence at the Ub C‐terminus, its site of attachment to target molecules. ATP‐dependent Ub activation is catalyzed by the E1 (Ub‐activating) enzyme, which adenylates the Ub C‐terminus, allowing the subsequent formation of a

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Figure 2 Overview of the Ub‐proteasome system. (A) Ubiquitin‐conjugation cascade and how Ub chain linkage type and length influence substrate fate. E1, Ub‐activating enzyme; E2, Ub‐ conjugating enzyme; E3, Ub‐ligase enzyme; and DUB, deubiquitinating enzyme. (B) Diversity in E3 ligases. E3s play crucial roles in substrate selection and can be regulated by localization, oligomerization, associated E2s, posttranslational modifications, and degradation. Hrd1p and Doa10p are yeast E3 ligases that are multispanning membrane proteins of the ER and, in the case of Doa10p, nuclear envelope. The mammalian SCF family of E3 ligases are mainly cytosolic and can recruit substrate adaptor proteins, the F‐boxes, with very diverse substrate specificities.

high‐energy thioester bond between the glycine residue of Ub and the cysteine residue on the E1 active site. Ub is then transferred from the E1 cysteinyl side chain to a cysteinyl group on one of several E2 (Ub‐conjugating) enzymes. Finally, one of hundreds of E3 (Ub‐ligase) enzymes, binds the Ub–E2 complex and the substrate, thus facilitating the transfer of Ub to a lysine residue in the substrate via an amide (isopeptide) bond (Hershko and Ciechanover, 1992). The functions of E3 ligases, in particular, are tightly regulated by signal‐ induced mechanisms, such as localization, oligomerization, degradation, and posttranslational modifications, which makes E3s the master orchestrators of specificity in the Ub conjugation cascade. This multistep mechanism, much like phosphorylation, endows protein ubiquitination with a high degree of specificity and flexibility, which is paramount to its important biological functions (Haglund and Dikic, 2005; Hershko and Ciechanover, 1998; Pickart, 2001, 2004; Varshavsky, 2005) (Fig. 2A).

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9. Ubiquitin Ligases We elaborate on Ub E3 ligases to some extent, as they are key players in several aspects of the immune system, including immune evasion (Liu, 2004) and also in ER quality control and degradation (Hirsch et al., 2004; Kostova and Wolf, 2003; Romisch, 2005), that yields to some of the ligands on class I MHC products. E3 ligases can be divided into two broad classes: the homologous to E6‐AP carboxyl terminus (HECT)‐domain ligases or the really interesting new gene (RING)‐like domain ligases. The first HECT E3 described, E6‐associated protein (E6‐AP), was shown to be required for ubiquitination and degradation of p53, mediated by the human papillomavirus protein E6 (Scheffner et al., 1993). In HECT E3s, Ub is transferred from the E2 to a conserved cysteine residue in the HECT domain, followed by attack of this thioester by a lysine on the substrate (Pickart, 2001). The RING‐CH domain is a ring finger motif with a cysteine residue in the fourth zinc‐coordinating position and a histidine residue in the fifth. RING‐type E3s are more abundant and do not form an obligatory thioester intermediate with Ub; rather they bring the Ub‐loaded E2s and the substrate into proximity, thus facilitating the Ub transfer from the E2 to the substrate (Pickart, 2001). RING‐type E3s can be single subunit E3s, which have both a RING‐finger domain and substrate recruitment domain(s) on the same protein, like MDM2, a key regulator of p53. Multisubunit E3s include the very diverse Cullin‐RING ligases (CRLs) (Petroski and Deshaies, 2005). CRLs are composed of a catalytic core that recruits the Ub‐loaded E2—formed by a nucleating Cullin protein and a RING finger protein—as well as a substrate recognition complex. The archetypal CRLs are the Skp‐1‐Cullin‐1‐F‐box protein complexes or SCF E3s. The Cullin subunit (any one of Cullin‐1, ‐2, ‐3, ‐4A, ‐4B, ‐5, or ‐7) forms an elongated bent backbone for the multisubunit ligase. The Cullin N‐terminus binds the S‐phase kinase‐related protein‐1 (Skp‐1), an adaptor that recruits any one of a number of substrate‐specific adaptor subunits called F‐box proteins. The F‐box protein is the main determinant in substrate specificity, as it binds the substrate through its particular substrate recognition domain (Jin et al., 2004a), although the RING box protein may participate (Jin and Harper, 2002). The Cullin N‐terminus binds the catalytic core composed of the RING‐box (Rbx) protein with its associated Ub‐loaded E2 (Zheng et al., 2002). This arrangement allows the F‐box protein to bring its bound substrate close to the ubiquitination machinery of the complex (Fig. 2B). Phosphorylation of the substrate very often regulates the F‐box protein–substrate interaction, converting the substrate into a form susceptible to E3 activity, adding an extra layer of control to the process (Joazeiro and Weissman, 2000; Schulman et al., 2000; Zheng et al., 2002). CRLs assemble with numerous substrate receptors. Cullins 2 and 5, for example, recruit substrates through suppressor of cytokine signaling/elongin‐BC

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(SOCS/BC) boxes and form the so‐called SCF2s and SCF5s complexes. In SCF2s and SCF5s, Skp‐1 is substituted by the Skp‐1‐like protein elongin C which binds the Ub‐like elongin B that binds the substrate adaptor subunit (Petroski and Deshaies, 2005). Many of these E3 complexes have important roles in the immune system (Liu, 2004; Liu et al., 2005). The nuclear factor‐kB (NF‐kB) transcription factor is a master organizer of both innate and adaptive immunity. NF‐kB is activated in response to TLR signaling on recognition of pathogen‐associated molecules like bacterial peptidoglycan (Liu et al., 2005) in a process that is crucially dependent on ubiquitination. One of the steps requires the cytosolic SCFb‐TrCP E3 complex. The SCFb‐TrCP substrate adaptor component is the F‐box protein b‐transducin repeat‐containing protein (b‐TrCP) that possesses WD repeats that bind to phosphorylated inhibitor of NF‐kB (IkB), inducing its ubiquitination and degradation. NF‐kB is thus released from the IkB‐NF‐kB dimer and translocates into the nucleus, activating downstream transcription. The elongin‐C‐elongin‐B‐Cullin‐5‐SOCS (ECS) complex uses SOCS proteins as the substrate adaptors. SOCS boxes bind Janus kinases (JAKs), which are recruited and activated in response to IFN and cytokine signaling, promoting ubiquitination and degradation of JAKs by the ECS complex. This, in turn, inhibits phosphorylation and activation of the signal transducer and activator of transcription (STAT) family of transcription factors that are crucial for the immune response following IFN and cytokine signaling and following viral infections (Liu et al., 2005). There are other families of E3s with noncanonical RING‐domains, like the K3 homologues and the Ufd2‐ homologous box (U‐box) E3s (which we discuss in more detail later). For a more comprehensive review of HECT and RING E3s and different classifications read (Ardley and Robinson, 2005; Coscoy and Ganem, 2003; Hatakeyama et al., 2001; Petroski and Deshaies, 2005; Sharrocks, 2006). 10. Ubiquitin Chains and Ubiquitin‐Like Modifiers (Ubls) Originally believed to always deliver a ‘‘kiss of death’’ and target the substrate for proteasomal degradation, the much more wide‐ranging effects of Ub conjugation are beginning to be appreciated. Chain length and linkage type also influence the outcome of the Ub‐conjugated substrate. The multiple Ub moieties in a polyUb chain (chains of 4 or more Ub moieties) are linked to one another by an isopeptide bond between a lysine residue on one Ub molecule (usually on Lys48) and the C‐terminal carboxyl group of the next Ub on the chain. At times, extension of a polyUb chain on a substrate conjugated with 1–3 ‘‘initiator’’ Ub moieties requires a special subclass of E3s, the UFD2‐homology box (U‐box) E3s (once called E4s) (Hoppe, 2005). Targeting of proteins for proteasomal proteolysis generally requires polyubiquitination in a lysine (Lys)

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48‐type linkage. By contrast to polyUb, substrate monoubiquitination or attachment of noncanonical Ub chains—Ub chains with non‐Lys48 linkages such as Lys63 and Lys29 linkages—usually have nonproteolytic functions in DNA repair, endocytosis, signal transduction, transcriptional regulation, and ribosomal function (d’Azzo et al., 2005; Pickart and Eddins, 2004). Monoubiquitination can occur on a single lysine residue or on several lysine residues in a substrate (multiubiquitination). Monoubiquitination is extremely important as a sorting signal in the endocytic pathway. For example, monoUb attachment is sufficient to induce endocytosis of growth hormone receptor and sorting to the lysosome for degradation (Hicke, 2001; Hicke and Dunn, 2003). Direct modification of the cargo (cis‐regulation) or modification of the protein‐ trafficking machinery (trans‐regulation) by monoUb could have many consequences for antigen presentation, as these processes rely heavily on events that take place in endolysosomal compartments. Noncanonical ubiquitin chains play many diverse roles in signaling pathways, in DNA replication and postreplication DNA repair, and modulating protein–protein interactions. For instance, activation of NF‐kB is tightly regulated by a balance between Lys48‐ and Lys63‐mediated ubiquitination of different components of the NF‐kB pathway. Triggering of many cell‐surface receptors leads to assembly of signaling complexes that recruit tumor necrosis factor receptor‐associated factor 6 (TRAF6), an E3 ligase that binds UBC13, promoting Lys63‐linked polyubiquitination of the g subunit of the inhibitor of NF‐kB kinase (IKK) complex, IKKg. This leads to activation of the IKK complex, which in turn results in IkB phosphorylation. Phosho‐IkB then recruits the SCF complex that catalyzes Lys48‐linked polyubiquitination of IkB and consequently activates NF‐kB (Karin and Ben‐Neriah, 2000). For comprehensive reviews see (Finley et al., 2004; Pickart and Eddins, 2004; Varshavsky, 2005). Ubiquitin‐like molecules or modifiers (Ubls) share structural homology with Ub and can also be conjugated onto protein substrates, mostly with outcomes other than proteasomal degradation. Ubls like the small ubiquitin‐like modifier SUMO, neuronal precursor cell‐expressed developmentally down‐regulated 8 (NEDD8) or IFN‐stimulated gene product of 15 kDa (ISG15), to name but a few, are implicated in important physiological processes like nuclear transport, maintenance of chromosome integrity, transcriptional regulation, cell cycle control, signaling and regulation of proteolysis (Hochstrasser, 2001; Schwartz and Hochstrasser, 2003). Ubls may regulate Ub‐mediated proteolysis or signaling through comodification of a substrate, thus modulating the effects of Ub conjugation (Lamsoul et al., 2005; Sobko et al., 2002) or by regulating the activity, specificity, localization, or stability of enzymes in the Ub‐conjugating cascade, as is the case for NEDD8 modification of Cullin‐RING E3s (Kawakami et al., 2001; Petroski and Deshaies, 2005; Wu et al., 2005).

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11. Deubiquitinating Enzymes Deubiquitinating enzymes can cleave isopeptide bonds to remove Ub from the substrate or from polyubiquitin chains. There are about 100 DUBs in the human genome, organized in five classes according to their catalytic domain structure: the ubiquitin‐specific proteases (USPs), the ubiquitin C‐terminal hydrolases (UCHs), the Machado‐Joseph disease proteases (MJDs), the ovarian tumor proteases (OTUs), and the JAB1/MPN/Mov34 proteases (JAMMs). The first four classes comprise cysteine‐type proteases, whereas JAMMs are metalloproteases. For a more detailed inventory of DUBs, read Nijman et al. (2005). DUBs have very diverse specificity properties, in terms of the ubiquitin or Ubl moiety itself (substrate specificity), in terms of the target protein to which the Ub or Ubl is attached (target specificity), and possibly in terms of the context provided by target and attached modification. DUB specificity in vivo can be further regulated by subcellular localization or association with different binding partners (Amerik and Hochstrasser, 2004; Li and Hochstrasser, 2003; Reyes‐ Turcu et al., 2006; Soboleva and Baker, 2004). DUB functions are therefore also extremely diverse, ranging from regulation of proteasome function, to regulation of chromatin structure, to membrane protein trafficking, and with obvious implications in processes such as cancer and neurodegeneration (Amerik and Hochstrasser, 2004; Nijman et al., 2005; Soboleva and Baker, 2004). 12. The Proteasome The proteasome, very abundant in the cytosol, is a multisubunit protease composed of the 20S and 19S proteasome complexes. The 20S proteasome (or central core particle) has the general architecture of a barrel, formed by four stacked rings of seven subunits each, the outer two rings being composed of a subunits and the innermost two rings of b subunits (Groll et al., 1997). The b subunits, which line the proteasome’s inner cavity, carry out the catalytic activity. For mammalian proteasomes, only three of the seven b subunits in each ring are catalytically active. Access to this cavity occurs through narrow pores (with a diameter on the order of 10–15 A˚) at both ends of the barrel, so it is usually assumed that protein substrates must be unfolded prior to their delivery to the catalytic chamber (Groll et al., 1997, 2000; Kohler et al., 2001). Also at both ends of the core particle there is the 19S cap complex, whose functions range from recognition of poly‐Ub chains on target proteins, to unfolding of the substrate to facilitate entry into the catalytic cavity, to deubiquitination activity (Adams, 2003; Heinemeyer et al., 2004; Rivett et al., 1997; Schmidt et al., 2005; Seeger et al., 1997). As mentioned earlier, the proteasome plays an instrumental role in class I MHC antigen presentation and activation of peptide‐specific CD8þ T cell responses. This process requires not only generation of peptides of the right

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quality—that is, right size and sequence to allow a correct fit into the peptide‐ binding cleft—but also in the right quantity to trigger a successful response, which is no small endeavor due to many destructive aminopeptidase activities in the cytosol (Shastri et al., 2005; Strehl et al., 2005). IFN‐g, a crucial component of the innate and adaptive antiviral immune responses, affords the immune system a competitive edge. IFN‐g induces expression of auxiliary b subunits, b1i, b5i, and b2i [also known as low molecular weight protein 2 (LMP2), LMP7 and multicatalytic endopeptidase‐like complex 1 (MECL1), respectively], as well as synthesis of the proteasome activator PA28, and of the proteasome maturation protein (POMP) (Strehl et al., 2005). The immunosubunits LMP2, LMP7, and MECL1 are incorporated into nascent proteasomes, replacing their endogenous counterparts and constituting the so‐called immunoproteasome. The proteasome activator PA28 (or 11S proteasome) binds to the outer rings of the 20S proteasome, thereby opening the central gate and facilitating substrate entry. POMP is important for assembly and maturation of the proteasome (Strehl et al., 2005). This IFN‐g‐induced proteolytic cascade, mediated by immunoproteasomes and PA28, might be induced to respond to a demand for high proteasome activity when the constitutive cascade is no longer sufficient, altering the proteolytic activity of the proteasome for maximal efficiency in production of the class I MHC peptide repertoire. IFN‐g treatment also activates a transcriptional program that increases the synthesis of class I MHC molecules themselves and that of components of the peptide‐ loading complex, thus increasing cell surface presentation (Kloetzel, 2004; Kloetzel and Ossendorp, 2004; Kruger et al., 2003; Rivett and Hearn, 2004; Van den Eynde and Morel, 2001). Therefore, even though class I MHC presentation is constitutive, it can be modulated in the course of an immune response, with a proposed role in the early stages of a cytotoxic response. It is thus not surprising that viruses have targeted the IFN‐g signaling cascade so aggressively (Alcami and Koszinowski, 2000; Hengel et al., 2005; Salazar‐ Mather and Hokeness, 2006). 13. ER Quality Control and Degradation Although tightly controlled, ER protein synthesis is not always successful. Proteins may sustain damage or fail to complete their synthesis early during biogenesis, or be trapped in an irreversible nonnative conformation, or a mutation may result in a structural alteration that leads to misfolding, as is the case for the cystic fibrosis conductance regulator (CFTR) (Jensen et al., 1995; Ward et al., 1995), mutant plasma a1‐antitrypsin (Teckman and Perlmutter, 1996), or tyrosinase (Halaban et al., 2000). They may also be expressed in the absence of their cognate subunits, as is the case for unassembled subunits of

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TCRa (Huppa and Ploegh, 1997; Yang et al., 1998). ER quality control is a homeostatic process that involves an elaborate machinery that recognizes and retains newly synthesized misfolded or misassembled proteins and targets many of them for degradation by the Ub‐proteasome system (Ellgaard and Helenius, 2003). This mode of degradation therefore samples sets of proteins otherwise targeted to extracellular space, where the degradation products are available as peptide ligands for class II MHC products. In addition to its role in ER quality control, this ER‐associated degradation (ERAD) can also be employed in the physiological regulated proteolysis of normal ER proteins whose degradation is subject to metabolic cues, such as hydroxymethylglutaryl‐coenzyme A reductase (HMGR) (Hampton, 2002; Hampton and Bhakta, 1997). A feature of this ER‐associated protein degradation is the spatial separation between targeting of substrates and their proteolysis, which requires substrate export from the ER lumen or membrane to the cytoplasm by a process termed dislocation (also called retrograde translocation or retrotranslocation) (Werner et al., 1996). Dislocation is a complicated multistep process that involves substrate recognition, targeting for dislocation, removal from the ER membrane, deglycosylation, ubiquitination, and finally proteolysis (Kostova and Wolf, 2003; Meusser et al., 2005; Romisch, 2005). The proteasome is usually considered to be a nonselective degradation apparatus, with selection of ERAD substrates being mediated mostly by the Ub ligases. However, the ER quality control E3 enzymes are mostly cytosolic or membrane‐associated and thus are separated from their substrates at least by the ER membrane. This invokes the existence of mechanisms, present in E3 ligases themselves or in upstream factors, which facilitate coupling of ERAD substrate recognition to ubiquitination by E3 ligases in the cytoplasm. 14. ERAD Substrate Recognition Owing to the extremely diverse nature of proteins that must be examined by the ER quality control machinery, a unifying model for how recognition of ERAD substrates takes place remains intractable. Nonetheless, misfolded proteins cleared from the ER enter the class I MHC‐processing pathway, and hence this route of degradation is an important aspect of the generation of class I MHC epitopes. Instead, ERAD is likely to be custom‐fitted to the client protein in question. For glycoproteins, a possible mechanism is the recognition of terminally misfolded proteins by the calnexin/calreticulin (CNX/CRT) lectin‐type chaperones, which retain immature glycoproteins in the ER until productive folding takes place (Ellgaard and Helenius, 2003; Hammond, 1994; Helenius and Aebi, 2004). Terminally misfolded proteins—that is, proteins that after

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extensive CXN/CRT cycle folding attempts still fail to acquire their native conformation—are trimmed by ER mannosidase I, leading to recognition by ER degradation‐enhancing alpha‐mannosidase‐like protein (EDEM), which presumably targets them for degradation (Eriksson et al., 2004; Jakob et al., 2001; Molinari et al., 2003). Most ER lumenal proteins require the lumenal chaperone BiP for degradation, while transmembrane proteins with large cytosolic domains usually rely on cytosolic chaperone systems, like the heat shock protein (HSP) complexes Hsp70/Hsp90 and Hsp40/Hsp70 (Ellgaard and Helenius, 2003; Romisch, 2005). Yet it appears that a protein’s lumenal or ER membrane localization matters less than the localization of the folding alteration within the polypeptide itself. CFTR whose cytoplasmic domains are recognized first by the Hsp70/Hsp90 cytoplasmic chaperone system may be targeted for degradation by the cytosolic Hsp70/Hsp90‐interacting CHIP E3 ligase cochaperone (Connell et al., 2001; Murata et al., 2001), even if the protein also has a misfolded lumenal domain (which might also target it to a BiP‐dependent degradation pathway). If the cytoplasmic domain is properly folded, then the lumenal domains are inspected, and if then the protein is recognized as misfolded, it is degraded in a process that involves BiP (Connell et al., 2001; Meacham et al., 2001; Vashist and Ng, 2004). This suggests that ER quality control uses sequential checkpoints to select degradation substrates and target them to the appropriate degradation pathway. In the case of nonglycosylated substrates, protein disulfide isomerase (PDI), one of a large number of ER‐resident oxidoreductases that catalyze disulfide bond formation and isomerization, can play a role in ER quality control by unfolding certain substrates prior to degradation. Another oxidoreductase, ERp57, interacts with CNX and CRT to facilitate folding, but in the event of a terminally misfolded protein may aid in transfer of proteins with improper disulfide bonds to the EDEM pathway (Hirsch et al., 2004). Another possibility, at least in yeast, is that misfolded proteins actually escape to the Golgi and are then recycled to the ER (Taxis et al., 2002; Vashist et al., 2001). This ER–Golgi shuttling model was proposed because mutations in several secretory pathway genes (like Ufe1p, Sec23p, and Erv29p) compromise degradation of ERAD substrates (Taxis et al., 2002; Vashist et al., 2001), invoking a functional secretory pathway for efficient degradation of misfolded proteins from the ER. The Golgi apparatus could presumably endow misfolded proteins with a signal for destruction, but such a modification has not been found. Since the Ufe1p and Sec23p have since been shown to be required for maintenance of proper ER structure (Prinz et al., 2000), the effects on protein degradation may simply be pleiotropic consequences of perturbing the normal arquitecture of the ER (Hammond et al., 1994; Romisch, 2005). Ubiquitination at the ER membrane is yet another mode of ERAD substrate selection.

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15. ERAD E3 Ligases Most ERAD E3s are at the ER membrane, as is the case for the yeast Hrd1p/ Der3p and Doa10p, or can be brought to the ER membrane on demand, as is the case for cytosolic SCF complexes and CHIP, for example (Kostova and Wolf, 2003; Meusser et al., 2005; Romisch, 2005). An E3 ligase can recruit distinct E2 Ub‐conjugating enzymes and/or distinct adaptor proteins (as we have seen for the SCF and ECS E3 complexes and will discuss later for ERAD E3s), thus conferring specificity in substrate selection. The yeast Hrd1p/Der3p E3 ligase was first discovered in a genetic screen for Saccharomyces cerevisiae genes involved in HMG‐CoA reductase degradation (Hrp) (Hampton et al., 1996) and is an ER‐resident protein with six predicted transmembrane domains and a C‐terminal RING‐finger motif facing the cytosol. Hrd1p can act in a complex with Ubc7p and Ubc6p to ubiquitinate substrate proteins (Fig. 2B). Ubc7p is a soluble protein that becomes active only when tethered to the ER membrane by the Ubc7p cofactor Cue1p membrane protein. Degradation of transcription factor Mata2–10 protein (Doa10p) is a transmembrane protein of the ER/nuclear envelope, which participates in yeast ERAD (Swanson et al., 2001). Doa10p is predicted to span the membrane 14 times (Kreft et al., 2006) and, like Hrd1p, uses Cue1p/Ubc7p and Ubc6p to ubiquitinate its substrates (Fig. 2B). However, the two ligases target different sets of substrates for degradation (Bays et al., 2001; Swanson et al., 2001). Hrd1p ubiquitination activity can be directed to a specific subset of ER degradation substrates, by virtue of its association of Hrd1p with Hrd3p and Der1p. Hrd3p is a single‐spanning ER membrane protein with a large ER luminal domain that can recognize misfolded proteins, thus possibly functioning as a substrate recruitment factor for the Hrd1p ligase complex (Gauss et al., 2006). The degradation from the ER‐1 protein (Der1p) spans the ER membrane four times and is required for degradation of some misfolded glycoproteins (Knop et al., 1996). Der1p may function as a substrate adaptor protein or even as a channel for ejection of degradation substrates from the ER membrane. Hrd3p can associate with Der1p, presumably enabling substrate delivery to downstream components. Hrd3p can even regulate Hrd1p activity, which is necessary for substrate extraction from the membrane and delivery to the proteasome (Gauss et al., 2006) (Fig. 3). These multifunctional protein complexes can therefore function in substrate selection in the ER lumen or membrane and even facilitate subsequent steps that lead to proteasomal degradation. Similarly, Doa10p can catalyze ubiquitination of both membrane and soluble proteins, yet the mechanisms of subsequent proteasome targeting differ (Ravid et al., 2006), presumably due to association with other regulatory proteins. The many layers of E3‐ mediated regulation of ERAD substrate selection are most likely just emerging.

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Figure 3 The yeast Hrd1p/Der3p‐Hrd3p E3 ligase participates in multiple steps of ERAD, from substrate selection in the ER membrane or lumen to ubiquitination in the cytosol. PNGase, peptide‐N‐glycanase; Ub, ubiquitin. See text for details.

16. Mammalian ERAD E3s In mammals, there are two predicted homologues of Hrd1p/Der3p, the HRD1 and the gp78 E3 ligases. Like its yeast counterpart, human HRD1 is involved in degradation from the ER (Kikkert et al., 2004). HRD1 may associate with UBC7 to catalyze ubiquitination of a subset of substrates, like TCRa and CD3d. HRD1 is not involved in the regulated degradation of the mammalian HMGR (Kikkert et al., 2004). HRD1 may also associate with SEL1L, a homologue of Hrd3p, as well as other ER membrane and ER membrane‐associated proteins, including Cdc48p(p97)/NPL4/UFD1, forming multisubunit complexes that seem to coordinate steps that range from substrate selection to delivery to the proteasome for at least a subset of ER degradation substrates (Lilley and Ploegh, 2005a; Ye et al., 2005). gp78 was identified as the tumor autocrine motility factor receptor (AMFR) (Nabi et al., 1992) and later as an E3 ligase due to its homology to Hrd1p and involvement in degradation of CD3d and apoliprotein B100 (Fang et al., 2001; Liang et al., 2003). gp78 is an ER‐resident

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protein, predicted to span the membrane five times, with a C‐terminal cytosolic RING‐domain and an additional UBC7 E2‐binding site, the Cue domain, arranged in tandem. While Hrd1p recruits UBC7 through the transmembrane Cue1p protein, it seems that convergent evolution has made the E3‐ and the E2‐docking protein come together in a single human protein. Curiously, gp78 is involved in sterol‐regulated Ub–dependent degradation of HMGR (Song et al., 2005), suggesting it constitutes the true functional homologue of yeast Hrd1p. Homocysteine‐induced endoplasmic reticulum protein (HERP) is a single‐ spanning ER membrane protein that is induced by the unfolded protein response (UPR) and also required for ERAD. HERP is proposed to improve ER protein folding and decrease protein load, protecting cells from ER‐stress–induced apoptosis (Kokame et al., 2000). Furthermore, HERP has an N‐terminal ubiquitin‐like domain (ULD) and is required for the degradation of conexin and CD3d (Hori et al., 2004; Sai et al., 2002). It forms a complex with HRD1, p97, Derlin‐1, and VIMP (Schulze et al., 2005), a VCP (p97)‐interacting membrane protein, that recruits p97 to Derlin‐1 (Ye et al., 2004). HERP may function as another adaptor protein in these multiprotein complexes at the ER membrane, influencing substrate selection. The Doa10p mammalian homologue, TEB4 (or MARCH‐VI), is a multiple‐ transmembrane‐domain‐containing protein of the ER membrane that functions as an E3 ligase: it has an N‐terminal noncanonical RING‐domain in the cytosol that catalyzes Ub conjugation and TEB4 self‐ubiquitination and degradation (Hassink et al., 2005), but its regulation is poorly characterized. Parkin is a cytosolic E3 ligase with a C‐terminal noncanonical double‐RING‐finger (RING‐IBR‐RING), and an N‐terminal Ub‐binding domain, believed to mediate proteasomal degradation of aggregation‐prone proteins (Imai et al., 2000), typical of Parkinson’s disease. Both phosphorylation (Yamamoto et al., 2005) and ER stress‐induced association with C‐terminus of Hsc70‐interacting protein (CHIP), involved in cytosolic chaperone‐dependent folding, regulate Parkin E3 ligase activity (Imai et al., 2002; Sahara et al., 2005), which could be beneficial for reduction of protein aggregates and cellular pathology. How specificity in substrate selection is conferred can be illustrated by E3s involved in glycoprotein turnover. The F‐box proteins Fbs1 and Fbs2 bind high mannose N‐linked glycoproteins (Winston et al., 1999). By using Fbs1/ Fbs2 as its substrate adaptor(s), the cytosolic SCFFbs1,Fbs2 E3 ligase is rendered specific for glycoproteins that have been dislocated from the ER (Yoshida et al., 2002, 2003) (Fig. 4A). The CHIP U‐box E3 ligase which is involved in the degradation of CFTR (Meacham et al., 2001) and glucocorticoid hormone receptor (Meacham et al., 2001), can be ‘‘manipulated’’ to function in ER glycoprotein turnover. CHIP usually serves as a cochaperone

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Fbs1, Fbs2

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Figure 4 Substrate specificity in mammalian ERAD E3s involved in glycoprotein turnover. (A) The cytosolic SCF E3 ligase catalyzes ubiquitination of dislocated N‐linked glycoproteins when complexed with the F‐box proteins Fbs1 and Fbs2; Rbx, RING‐box domain. (B) CHIP, C‐terminus of Hsc70‐interacting protein, is usually a cochaperone for the heat shock protein (Hsp) chaperone system Hsp70/Hsp90, ubiquitinating misfolded proteins bound to Hsps. (C) When complexed with Fbs1 or Fbs2, CHIP ubiquitinates dislocated glycoproteins. E2, Ub‐conjugating enzyme; Ub, ubiquitin.

for the cytosolic heat shock protein Hsp70/Hsp90 chaperone system. The CHIP N‐terminal TPR motif recruits Hsp chaperones loaded with misfolded proteins, whereas its C‐terminal U‐box RING domain recruits E2 enzymes (Murata et al., 2003) (Fig. 4B), effectively linking protein folding with ubiquitination. The CHIP E3 ligase activity can be directed to ER glycoprotein turnover by binding to Fbs2 (Nelson et al., 2006), through an interaction between its TPR motif and a PEST motif in Fbs2 (Fig. 4C). N‐linked glycans can, therefore, function in ER quality control not only to regulate ER retention in the folding cycle, but also to function in ERAD substrate selection and ubiquitination, adding an additional layer of complexity and specificity to glycoprotein quality control (Nelson et al., 2006; Yoshida, 2003).

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17. The Elusive Dislocon Export of proteins through the ER membrane most likely takes place via an aqueous channel that allows the passage of polypeptides through the highly hydrophobic ER membrane environment while maintaining proper ionic balance between the ER and the cytoplasm. The Sec61 channel, the very same channel responsible for protein import into the ER, was initially thought to mediate transport in the reverse direction (hence the name retrograde translocation or retrotranslocation also coined for the dislocation process) (Pilon et al., 1997; Plemper et al., 1997, 1998; Wiertz et al., 1996b), with accessory factors regulating directionality and specificity of the channel. The extent to which Sec61 is involved in ER dislocation or the identity of the ‘‘dislocon’’ are not without controversy, and the search for a dislocation channel(s) is a subject of intense research (Meusser et al., 2005; Romisch, 2005). Mammalian Derlin‐1, a member of the Der1p‐Like (Derlin) family of yeast Der1p homologues, is involved in dislocation from the ER (Lilley and Ploegh, 2004; Ye et al., 2004) and was proposed to constitute a channel for protein export from the ER membrane to the cytosol (Lilley and Ploegh, 2004; Ye et al., 2004). Like their yeast homologue, Derlins 1, 2, and 3 are tetraspanning ER membrane proteins that can homo‐ and heteroligomerize and could presumably form higher order structures with channel‐like properties (Lilley and Ploegh, 2004; Ye et al., 2004). Conclusive evidence for a role of Derlin‐1 as a channel is still unavailable, and in any case, Derlin‐1 is unlikely to be the only channel, as turnover of some ERAD substrates does not rely on Derlin‐1 function (Kreft et al., 2006; Lilley and Ploegh, 2004). Derlins 2 and 3 are obvious candidates that could function in place of Derlin‐1. Alternatively, Derlins may act to deliver a particular substrate to a channel/another adaptor in its cognate dislocation pathway. In fact, Derlins form a large, multiprotein complex with p97 and the Hrd1p and Hrd3p mammalian homologues HRD1 and SEL1L, respectively (Lilley and Ploegh, 2005a; Ye et al., 2005), suggesting a very intimate connection between substrate recognition, export through the membrane, ubiquitination, and extraction into the cytoplasm. The existence of such a complex that would integrate all of these different functions, including formation of a channel or dislocon, would offer obvious advantages in terms of control of both specificity and directionality of the dislocation process. We shall return to this substrate ‘‘guidance’’ theme. 18. Driving Dislocation and the Ub‐Binding Route to the Proteasome A cytosolic complex containing the AAA ATPase Cdc48p (yeast) [valosin‐containing protein (VCP)/p97 (in mammals)] and its cofactors nuclear protein localization 4 (Npl4p) and ubiquitin‐fusion degradation 1 (Ufd1p), was recently shown

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to participate in ER degradation (Lord et al., 2002; Romisch, 2005; Ye et al., 2001). Cdc48p/p97 is an essential protein of the AAA ATPase (ATPases associated with various cellular activities) family, conserved from archaea to mammals, whose functions include mitotic spindle disassembly, membrane traffic and fusion, nucleic acid repair and replication, and Ub‐ proteasome degradation (Woodman, 2003). Cdc48p/p97 is a motor protein that generates energy from ATP binding and hydrolysis; it forms a homohexameric barrel structure, with each subunit containing two AAA domains that contain the Walker motifs essential for ATPase activity, and the 6 subunits arranged in a ring with a pore in the center (Zhang et al., 2000). Cdc48p/p97 interacts with many different adaptor proteins, which regulate its function (Dreveny et al., 2004). P97 can recognize denatured proteins nonspecifically (Thoms, 2002) and has an affinity for polyubiquitin chains (Ye et al., 2003). When complexed with the polyUb‐binding Ufd1p and Npl4p, Cdc48p/p97 activity is directed to ER degradation (Ye et al., 2001). Both in yeast and in mammals, the trimeric Cdc48p(p97)/NPL4/UFD1 complex is proposed to function in a postubiquitination, preproteasomal step (Bays and Hampton, 2002; Jarosch et al., 2002), in one of two fashions: the ATP‐hydrolytic activity of the AAA ATPase p97 may provide the driving force to extract substrates through the ER membrane, or may be required to liberate already dislocated substrates from the cytosolic face of the ER membrane (Braun et al., 2002; Flierman et al., 2003; Hirsch et al., 2004; Kostova and Wolf, 2003; Meusser et al., 2005). The proteasome presumably interacts with the ER membrane (Hirsch and Ploegh, 2000), either directly or through a receptor that docks the proteasome to the ER membrane, perhaps Sec61 (Kalies et al., 2005). Notwithstanding, the Cdc48p(p97)/NPL4/UFD1 complex or other accessory factors might aid substrate feeding to the proteasome (Hartmann‐ Petersen and Gordon, 2004a; Richly et al., 2005). Ubiquitin‐binding factors, such as Rad23p and Dsk2p (in yeast), have a ubiquitin‐associated (UBA) motif that binds polyubiquitin chains and a ubiquitin‐like (UBL) motif that binds to the 19S proteasome, and these are required for efficient degradation of a model ERAD substrate (Elsasser et al., 2004; Schauber et al., 1998; Wilkinson et al., 2001). The yeast Ub regulatory X domain‐containing Ubx2p/Sel1p protein, an integral ER membrane protein, was recently shown to recruit the Cdc48p/Npl4p/Ufd1p complex to the ER membrane, thereby facilitating the transfer of polyubiquitinated substrates from the E3 ligases Hrd1p and Doa10p to Cdc48/p97 (Neuber et al., 2005; Schuberth and Buchberger, 2005). These dual function Ub‐binding factors effectively serve as bridges between the p97/NPL4/UFD1 complex and the proteasome. As more of these Ub‐ and proteasome‐binding proteins are discovered (Buschhorn et al., 2004; Decottignies et al., 2004; Medicherla

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et al., 2004; Mullally et al., 2006), a ‘‘guidance’’ model, in which ERAD substrates are escorted from a dislocation channel to the proteasome by a cascade of Ub‐binding factors, gains strength (Hartmann‐Petersen and Gordon, 2004b; Hartmann‐Petersen et al., 2003; Hendil and Hartmann‐Petersen, 2004; Richly et al., 2005). These interactions could be responsible for maintaining the substrate in a proteolysis‐competent state and protect it from premature deubiquitination, as well as contribute to directionality of dislocation (Hendil and Hartmann‐Petersen, 2004; Meusser et al., 2005; Romisch, 2006). In fact, it seems Cdc48p/p97 may even be capable influencing substrate fate (Rumpf and Jentsch, 2006). Cdc48p/p97 can simultaneously bind Ufd2p, a U‐box E3 that catalyzes polyubiquitin chain extension, and one of two factors that can counteract its action: Otu1p, a DUB, and Ufd3p protein, a WD40 repeat protein of unknown function that has been shown to be required for Ub‐dependent proteolysis (Ghislain et al., 1996; Johnson et al., 1995). Otu1p can disassemble the polyUb chains, whereas Ufd3p competes with Ufd2p for the same docking site on Cdc48p/p97. Presumably, Cdc48p/p97 can selectively recruit different substrate processing cofactors and thus tip the balance toward substrate degradation or release from the degradation cascade (Rumpf and Jentsch, 2006), suggesting a very tight regulation of proteasomal proteolysis. These aspects are important not only to understand how these pathways contribute to class I MHC–peptide epitope presentation, but also how viruses manipulate these routes to avoid detection. 19. Peptide N‐Glycanase PNGase is a cytosolic deglycosylating enzyme that presumably removes N‐linked glycan chains from misfolded substrates prior to proteasomal degradation (Hirsch et al., 2003; Suzuki et al., 2000). Both in yeast and mammals, there is generally a tightly knit relationship between PNGase and the proteasome: PNGase interacts with (at least) the S4 and S5 subunits of the mammalian 19S proteasome and the Ub‐binding factor Rad23p (HR23B in mammals), which seems to recognize only deglycosylated degradation substrates (Katiyar et al., 2004). This suggests that misfolded protein substrates may first be deglycosylated by ER‐associated or free PNGase, then identified by the HR23B adaptor protein, and subsequently targeted to the nearby proteasome (Katiyar et al., 2004). Mammalian PNGase also associates with the ER membrane gp78 E3 ligase and the cytosolic p97 and Y33K, a UBA/UBX domain protein (Li et al., 2006a). A gp78‐Y33K‐p97‐PNGase‐ HR23B complex could therefore be formed that recruits PNGase to the cytosolic face of the ER membrane that couples the activities of dislocation, ubiquitination, and deglycosylation and escorts misfolded glycoproteins to the proteasome (Kim et al., 2006a; Li et al., 2005, 2006a).

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20. Viral Interference with Class I MHC Antigen Presentation Viruses keep evolving and developing sophisticated immune evasion strategies. In particular, they have targeted virtually every step of the class I MHC antigen presentation pathway, inhibiting proteolysis and generation of the antigenic peptide [Epstein–Barr virus (EBV) nuclear antigen‐1 or EBNA‐1, HCMV E protein pp65, and HIV Tat], inhibiting peptide loading and assembly in the ER (HSV ICP47, HCMV US6, bovine herpes‐virus‐1 UL49.5), retaining class I MHC molecules in the ER (adenovirus E3/19K and HCMV US3), blocking their exit from the ER‐to‐Golgi complex (ERGIC) (MCMV m152), misdirecting MHC complexes to lysosomal compartments (MCMV m06 and HHV‐7 U21), internalizing MHC complexes from the cell surface (KSHV K3 and K5 and HIV Nef ), encoding homologues of class I MHC as decoys for NK cells (HCMV UL18 and UL142 and MCMV m04), and causing degradation of class I MHC products by the Ub‐proteasome system (HCMV US2 and US11 and MHV‐68 mK3) (Fig. 5). Because these topics have been the subject of numerous

Figure 5 HCMV interference with class I MHC antigen presentation. HCMV immunoevasins aimed at inhibition of cytotoxicity by CD8þ T cells and NK cells are in red. TCR, T cell receptor; PLC, peptide‐loading complex; TAP, transporter associated with antigen presentation; CRT, calreticulin.

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reviews (Alcami and Koszinowski, 2000; Ambagala et al., 2005; Hengel and Koszinowski, 1997; Hengel et al., 1998, 1999; Lybarger et al., 2003; Mocarski, 2004; Yewdell and Hill, 2002), we shall discuss only a few of these mechanisms in more detail, particularly those exploited by HCMV. 21. Human Cytomegalovirus The b‐herpesvirus HCMV is extremely successful in evolutionary terms: it is a ubiquitous, highly species‐adapted pathogen that is able to establish a life‐long persistent infection with minimal or no disease symptoms in the immunocompetent host. Prolonged latency periods (a dormant state with minimal production of viral proteins and absence of viral progeny) and controlled sporadic reactivation ensure transmission to a new host, and thus survival of both host and virus. Perturbation of this delicate balance leads to life‐threatening infections in immunocompromised patients, transplant recipients and infected newborns and illustrates how the outcome of this host–virus relationship is dependent on viral manipulation of the host immune response (Hengel et al., 1998; Klenerman and Hill, 2005). The several HCMV‐encoded immunoevasins (Jones et al., 1995) are presumably aimed primarily, but not solely, at control of the CD8þ T cell and NK cell responses (Falk et al., 2002; Mocarski, 2004; Pinto and Hill, 2005; Yewdell and Hill, 2002). Here we will discuss the HCMV immunoevasins that interfere with class I MHC antigen presentation, US3, US6, US10, US2, US11, UL16, UL18, UL40, UL141, and UL142. In light of our most recent findings, we will elaborate on the mechanism of ER dislocation co‐opted by the HCMV US2 and US11 immunoevasins. More specifically, we discuss the similarities and the differences between the two cellular ERAD pathways that US2 and US11 have allowed us to uncover and the possible implications for ER dislocation. We will extend this by comparing the HCMV US2‐ and US11‐mediated dislocation of human class I MHC HC molecules with dislocation of murine HCs by the MHV‐68 mK3 immunoevasin. 22. HCMV Interference with Class I MHC Antigen Presentation If one goes back to the steps we depicted for antigen presentation (Fig. 1) and then examines the immunoevasins encoded by HCMV, we will find that this herpesvirus exploits many aspects of the antigen presentation pathway thus defined. The HCMV phosphoprotein pp65 tegument protein mediates the phosphorylation of the HCMV immediate early antigen‐1 (IE‐1) during HCMV infection. Phosphorylation of IE‐1 interferes with the presentation of IE‐1‐derived antigens (Gilbert et al., 1996). The US3 protein binds to and retains some class I MHC locus products in the ER membrane (Ahn et al., 1996; Jones et al., 1996). US3 is a type I membrane glycoprotein with an Ig‐like

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lumenal domain that is essential for its own retention, albeit transient, in the ER (Lee et al., 2003). US3 eventually travels to the lysosome where it is degraded (Gruhler et al., 2000). Some evidence suggests that ER retention of class I MHC complexes by US3 depends on the ER localization signal on US3 and perhaps the ability of the US3 luminal domain to oligomerize (Misaghi et al., 2004b) as determinants of retention of both molecules (Lee et al., 2003). Another model suggests that association of US3 with tapasin, which inhibits tapasin, is sufficient to mediate ER retention (Park et al., 2004). Class I MHC alleles that require peptide optimization by tapasin may be retained in the ER, whereas tapasin‐independent locus MHC products are spared from retention. In fact, there is a perfect correlation between tapasin‐dependence and US3 sensitivity (Park et al., 2004). Both mechanisms, tapasin inhibition and direct binding, may be in place, perhaps allowing US3 to retain a larger repertoire of class I MHC locus products. US6 inhibits peptide loading of the class I MHC molecules by blocking the TAP transporter (Ahn et al., 1997; Hengel et al., 1997; Lehner et al., 1997). US6 is an ER‐resident type I membrane glycoprotein with a bulky lumenal domain that binds the core transmembrane domains of the TAP subunits, TAP 1 and TAP 2, from within the ER lumen, inhibiting ATP binding (Hewitt et al., 2001; Kyritsis et al., 2001) and thus TAP‐mediated peptide translocation into the ER. The US6 luminal domain oligomerizes and may form a bridge between the TAP 1 and TAP 2 subunits to effectively block TAP activity (Halenius et al., 2006). US10 delays trafficking of class I MHC HCs and stalls them in the ER; although the block is not absolute and the mechanism is poorly characterized, US10 expression results in downregulation of class I MHC from the cell surface (Furman et al., 2002a). US2 and US11 catalyze destruction of class I MHC HCs from the ER membrane by targeting them to the Ub‐proteasome system (Wiertz et al., 1996a,b), a process we discuss in detail in a later section. Expression of each individual immunoevasin results in reduction of cell surface expression of class I MHC peptide‐loaded complexes and evasion of CD8þ T cell‐mediated lysis (Ahn et al., 1996; Jones et al., 1995). Besides CD8þ T cell recognition, HCMV can frustrate NK cell recognition (Orange et al., 2002). Protection of HCMV from NK cell‐mediated lysis can be mediated by several immunoevasins, HCMV UL16, UL40, UL18, UL141, UL142, and pp65 (Lodoen and Lanier, 2005; Orange et al., 2002; Rajagopalan and Long, 2005; Reyburn et al., 1997; Wills et al., 2005). The activating receptor NKG2D on the NK cell recognizes divergent families of class I MHC‐related ligands, like the MIC and ULBP products. HCMV UL16 retains the ULBP 1, ULBP 2, and MIC‐B NKG2D ligands in the ERGIC compartment of the target cell, preventing NKG2D recognition and thus NK cell activation (Dunn et al., 2003). HCMV UL141 and pp65 act at the level of the NK effector cell rather than the APC. The result is, nevertheless, the same: prevention of

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NK cell activation. UL141 downregulates the NK cell‐activating receptors CD226 (DNAM‐1) and CD96 (TACTILE) (Tomasec et al., 2005). The pp65 tegument protein engages the activating receptor NKp30 and antagonizes its effects, dampening NK cell‐mediated cytotoxicity. How a tegument protein gains access to the receptor on the NK cell is unknown, but pp65 engagement of the NKp30 receptor causes dissociation of a receptor‐associated signaling module (Orange et al., 2002), disrupting the activating signaling pathway that would lead to NK cell activation (Arnon et al., 2005). NK cell responses also rely on receptors that recognize class I MHC locus products. The killer cell immunoglobulin‐like receptor (KIR) genes encode a family of activating and inhibitory receptors that recognize human leukocyte antigen (HLA)‐A, ‐B, and ‐C. The CD94/NKG2 receptors recognize the nonclassical class I MHC molecule HLA‐E. HLA‐E presents fragments derived from the signal sequences of classical class I MHC molecules, which delivers an inhibitory signal to CD94/NKG2 receptors on NK cells. UL40 encodes a peptide whose sequence is exactly homologous to the HLA‐E binding leader peptide from HLA‐C locus products. UL40 therefore loads HLA‐E and maintains HLA‐E on infected cells (Tomasec et al., 2000; Ulbrecht et al., 2000) even when other class I MHC products are downregulated. UL18 encodes a class I MHC‐like molecule that engages the inhibitory CD85j/LIR‐1/ILT‐2 receptor on the NK cell, thus inhibiting NK cell effector functions (Cosman et al., 1997; Reyburn et al., 1997). However, UL18 and UL40 expression may be insufficient to confer target cell protection (Falk et al., 2002; Leong et al., 1998). UL18 and UL40 may, in fact, be more relevant for control of viral infection by T cells, with HLA‐E‐restricted CD8þ T cells playing a role in lysis of cells expressing UL40, and non‐MHC‐ restricted CD8þ T cells playing a role in lysis of UL18‐positive cells (Pietra et al., 2003; Romagnani et al., 2004; Saverino et al., 2004). CD85j is an invariant receptor expressed by many T cells and responsible for transduction of inhibitory signals that downregulate antigen‐specific T cell functions (Merlo et al., 2001; Saverino et al., 2000). CD85j interaction with UL18 on CD8þ T cells occurs in a TCR‐independent manner and leads to activation (not inhibition) of non‐MHC‐ restricted CD8þ T cells (Saverino et al., 2004). This expands the repertoire of T cell activation mechanisms and is probably a viral strategy of ensuring survival of the host, by allowing some level of protection from the initial wave of NK cell‐ mediated antiviral response. In vivo HCMV‐infected APCs are faced with multiple immunoevasins displaying allelic preferences and expression patterns that are both spatially and temporally regulated. The many immunoevasins expressed by HCMV are thus likely to have both synergistic and antagonistic interactions (Ahn et al., 1996; Farrell et al., 2000; Klenerman and Hill, 2005; Mocarski, 2004; Reddehase, 2002; Yewdell and Hill, 2002), as has been experimentally verified for murine cytomegalovirus (Wagner et al., 2002).

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23. Dislocation from the ER: HCMV US11 and US2 Evidence of ER‐to‐cytosol transport or dislocation, a crucial ER quality control step now considered of general importance in dispensing with misfolded or misassembled ER proteins, was initially provided by studying the mechanism of action of the HCMV US2 and US11 immunoevasins (Wiertz et al., 1996a,b). The viral proteins appropriate this cellular quality control process to extract (dislocate) class I MHC HCs from the ER membrane. On arrival in the cytoplasm, the dislocated HC molecules are destroyed by the proteasome; destruction of the HC component of the class I MHC complex by US2 and US11 abolishes cell‐surface expression of class I MHC complexes and, consequently, presentation of viral peptides to CD8þ T cells, allowing HCMV to remain undetected (Wiertz et al., 1996a,b). US2‐ and US11‐mediated HC dislocation from the ER membrane is not only an ingenious viral immune evasion strategy, but also a useful case study in ER quality control and degradation. One theme that arises from the characterization of this process over the 10 years that have passed since its discovery is that HC dislocation is unique in many respects: HC molecules do not meet the requirement of being either misfolded or misassembled, yet their dislocation takes place by virtue of the presence of US2 or US11; the speed of HC degradation is unrivaled by that of any other ER‐associated degradation substrates: HC half‐life is reduced from hours to a mere 2–5 min in cells infected by HCMV or in cells expressing either US2 or US11 (Wiertz et al., 1996a,b); both US2 and US11 have stringent requirements in terms of which HLA alleles (Barel et al., 2003, 2006; Machold et al., 1997) or assembly, folding and ubiquitination status of the class I MHC complex (Blom et al., 2004; Furman et al., 2003; Gewurz et al., 2001) either viral protein is able to target for dislocation and proteasomal destruction. Notwithstanding the unique nature of this virus‐mediated process, knowledge from the US11 pathway, and in particular the identification of the Derlin proteins, has widened our understanding of the cellular factors involved in ER dislocation more generally (Lilley and Ploegh, 2004, 2005a). The US11 transmembrane domain (TMD) is crucial for US11 function: more specifically, mutation of a polar amino acid, glutamine (Q) 192, within the US11 TMD to a hydrophobic leucine (L) residue renders this US11 Q192L mutant inactive in dislocating HCs from the ER membrane. In a screen for proteins that interact specifically with the active version of US11, work from our laboratory showed that the aforementioned Derlins, the mammalian Der1p homologues, are involved in HC dislocation mediated by US11, but not by US2 (Lilley and Ploegh, 2004). The fact that the dislocation mechanism used by US2 is not dependent on the Derlins prompted us to investigate what other ERAD

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pathway is being co‐opted by the HCMV US2 immunoevasin and allowed us to uncover an unexpected ERAD player.

24. Signal Peptide Peptidase Is Required for Dislocation from the ER US2 is an ER‐resident type I membrane glycoprotein of only 199 amino acids, with a noncleavable signal sequence (Gewurz et al., 2002), a lumenal domain that dictates an allele‐specific association with the lumenal domain of HC (Gewurz et al., 2001), a transmembrane segment, and a short cytosolic tail of only 14 amino acids (residues 185–199), with no obvious sequence homology to known cellular proteins. The US2 tail is essential for dislocation: US2186, a cytosolic tail deletion mutant of US2, is dislocation incompetent (Furman et al., 2002b). By using an affinity purification approach similar to that used for US11 (Lilley and Ploegh, 2004), signal peptide peptidase (SPP) was found as a specific interacting partner for dislocation‐competent (active) US2 (Loureiro et al., 2006), an interaction that relies solely on the presence of the highly hydrophobic US2 tail. More importantly, reduction of SPP levels by RNA interference led to inhibition of class I MHC HC dislocation and to the discovery of SPP as a necessary factor for the US2‐mediated ER dislocation pathway. SPP is an ER‐resident protein of approximately 45 kDa that is predicted to span the ER membrane seven to nine times (Friedmann et al., 2004), and a member of the presenilin (PS)/SPP‐Like (SPPL) superfamily of intramembrane‐cleaving aspartic proteases (Weihofen et al., 2002). These proteases are characterized by the ability to cleave substrate polypeptides within a transmembrane region and by possessing two active site aspartate (D) residues (italicized) within the conserved motifs YD and LGLGD in adjacent membrane‐spanning regions (Martoglio and Golde, 2003; Wang et al., 2006a; Weihofen et al., 2002). There are seven related members of the PS/SPPL superfamily in the human genome: PS‐1, PS‐2, SPP and four SPP‐Like proteins, SPP2a, SPP2b, and SPP2c, and SPP3 (Martoglio and Golde, 2003). Presenilins 1 and 2 are the catalytic components of g‐secretase, a tetrameric complex containing PS and three other subunits. PSs play a role in processing of the b‐amyloid precursor protein (APP) into Ab40 and Ab42, peptides that constitute the principal components of the b‐amyloid plaques in Alzheimer’s disease (AD); PSs are also required for development due to processing of the Notch receptor by g‐secretase (Selkoe and Kopan, 2003) and might be involved in intracellular trafficking (Sisodia and St George‐Hyslop, 2002; Wang et al., 2006c). The function of the four SPP‐like proteins is, at this point, unknown, but the role of intramembrane‐cleaving proteases is usually to liberate signaling molecules from membrane‐bound precursors with consequent activation or repression

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of signaling cascades (Fortini, 2002; Kopan and Ilagan, 2004; Martoglio and Golde, 2003; Parent et al., 2005; Xia and Wolfe, 2003). 25. SPP and Generation of HLA‐E Epitopes In humans, SPP performs an important immunological function as it generates the peptide ligands for the nonclassical class I MHC molecule HLA‐E. On insertion of secretory or type II membrane proteins into the ER, their signal sequence is cleaved by the ER luminal protein signal peptidase, leaving the signal peptide anchored in the ER membrane. The ER membrane–anchored signal peptide is subsequently cleaved by the intramembrane‐cleaving SPP within the transmembrane region. The resulting signal peptide fragments are released into the cytosol (N‐terminal portion) or into the ER lumen (C‐terminal portion). The latter peptides may easily bind to class I MHC molecules in the ER lumen. The HLA‐A2 molecule, for instance, is known to bind signal sequence‐derived peptides. The N‐terminal signal sequence fragments are TAP‐transported into the ER lumen and bind to HLA‐E (Lemberg et al., 2001). By presenting fragments derived from the signal sequences of classical class I MHC molecules, HLA‐E monitors the presence of classical class I MHC molecules. This is, as we discussed earlier, of crucial importance for NK cell recognition. 26. SPP and Processing of the Hepatitis C Virus Core Protein SPP is involved in processing of the hepatitis C virus (HCV) core protein (McLauchlan et al., 2002). HCV is a single‐stranded RNA virus with a single open reading frame encoding a large polyprotein. The N‐terminal portion of the HCV polyprotein encodes the structural components of the HCV virion, the core protein (thought to constitute the virion capsid), and the E1 and E2 envelope glycoproteins. The mature structural components of the HCV virion are produced through a series of cleavage events catalyzed by cellular proteases. The core protein is the most N‐terminal portion of the polyprotein and is followed by the signal sequence of the E1 envelope glycoprotein. The E1 signal sequence targets the polyprotein to the ER membrane and induces translocation of E1 into the ER lumen. Cleavage by signal peptidase liberates the N‐terminal end of E1, leaving the core protein anchored (by the E1 signal peptide) in the ER membrane. SPP‐mediated intramembrane proteolysis of the E1 signal sequence then results in release of the HCV core protein from the ER membrane, thus freeing the mature core protein for incorporation into lipid droplets (Martoglio and Golde, 2003). SPP‐mediated HCV core protein maturation and trafficking to lipid droplets and the outer mitochondrial membrane

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may be critical for viral assembly and life cycle (Ait‐Goughoulte et al., 2006) and may also affect cellular lipid metabolism and apoptosis, as HCV core protein‐ transgenic mice display liver pathologies, mitochondrial injury, and enhanced oxidative stress (Chou et al., 2005; Korenaga et al., 2005; Meyer et al., 2005; Okuda et al., 2002; Omura et al., 2005; Schwer et al., 2004; Suzuki et al., 2005). SPP‐mediated cleavage of the HCV core protein may thus modulate these important cellular functions of HCV. 27. SPP and Calmodulin Signaling SPP may regulate the interaction of signal peptide remnants of HIV gp160 envelope protein and preprolactin (p‐Prl) with calmodulin. A characteristic feature of a signal sequence is its tripartite structure: a polar N‐terminal n‐region, a hydrophobic core (h‐region) of 7–15 residues, and a polar C‐terminal c‐region that contains the consensus sequence for signal peptide cleavage (von Heijne, 1985). The n‐region of most signal sequences comprises only a few residues. However, some signal sequences have extended n‐regions of up to 150 residues. The function of such long n‐regions is not known. Both the p‐Prl and the gp160 signal sequence have an extended basic n‐region that can potentially form a basic amphiphilic alpha‐helix, a feature of CaM‐binding domains (O’Neil and DeGrado, 1990), not found in the majority of signal sequences. SPP‐mediated cleavage releases this CaM‐binding domain on the N‐terminal fragment of the p‐Prl and gp160 signal peptides into the cytosol. The functional and physiological significance of an interaction between the p‐Prl and p‐gp160 signal peptide fragments that are released into the cytosol and calmodulin (CaM) could be due to a regulatory function of the signal peptide fragments. CaM‐dependent processes could be enhanced or inhibited depending on the amounts of CaM‐binding signal peptide fragments generated and released into the cytosol (Martoglio et al., 1997). 28. SPP Peptide Peptidase and Development The SPP orthologues in Drosophila melanogaster, Spp, and in Caenorhabditis elegans, Imp‐2, play an essential role in development: SPP protease activity seems to be essential for larval development both in the fly and in the nematode (Casso et al., 2005; Grigorenko et al., 2004). Although the mechanism by which SPP mutations impairs developmental processes in the fly is currently unknown, in C. elegans the molting defect induced by imp‐2 deficiency was mimicked by cholesterol depletion and by deficiency in Irp‐1, a homologue of mammalian lipoprotein receptor‐related protein (LRP) receptors suggesting a role in cholesterol and lipid metabolism (Grigorenko et al., 2004).

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29. SPP and ER Quality Control The possibility of a role for SPP in ER quality control was first advanced by High and colleagues, who reported an association between SPP and a truncated version of a polytopic ER protein (opsin) in an in vitro system (Crawshaw et al., 2004) and proposed SPP to be implicated in the recognition of misassembled transmembrane domains during membrane protein quality control at the ER. US2‐mediated dislocation of class I MHC HC molecules presents the first functional evidence of such a role for the intramembrane‐ cleaving protease SPP. The US2 tail is necessary and sufficient to recruit SPP, and structural predictions suggest that the US2 tail suggests may adopt a 310 helical conformation that could form a protein–protein interaction domain (Oresic et al., 2006). SPP is crucial for dislocation by US2, as reduction of its levels by RNA interference blocks HC degradation. The question remains as to the detailed mechanism of its involvement. An obvious possibility is involvement of the catalytic activity of SPP. This would imply a cleavage event during dislocation, such as within the TMD of US2 or HC or another factor, unknown at this point; a postcleavage function of one of these protein fragments could play a regulatory role in the process. SPP‐ mediated intramembrane proteolysis requires, among other things, a membrane protein substrate to have access to its catalytic core in a type II orientation (Lemberg and Martoglio, 2002; Martoglio and Golde, 2003). Both US2 and HC are type I membrane glycoproteins, so the topology of their transmembrane and tail segments is opposite to that of predicted SPP substrates. SPP‐mediated intramembrane cleavage within the HC TMD is inconsistent with the observed recovery of full length HC in the dislocation reaction (Blom et al., 2004; Misaghi et al., 2004a; Wiertz et al., 1996a,b). For US2, while a suggested protein–protein interaction domain in the US2 tail (Oresic et al., 2006) may mediate binding to SPP, intramembrane cleavage of the US2 TMD by SPP in this inverted orientation would presumably not occur, as seen for other proteases (Roques et al., 1983; Tarasova et al., 2005). However, the proposed bent‐helix conformation on the US2 tail (Oresic et al., 2006) might allow US2 to conform to the requirements for SPP cleavage. Whether SPP‐mediated US2 cleavage takes place is unknown at this point. One can speculate that this putative processing of US2 by SPP, as for the HCV core protein (McLauchlan et al., 2002), could be necessary for ‘‘maturation’’ of US2 into a dislocation‐active form. Binding of US2 to SPP could still modulate SPP enzymatic activity and thus affect dislocation. The possibility remains that SPP mediates cleavage in trans of an unidentified factor whose function is important. Experiments with SPP inhibitors and catalytic mutants of SPP should allow an assessment of the contribution of the proteolytic properties of SPP to dislocation.

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The involvement of SPP need not be related to its catalytic activity. Substrate recruitment and subsequent cleavage by SPP may be separable events, as shown for the related PS (Kornilova et al., 2003; Lemberg and Martoglio, 2004). Some intracellular cleavage products of g‐secretase are proposed to be intermediates that are destined for degradation (Kopan and Ilagan, 2004; Parent et al., 2005). g‐secretase‐mediated cleavage of a large number of type I transmembrane proteins releases their C‐terminal fragments (CTFs). PS1 deficiency causes delayed turnover and subsequent accumulation of some g‐secretase substrates as full‐length proteins (Esselens et al., 2004; Wang et al., 2006c; Wilson et al., 2004c), suggesting cleavage of the CTFs as a prelude to degradation. Treatment with g‐secretase inhibitors, however, does not phenocopy PS deficiency (Wang et al., 2006c), suggesting that this effect is independent of g‐secretase activity. Therefore, SPP may be crucial for HC dislocation irrespectively of its catalytic properties. Binding of US2 to SPP could presumably modify the SPP structure in a way that affects dislocation. Alternatively, recruitment of SPP by US2 could perhaps nucleate assembly of a dislocation complex, much like US11 and the Derlins (Lilley and Ploegh, 2005a). SPP may be a component of an ERAD pathway for a subset of ER degradation substrates that includes misfolded transmembrane proteins, such as truncated opsin (Crawshaw et al., 2004), and that is recruited by US2 to dislocate HC (Fig. 6, A arrow). Experiments to address the identity of SPP‐associated proteins may prove informative. Curiously, another class of intramembrane‐cleaving proteases, the rhomboid serine proteases, share a homology domain of unknown function with the Derlins (Lemberg et al., 2005). It is tempting to speculate that our observations extend the connection from regulated intramembrane proteolysis to a direct involvement in ER dislocation. An involvement of SPP with the UPR is also a possibility. Cells deficient for the X‐box binding protein‐1 transcription factor (XBP‐1) show upregulated levels of SPP transcripts (Shaffer et al., 2004), but this aspect of the process remains to be explored. Although removal of signal peptide remnants from the ER membrane, assigned to SPP in animals and plants, is not a function exclusive to higher eukaryotes, a gene that encodes an orthologue of this enzyme is absent from the yeast genome (Martoglio, 2003; Weihofen et al., 2002). The role of SPP in higher eukaryotes might therefore not be limited to signal peptide processing but extend to processes such as protein dislocation from the ER. PS and the other SPP‐like members of the PS/SPPL superfamily of intramembrane‐cleaving aspartic proteases, so far of unknown function (Martoglio and Golde, 2003), and some of which may not reside in the ER (Krawitz et al., 2005), may likewise be involved in disposal of different degradation substrates. HCMV might just be exploiting a cellular degradation pathway that involves intramembrane‐cleaving proteases for disposal of class I MHC HC.

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Figure 6 The US2 dislocation pathway requires signal peptide peptidase. The US2 cytosolic tail recruits SPP (A), which is required for dislocation of class I MHC HC. An additional step critical for dislocation is dependent on interactions involving the US2 transmembrane domain (US2 TMD) and SPP (B) or other protein(s) so far unidentified (C). Ub, ubiquitin; TMD, transmembrane domain. PNGase, peptide‐N‐glycanase.

The US2 TMD, although dispensable for interaction with SPP, is also required for HC dislocation (Loureiro et al., 2006). HC dislocation is therefore dependent not just on (US2 tail‐mediated) recruitment of SPP, but also on additional (US2 TMD‐mediated) interactions within the plane of the membrane. The US2 TMD may be involved in further engagement of SPP (SPP‐mediated cleavage or otherwise) (Fig. 6, B arrow), or alternatively, in the recruitment or engagement of other protein(s) involved in dislocation (Fig. 6, C arrow). ER membrane E3s or their adaptor subunits are likely candidates. SPP is most certainly not the sole host‐derived component of the US2 dislocation pathway, and a putative multiprotein complex (cascade of adaptor proteins) analogous to that found for US11 is likely to be found that evokes many (complicated) links between the ERAD machineries at the level of the ER membrane and the cytosol. Uncovering the identity of the additional cellular partners of this HCMV US2 immunoevasin will certainly give us insight into the dislocation mechanism.

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30. Three Routes of Pathogen‐Mediated ER Protein Disposal A theme that arises from analysis of the US2‐ and US11‐mediated HC dislocation processes is that these are only superficially similar: although HC dislocation by US2 and US11 has the same outcome (proteasomal degradation) of HC and shares many if not all of the steps that take place after extraction of the HC from the ER membrane (such as deglycosylation and degradation kinetics), the differences between the pathways are rather striking in terms of the steps prior to dislocation. As we mentioned earlier, US2 and US11 have different allele and substrate folding, assembly, and ubiquitination requirements. Derlin‐1 is crucial for US11‐ but not US2‐mediated dislocation of HCs (Lilley and Ploegh, 2004; Ye et al., 2004), and, conversely, SPP is required by US2 but not by US11 (Loureiro et al., 2006). This suggests that the HCMV immunoevasins US2 and US11 are targeting HC molecules to distinct ER dislocation pathways, perhaps by serving as adaptor molecules aiding in recruitment and/or assembly of distinct multiprotein complexes at the ER membrane (Lilley and Ploegh, 2004, 2005b). The murine g‐herpesvirus 68 (MHV‐68) mK3 also targets newly synthesized murine class I MHC HC for dislocation from the ER and proteasomal degradation (Boname and Stevenson, 2001; Lybarger et al., 2005; Wang et al., 2005; Yu et al., 2002). MHV‐68 mK3 belongs to a family of structurally related molecules, the K3 homologues, which have E3 ligase activity. K3 homologues are present in several different g‐herpesviruses and poxviruses (Coscoy and Ganem, 2000; Ishido et al., 2000; Mansouri et al., 2003; Stevenson et al., 2000). All K3 homologues possess a noncanonical RING‐finger domain with ubiquitin ligase (E3) activity [also called a plant homeodomain (PHD) or leukemia‐associated protein (LAP) domain], and a conserved integral membrane topology, with the transmembrane domains and cytosolic C‐terminal tails mediating interaction with the substrate (Coscoy and Ganem, 2003). The mK3 PHD/LAP‐family E3 is a type III ER membrane protein with the PHD/LAP RING‐related domain facing the cytosol (Boname and Stevenson, 2001; Sanchez et al., 2002). mK3‐mediated degradation of murine HCs is absolutely dependent on components of the PLC: association of mK3 with TAP and tapasin presumably imposes the necessary proximity and/or orientation of the mK3 RING domain that allows mK3 to specifically ubiquitinate class I MHC HC as they enter the PLC (Lybarger et al., 2005; Wang et al., 2004, 2005). mK3‐mediated dislocation of murine HCs is dependent on the ATPase activity of p97 and physical association with Derlin‐1 and VIMP (Wang et al., 2006d). This is reminiscent of the pathway that HCMV US11 is proposed to co‐opt for dislocation of mammalian class I MHC molecules. In a sense, it appears that mK3 may be a more evolved immunoevasin that can couple the

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ability to recruit other components of the dislocation machinery, like US11 and presumably US2, with E3 ligase activity, in one viral polypeptide. There are several mammalian K3 homologues, the membrane‐anchored RING‐CH (MARCH) proteins (Bartee et al., 2004; Goto et al., 2003), which are likely to be the cellular ancestors of MHV‐68 mK3. mK3‐mediated ubiquitination of the murine class I MHC HC cytosolic tail, the portion of the HC molecule more likely to come into contact with the cytosolic RING‐CH domain of mK3, is not required for dislocation even though ubiquitination and presence of the cytosolic tail are essential for dislocation (Wang et al., 2005). In fact, like mK3, HCMV US2 and US11 also induce ubiquitination‐dependent degradation of class I MHC molecules in a cytosolic tail lysine‐independent fashion (Furman et al., 2003; Shamu et al., 1999). How, then, does mK3 access the luminal domain of HC molecules and trigger its ubiquitination? Access of the HC lumenal domain to the cytosol would invoke a ‘‘partial dislocation’’ model that has been proposed for HCMV US2 and US11 (Furman et al., 2003; Shamu et al., 1999): the HC luminal domain must begin to emerge in the cytosolic face of the ER so ubiquitination can take place. This would mean that the trigger for dislocation would reside upstream from tail ubiquitination. An alternative explanation would be that the class I MHC tail lysine mutant HC molecules are dislocated as a ‘‘bystander effect’’ of dislocation of wild type molecules by mK3, simply because they are in the proximity of the dislocation machinery that has been recruited by the viral protein for the wild type HC clientele. The HCMV US2 and US11 and MHV‐68 mK3 immunoevasins all target nascent class I MHC HCs for degradation by inducing their dislocation from the ER membrane. The mechanisms used by US2, US11, and mK3, however, when analyzed in detail, are strikingly different. For instance, the stage of class I MHC HC biosynthesis that is targeted by each viral protein is distinct: US11 is the most promiscuous, targeting multiple class I MHC assembly intermediates, whereas US2 targets only properly folded class I MHC complexes and mK3 targets predominantly incompletely assembled HC while in association with the peptide‐loading complex. The main difference resides in the fact that mK3 encompasses ubiquitination activity (by means of its RING domain), substrate selection (by means of its association with the peptide‐loading complex), and recruitment of ER membrane and cytosolic factors necessary for dislocation (like Derlin‐1 and p97) all in one polypeptide. HCMV US2 and US11 do not possess E3 ligase activity and possess no obvious sequence similarity with known genes/proteins that would hint at their function. However, at least US11, and presumably also US2, are still able to induce assembly of a dislocation complex (Fig. 7) that encompasses all the necessary ERAD activities.

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Figure 7 Three viral immunoevasins that co‐opt distinct ERAD pathways. The HCMV US11 immunoevasin delivers class I MHC HC molecules to Derlins for dislocation from the ER membrane and degradation, whereas HCMV US2 uses a pathway that is dependent on signal peptide peptidase. The MHV‐68 mK3 immunoevasin is an E3 ligase that uses the PLC as a platform to target murine class I MHC molecules for ubiquitination and degradation. Although the three pathways are superficially similar, the substrate selection and targeting steps at the ER membrane are very distinct. Ub, ubiquitin; E1, Ub‐activating enzyme; E2, Ub‐conjugating enzyme; E3, Ub‐ligase enzyme; PNGase, peptide‐N‐glycanase; SPP, signal peptide peptidase; TAP, transporter associated with antigen presentation; CRT, calreticulin.

The knowledge that the TMD of US11 is essential for its function (Lilley et al., 2003), led to the discovery of Derlins (Lilley and Ploegh, 2004) and of multiprotein complexes at the ER membrane that function in US11‐mediated dislocation of HCs and US11‐independent dislocation of a subset of ERAD substrates (Lilley and Ploegh, 2005a; Oda et al., 2006; Ye et al., 2005). US2 uses its cytosolic tail to recruit SPP and its TMD for an additional step (so far unknown) also critical for HC dislocation (Loureiro et al., 2006), presumably resulting in recruitment of US2‐specific‐components of the ER dislocation machinery. The mechanism by which US2 operates will hopefully be clarified as we continue characterizing the structural and host cofactor requirements for its function in dislocation. The mechanistic details of dislocation catalyzed

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by the HCMV US2 and US11 and the MHV‐68 mK3 immunoevasins must be quite diverse and their study will most certainly keep providing new insights into ER dislocation. Furthermore, it is nothing short of remarkable how sequence and structurally unrelated herpesvirus proteins have converged into (although only superficially) similar mechanisms to dislocate newly synthesized class I MHC molecules from the ER membrane. 31. Pathogen Interference with Class II MHC Antigen Presentation Class II MHC‐restricted CD4þ T cells are crucial for lymphocyte activation, antibody responses, and coordination of the immune response and rely on activation by the professional APCs. Immunoevasins aimed at interfering with class II MHC antigen presentation are not expected to block presentation of exogenous antigens to CD4þ T cells, unless the APC is infected by the virus (Yewdell and Hill, 2002). Class II MHC expression can, however, be modulated by IFN and receptor signaling through the CIITA transcription factor (Boss and Jensen, 2003). There are relatively few examples of viruses and bacteria that directly infect APCs and affect IFN‐induced class II MHC expression or directly interfere with class II MHC‐restricted antigen presentation (Hmama et al., 1998; Hussain et al., 1999; Miller et al., 1998; Rinaldo, 1994; Schuller et al., 1998; Srisatjaluk et al., 2002; Zhong et al., 1999). It is likely that more examples will be found as this important question is being revisited. For viruses that do not infect professional APCs, the route to avoiding helper T cell and antibody‐ mediated responses is to interfere with activation of CD4þ T cells by APCs. In this section, we present an overview of some of the mechanisms used mostly by viral pathogens to actively subvert class II MHC antigen presentation (Fig. 8). The degree to which other pathogens, like bacteria and parasites, actively interfere with class II MHC antigen presentation, by encoding ‘‘immunoevasins’’ rather than passively, due to their residence in endocytic compartments, is difficult to discern. 32. Inhibition of Recognition at the Surface of the APC Epstein–Barr virus is an example of a virus that can infect and establish latency in B lymphocytes and is associated with a number of malignancies. The product of the BZLF2 EBV gene, gp42, is a bifunctional protein. First, it functions as the coreceptor for viral entry into B cells by binding to the HLA‐ DR product. Second, gp42 is generated through proteolytic cleavage in the ER and matures into a secreted form that binds class II MHC molecules at the cell surface. gp42 bound to class II MHC molecules at the cell surface prevents

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Figure 8 Pathogen interference with class II MHC antigen presentation. TCR, T cell receptor; PLC, peptide‐loading complex. Nef, HIV‐1 Nef; gp42, EBV glycoprotein 42; BPV E5, bovine papillomavirus protein E5; HPV E5, human papillomavirus protein E5; VacA, H. pylori VacA toxin; US2, HCMV US2; Vpu, HIV‐1 Vpu; gp160, HIV‐1 glycoprotein 160; MVB, multivesicular body; Ii, invariant chain. The immunoevasins and pathways depicted in red take place in the effector CD4þ T cell, whereas those in blue occur in the antigen‐presenting cell.

TCR‐(peptide‐loaded class II MHC) interactions and CD4þ T cell activation (Li et al., 1997; Ressing et al., 2003, 2005; Spriggs et al., 1996).

33. Class II MHC Downregulation from the Surface of the APC The HIV‐1 Nef protein downregulates class II MHC molecules from the cell surface by restructuring the endocytic pathway such that invariant chain (Ii) degradation is impaired and immature class II MHC complexes (abIi) are granted increased access to the cell surface (Stumptner‐Cuvelette et al., 2001). Nef induces a reduction of surface levels of peptide‐loaded class II MHC as well as a strong accumulation of surface‐displayed immature class II MHC complexes, still containing (and thereby blocked by) intact invariant chain (Ii).

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Nef expression results in accumulation of both class II MHC and invariant chain (Ii) in multivesicular bodies (MVBs) (Stumptner‐Cuvelette et al., 2003). MVBs are a specialized type of endosome that constitutes a major pathway of delivery of transmembrane proteins for lysosomal degradation (Hurley and Emr, 2006). Sequestering in MVBs suggests a reduced capacity of immature class II MHC complexes to reach lysosomes, either due to a defect in class II MHC sorting to the lysosomes or due to slower internalization of immature complexes. The mechanism is still unclear (Stumptner‐Cuvelette et al., 2003). The HCMV US2 immunoevasin was proposed to downregulate class II MHC from the cell surface, presumably by targeting HLA‐DRa and HLA‐ DMa for degradation by the proteasome, thus inhibiting antigen presentation to CD4þ T cells (Chevalier et al., 2002; Hegde and Johnson, 2003; Tomazin et al., 1999). This effect of US2, however, was only seen in cell lines in which induction of class II MHC was induced by stable transfection with the class II MHC trans‐activator (CIITA), but not in human DCs or several other cell lines, which express class II MHC endogenously (Rehm et al., 2002). Presumably the relative expression levels of class II MHC and/or US2 could account for the observed differences, and in fact, in CIITA‐transfected cells, even US3 was seen to downregulate class II MHC molecules (Hegde et al., 2002). HSV‐1 can downregulate surface expression of class II MHC complexes in B cells and inhibits the ability of B cells to stimulate CD4þ T cells. HSV‐1 inhibits synthesis of Ii and also encodes an envelope glycoprotein B (gB) that binds both HLA‐DR and HLA‐DM (Neumann et al., 2003; Sievers et al., 2002). By binding to HLA‐DR, gB affects trafficking of the molecule in the secretory pathway and by binding HLA‐DM it sequesters this peptide editor and thus prevents peptide loading of class II MHC molecules that may have escaped (Neumann et al., 2003). 34. CD4 Downregulation from the Surface of the CD4þ T Cell The CD4 protein serves as the primary cellular receptor for HIV at the surface of CD4þ cells. However, its presence inhibits virus budding and interferes with incorporation of the gp120 protein into the budding virion, not to mention its crucial role in eliciting of a CD4þ T cell response (Keppler et al., 2006; Lama et al., 1999; Ross et al., 1999). Not surprisingly, three HIV‐1 proteins, Nef, Vpu, and gp160 dramatically reduce the steady state levels of CD4 on the cell surface (Mangasarian et al., 1999; Piguet et al., 1999a,b). One of the many mechanisms used by Nef involves acceleration of the constitutive endocytosis of CD4. In T cells, CD4 is stabilized at the cell surface by p56Lck, a Src‐family tyrosine kinase, which binds a dileucine motif in the CD4 cytoplasmic tail, preventing CD4 from being recruited into clathrin‐coated pits

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(Aiken et al., 1994; Jin et al., 2005). Nef can displace Lck by directly binding a dileucine sorting motif in the CD4 tail, overcoming the normal Lck phosphorylation‐dependent route of CD4 downregulation. Nef itself contains a C‐terminal dileucine motif that recruits a subunit of the tetrameric adaptor protein complex‐2 (AP‐2), a component of clathrin‐coated pits at the cell membrane. Thus, Nef connects CD4 to AP‐2 on clathrin‐coated pits, triggering rapid CD4 endocytosis (Jin et al., 2004b, 2005; Mangasarian et al., 1999; Piguet et al., 1998, 1999a). Nef can bind not only the AP‐2 components of clathrin‐coated pits, but also the regulatory V1H subunit of the vacuolar proton (Hþ) ATPase. V1H (also called NBP1 or Nef binding protein‐1) binds AP‐2 in clathrin‐coated vesicles. By binding V1H, Nef strengthens its weak direct interaction with AP‐2 (Geyer et al., 2002; Mandic et al., 2001). Again, by binding both CD4 (using the aforementioned dileucine motif in the cytoplasmic tail) and V1H, Nef directs internalization of CD4 from the cell surface. The endocytosed CD4 accumulates in early endosomes from where it is sorted to lysosomes for degradation. Adaptor protein (AP) complexes mediate transport of proteins to numerous compartments within the cell. Whereas AP‐2 initiates early endocytic vesicle formation at the cell membrane, AP‐1 is involved in vesicle formation at the TGN and vesicle targeting to early endosomes and AP‐3 participates in vesicle formation at the TGN and targeting to late endocytic/lysosomal compartments (Bonifacino and Traub, 2003). The ‘‘coat’’ on vesicles in the endocytic pathway is composed of AP complexes and another set of coat proteins, clathrin in clathrin‐coated vesicles, and COP proteins, in COP‐coated vesicles. The COP proteins are involved in vesicle trafficking early in the secretory pathway between the ER and the Golgi (McMahon and Mills, 2004). Trafficking in the endocytic pathway can be targeted by HIV Nef by direct binding to AP complexes or by interference with the recruitment and release cycles of AP complexes from vesicle membranes. AP complexes cycle from the cytosol to vesicles in a process dependent on the GTPase cycle of ADP‐ribosylation factor‐1 (ARF1). Nef can bind to and stabilize the small GTPase ARF1 on the endosomal membrane, preventing AP complexes from being released, affecting trafficking of host molecules (including CD4) along the endocytic pathway. Nef can also mediate the formation of a ternary complex composed of Nef, ARF1, and a component of the COP‐I coat, bCOP. COP‐I‐coated vesicles are mostly subject to retrograde transport within the Golgi and between the Golgi and the ER, but some are involved in transport from early to late endosomes (McMahon and Mills, 2004), and thus association of Nef with ARF1 and bCOP mediates targeting of CD4 for lysosomal degradation (Faure et al., 2004). Nef has a preference for AP‐1 and AP‐3 complexes in vitro (Janvier et al., 2003b), suggesting that the Nef modus operandi is mostly

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at the level of endosomal membranes. Downregulation from the cell surface by binding AP‐2 does not seem to be a major route for CD4 downregulation (Rose et al., 2005). Which one of these strategies—downregulation from the cell surface or intracellular retention—or what combinations of these strategies and adaptor protein complexes are used may allow the HIV‐1 Nef protein to downregulate cell surface expression of CD4 perhaps in different cell types (Mangasarian and Trono, 1997). Vpu targets newly synthesized CD4 molecules in the ER for proteasomal degradation (Kerkau et al., 1997) by recruiting the cytosolic F‐box protein b‐TrCP, the receptor component of the SCFb‐TrCP E3 ligase, to the ER membrane (Margottin et al., 1998). A motif in the Vpu C‐terminus binds a WD repeat on b‐TrCP, directing SCFb‐TrCP E3 ligase activity to catalyze the ubiquitination of lysine residues on the CD4 tail, which serves as the trigger for CD4 degradation (Margottin et al., 1998; Schubert et al., 1998). gp160 retains newly synthesized CD4 molecules in the ER (Crise and Rose, 1992; Kimura et al., 1994). In the absence of CD4, HIV gp160 is posttranslationally cleaved into its gp120 and gp41 subunits at the level of the ER‐to‐Golgi compartment (ERGIC). In the presence of CD4, gp160 forms a complex with CD4 that mediates ER retention of both molecules and downregulation of CD4 from the cell surface. In the context of an HIV‐infected cell, coexpression of Vpu liberates gp160 from the complex, ensuring gp160 translocation to the ERGIC and ensuing maturation, and simultaneously accelerating CD4 turnover (Crise and Rose, 1992; Rose et al., 2005). HIV downregulates class I MHC, class II MHC, CD4, and CD1d, a class I MHC‐like molecule that presents lipid antigens (Le Gall et al., 1998; Piguet et al., 1999b), from the cell surface by virtue of its Nef, Vpu, and gp160 proteins, reflecting its ability to manipulate various aspects of the immune response (Joseph et al., 2005; Piguet et al., 1999b). Furthermore, each of these mechanisms may be more far‐reaching than downregulation of each individual receptor. For instance, by directly associating with the proton (Hþ) ATPase, which is required for the acidification of lysosomes, Nef not only downregulates CD4 but may also interfere with the pH of class II MHC‐positive compartments, affecting antigen processing by lysosomal proteases and class II MHC antigen presentation. This strategy is used by other pathogens: the E5 protein from both human and bovine papillomavirus (Andresson et al., 1995) and the VacA toxin secreted by Helicobacter pylori (Molinari et al., 1998) actively manipulate acidification in the endocytic pathway, resulting in impaired CD4þ T cell‐mediated responses (Brodsky et al., 1999). By binding to AP complexes or to the small GTPase ARF1, Nef may interfere with trafficking and alter the fate of numerous host molecules in the endocytic pathway (Janvier et al., 2003a), affecting aspects that range from viral replication

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to modulation of the immune system (Lama and Ware, 2000; Sol‐Foulon et al., 2002; Swigut et al., 2001). 35. Pathogen Manipulation of the Ubiquitin‐Proteasome System Intracellular pathogens exploit the ubiquitin‐proteasome system mostly to destroy or avoid destruction of specific cellular proteins. This serves to create a more hospitable environment for themselves in the host cell, or to prevent destruction of their own proteins, and so to ensure replication or avoid detection by the immune system. Many events at the initial stage of infection, entry into the cell, are controlled by signaling pathways—for instance, those involved in cytoskeletal rearrangements or in receptor engagement—that rely heavily on the Ub‐proteasome system. The same is true for signaling cascades involved in cell survival, differentiation, and proliferation. Not surprisingly, pathogens have devoted much energy and coding capacity to interfere with cell signaling events through interference with the Ub‐proteasome system. An illustrative example is that of tumor viruses and degradation of cellular tumor suppressor proteins, like the retinoblastoma protein or p53, often resulting in malignant transformation (Shackelford and Pagano, 2005). There have been a number of recent reviews on viral interference with signaling pathways, so we will not describe this aspect in detail here. For comprehensive reviews, the reader is referred to Banks et al. (2003) and Shackelford and Pagano (2004, 2005). In this chapter, we will focus on a few examples of viral and bacterial interference with the Ub‐proteasome system that illustrate how the latter is crucial for aspects of pathogen life cycles that are as distinct as integration into host chromosomes, exit from the host cell, or RNA interference, not to mention control of the immune response. As mentioned in the first section of this chapter, proteasome‐mediated degradation of cytoplasmic proteins as well as the proteolytic events in the endolysosomal system are important for class I and class II MHC presentation and cross‐presentation. Mounting of an immune response involves complicated signaling cascades like NF‐kB and JAK/STAT signaling, which are heavily dependent on the Ub‐proteasome system, and that can be manipulated from early events of cell‐surface receptor‐mediated signaling, for example, to later events of ubiquitination and deubiquitination of downstream targets. Certainly, because of the central role of the Ub‐proteasome system in many different aspects of cellular physiology, interference with this pathway will have pleiotropic effects, and which of the observed effects predominates may be difficult to discern. It is not our intention to make the following a comprehensive list. We rather propose to provide an overview of the possibilities of manipulation of this system that have been described for pathogens (Fig. 9). We discuss in more

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Figure 9 Pathogen interference with the Ub‐proteasome system. Pathogens interfere with the Ub‐proteasome system not only to manipulate antigen presentation and other aspects of the immune system, but for processes as distinct as chromosomal integration, virus budding, RNA interference, and many others. They may rely on hijacking of host E3 ligase activities or encode their own. Pathogens can manipulate host DUBs as well as encode DUB activities. The function of pathogen‐encoded DUBs remains a mistery.

detail pathogen‐encoded modulators of the ubiquitin‐proteasome system, ranging from pathogen‐encoded proteolysis‐resistant peptides, to ubiquitin ligases and DUB, with obvious implications for the viral or bacterial life cycle or with consequences for the control of host immune responses. We elaborate on novel pathogen‐encoded DUBs and their putative functions. 36. Interference with Proteasomal Proteolysis The classic example of a viral protein that interferes with proteasomal processing is the EBNA‐1. The EBNA‐1 protein contains an internal repeat exclusively composed of glycines and alanines, the Gly‐Ala repeat, which not only interferes with its own proteasomal proteolysis (Levitskaya et al., 1995), but also reduces its rate of translation, blocking viral DRIPs formation and inhibiting the presentation

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of EBNA‐1 class I MHC‐restricted T cell epitopes (Yin et al., 2003). A single residue change in the mouse leukemia virus (MuLV)‐derived CTL epitope (from KSPWFTTL to RSPWFTTL) can eliminate the proteolytic cleavage site required for its presentation (Ossendorp et al., 1996). As discussed earlier, phosphorylation of the HCMV IE‐1 by the HCMV pp65 tegument protein interferes with production of IE‐1‐derived peptides (Gilbert et al., 1996). The HIV‐1 transcriptional activator (Tat) protein manipulates 26S proteasome function by directly interacting with the LMP7 and MECL1 subunits of the proteasome and competing with the 11S proteasome for binding to the 20S proteasome (Andre et al., 1998; Apcher et al., 2003), leading to inhibition of proteolytic activity. Tat also acts at the transcriptional level by modifying proteasome composition by upregulating the LMP7 and MECL1 subunits and downregulating the LMP2 subunit, leading to an increased presentation of cryptic and subdominant CTL epitopes (Gavioli et al., 2004). By preventing display of viral peptides, these viral proteins interfere with cytotoxic T cell recognition. 37. Control of Infection Salmonella enterica serovar typhimurium is an important bacterial pathogen, the causative agent of food poisoning and typhoid fever. S. typhimurium temporally regulates the initial phase of bacterial internalization and host cell recovery after invasion through two type III secretion system (TTSS)‐delivered substrates, SptP and SopE with different proteasomal half lives (Kubori and Galan, 2003). SopE is a bacterial guanine nucleotide exchange factor (GEF) delivered by a TTSS that mimics a host cell GEF for the small Rho GTPases Cdc42 and Rac1, involved in actin remodeling and formation of membrane extensions required for bacterial engulfment. SptP, delivered by another TTSS, is a Rho GTPase activating protein (GAP) factor for Cdc42 and Rac1, which accelerates GTP hydrolysis. SpT plays a role once bacterial internalization has taken place, inactivating the Rho GTPases, inhibiting actin polimerization, and assuring closure of the plasma membrane and recovery of the normal cellular architecture (Pizarro‐Cerda and Cossart, 2006). Salmonella initially delivers equal amounts of SopE and SptB to the host cell. However, 15–20 min after infection SopE is rapidly degraded, whereas SptB degradation occurs only slowly after 3 h, thereby efficiently timing the actin remodeling events that lead to the initial bacterial engulfment and the later host cell recovery. Both processes are proteasome‐dependent and catalyzed by the N‐terminus of the bacterial proteins, but the mechanism and the host factors involved are currently unknown (Kubori and Galan, 2003). The HIV‐1 Vif protein targets the RNA editing protein APOBEC3G for degradation by a cellular E3 ligase to allow production of infectious viral

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progeny (Yu et al., 2003). The apolipoprotein B mRNA editing enzyme, catalytic polypeptide‐like 3G (APOBEC3G) and related cytidine deaminases are involved in mRNA editing and in immunoglobulin gene class switching and hypermutation but are also potent antiretroviral enzymes. The cytosolic APOBEC3G is incorporated into budding virions, where on infection of new target cells, its cytidine deaminase activity induces G to A hypermutation on the minus‐strand viral DNA, resulting in abortive infection (Bishop et al., 2004b; Zhang et al., 2003). Vif hijacks the elongin‐C‐elongin‐B‐Cullin‐5‐E3 (ECS) complex. The ECS5 E3s recognize substrate receptor proteins containing a BC‐box. Vif possesses a BC‐box motif, through which it binds elongin C (Luo et al., 2005). Vif hijacks the E3 ligase complex and by bridging APOBEC3G and elongin C targets APOBEC3G for ubiquitination and degradation (Yu et al., 2003). This ultimately allows production of infectious virus progeny (Bieniasz, 2004; Bishop et al., 2004a; Harris and Liddament, 2004). 38. Virus Budding The endosomal sorting complexes required for transport (ESCRT) are highly conserved from yeast to mammals and consist of the ESCRT I, II, and III complexes. ESCRT complexes are composed of vacuolar protein sorting (VPS) proteins (in yeast), which are recruited from the cytoplasm to promote sorting of ubiquitinated proteins to MVBs (Katzmann et al., 2002). MVBs are formed by invagination of the late endosome membrane, which generates internal vesicles into which proteins destined to the lysosomes are sorted; they are critical for receptor downregulation and other normal and pathological cell processes (Hierro et al., 2004; Hurley and Emr, 2006; Kostelansky et al., 2006), as well as for virus budding. The tumor susceptibility gene TSG101, the mammalian homologue of yeast VPS23, is essential for sorting of ubiquitinated proteins to MVBs (Babst et al., 2000). TSG101 recruits hepatocyte growth factor–regulated tyrosine kinase substrate (HRS), the mammalian homologue of yeast VPS27, to the endosomal membrane. HRS nucleates recruitment of the ESCRT‐1 complex, which in turn recruits ESCRT 2 and ‐3, leading to formation of the inner membranes of the MVB. TSG101 is a noncanonical Ub E2 variant (UEV) protein—it does not possess Ub‐conjugating activity, only an N‐terminal UEV domain that binds ubiquitin. Binding of the TSG101 UEV domain to a P(S/T)AP tetrapeptide motif on HRS recruits HRS to the endosomal membrane, triggering assembly of ESCRT complexes and genesis of the MVB (Clague and Urbe, 2003; Garrus et al., 2001; Pornillos et al., 2002, 2003). Many viruses such as HIV‐1 and Ebola recruit this Ub‐dependent sorting machinery to the viral release sites at the plasma membrane to promote virus budding from host cells

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(Li and Wild, 2005; Liu, 2004), by making use of conserved P(S/T)AP, PPXY, or FPIV motifs, also called late budding domains (Bieniasz, 2006). Retroviruses like HIV use the P(S/T)AP motif on the L‐domain contained in their GAG protein to mimic the TSG101‐recruiting activity of the HRS protein (Klinger and Schubert, 2005; Martin‐Serrano et al., 2003; Sorin and Kalpana, 2006; Stuchell et al., 2004). Other viruses can also recruit neuronal precursor cell‐expressed developmentally downregulated 4 (NEDD4) and NEDD‐4‐like HECT E3 ligases to facilitate viral budding (Bieniasz, 2006; Harty et al., 2001; Sakurai et al., 2004; Yasuda et al., 2003). NEDD4 E3s ligases are a diversified group of orthologues of yeast Rsp5p and are involved in a wide range of processes such as receptor internalization and degradation (presumably through the endolysosomal pathway), maintenance of EBV latency, and regulation of cytokine signaling. NEDD4 E3s have a catalytic C‐terminal HECT domain, two or more central N‐terminal WW domains, and an N‐terminal C2 domain. The N‐terminal C2 domain seems to be responsible for membrane association and cellular localization of the protein. The WW domain is a protein–protein interaction module of about 35 amino acids with two crucial tryptophan (W) residues spaced 20–22 amino acids apart, which appears to mediate substrate selection. It binds mostly proline‐rich motifs, such as the PPXY motif present in many cellular proteins, targeting them for degradation (Ingham et al., 2004, 2005). Viral proteins with the PPXY motif can therefore bind these WW domains on Nedd4 E3s, but the nature of these interactions and how they facilitate virus budding is so far unknown. Some viruses, like Ebola, have two different late budding motifs, PPXY and P(S/T)AP, and the Ebola late domain‐containing VP40 matrix protein can recruit both TSG101 and NEDD4 for effective budding (Licata et al., 2003; Liu, 2004; Yasuda et al., 2003). The nonenveloped adenovirus possesses a PPXY motif on its penton base protein, which is essential for virus internalization that can interact with several NEDD4 HECT E3 ligases (Galinier et al., 2002). Whether or not this interaction or E3 ligase activity is required for adenovirus entry is currently unknown. 39. Bacterial Chromosome Integration Agrobacterium tumefaciens exploits a host cell SCF E3 ligase for integration of its T‐DNA by encoding an F‐box protein (Tzfira et al., 2004b). Agrobacterium is a common phytopathogenic bacterium that induces ‘‘crown gall’’ disease in plants by transfer and integration of a segment of its tumor‐inducing (Ti) plasmid DNA into the plant genome. This process relies also on delivery of several virulence (Vir) proteins into the host cell, such as the bacterial VirE2 protein, which is thought to package and protect the transported T‐DNA

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molecule, and, together with the host plant VIP1 protein, assist its nuclear import (Tzfira et al., 2004a). However, disassembly of this VirE2/T‐DNA/VIP1 complex must occur before integration and involves intranuclear proteolysis of VirE2 and VIP1 induced by the VirF protein, an F‐box‐domain–containing protein. The bacterial VirF F‐box hijacks the SCF E3 plant homologue. The VirF‐containing SCFVirF then leads to degradation of VIP1 and VirE2, and integration of the Agrobacterium T‐DNA (Tzfira et al., 2004b). We consider it unlikely that this possibility has been exploited only by plant pathogens. 40. ISGylation and deISGylation The host innate response triggered by type I interferon (IFN‐a and ‐b) innate response is crucial in early immunity against viruses, bacteria, and some parasites, acting to limit pathogen infection limiting replication of the pathogen and constraining cellular permissiveness to infection (Smith et al., 2005). Type I interferon receptor signaling occurs through the JAK/STAT pathway and leads to transcription of several IFN‐stimulated genes (ISGs), of which the gene encoding the Ub‐like modifier ISG15 is one of the most strongly induced (Farrell et al., 1979). ISG15 and protein modification by ISG15 (ISGylation) are induced by viral and bacterial infection or other stresses, suggesting important roles for the ISG15 system in innate immune responses (Liu et al., 2005; Ritchie and Zhang, 2004). ISG15 is conjugated onto several signaling molecules with immunomodulatory functions, like JAK/STAT proteins (Giannakopoulos et al., 2005; Malakhov et al., 2003). The ISGylation cascade is initiated by ISG15 activation by an E1‐like enzyme, UBE1L, transfer to the ISG‐conjugating UBCH8 enzyme (Zhao et al., 2004), and incorporation into UBCH8‐compatible Ub E3 ligases (Dastur et al., 2006; Zou and Zhang, 2006). A deISGylating enzyme, ubiquitin‐binding protein 43 (UBP43), also called USP18, specifically removes ISG15 from ISGylated substrates (Malakhov et al., 2002), and is regulated by ubiquitination by the SCFSkp2 E3 (Tokarz et al., 2004). USP18 is unlikely to be the sole enzyme capable of acting on ISG15 conjugates. The role of ISG15 in orchestration of the innate antiviral response sets the ground for pathogen interference. Although the mechanism is unclear, viral replication in UBP43 knockout mice is impaired, a phenotype that was initially attributed to inhibition of deISGylation and deregulation of STAT signaling (Ritchie et al., 2002). There are conflicting views on this (Dao and Zhang, 2005; Kim et al., 2006b; Knobeloch et al., 2005) and UBP43 may in fact inhibit type I IFN signaling irrespectively of its ISG15 isopeptidase activity, by binding to the IFN receptor and blocking the interaction between JAK and the IFN receptor (Malakhova et al., 2006). IFN‐mediated inhibition of HIV replication and budding is dependent on ISG15 (Kunzi and Pitha, 1996; Pitha, 1994; Poli et al., 1989). Expression of ISG15 inhibits ubiquitination of the HIV‐1 Gag protein and TSG101, and disrupts the interaction of the Gag late

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budding domain with TSG101 (Okumura et al., 2006). Either of these ISG15 effects and/or additional mechanisms of action could lead to failure to recruit ESCRT complexes and inhibition of HIV budding. ISG15 is strongly induced by infection with influenza B virus. The exact cellular function and targets of ISG15 are not known, but the NS1 protein of influenza B viruses (NS1B) blocks its conjugation to target proteins: the NS1B N‐terminus binds ISG15, inhibiting activation of ISG15 by its E1 enzyme, UBE1L. Influenza A viruses also manipulate cellular ISGylation processes, through an even less well characterized mechanism: the influenza A virus NS1A protein does not directly bind the ISG15 protein, but little or no ISG15 protein is produced during infection (Yuan and Krug, 2001; Yuan et al., 2002). Whether this reflects ISGylation, deISGylation, and/or UBP43 ubiquitination‐mediated control of the IFN innate immune response is so far unknown. 41. Control of Inflammation Suppression of NF‐kB signaling is a common theme for many viral as well as bacterial pathogens and can be certainly achieved through modulation of Ub‐dependent events in the NF‐kB cascade (Bowie et al., 2004; Hiscott et al., 2001; Mason et al., 2004). The human enteric flora may influence intestinal epithelium inflammatory tolerance by inhibiting the NF‐kB pathway, which can be achieved by blocking any one of the many Ub‐dependent steps that control it. Shigella flexneri, which causes severe diarrhea in humans, injects TTSS‐effector proteins into host cells to induce their entry into epithelial cells or trigger apoptosis in macrophages. The OspG effector is a serine/ threonine kinase that binds various E2s, including UbcH5, a component of the SCFb‐TrCP E3 complex. OspG binding to UbcH5 inhibits the SCFb‐TrCP complex and thereby phospho‐IkB degradation, blocking NF‐kB signaling in response to the bacterial infection. The Cullin subunit of the SCFb‐TrCP complex is itself regulated by NEDD8 attachment (Pan et al., 2004). Certain enteric bacteria can lead to rapid deneddylation of Cullin‐1 and consequent repression of the NF‐kB pathway (Collier‐Hyams et al., 2005), but the bacterial activities responsible are still to be determined. In any case, because NF‐kB is crucial for both the initial innate inflammatory response and for coordination of the adaptive immune response, dampening of inflammation may endow Shigella and other enteric bacteria with the ability to invade and later colonize the gastrointestinal epithelium (Kim et al., 2005). 42. Posttranscriptional Gene Silencing RNA interference (or posttranscriptional gene silencing) in plants and invertebrate animals is important for many regulatory processes and a primitive form of antiviral immunity that is nucleic acid based. Consequently, plant

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viruses have evolved proteins, the so‐called silencing suppressors, which directly bind to and inactivate the plant microRNAs (Ding et al., 2004; Zamore, 2004). Poleroviruses are small positive‐strand RNA viruses that cause leafroll phenotypes in many plant species. Poleroviruses encode a silencing suppressor P0, which is a viral F‐box protein (Barry and Fruh, 2006). P0 has a minimal conserved F‐box motif that can bind the plant Skp‐1 homologue and form a functional complex with the plant Cullin‐1 homologue. The P0 F‐box is required for polerovirus infectivity and its silencing suppressor function (Pazhouhandeh et al., 2006). P0 presumably binds the microRNAs and targets them for Cullin‐4A‐dependent degradation. This constitutes a remarkable finding, as degradation of RNA (and not protein) by an E3 ligase had not been documented before. This primitive form of antiviral immunity, although widely used as a laboratory tool was only recently shown to occur in the context of a natural viral infection in jawed vertebrates (Browne et al., 2005). HIV‐1 encodes viral small interfering RNA (siRNA) precursors that provoke RNA silencing in human cells, so not surprisingly, the HIV‐1 Tat protein also contains a silencing suppressor function (Bennasser et al., 2005), that prevents Dicer from processing the precursor double‐stranded RNAs into siRNAs. Suppression of RNA silencing by other RNA viruses is likely to occur. It will be interesting to see whether the mechanism used by the plant poleroviruses is conserved and whether manipulation of the Ub‐proteasome system for suppressing RNA interference is more widely used by mammalian RNA viruses. 43. Downregulation of Cell Surface Receptors by Pathogen‐Encoded E3s As mentioned earlier, several g‐herpesviruses and poxviruses encode PHD/ LAP E3s, the K3 homologues, that include the Kaposi’s‐sarcoma‐associated herpesvirus (KSHV) kK3 and kK5 (also called modulator of immune recognition MIR‐1 and MIR‐2, respectively), the MHV‐68 mK3, and the rabbit myxoma virus M153R (Coscoy and Ganem, 2003). We discussed mK3 which catalyzes ubiquitination and proteasomal degradation of nascent murine class I MHC HCs after dislocation from the ER membrane (Lybarger et al., 2005; Wang et al., 2005). KSHV kK3 and kK5 and myxoma virus M153R catalyze ubiquitination of cell surface class I MHC HC, thus providing the trigger for their internalization from the cell membrane, as well as for sorting through MVB formation to lysosomal degradation (Duncan et al., 2006). In addition to downregulation of class I MHC molecules, some of these K3 homologues target the lymphocyte costimulatory molecules CD86 (B7.2) and intercellular adhesion molecule ICAM‐1, CD1d, and CD4 (Coscoy and Ganem, 2003;

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Coscoy et al., 2001; Lehner et al., 2005; Mansouri et al., 2003), as well as the Fas/CD95 death receptor (Collin et al., 2005; Guerin et al., 2002). By targeting not only class I MHC molecules, the viral K3 homologues are adding an extra layer of protection predicted to be important for viral escape after reactivation from latency (Lehner et al., 2005). The K3 homologues are most likely making use of old ideas: the mechanisms used by the viral Ub ligases have probably been ‘‘borrowed’’ from their mammalian counterparts, the MARCH proteins, and will certainly continue to yield insights into the Ub‐proteasome system. 44. Programmed Cell Death in Plants A bacterial E3 ligase was recently shown to inactivate another type of immune response in plants (Janjusevic et al., 2006). Antipathogen responses in plants, although not as sophisticated as those afforded by the vertebrate immune system, are nevertheless quite efficient. One mechanism, immunity‐induced programmed cell death (PCD), is a response that sacrifices a limited portion of the plant to limit spread of the infection. The Pseudomonas syringae bacterium, which causes disease in tomato and Arabidopsis, delivers its AvrPtoB protein into plant cells through a type III secretion system. AvrPtoB can inhibit PCD in susceptible hosts, allowing Pseudomonas to cause systemic infection and disease. The AvrPtoB C‐terminus encodes a U‐box E3 ligase activity necessary for the pathogenic role of AvrPtoB, since mutation of the putative E2‐recruitment sites abolishes the anti‐PCD and virulence activities of the AvrPtoB protein (Janjusevic et al., 2006). Determining the host targets of the Pseudomonas E3 will be crucial in clarifying its mechanism of action and might even explain plant susceptibility to infection. Such experiments may also assist in the identification of similar targets in mammalian species infected with comparable Gram‐negative microbes. Furthermore, it is rather striking that another pathogen uses a mimic of a host E3 ligase to encode an immunomodulatory function. There are bound to be others. 45. Cytokine Responses Mumps virus and other Paramyxoviridae family members hijack the Cullin‐4A‐ SCFb‐TrCP E3 ligase to suppress IFN‐ as well as IL‐6‐mediated signaling (Ulane et al., 2003), important for control of inflammation and apoptosis during inflammation (Hodge et al., 2005). IFN and IL‐6 signaling activates their cognate STAT factors and transcription of genes involved in IFN and cytokine signaling. Some of these paramyxoviruses use their V protein to bind DDB1, the Cullin‐4A‐SCFb‐TrCP E3 substrate adaptor that recruits STAT proteins to the E3 complex, targeting STATs for proteasome‐mediated

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degradation (Li et al., 2006b; Ulane and Horvath, 2002; Ulane et al., 2005). Many other viruses target STAT transcription factors for proteasomal degradation (Garcin et al., 2002; Lin et al., 2005; Ramaswamy et al., 2004; Zimmermann et al., 2005). Recruitment of Cullin E3s is likely to be a more widely used viral strategy of interfering with IFN and cytokine signaling. The interleukin‐2 (IL‐2)‐inducible deubiquitinating enzyme DUB‐2 is induced by IL‐2 stimulation and may regulate IL‐2 signaling. The IL‐2‐inducible DUB‐2 is constitutively expressed in cells transformed by human T cell leukemia virus‐1 (HTLV‐1). Like other cytokines, IL‐2 is an important modulator of apoptosis in T cells that acts through the STAT pathway. Although the mechanism is not at all clear, DUB‐2 activity prolongs IL‐2‐stimulated phosphorylation and transcriptional activity of STAT5, inhibiting T cell apoptosis in the aftermath of the immune response (Migone et al., 2001; Shackelford and Pagano, 2004) and possibly contributing to cell immortalization. Cytokine‐inducible DUBs could interfere with cytokine signaling, thereby playing an active role in modulation of the immune response. The IL‐1‐inducible DUB‐1 is specifically induced by interleukin 3 (IL‐3), GM‐CSF, and IL‐5, which suggests a role in responses mediated by these cytokines (D’Andrea and Pellman, 1998) and might constitute a target for pathogen modulation of the immune response through interference with the Ub‐proteasome system. Although a review of this rapidly expanding field is beyond the scope of this chapter, even this simple example suffices to demonstrate how both Ub addition and removal control immune physiology, and consequently are likely targets for interference by pathogens. 46. Pathogen‐Encoded DUBs Bacteria of the Yersinia genus are the causal agents of plague, septicemia, and gastrointestinal syndromes. Enteropathogenic Yersinia species are extracellular multiplying Gram‐negative bacteria that make use of type III secretion systems to inject virulence factors into host cells. The Yersinia YopJ virulence factor encodes a protein reported to be a cysteine protease that can cleave Ub and SUMO. The YopJ DUB inhibits NF‐kB and mitogen‐activated protein kinase (MAPK) pathways, a function ascribed previously to its somewhat promiscuous deubiquitination of critical cellular proteins, such as TRAF2, TRAF6, and IkB. The MAPK/JUN pathway is involved in transcriptional control of several cytokine genes. The YopJ activity induces macrophage death and blocks their ability to activate these inflammatory pathways (Orth, 2002; Orth et al., 2000; Zhou et al., 2005). However, recent reports show that the true function of YopJ is that of a serine/threonine acetyltransferase (Mukherjee et al., 2006). The way by which the functions assigned to YopJ have changed as the field advances demonstrates the complexity of assigning a function to proteins that lack obvious

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mammalian counterparts. Both Salmonella and the plant pathogen Xanthomonas secrete proteins with homology to YopJ, but their putative DUB activity or function in the host have not been assessed yet (Gurlebeck et al., 2006; Hardt and Galan, 1997). A novel viral USP or deubiquitinating enzyme, UL36USP, was recently identified in herpes simplex virus‐1 (HSV‐1) by labeling with an Ub‐derived probe (Kattenhorn et al., 2005). The UL36USP is located at the N‐terminus of the UL36, the large tegument protein of HSV‐1, an a‐herpesvirus. Despite the overall low sequence homology—at the exception of almost only the amino acid residues composing the catalytic triad—the UL36USP activity is well conserved in all members of the Herpesviridae family, as the homologous proteins in murine cytomegalovirus (a b‐herpesvirus) and EBV (a g‐herpesvirus) also exhibit DUB activity in vitro (Schlieker et al., 2005). One of the two severe acute respiratory syndrome (SARS) Coronavirus proteases responsible for cleavage of the replicase polyprotein, the SARS‐CoV papain‐like protease PLpro, is also a DUB (Barretto et al., 2005; Lindner et al., 2005), predicted to be structurally similar to human USP7 (Hu et al., 2002). The SARS‐CoV Plpro protease activity is involved in the processing of the viral polyprotein, thereby contributing to replication of the viral RNA genome. The function of the SARS virus deubiquitinating activity, which extends to ISG15‐removal activity (Lindner et al., 2005), is unknown at this point. The adenovirus proteinase (Avp) and the human cytomegalovirus UL48 protein also encode DUB activities, but the viral and/or cellular targets remain unidentified (Balakirev et al., 2002; Wang et al., 2006b). Chlamydia trachomatis, an obligate intracellular bacterium that causes a variety of diseases in humans has two genes, ChlaDub1 and ChlaDub2, whose products encode deubiquitinating and deNEDDylating activities (Misaghi et al., 2006). Unlike C. pneumoniae whose genome is devoid of ChlaDub genes, C. trachomatis is able to block NF‐kB signaling and thus the inflammatory response, as well as host cell apoptosis. Both processes could be modulated by the C. trachomatis DUBs. These unexpected DUB activities encoded by pathogens suggest a novel strategy of modulation of host defense by manipulating the cellular ubiquitination machinery. Although the experimental evidence is at best tenuous, one can speculate that many of these other pathogen‐encoded DUBs may be important for modulation of the Ub‐proteasome system during the pathogen life cycle and/or in the context of immune evasion. The fact that these DUB activities are conserved suggests functional importance. They may be delivered to the host cell at the time of infection (for instance, by a bacterial type III secretion system or by a viral tegument protein) or may be transcribed early during infection. The pathogen‐encoded DUB could interfere with the levels of Ub‐ or Ubl‐conjugated proteins, thereby altering cellular processes

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ranging from signaling pathways, protein degradation, antigen presentation, vesicular trafficking, and many others. 47. Conclusions and Future Directions We have elaborated on several examples of how the host immune system can be manipulated by a pathogen‐encoded E3 or pathogen hijacking of a host E3. Similarly—and although the sample pool is rather small at the moment—there is no reason to believe that encoding their own DUBs and/or hijacking of host DUBs has not ‘‘occurred’’ to pathogens. An attractive possibility is that some of these activities are aimed at manipulating the host immune system. Possible targets would include pathways involved in ‘‘housekeeping’’ processes like life and death of the cells of the immune system, and extend to, for example, interference with antigen presentation. These DUB activities could manipulate membrane trafficking in ways that would prevent MHC molecules from reaching the cell surface, or prevent proteasomal degradation of pathogen‐derived proteins. This initial window of deubiquitinating activity could help viruses escape detection. Bacterial pathogens could benefit from targeting of transcription factors and cytokine signaling networks that are crucial for coordination of macrophage effector functions. Other immune responses that could, in principle, be controlled by DUBs are IFN and NF‐NF‐kB signaling, and the posttranscriptional gene silencing ‘‘immune response’’ at least in plants. The possibilities are endless. Exploitation of the Ub‐proteasome system by pathogens to enhance their own survival is beginning to emerge as a central theme. The development of the appropriate genetic and biochemical tools will help place this subject in the realm of essential host–pathogen interactions. Acknowledgments We thank Howard C. Hang for help with the text and the figures and apologize to all colleagues whose work we may have not included. This work was supported by NIH grants to H.L.P.

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Yoshida, Y., Chiba, T., Tokunaga, F., Kawasaki, H., Iwai, K., Suzuki, T., Ito, Y., Matsuoka, K., Yoshida, M., Tanaka, K., and Tai, T. (2002). E3 ubiquitin ligase that recognizes sugar chains. Nature 418(6896), 438–442. Yoshida, Y., Tokunaga, F., Chiba, T., Iwai, K., Tanaka, K., and Tai, T. (2003). Fbs2 is a new member of the E3 ubiquitin ligase family that recognizes sugar chains. J. Biol. Chem. 278(44), 43877–43884. Yu, X., Yu, Y., Liu, B., Luo, K., Kong, W., Mao, P., and Yu, X. F. (2003). Induction of APOBEC3G ubiquitination and degradation by an HIV‐1 Vif‐Cul5‐SCF complex. Science 302(5647), 1056–1060. Yu, Y. Y., Harris, M. R., Lybarger, L., Kimpler, L. A., Myers, N. B., Virgin, H. W., Vth, and Hansen, T. H. (2002). Physical association of the K3 protein of gamma‐2 herpesvirus 68 with major histocompatibility complex class I molecules with impaired peptide and beta(2)‐microglobulin assembly. J. Virol. 76(6), 2796–2803. Yuan, W., and Krug, R. M. (2001). Influenza B virus NS1 protein inhibits conjugation of the interferon (IFN)‐induced ubiquitin‐like ISG15 protein. EMBO J. 20(3), 362–371. Yuan, W., Aramini, J. M., Montelione, G. T., and Krug, R. M. (2002). Structural basis for ubiquitin‐ like ISG 15 protein binding to the NS1 protein of influenza B virus: A protein‐protein interaction function that is not shared by the corresponding N‐terminal domain of the NS1 protein of influenza A virus. Virology 304(2), 291–301. Zamore, P. D. (2004). Plant RNAi: How a viral silencing suppressor inactivates siRNA. Curr. Biol. 14(5), R198–R200. Zhang, H., Yang, B., Pomerantz, R. J., Zhang, C., Arunachalam, S. C., and Gao, L. (2003). The cytidine deaminase CEM15 induces hypermutation in newly synthesized HIV‐1 DNA. Nature 424(6944), 94–98. Zhang, X., Shaw, A., Bates, P. A., Newman, R. H., Gowen, B., Orlova, E., Gorman, M. A., Kondo, H., Dokurno, P., Lally, J., Leonard, G., Meyer, H., et al. (2000). Structure of the AAA ATPase p97. Mol. Cells 6(6), 1473–1484. Zhao, C., Beaudenon, S. L., Kelley, M. L., Waddell, M. B., Yuan, W., Schulman, B. A., Huibregtse, J. M., and Krug, R. M. (2004). The UbcH8 ubiquitin E2 enzyme is also the E2 enzyme for ISG15, an IFN‐alpha/beta‐induced ubiquitin‐like protein. Proc. Natl. Acad. Sci. USA 101(20), 7578–7582. Zheng, N., Schulman, B. A., Song, L., Miller, J. J., Jeffrey, P. D., Wang, P., Chu, C., Koepp, D. M., Elledge, S. J., Pagano, M., Conaway, R. C., Conaway, J. W., et al. (2002). Structure of the Cul1‐ Rbx1‐Skp1‐F boxSkp2 SCF ubiquitin ligase complex. Nature 416(6882), 703–709. Zhong, G., Fan, T., and Liu, L. (1999). Chlamydia inhibits interferon gamma‐inducible major histocompatibility complex class II expression by degradation of upstream stimulatory factor 1. J. Exp. Med. 189(12), 1931–1938. Zhou, H., Monack, D. M., Kayagaki, N., Wertz, I., Yin, J., Wolf, B., and Dixit, V. M. (2005). Yersinia virulence factor YopJ acts as a deubiquitinase to inhibit NF‐kappa B activation. J. Exp. Med. 202(10), 1327–1332. Zimmermann, A., Trilling, M., Wagner, M., Wilborn, M., Bubic, I., Jonjic, S., Koszinowski, U., and Hengel, H. (2005). A cytomegaloviral protein reveals a dual role for STAT2 in IFN‐{gamma} signaling and antiviral responses. J. Exp. Med. 201(10), 1543–1553. Zingoni, A., Sornasse, T., Cocks, B. G., Tanaka, Y., Santoni, A., and Lanier, L. L. (2005). NK cell regulation of T cell‐mediated responses. Mol. Immunol. 42(4), 451–454. Zou, W., and Zhang, D. E. (2006). The interferon‐inducible ubiquitin‐protein isopeptide ligase (E3) EFP also functions as an ISG15 E3 ligase. J. Biol. Chem. 281(7), 3989–3994.

Index

A Abortive infection, 272 Acrosclerosis, 93 Adaptive immune systems, dysregulation of, 41 Adaptive immunity, 227 costimulators of, 204 Adaptor protein-2 (AP2), 170 Adaptor protein complex-2 (AP-2), 267 Adaptor protein(s), 243, 245, 260 HR23B, 249 Adenovirus E3/19K, 250 Adenovirus proteinase (Avp), 279 Adhesion molecules, 45 Adoptive transfer experiments, 35, 85 ADP-ribosylation factor-1 (ARF1), GTPase cycle of, 267–268 AECA-dependent cellular cytotoxicity (ADCC), 100 African Americans, 13 Agrobacterium tumefaciens, 273 Aire, 139 Alzheimer’s disease (AD), 255 Amelanosis, 103 Amelanotic Smyth line, 72 American Rheumatism Association (ARA), diagnosis criteria for SLE, 3–4 b-Amyloid precursor protein (APP), 255 ANAs. See Anti-nuclear autoantibodies Anemia, 19 Angiotensin-converting enzyme (ACE), 99 Antibody-dependent cellular mediated cytotoxicity (ADCC), 7 Anticardiolipin antibodies, 99 Anticentromere antibodies, 98 Anti-chromatin autoantibodies, 35 locus associated with, 36 Anti-dsDNA autoantibodies, 19, 29, 32, 37 Anti-endothelial cell antibodies (AECA), 99

307

Antigen presentation, 230 MHC class I, 230–231 MHC class II, 233–234 Antigen-presenting cells (APCs), 8, 86, 158, 201, 227–228 Antigen processing and presentation by class I and II MHC molecules, 230 Antigens. See also specific antigens B-G, 74 Antigen-specific T cell responses, 190 assessing T cells in peripheral tissues, 195–196 in-depth molecular monitoring of, 194–195 measuring CD4 T cells, 193–194 measuring CD8 T cells, 191–193 Anti-GP70 autoantibodies, 38 Anti-melanocyte autoantibodies, 106 Anti-nuclear antibodies, 34, 98 Anti-nuclear autoantibodies (ANAs), 3, 7, 18–19, 41, 98 production, 36 Antitopoisomerase-I (anti-Scl-70) antibodies, 98 a1-Antitrypsin, 240 Antitumor immunity, 121 APC surface, class II MHC downregulation from, 265–266 inhibition of recognition at, 264–265 APOBEC3G, 271–272 Apoliprotein B100, 244 Apoptosis, 10–11, 22, 99, 169, 234, 257, 275 activation-induced, 38 control of, 229 endothelial cell, 100 ER-stress–induced, 245 of PBMCs, 17 of T cells, 16, 206, 278 upregulation of, 18

308 Asparaginyl endopeptidase (AEP), 234 Aspartic proteases, 255, 259 Atherosclerosis, 74 Autoantigens (Ags), 5, 120, 127 Autocrine motility factor receptor (AMFR), 244 Autoimmune disorders, chicken models for, 75 Autoimmune gastritis, 138, 140 Autoimmune polyglandular syndromes, 145 Autoimmune thyroiditis, 83 spontaneously occurring, 86 Autoimmunity due to mutations in FoxP3, 140–141 intrinsic and extrinsic drivers of, 138–140 nTreg-mediated control of, 141–144 spontaneous, 138 Autoreactivity, 86 Avian encephalomyelitis virus, 85 Avian flu, 74 Avian leucosis virus (ALV), 85, 91 Avian Rous sarcoma virus, 73 Avian scleroderma, 100 AvrPtoB protein, 277 B Bacterial chromosome integration, 273–274 Bacterial peptidoglycan, 227, 237 Bad, 11 B and T lymphocyte attenuator (BTLA). See BTLA Bax, 11 B cell receptor (BCR), 158, 228 B cells, 5–7, 73 activation, 37 autoreactive, chromatin-specific, 35 coreceptor, 7 dysregulation of, 42–43 splenic, 30 Th-mediated production of IgG autoantibodies by, 8 Bcl-2 protein, 135 Bcl-x, 11 BDCA receptors, 12 ‘‘Bedrock principle’’ of vaccine strategies, 229 B-G antigens, 74 BJAB (B cell) tumor line, 171 Bleomycin, 93 B lymphocytes, 202

i nd e x B-lymphocyte stimulator (BLyS), 42 antagonists of, 43 BORIS (Brother of the Regulator of Imprinted Sites), 189 British Isles Lupus Assessment Group (BILAG) index, 3 Bronchitis virus, 107 BTLA, 157, 164 bound to HVEM, structural characterization of, 164 expression and regulation on T Cells and APCs, 165–167 human interaction with human CMV UL144, 165 and HVEM ligand discovery, 158, 160–161 inhibition of T cell–dependent inflammation, 176 Bursa-derived lymphocytes (B cells), 73 Bursa of Fabricius, 73 Bursectomy, 106 neonatal and in ovo, 85 Bursitis virus, 85 BZLF2 EBV gene, 264 C Calcinosis, 93 Calmodulin (CaM), 257 signaling, and SPP, 257 Calnexin (CNX), 230, 241 Calpain, 11 Calreticulin (CRT), 230, 241–242 Cancer immunotherapy, 206 Cancer/testis (CT) antigens, 189 Cancer vaccines, 205–206 effective, hurdles in achieving, 208 Candida albicans infection, control of, 132–133 Candidate mutations, 82–83 Carcinomas head and neck, 207 lung, 200 Caspases, 11 Cathepsin (Cat) family, cysteine proteases of, 234 Caucasians, 12 Causative mutations, identification of, 81–83 CD4þCD25þ nTreg cells, 120–121 Cdc48p/p97, 248 CD4 downregulation, from CD4þ T cell surface, 266–269

index þ

CD4 effector T cells, 131, 133 CD4 endocytosis, 266–267 CD85j/LIR-1/ILT-2 receptor, 253 CD94/NKG2 receptors, 253 CD4þ T cell activation, 265 CD8þ T cell-mediated lysis, 252 CD8þ T cell recognition, 252 CD4 T cell responses, 200 Melan-A/MART-1, 201–202 NY-ESO-1, 201 SSX-2 and SSX-4, 201 CD4þ T cells, 142 CD8þ T cells and NK cells, pathogen recognition by, 232–233 CD8 T cells, priming of, 205 CD4þ T cell surface, CD4 downregulation from, 266–269 CD8þ T lymphocytes, 232 Cell death, programmed, in plants, 277 Cell-surface markers, 122 Cell-surface receptors B7 family of, 158 downregulation by pathogen-encoded E3s, 276–277 ‘‘Cell-trackers,’’ 196 Cerebritis, 19 CFTR. See Cystic fibrosis conductance regulator Chemokine receptors, 123, 133 Chemokines, 18 Chemotaxis, 17 Chicken embryonic fibroblasts (CEF), 101 Chicken genome, sequencing of, 72 Chicken genomics, progress in, 75 Chickens, Obese strain (OS) of, 72 Chinese hamster ovary (CHO) cells, 161 CHIP. See C-terminus of Hsc70-interacting protein CHIP E3 ligase, 245, 246 cochaperone, 242 ChlaDub1 and -2, 279 Chlamydia trachomatis, 279 Chorioallantoic membrane (CAM) assay, 74 Chromatin, 21 Class I and II MHC molecules, antigen processing and presentation by, 230 Class II MHC antigen presentation, pathogen interference with, 265

309 Class II MHC-associated Ii peptide (CLIP), 233 Class II MHC trans-activator (CIITA), 264, 266 Class I MHC antigen presentation, HCMV interference with, 251 Class I MHC HC biosynthesis, 262 Collagen, 100 deposition, 99 production, 101 Colloid antigen (CA), 84 ConA, 177 ConA-challenge, 178 Conexin, degradation of, 245 COP proteins, 267 Coronavirus proteases, 279 Corticosterone binding globulin (CBG), 87 Costimulatory and inhibitory receptors, of immunoglobulin superfamily and TNFR families, 159 COX-2 inhibitors, 207 CpG motifs, 202 C-reactive protein (Crp), 36 CREST syndrome, 93 ‘‘Crown gall’’ disease, 273 CT antigens, 190 C-terminal fragments (CTFs), 259 C-terminus of Hsc70-interacting protein (CHIP), 245 CTL activation, 203 Ctla4 gene, 8 CTL induction, stimulators for, 203 Cue1p membrane protein, 243 Cullin-1, 275 Cullin-4ASCFb-TrCP E3 ligase, 277 Cullin-RING E3s, 238 Cullin-RING ligases (CRLs), 236 Cyclosporin A, 138, 177 Cysteine proteases of cathepsin (Cat) family, 234 Cysteine-rich domains (CRDs) of HVEM, 165 of TNFRs, 163–164 Cystic fibrosis, 23 Cystic fibrosis conductance regulator (CFTR), 240, 242 degradation of, 245 Cytokine responses, 277–278

310 Cytokines, 227. See also specific ones associations with SLE, 14–15 IFN-a, 19–20 IFN-g, 19 IL-6, 16 IL-10, 16–17 IL-1b, 13, 16 monocyte chemoattractive protein-1 (MCP-1), 18–19 recombinant, 87 regulatory, 205 TNF-a, 17–18 toll-like receptor (TLR), 20–21 TLR3, 22 TLR7, 21–22 TLR9, 21 Cytokine signaling, 229 Cytokine signaling/elongin-BC, suppressor of (SOCS/BC) boxes, 236 Cytomegalovirus (CMV), 160 human, 251. See also HCMV Cytomegalovirus, murine, 253 Cytosine phosphoguanine (CpG), 13 Cytotoxicity, NK cell-mediated, 253 Cytotoxic T cells (CTLs), 188, 232 human, 187 Cytotoxic T lymphocyte antigen-4 (CTLA-4), 158 inhibitory activity of, 170 polymorphisms in, 8 role in nTreg cell development and function, 130–131 Cytotoxic T lymphocytes (CTLs). See Cytotoxic T cells D Defective ribosomal products (DRIPs), 232 Delayed amelanosis (DAM) chicken, 102 Dendritic cells (DCs), 11–13, 165, 189, 226 accumulation in lymphoid organs, 175 Derlin proteins, 254, 259 derlin-1, 245, 247, 261 discovery of, 263 Dermal lymphoid aggregates (DLA), 106 Der1p, 243 Deubiquitinating enzymes (DUBs), 234 inventory of, 239 pathogen-encoded, 270, 278–280 Dexamethasone, 122 Diabetes, 135

i nd e x T1D models, 142–144 Diarrhea, 275 Dislocation, 241 ‘‘bystander effect’’ of, 262 HCs, 260–261 US11-mediated dislocation of, 263 Dislocon, elusive, 246–247 Distal polyarthritis, 94 DNA replication, 238 sequence analysis, 82 DNAM-1, 253 Doa10p, 243 DUBs. See Deubiquitinating enzymes E E6-associated protein (E6-AP), 236 EAT, 86 Eczema, 140 EDEM pathway, 242 Effector CD4 T cells, 205 Effector cells, 85–86, 100, 195 IL-2 production from, 130 Effector T cells, 86, 121, 135, 137, 140, 143–144 frequency of, 142 inhibitors of, 206 E3 ligase, 244, 268 activity, 261–262 bacterial, 277 cytosolic, 245 ELISPOT assay, 191 Elongin B, Ub-like, 237 Elongin-C-elongin-B-Cullin-5-E3 (ECS) complex, 272 Elongin-C-elongin-B-Cullin-5-SOCS (ECS) complex, 237 Encephalomyelitis, 127 Endocytosis, 170, 238 Endogenous viral (ev) genes, 104 Endopeptidases, ER-resident, 231 Endoplasmic reticulum (ER) dislocation, 226 dislocation pathway, US2-mediated, 255 mannosidase I, 242 membrane-associated proteins, 244 proteins disposal, pathogen-mediated, routes of, 261–264 misfolded/misassembled, 254

index quality control and degradation, 240–241 End organ disease, 30, 43 End organ targeting, 43–45 Endosomal sorting complexes required for transport (ESCRT) complexes, 272 Enteropathy, X-linked, 140 Enzymes, deubiquitinating, 239 Eosinophils, 44 Epistasis, 38 Epstein–Barr virus (EBV), 264 Epstein–Barr virus (EBV) nuclear antigen (EBNA), 250 EBNA-1 protein, 270 ERAD. See ER-associated degradation ER-associated degradation (ERAD), 241 E3 ligases, 243 mammalian, 244–246 substrate recognition, 241–242 ER degradation-enhancing alphamannosidase-like protein (EDEM), 242 ERK MAP kinases, 126 ERp57, 231 ER-resident oxidoreductases, 242 ER-to-Golgi compartment (ERGIC), 268 ER-to-Golgi complex (ERGIC), 250 ESCRT complexes, 275 Esophageal dysmotility, 93 Etanercept, 11 Ethylnitrosourea, 94 European Consensus Lupus Activity Measure (ECLAM), 3 Experimental autoimmune encephalomyelitis (EAE), 86 Expressed sequence tags (ESTs), 76, 91 Expression analysis, 82 F Fas/CD95 death receptor, 277 Fas ligand (FasL), 11 expression, 100 FATAL (fluorometric assessment of T lymphocyte antigen-specific lysis) assay, 192 F-box proteins, 236, 245 viral, 276 Fcgr3a gene, 7 Fcg receptors, 43, 100 FcgRI, FcgRII, and FcgRIII, 12 FcgRIIIa, 7 FcgRIIIb, 44

311 FcgRII, polymorphisms of, 13 Fc receptors, 26 Fibroblasts, 100 Fibrosis, 94, 100–101 Fine mapping analysis, 35 Flagellin, 132 Flow cytometry CTL (FCC) assay, 192 Flu, avian, 74 Fluorolysis assay, 192 Food and Agriculture Organization (FAO), focus of, 74 Food poisoning, casuative agent of, 271 FOXP3 cloning of, 194 expression, 122, 136–137, 140 transcription factor, 121 FPIV motif, 273 G GAG. See Glycosaminoglycans Galectin-1, 123 Gene-mapping, 77, 82 Gene silencing, posttranscriptional, 275–276 Genetic diversity, loss in poultry/livestock breeds, 74 Genetic markers, 80 GILT. See IFN-g-inducible lysosomal thiol reductase GITR/GITR-L interactions, role in function of nTreg cells, 131–132 Global agriculture, animal genetic resources for, 74 Glomerulonephritis, 11 Glucocorticoid-increasing factors (GIF), 87 Glucocorticoid-induced TNF receptor (GITR), 123 Glycophosphoinositol (GPI), 10 Glycoprotein B (gB), 266 Glycosaminoglycans (GAG), 93, 273 Gp42, 264 Gp100, 189–190, 203 Gp160, 266, 268 Gp78 E3 ligases, 244 Gp120 protein, 266 Graft rejection, 121 Graft-versus-host disease, 133 Graft-versus-host reaction (GvHR), 74 Granulopoeisis, 9 Granzymes, 199–200 Graves’ disease, 90

312 Growth hormone receptor, endocytosis of, 238 GTPase activating protein (GAP), 271 Guanine nucleotide exchange factor (GEF), 271 H Hashimoto disease, human, 72 Hashimoto-like autoimmune thyroiditis, 71 Hashimoto thyroiditis, 90 human, 83 HC dislocation, 263 HCMV. See Human cytomegalovirus HCMV E protein pp65, 250 HCMV IE-1, 271 HCV. See Hepatitis C virus Head and neck carcinomas, 207 Heat shock protein (HSP) complexes, 241–242 HECT-domain ligases, 236 HECT E3 ligases, 273 Helicobacter pylori, 268 Hepatitis C virus core protein processing, and SPP, 256–257 Hepatitis C virus (HCV), 256–258 Hepatocyte growth factor–regulated tyrosine kinase substrate (HRS), 272–273 Herpes simplex virus-1 (HSV-1), 266, 279 Herpesvirus entry mediator (HVEM), 155. See also HVEM deficiency, and T cell-dependent inflammation, 177–178 expression and regulation on T Cells and APCs, 165–167 and its ligands, modeling of, 162 ligand for BTLA, identification of, 161 ligation, consequences of HVEM-mediated activation in B Cells, DCs, and NK cells, 174–175 HVEM signaling in T cells, 172–173 and TNF ligands structural characterization of interaction between, 163 Herpesviruses, 107 g-herpesviruses, 261, 276 b-herpesvirus HCMV, 251 Herpesvirus-1 glycoprotein D (HSV1 gD), 158 High-throughput expression analysis, 75 Hispanics, 12 HIV, 206, 273 receptor for, 266

i nd e x replication, inhibition of, 274 HIV-1, 272, 276 HIV-1 Gag protein, 274 HIV gp160, 257, 268 HIV Nef, 265, 267–268 HIV-1 proteins Nef, Vpu, and gp160, 266 HIV Tat, 250 HIV-1 Tat protein, 276 HIV-1 Vif protein, 271 HLA. See Human leukocyte antigen HMG-CoA reductase degradation (Hrp), 243 HMGR. See Hydroxymethylglutaryl-coenzyme A reductase Homocysteine-induced endoplasmic reticulum protein (HERP), 245 Host–pathogen interactions, Ag presentation and Ub-proteasome system in, 227–229 antigen presentation, 230 bacterial chromosome integration, 273–274 CD4 downregulation from CD4þ T cell surface, 266–269 cell surface receptors, downregulation by pathogen-encoded E3s, 276–277 class II MHC antigen presentation, 233–234 class II MHC antigen presentation, pathogen interference with, 264 class II MHC downregulation from APC surface, 265–266 class I MHC antigen presentation, 230–231 HCMV interference with, 251–253 viral interference with, 250–251 cytokine responses, 277–278 deubiquitinating enzymes, 239 dislocation from ER HCMV US11 and US2, 254–255 signal peptide peptidase (SPP) for, 255–256 driving dislocation and Ub-binding route to proteasome, 247–249 DUBs, pathogen-encoded, 278–280 the elusive dislocon, 247 ERAD E3 ligases, 243 ERAD E3s, mammalian, 244–246 ERAD substrate recognition, 241–242 ER quality control and degradation, 240–241 human cytomegalovirus, 251 infection, control of, 271–272 inflammation, control of, 275

index inhibition of recognition at APC surface, 264–265 ISGylation and deISGylation, 274–275 manipulation of host response by pathogens, 229–230 pathogen-mediated ER protein disposal, routes of, 261–264 pathogen recognition by CD8þ T cells and NK cells, 232–233 peptide N-glycanase, 249 posttranscriptional gene silencing, 275–276 programmed cell death in plants, 277 proteasomal proteolysis, interference with, 270–271 the proteasome, 239–240 signal peptide peptidase (SPP) and calmodulin signaling, 257 and ER quality control, 258–260 and generation of HLA-E epitopes, 256 peptide peptidase and development, 257 and processing of hepatitis C virus core protein, 256–257 ubiquitin chains and ubiquitin-like modifiers, 237–238 ubiquitin conjugation cascade, 234–235 ubiquitin ligases, 236–237 ubiquitin-proteasome system, 234 pathogen manipulation of, 269–270 virus budding, 272–273 HR23B adaptor protein, 249 Hrd1p/Der3p E3 ligase, yeast, 244 Hrd1p/Der3p-Hrd3p E3 ligase, yeast, 244 HSV-1. See Herpes simplex virus-1 HSV-1 gD proteins, 158, 164 HSV-thymidine kinase, 196 Human autoimmune diseases, spontaneous chicken models for, 71 Human cytomegalovirus (HCMV), 251 immunoevasins, 226, 251 US2, 260, 262–264, 266 US3, 250 US2 and US11, 254–255, 262, 264 infection, 251 interference with class I MHC antigen presentation, 250–251 pp65 tegument protein, 271 Human leukocyte antigen (HLA) -A, -B, and -C, 253 HLA-DP4, expression of, 201 HLA-DR and HLA-DM, 266

313 HLA-E, 256 epitopes generation, and SPP, 256 Human papillomavirus protein, 236 Human systemic sclerosis, 72 Human T cell leukemia virus-1 (HTLV-1), 278 Humoral autoimmunity, 37–38 HVEM-BTLA pathway, viral modulators of HSV1 gD, as T cell inhibitor and mediator of viral fusion, 164–165 human BTLA interacts with human CMV UL144, 165 HVEM Fc fusion proteins, 174 HVT. See Turkey herpesvirus Hydroxymethylglutaryl-coenzyme A reductase (HMGR), 241 degradation of, 245 mammalian, 244 Hypergammaglobinemia, 6, 34 Hypergammaglobulinemia, 32 Hypothalamo-pituitary-adrenal (HPA), 87 Hypothyroidism, 92–93, 104 SAT-based, 83 I IFA, vaccination with, 203 IFN. See Interferon IFN-a. See Interferon-alpha IFN gamma receptor (IFN-gR), 19 IFN-g ELISPOT assay, 193 IFN-g-inducible lysosomal thiol reductase (GILT), 234 IFN-stimulated genes (ISGs), 274 IgG antibodies, 35 IgG autoantibodies, 34, 40 Th-mediated production by B cells, 8 IL. See Interleukin Immediate early antigen-1 (IE-1), 251 Immune complexes (ICs), 2 ANA-containing, 3 receptors for, 43–44 Immune dysregulation, 140 Immune homeostasis, 124, 131 Immunity, innate and adaptive, cross talk between, 229. See also specific immunity Immunoevasins, 251, 253 HCMV US2, 254–255, 260, 262–264, 266 US11, 254 viral, 262–264 Immunoglobulin A (IgA) nephropathy, 177 Immunoproteasome, 240

314 Immunosurveillance, 187 Immunotyrosine inhibitory motifs (ITIMs), 170, 172 Indium-oxyquinoline (111In-oxine), 196 Indoleamine deoxygenase (IDO), 206 Inducible costimulator (ICOS), 158 Infection, abortive, 272 control of, 271–272 HCMV, 251 influenza B virus, 275 Inflammation, 227 control of, 275 T cell-dependent, and HVEM deficiency, 177–178 Inflammatory T cells, 228 Infliximab, 20 Influenza A viruses, NS1 protein of (NS1A), 275 Influenza B viruses, NS1 protein of (NS1B), 275 Inhibitor of NF-kB (IkB), 237 Lys48-linked polyubiquitination of, 238 Inhibitor of NF-kB kinase (IKK), 238 Inhibitory receptors, 253 CD28 family, mechanisms of, 167–169 BTLA and PD-1, role in anergic T cells, 173 BTLA signaling, early events in, 170–172 BTLA signaling, human and murine, differences between, 172–173 inhibition by CTLA-4 and PD-1, 169–170 Inhibitory tyrosine-based switch motif (ITSM), 170 Innate immune systems, dysregulation of, 41–42 Innate immunity, 227 receptors, 204 Interferon-alpha (IFN-a), 11–13, 19–20 Interferon-g (IFN-g), 19, 31, 90, 177, 205, 232 hyporesponsiveness, 199 production, 133 Interferon (IFN), 227 (IFN-a, -b), 274 production, 43 type I, 27, 203 gene cluster, 36–37

i nd e x Interferon inducible genes (IFI), 36 Interleukin-1, 87 Interleukin-1b (IL-1b), 132 Interleukin-2 (IL-2), 169, 278 bioactivity, 98 key role in nTreg cell development, function, and homeostasis, 134–135 production, 129 from effector cells, 130 receptor (R) alpha (a) chain, 120 receptors, hyperexpression of, 85 synthesis, 135 Interleukin-6 (IL-6), 12, 16, 87, 132 Interleukin-7 (IL-7), 136 Interleukin-10 (IL-10), 16–17, 31, 121–122 International Chicken Genome Sequencing Consortium, 72, 75–77 International Chicken Polymorphism Map Consortium, 77–78 Intracellular adhesion molecule-1 (ICAM-1), 45 In vitro stimulation (IVS), 197 Ionomycin, 126 IPEX (immune dysregulation, polyendocrinopathy, enteropathy, X-linked), 140–141, 144 patients, 140 IRF5, 26 ISG15 (IFN-stimulated gene product of 15 kDa), 238, 275 isopeptidase activity, 274 ISGylation and deISGylation, 274–275 Islet Ags, 127 J JAB1/MPN/Mov34 proteases (JAMMs), 239 JAK/STAT pathway, 274 signaling, 269 Janus kinases (JAKs), 237 K Kaposi’s-sarcoma-associated herpesvirus (KSHV), 276 Keratinocytes, 105, 188 Kidney, as SLE target, 3 Kidney disease, 36–38 Killer cell immunoglobulin-like receptor (KIR), 253

index

L

LAG-3, 123 Laryngotracheitis virus, 85 Latency associated peptide, 136 Leucine zipper, 125 Leucosis, 85 Leukemia-associated protein (LAP), 261 LFA-1, 123 LIGHT, 160–161, 163–164, 174–175 costimulation of T cells through HVEM and stimulation of inflammation through LTbR, 176–177 deficiency, 177 expression and regulation on T Cells and APCs, 165–167 T cell expression of, 176 transgenic expression of, 177 Lineage markers, 120 Linkage mapping, 78–81 Linker for activation of T cells (LAT), 169 Lipemia, 83 Lipid antigens, 268 Lipopolysaccharide (LPS), 132, 166 Lipoprotein receptor-related protein (LRP) receptors, 257 Livestock breeds, loss of genetic diversity in, 74 Low molecular weight proteins (LMPs), 240 LPS. See Lipopolysaccharide LPS receptor, CD14, expression of, 9 LTbR (lymphotoxin b receptor), 160, 163 expression, 175, 177 Lung cancer, 207 Lung carcinomas, 200 Lupus autoantigens, major source of, 5 Lupus nephritis, 38 Lupus-prone inbred mouse strains, commonly utilized, 28–33 Lympadenopathy, 37 Lymphoadenopathy, 38 Lymphocytes bursa-derived (B cells), 73 tumor-associated, 200 Lymphopenia, 19 chemotherapy-induced, 135 induction of, 200 Lymphoproliferation (lpr), 32, 42 Lymphotoxin a (LTa), 160–161, 163 Lysosomal-associated membrane glycoproteins (LAMPs), 192

315 Lysosomal thiol reductase, IFN-g-inducible (GILT), 234 Lysosomes, 266 acidification of, 268 M Machado-Joseph disease proteases (MJDs), 239 MAGE-A1 and -A3 antigens, 197 Major histocompatibility complex (MHC). See also MHC antigen presentation pathways, 226 class I antigens, 74 immune genes, 139 molecules, class I & II, 227 Mapping high-resolution, 81 linkage, 78–81 spatial, 196 MARCH (membrane-anchored RING-CH) proteins, 262, 277 MARCH-VI, 245 Marek’s disease virus (MDV), 73 MDV-1, 107 Markers activation, 30 cell-surface, 122 flanking, 79 genetic, 80 lineage, 120 surface, 123 Mata2–10 protein, 243 MCP-1. See Monocyte chemoattractive protein-1 Melan-A/MART-1, 189–190, 201–203 Melan-A/MART-1 antigen in TIL populations, 199 Melanocytes, 103, 105 destruction of, 82, 102 Melanoma antigens, 188–190 cells, 188 metastases, 198 ‘‘Melanoma model,’’ 187 Melanoma tumors, 201 Melanosomes, autophagocytosis of, 105 Membrane immunoglobulin (mIg), 228 Metalloproteases, 239 Metastasis, 199 Metastatic melanoma, 200, 207

316 MHC class I antigen presentation, 230–231 HCMV interference with, 251–253 viral interference with, 250–251 antigen presentation pathway, 232 HC biosynthesis, 262 MHC class II antigen presentation, 233–234 pathogen interference with, 265 antigen presentation, pathogen interference with, 264 downregulation from APC surface, 265–266 expression, 106 genes, polymorphism of, 193 MHV-68 mK3, 262, 264 b2-Microglobulin (b2m), 230 MIP-1a, MIP-1b, 133 Mitogen-activated protein kinase (MAPK), 278 Mitotic spindle disassembly, 248 Mixed lymphocyte reaction (MLR), 174 Modulator of immune recognition (MIR), 276 Modulatory genes, 88 Monocytes and polymorphonuclear cells, 9–11 Monocytosis, 31 Mononuclear cell (MNC), 83 Monoubiquitination, 238 Motor protein, 248 Mouse leukemia virus (MuLV), 271 Multicatalytic endopeptidase-like complex 1 (MECL1), 240 Multiubiquitination, 238 Multivesicular bodies (MVBs), 266 Mumps virus, 277 Murine cytomegalovirus, 253 Murine g-herpesvirus 68 (MHV-68), 261 Mutations. See specific mutations Myasthenia gravis, 145 Mycoplasma gallisepticum, 85 Mycoplasma synovie, 85 N Natural killer (NK) cells, 100, 202–203, 227–228, 232 activation, 252–253 and CD8þ T cells, pathogen recognition by, 232–233 cytotoxicity, 232 decoys for, 250

i nd e x demonstration of ADCC in the presence of Rituximab, 7 proliferation, 175 recognition, 252 NBP1 (Nef binding protein-1), 267 Necrosis, 94 NEDD4 E3s ligases, 273 NEDD4 HECT E3 ligases, 273 NEDD (neuronal precursor cell-expressed developmentally downregulated), 238, 273 Nef binding protein-1 (NBP1), 267 Nef, HIV-1 protein, 266 Nephritis, 3, 18–19, 31 increased risk in SLE patients, 44 progression of, 21 Nerve growth factor receptor (NGFR), 158 Neuropilin-1, 123 Neurotropins, NGFR binding, 158 Neutrophils, 44, 227 Newcastle disease virus, 85, 107 NK cell-activating receptors, 253 NK cell-mediated cytotoxicity, 253 NK cell surface receptors, 232 NK effector cell, 252 NKp30 receptor, 253 Notch receptor, 255 nTreg cells cell deficiency, 139 dysfunction, 145 peripheral homeostasis of, 130 Nuclear antigens, 34–35, 41 breaking immune tolerance to, 40 Nuclear factor-kB (NF-kB), 237 inhibitor of (IkB), 237 and JAK/STAT signaling, 269 pathway, 238 signaling, suppression of, 275 Nuclear protein localization 4 (Npl4p), 247 Nucleic acid, repair and replication, 248 Nucleotide-binding oligomerization domain (NOD) proteins, 227 NY-ESO-1, 189, 201 O Obese strain (OS) of chickens, 72 Okadaic acid, 170 Oklahoma Medical Research Foundation (OMRF), 25

index Opsin, 258 truncated, 259 OspG effector, 275 OS thymocytes, 87 OS thyroid epithelial cells, 90 Ovarian tumor proteases (OTUs), 238 Oxidative stress, 257 P P53, 269 P97, 245 Pancreatic lymph node (pLN), 142–143 Papillomavirus, 268 PA28, proteasome activator, 240 Parkin, 245 Parkinson’s disease, 245 Pathogen-associated molecular patterns (PAMPs), 20, 132, 227 Pathogens, manipulation of host response by, 229–230 PDCD-1, 42 PD-1 (programmed death-1), 158, 205 pathway, uncoupling of, 206 Peptide-loading complex (PLC), 230–231 Peptide N-glycanase, 249 Perforin, 199–200 Peripheral blood lymphocytes (PBL), 87 Peripheral blood mononuclear cells (PBMCs), 200 Peripheral homeostasis, nTreg cell, 137 Peripheral T cell tolerance, 139 regulation of, 120 Peripheral Treg cells, 130 PEST motif, 246 Phagocytes, 227 Phagocytosis, 228 of apoptotic cells, 31 Fc- and complement-mediated, 10 Phenotypic effect, 82–83 Phenotyping, 192 Phosphatidylinositol 3-kinase (PI3K), 170 Phosphatidylinositol 3-kinase (PI3K) pathway, 126 Phosphatidylinositol 3-kinase (p85 PI3K), 171 Phosphoprotein pp65 tegument protein, 251 Phytohemagglutinin (PHA), 16 Plague, 278 Plant homeodomain (PHD), 261

317 Plants, programmed cell death in, 277 Plasmacytoid dendritic cells (pDCs), 12–13, 202–203 Platelet activation, 99 Platelet-derived growth factor (PDGF), 171 Platelet endothelial cell adhesion molecule-1 (PECAM-1) receptors, 171 PLpro, 279 PNGase. See Peptide N-Glycanase Poleroviruses, 276 Poly(ADP-ribose) polymerase (PARP), 11 Polyendocrinopathy, 140 Polymorphism(s), 8 in apoptosis-related genes, 11 in exon 1 and SLE, linkage between, 9 of FcgRII, 13 of MHC-II genes, 193 Polymorphonuclear cells (PMNs), 10 Poultry, loss of genetic diversity in, 74 Poxviruses, 261, 276 PPXY motif, 273 Preprolactin (p-Prl), 257 Presenilins, 255 Programmed cell death (PCD), 277 Prostacyclin, 99 Proteasomal proteolysis, 237, 270 interference with, 270–271 Proteasome, 239–240 Proteasome maturation protein (POMP), 240 Proteasome, Ub-binding route to, and driving dislocation, 247–249 Protein disulfide isomerase (PDI), 242 Protein kinase B (PKB), 170 Protein kinase R (PKR), 227 Protein phosphatase 1, 170 Protein tyrosine phosphotase PTPN22, 26 Proteinuria, 3 Pseudomonas syringae, 277 Psoriasis, 145 P(S/T)AP motif, 273 PTPN22. See Protein tyrosine phosphotase Q Quantitative trait locus (QTL) analysis, 81 R RANTES, 133 Raynaud’s phenomenon, 93 Receptor endocytosis, 234

318 Receptors. See also specific receptors activating and inhibitory, 232 costimulatory and inhibitory, 158 costimulatory and inhibitory, of immunoglobulin superfamily and TNFR families, 159 inhibitory, 253 gc receptor, 136 TNFRs, TNFs, BTLA, and HVEM, interactions between, 160 Recombinant vaccinia viruses, 203 Red jungle fowl (RJF) inbred, 75 Regulatory cytokines, 205 Regulatory T cells, naturally occurring (nTreg) control of autoimmune responses by autoimmunity, as result of mutations in FoxP3, 140–141 autoimmunity, intrinsic and extrinsic drivers of, 138–140 nTreg-mediated control of autoimmunity in the mouse, 141–144 spontaneous autoimmunity in humans, 144–145 factors regulating expansion and specificity of regulation of expansion and survival, 126–127 repertoire and Ag specificity, 127–129 growth factor–mediated control of nTreg cell development, function, and homeostasis key role of IL-2, 134–135 role of other gc cytokines, 135–136 TGF-b, controversial role of, 136–137 innate/adaptive inflammatory signals for development and function of chemokine receptor-mediated control of nTreg cell homing, 133–134 modluation by toll-like receptors– dependent signals, 132–133 role of B7/CD28 pathway, 129–130 role of CTLA-4, 130–131 role of GITR/GITR-L interactions, 131–132 phenotype of CD4þCD25þ cell-surface biomarkers in humans, 123–124 cell-surface biomarkers in mice, 122–123

i nd e x FoxP3 as biomarker and ‘‘master regulator,’’ 124–126 Regulatory T (Treg) cells, 206, 228. See also T regulatory cells induced (iTreg), 120–122 naturally occurring (nTreg), 120–121 Reoviruses, 85 Restriction fragment length polymorphism (RFLP), 100 Retinoblastoma protein, 269 Retrograde translocation. See Dislocation Retrotranslocation. See Dislocation Retrovirus HIV, 273 Rheumatic disease, 41 Rheumatoid arthritis, 145 Rheumatoid factor (RF), 12 Rho GTPases, 271 Rhomboid serine proteases, 259 RING-box (Rbx) protein, 236 RING-finger motif, 243 RING finger protein, 236 RING-IBR-RING, 245 RING-like domain ligases, 236 Rituximab efficacy in the treatment of SLE, 6 FcgRIIIa-VV affinity for, 7 RNA editing protein, 271 silencing, 276 viruses, 276 RNase protection assays (RPA), 101 Rous-associated virus type I and II, 85 Rous sarcoma virus, 73 Rsp5p, yeast, 273 S Saccharomyces cerevisiae, genetic screen for, 243 Salmonella, 279 Salmonella enterica serovar typhimurium, 271 SARS-CoV Plpro protease activity, 279 SAT. See Spontaneous autoimmune thyroiditis SCF E3 ligase, 273 Sclerodactyly, 93 Scleroderma, 72, 90 in UCD-200 line, genetic dissections of, 75 Scleroderma-like disease, 71 Sclerosis, 145 Scurfy, 144 Sec61 channel, 247

index Sec23p, for ER structure maintenance, 242 g-Secretase, 255, 259 l-Selectin, 45 Self-antigens, 189 Self-recognition-controlling cells, 86 Self-tolerance, 140 Septicemia, 278 Serum amyloid P component (SAP), 36 Severe acute respiratory syndrome (SARS), 279 Sheddase, 18 Shigella flexneri, 275 Signal peptide peptidase (SPP), 255. See also Host–pathogen interactions and calmodulin signaling, 257 and development, 257 for dislocation from ER, 255–256 and ER quality control, 257–260 and generation of HLA-E epitopes, 256 inhibitors, 258 mutations, 257 and processing of hepatitis C virus core protein, 256–257 protease activity, 257 Silencing suppressors, 276 Single nucleotide polymorphisms (SNPs) detection and cataloguing of, 26 in TCRb and TCRz, 9 Skin cancers, 188 SLAM (Systemic Lupus Activity Measure), 3 index, 10 SL chickens multifactorial nature of, 105–107 scientific value of, 107–108 vitiligo, 102–105 SLE. See Systemic lupus erythematosus SLEDAI (Systemic Lupus Erythematosus Activity Index), 3, 12, 18, 20 measure, 10 score, 16 Smad3 gene, 136 Small nuclear ribonuclear particles (snRNP), 22 Small ubiquitin-like modifier (SUMO), 238 Smyth line with vitiligo (SLV), 72 SNP. See Single nucleotide polymorphisms SNP typing, 26 SOCS proteins, 237 SONIA (systemic onset juvenile idiopathic arthritis), 20

319 SopE, 271 Spatial mapping, 196 SPECT-CT imaging systems, 196 SPECT (single photon emission computed tomography), 196 S-phase kinase-related protein-1 (Skp-1), 236 Splenocytes, 142 Splenomegaly, 34, 36–38 Spontaneous autoimmune diseases, avian models with, 71 chicken genomics and its application causative mutations, identification of, 81–83 chicken genome, 75–77 chicken, highly polymorphic species, 77–78 linkage mapping, 78–81 Obese strain (OS) of chickens breeding and maintenance of, 92 effector mechanisms and immunoregulation, 85–87 endocrinology, 87–88 genes responsible for SAT development, 90–92 histologic and immunologic hallmarks of, 83–85 immunogenetics (genetically determined target organ susceptibility), 88–90 SL chicken model multifactorial nature of, 105–107 scientific value of, 107–108 vitiligo, 102–105 UCD-200 line of chickens clinical features and pathogenesis, 92–94 UCD-200 and 206 chickens, 94–102 Spontaneous autoimmune neuropathy, 135 Spontaneous autoimmune thyroiditis (SAT), 72, 83 genetic basis for target organ susceptibility in, 89 Spontaneous tumor progression, 187 SPP-like proteins, 255 SptP, 271 Src-family tyrosine kinase, 266 Src homology 2 (SH2) domain containing phosphatase 1 (SHP1) and SHP2, 169 SSX-2 and SSX-4, 201 STAT (signal transducer and activator of transcription), 237, 277 signaling, 274

320 SUMO. See Small ubiquitin-like modifier Suppression, nTreg cell–mediated, 130–133, 136 Suppression substractive hybridization (SSH), 91 Suppressor activity, 130 induction of, 129 Suppressor of cytokine signaling/elongin-BC (SOCS/BC) boxes, 237 Surface markers, 123 Systemic autoimmunity, 30, 32–33, 40, 42 Systemic lupus erythematosus (SLE), 145 causative disease alleles, 2 clinical presentation disease course of, 3 heterogeneity in pathogenesis, 3 disease development, modeling pathway I, 40–41 pathway II, 41–43 pathway III, 43–45 factors responsible for, 2 gender bias in disease incidence, 4–5 genetic interactions and disease development, 39–40 genetic linkage and association studies of, 24 immunological alterations in B cells, 5–7 cytokines, 13–20 dendritic cells, 11–13 monocytes and polymorphonuclear cells, 9–11 T cells, 7–9 immunosuppressive drug therapies, 2 incidence and severity of, in United States, 2 methods for measurement of disease activity, 3 murine models of lupus-prone inbred mouse strains, commonly used, 28–33 lupus-prone strains, linkage analysis and congenic dissection of, 33–38 patients with frequency of polymorphisms in UTR, 7 susceptibility, gene-dosage effect on, 17 susceptibility in humans, genetics of, 22 association analysis, 26 future prospects for analysis of, 26–27 SLE with affected sibpairs, linkage analysis of, 23–25 Systemic sclerosis (SSc), 92

i nd e x best animal model for, 102 human and avian, comparison of clinical, biochemical, immunological, and pathologic features of, 95 T TACTILE, 253 Tapasin, 231, 252 TAP transporter, 252 T cell(s), 7–9, 35 activation, 177 apoptosis, 206, 278 ‘‘avidity,’’ 194 blockade of costimulation, 31 diabetogenic, 142 expansion, 129 growth factor, 134 helper and cytotoxic, 98, 106 immunity, 206 inflammatory, 228 LIGHT-deficient, 178 maturation, 120 Melan-A-specific, 197 migration and recruitment into sites of inflammation, 133 mitogens, 85 quantification, 190 tumor antigen-specific, 197, 199–200, 205 T cell receptor b and z chains (TCRb and TCRz) SNPs in, 9 T cell receptors (TCRs), 84, 105, 158, 190, 227, 231 T cell responses antigen-specific protective, putative properties of, 208 CD4, 198–200 tumor antigen-specific, regulation of, 206–207 vaccine-induced peptide-based vaccines in melanoma, 202–205 recruiting CD4 T cells, 205–206 sustaining antigen-specific T cell response, 205 T cell vaccines, 202–203 TCR affinity, 194–195 TCRg/d, 98 TCR gene expression, 138 TCRz, 169–170

index TEB4, 245 T effector cells. See Effector T cells Telangiectasia, 93 Tetanus antigens, 192 Tetraiodothyronine, 87 TgAAb, 85–86, 92 autoantibodies against thyreoglobulin, 84 TGF-b, 121–122 TGF-b type II receptor, 136 T helper 1 (TH1) cells, 161 T helper 1 (TH1)-selective expression, 157 b-Thromboglobulin, 99 Thrombomodulin, 99 Thromboxan, 99 Thymectomy, 138 day 3, 138, 140 neonatal, 84, 86 Thymic epithelial cells (TEC), 85 Thymic nurse cells (TNC), 85 Thymocytes, 130 Thymopoiesis, 144 Thymus, 86, 97, 130 Thyroglobulin, 84 Thyroid antigens, 84 Thyroid gland, MNC infiltration of, 83 Thyroid infiltration, 85 Thyroiditis, 83 in OS line, genetic dissections of, 75 spontaneous autoimmune, 72 susceptibility gene, 87–88 Thyroid-stimulating hormone (TSH), 87 Thyroxine, 83, 87 T lymphocytes, 38, 98, 199 dysregulation of, 42 subsets of, 227 TNF alpha converting enzyme (TACE), 18 TNFR-associated factor (TRAF), 173 TNFR decoy receptor 3 (DcR3), 163 TNFR I and II, 18 TNF signaling, 18 Toll-like receptors (TLRs), 203, 227 TLR-9, 13, 202 TLR-3, polymorphisms, 22 TPR motif, 246 Transcription factors, STAT family of, 237 Transforming growth factor-beta (TGF-b), 101 Transmembrane domain (TMD), 254 Transmigration, 17 Transporter associated with antigen presentation (TAP), 231

321 b-TrCP (b-transducin repeat-containing protein), 237, 268 TRECs (T cell receptor recombination excision circles), 123, 197 Treg-mediated suppression, functional significance of cell-surface molecules in, 128 T regulatory cells (Tregs), 86, 98. See also Regulatory T (Treg) cells peripheral, 130 expansion of, 135 Triiodothyronine, 87 T suppressor cells, 86 TTSS-effector proteins, 275 TTSS (two type III secretion system), 271 Tumors, 206 immunity, 131 rejection, 200 vaccines, 207 Tumor antigens, 187 classification of, 189 expression, 190 T cell–defined, 189 Tumor antigen-specific T cells, 205 responses, regulation of, 206–207 Tumor infiltrating lymphocytes (TILs), 188 Tumor necrosis factor receptor-associated factor 6 (TRAF6), 238 Tumor-necrosis factor receptor (TNFR) family, 157–158 Tumor-necrosis factor (TNF) ligands for HVEM, identification of, 161, 163 Tumor necrosis factor (TNF)-a, 11, 17–18, 31, 87 Tumor suppressor proteins, 269 Tumor susceptibility gene TSG101, 272 Turkey herpesvirus (HVT) vaccination, 104 Typhoid fever, casuative agent of, 271 Tyrosinase, 189–190, 240 Tyrosine kinases, 169 Tyrosine motifs, 170–171 Tyrosine phosphatases, 170 U Ub-binding factor Rad23p, 249 UBCH8 enzyme, 274 Ub E2 variant (UEV) protein, 272 Ubiquitin, 234

i nd e x

322 Ubiquitin-associated (UBA) motif, 248 Ubiquitinate substrate proteins, 243 Ubiquitination, tail, 262 Ubiquitin-binding factors, 248 Ubiquitin-binding protein 43 (UBP43), 274 Ubiquitin chains and ubiquitin-like modifiers, 237–238 Ubiquitin conjugation cascade, 234–235 Ubiquitin C-terminal hydrolases (UCHs), 239 Ubiquitin-fusion degradation 1 (Ufd1p), 247 Ubiquitin ligase (E3) activity, 261 Ubiquitin ligases, 236–237 Ubiquitin-like domain (ULD), 245 Ubiquitin-like molecules/modifiers (Ubls), 238 Ubiquitin-like (UBL) motif, 248 Ubiquitin-proteasome system, 234, 269 degradation of class I MHC products by, 250 pathogen manipulation of, 269–270 Ubiquitin-specific proteases (USPs), 239 Ub-proteasome degradation, 248 Ubx2p/Sel1p protein, 248 UFD2-homology box (U-box) E3s, 237 Ufd3p protein, 249 Ufe1p, for ER structure maintenance, 242 ULBP products, 252 UL48 protein, 279 UL36USP, in HSV–1, 279 Unfolded protein response (UPR), 245 University of California at Davis-200 and -206 (UCD-200 and -206) lines, 72 Untranslated region (UTR), frequency of polymorphisms in SLE patients, 7 US2, 255, 259 dislocation pathway, requirement of SPP, 260 and US11, 226 USP18, 274 US2 TMD, 260 V VacA toxin, 268 Vaccination antigen-specific, 188 with CpG, IFA, and Melan-A peptide, 205 HVT, 104, 107 Vaccine(s) effective cancer vaccines, hurdles in achieving, 208 human T cell, 190 IFA, 205

immunity, 204 strategies, bedrock principle of, 229 T cell vaccines, 202–203 tumor, 207 Vaccinia viruses, recombinant, 203 Vacudes, 85 Vacuolar protein sorting (VPS) proteins, 272 Val14Met, 19 Valosin-containing protein (VCP), 245, 247 Vascular endothelial cell apoptosis, 100 Vasculitis, 45 VCP. See Valosin-containing protein Versatile fluorometric technique (VITAL) assay, 192 Vesicular stomatitis virus (VSV), 21 V1H. See NBP1 Vif, 271–272 VIMP, 245, 261 VIP1 protein, 274 Viral Ags, 129 Viral disease, susceptibility and resistance against, genetic basis for, 74 Viral dsRNA, 227 VirE2 protein, bacterial, 273 VirE2/T-DNA/VIP1 complex, 274 VirF protein, 274 Virulence (Vir) proteins, 273 Virus. See specific viruses Virus budding, 272–273 Vitamin D3, 122 Vitiligo, 102–105 in Smyth line, genetic dissections of, 75 VP40 matrix protein, 273 Vpu, 266, 268 W Walker motifs, 248 von Willebrand factor (vWF), 99 WT nTreg cells, 135, 137, 142 X Xanthomonas, 279 X-box binding protein-1 transcription factor (XBP-1), 259 X-linked lymphoproliferative disease, 194 Y Y-chromosome associated accelerator (yaa), 22, 31–32, 35–36 locus, 28

index Yeast Hrd1p/Der3p and Doa10p, 243 Hrd1p/Der3p-Hrd3p E3 ligase, 244 Rsp5p, 273 VPS27, 272 Yersinia, 278 YopJ virulence factor, 278

323 YopJ activity, 278 Y201, phosphorylation of, 170 Z ZAP70, 170 Zinc-finger protein gene CTCF, 189 Zinc-finger regions, 125

Contents of Recent Volumes

Hypermutation Defects (Hyper-IgM Syndromes) Anne Durandy, Patrick Revy, and Alain Fischer

Volume 82 Transcriptional Regulation in Neutrophils: Teaching Old Cells New Tricks Patrick P. McDonald

The Biological Role of the C1 Inhibitor in Regulation of Vascular Permeability and Modulation of Inflammation Alvin E. Davis, III, Shenghe Cai, and Dongxu Liu

Tumor Vaccines Freda K. Stevenson, Jason Rice, and Delin Zhu Immunotherapy of Allergic Disease R. Valenta, T. Ball, M. Focke, B. Linhart, N. Mothes, V. Niederberger, S. Spitzauer, I. Swoboda, S.Vrtala, K. Westritschnic, and D. Kraft Interactions of Immunoglobulins Outside the Antigen-Combining Site Roald Nezlin and Victor Ghetie

Index

Volume 83 Lineage Commitment and Developmental Plasticity in Early Lymphoid Progenitor Subsets David Traver and Koichi Akashi

The Roles of Antibodies in Mouse Models of Rheumatoid Arthritis, and Relevance to Human Disease Paul A. Monach, Christophe Benoist, and Diane Mathis

The CD4/CD8 Lineage Choice: New Insights into Epigenetic Regulation during T Cell Development Ichiro Taniuchi, Wilfried Ellmeier, and Dan R. Littman

MUC1 Immunology: From Discovery to Clinical Applications Anda M. Vlad, Jessica C. Kettel, Nehad M. Alajez, Casey A. Carlos, and Olivera J. Finn

CD4/CD8 Coreceptors in Thymocyte Development, Selection, and Lineage Commitment: Analysis of the CD4/CD8 Lineage Decision Alfred Singer and Remy Bosselut

Human Models of Inherited Immunoglobulin Class Switch Recombination and Somatic

Development and Function of T Helper 1 Cells Anne O’Garra and Douglas Robinson

325

326 Th2 Cells: Orchestrating Barrier Immunity Daniel B. Stetson, David Voehringer, Jane L. Grogan, Min Xu, R. Lee Reinhardt, Stefanie Scheu, Ben L. Kelly, and Richard M. Locksley Generation, Maintenance, and Function of Memory T Cells Patrick R. Burkett, Rima Koka, Marcia Chien, David L. Boone, and Averil Ma CD8þ Effector Cells Pierre A. Henkart and Marta Catalfamo An Integrated Model of Immunoregulation Mediated by Regulatory T Cell Subsets Hong Jiang and Leonard Chess Index

Volume 84 Interactions Between NK Cells and B Lymphocytes Dorothy Yuan Multitasking of Helix-Loop-Helix Proteins in Lymphopoiesis Xiao-Hong Sun Customized Antigens for Desensitizing Allergic Patients Fa¨tima Ferreira, Michael Wallner, and Josef Thalhamer Immune Response Against Dying Tumor Cells Laurence Zitvogel, Noelia Casares, Marie O. Pe¨quignot, Nathalie Chaput, Mathew L. Albert, and Guido Kroemer HMGB1 in the Immunology of Sepsis (Not Septic Shock) and Arthritis

co n t e nt s o f re c e nt vo l um es Christopher J. Czura, Huan Yang, Carol Ann Amella, and Kevin J. Tracey Selection of the T-Cell Repertoire: Receptor-Controlled Checkpoints in T-Cell Development Harald Von Boehmer The Pathogenesis of Diabetes in the NOD Mouse Michelle Solomon and Nora Sarvetnick Index

Volume 85 Cumulative Subject Index Volumes 66–82

Volume 86 Adenosine Deaminase Deficiency: Metabolic Basis of Immune Deficiency and Pulmonary Inflammation Michael R. Blackburn and Rodney E. Kellems Mechanism and Control of V(D)J Recombination Versus Class Switch Recombination: Similarities and Differences Darryll D. Dudley, Jayanta Chaudhuri, Craig H. Bassing, and Frederick W. Alt Isoforms of Terminal Deoxynucleotidyltransferase: Developmental Aspects and Function To-Ha Thai and John F. Kearney Innate Autoimmunity Michael C. Carroll and V. Michael Holers

c o nt e n t s of re c e n t vo l u m es Formation of Bradykinin: A Major Contributor to the Innate Inflammatory Response Kusumam Joseph and Allen P. Kaplan Interleukin-2, Interleukin-15, and Their Roles in Human Natural Killer Cells Brian Becknell and Michael A. Caligiuri Regulation of Antigen Presentation and CrossPresentation in the Dendritic Cell Network: Facts, Hypothesis, and Immunological Implications Nicholas S. Wilson and Jose A. Villadangos Index

327 Gary W. Litman, John P. Cannon, and Jonathan P. Rast The Repair of DNA Damages/Modifications During the Maturation of the Immune System: Lessons from Human Primary Immunodeficiency Disorders and Animal Models Patrick Revy, Dietke Buck, Franc,oise le Deist, and Jean-Pierre de Villartay Antibody Class Switch Recombination: Roles for Switch Sequences and Mismatch Repair Proteins Irene M. Min and Erik Selsing Index

Volume 87 Role of the LAT Adaptor in T-Cell Development and Th2 Differentiation Bernard Malissen, Enrique Aguado, and Marie Malissen The Integration of Conventional and Unconventional T Cells that Characterizes Cell-Mediated Responses Daniel J. Pennington, David Vermijlen, Emma L. Wise, Sarah L. Clarke, Robert E. Tigelaar, and Adrian C. Hayday Negative Regulation of Cytokine and TLR Signalings by SOCS and Others Tetsuji Naka, Minoru Fujimoto, Hiroko Tsutsui, and Akihiko Yoshimura Pathogenic T-Cell Clones in Autoimmune Diabetes: More Lessons from the NOD Mouse Kathryn Haskins The Biology of Human Lymphoid Malignancies Revealed by Gene Expression Profiling Louis M. Staudt and Sandeep Dave New Insights into Alternative Mechanisms of Immune Receptor Diversification

Volume 88 CD22: A Multifunctional Receptor That Regulates B Lymphocyte Survival and Signal Transduction Thomas F. Tedder, Jonathan C. Poe, and Karen M. Haas Tetramer Analysis of Human Autoreactive CD4-Positive T Cells Gerald T. Nepom Regulation of Phospholipase C-g2 Networks in B Lymphocytes Masaki Hikida and Tomohiro Kurosaki Role of Human Mast Cells and Basophils in Bronchial Asthma Gianni Marone, Massimo Triggiani, Arturo Genovese, and Amato De Paulis A Novel Recognition System for MHC Class I Molecules Constituted by PIR Toshiyuki Takai Dendritic Cell Biology Francesca Granucci, Maria Foti, and Paola Ricciardi-Castagnoli

328 The Murine Diabetogenic Class II Histocompatibility Molecule I-Ag7: Structural and Functional Properties and Specificity of Peptide Selection Anish Suri and Emil R. Unanue RNAi and RNA-Based Regulation of Immune System Function Dipanjan Chowdhury and Carl D. Novina Index

Volume 89 Posttranscriptional Mechanisms Regulating the Inflammatory Response Georg Stoecklin Paul Anderson Negative Signaling in Fc Receptor Complexes Marc Dae¨ron and Renaud Lesourne The Surprising Diversity of Lipid Antigens for CD1-Restricted T Cells D. Branch Moody Lysophospholipids as Mediators of Immunity Debby A. Lin and Joshua A. Boyce Systemic Mastocytosis Jamie Robyn and Dean D. Metcalfe Regulation of Fibrosis by the Immune System Mark L. Lupher, Jr. and W. Michael Gallatin Immunity and Acquired Alterations in Cognition and Emotion: Lessons from SLE Betty Diamond, Czeslawa Kowal, Patricio T. Huerta, Cynthia Aranow, Meggan Mackay, Lorraine A. DeGiorgio, Ji Lee, Antigone Triantafyllopoulou, Joel Cohen-Solal Bruce, and T. Volpe Immunodeficiencies with Autoimmune Consequences

co n t e nt s o f re c e nt vo l um es Luigi D. Notarangelo, Eleonora Gambineri, and Raffaele Badolato Index

Volume 90 Cancer Immunosurveillance and Immunoediting: The Roles of Immunity in Suppressing Tumor Development and Shaping Tumor Immunogenicity Mark J. Smyth, Gavin P. Dunn, and Robert D. Schreiber Mechanisms of Immune Evasion by Tumors Charles G. Drake, Elizabeth Jaffee, and Drew M. Pardoll Development of Antibodies and Chimeric Molecules for Cancer Immunotherapy Thomas A. Waldmann and John C. Morris Induction of Tumor Immunity Following Allogeneic Stem Cell Transplantation Catherine J. Wu and Jerome Ritz Vaccination for Treatment and Prevention of Cancer in Animal Models Federica Cavallo, Rienk Offringa, Sjoerd H. van der Burg, Guido Forni, and Cornelis J. M. Melief Unraveling the Complex Relationship Between Cancer Immunity and Autoimmunity: Lessons from Melanoma and Vitiligo Hiroshi Uchi, Rodica Stan, Mary Jo Turk, Manuel E. Engelhorn, Gabrielle A. Rizzuto, Stacie M. Goldberg, Jedd D. Wolchok, and Alan N. Houghton Immunity to Melanoma Antigens: From Self-Tolerance to Immunotherapy Craig L. Slingluff, Jr., Kimberly A. Chianese-Bullock, Timothy N. J. Bullock, William W. Grosh, David W. Mullins,

c o nt e n t s of re c e n t vo l u m es Lisa Nichols, Walter Olson, Gina Petroni, Mark Smolkin, and Victor H. Engelhard Checkpoint Blockade in Cancer Immunotherapy Alan J. Korman, Karl S. Peggs, and James P. Allison Combinatorial Cancer Immunotherapy F. Stephen Hodi and Glenn Dranoff Index

Volume 91 A Reappraisal of Humoral Immunity Based on Mechanisms of Antibody-Mediated Protection Against Intracellular Pathogens Arturo Casadevall and Liise-anne Pirofski Accessibility Control of V(D)J Recombination Robin Milley Cobb, Kenneth J. Oestreich, Oleg A. Osipovich, and Eugene M. Oltz

329 Targeting Integrin Structure and Function in Disease Donald E. Staunton, Mark L. Lupher, Robert Liddington, and W. Michael Gallatin Endogenous TLR Ligands and Autoimmunity Hermann Wagner Genetic Analysis of Innate Immunity Kasper Hoebe, Zhengfan Jiang, Koichi Tabeta, Xin Du, Philippe Georgel, Karine Crozat, and Bruce Beutler TIM Family of Genes in Immunity and Tolerance Vijay K. Kuchroo, Jennifer Hartt Meyers, Dale T. Umetsu, and Rosemarie H. DeKruyff Inhibition of Inflammatory Responses by Leukocyte Ig-Like Receptors Howard R. Katz Index