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Characterization of the disease pathogenesis of Schimke immuno-osseous dysplasia Morimoto, Marie 2016

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CHARACTERIZATION OF THE DISEASE PATHOGENESIS OF SCHIMKE IMMUNO-OSSEOUS DYSPLASIA  by  MARIE MORIMOTO  B.Sc., The University of British Columbia, 2009  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  August 2016  © Marie Morimoto, 2016   ii Abstract Schimke immuno-osseous dysplasia (SIOD) is a rare autosomal recessive multisystemic disorder characterized by disproportionate short stature due to skeletal dysplasia, renal disease due to focal segmental glomerulosclerosis (FSGS), T-cell immunodeficiency, and vascular disease. SIOD is caused by mutations in the SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1 (SMARCAL1) gene, which encodes for a DNA annealing helicase with roles in DNA replication, DNA repair, and gene expression. Although SMARCAL1 functions to maintain genomic integrity, it is not known how SMARCAL1 deficiency leads to the various clinical features of SIOD. My aim was therefore to characterize the molecular pathogenesis of the dental, vascular, renal, and immune features. Given that SMARCAL1 has a role in modulating gene expression and that phenotypic changes are typically preceded by changes in gene expression, I hypothesized that SMARCAL1 deficiency pathologically alters the expression of key genes that lead to the clinical features of SIOD. To test this, SIOD patient tissues were studied using molecular biological analyses. With respect to vascular disease, an SIOD aorta had decreased expression of elastin, and both transcriptional and post-transcriptional mechanisms contributed to the elastin deficiency. Elastin is critical for the structural integrity of the arteries and its deficiency is a known cause of vascular disease. With respect to renal disease, SIOD glomeruli have increased expression and activation of the Wnt and Notch signaling pathways. Wnt and Notch signaling are required for kidney development and the postnatal reactivation of these pathways is an established cause of FSGS. With respect to immune disease, SIOD T cells have decreased mRNA and protein expression of interleukin 7 receptor alpha chain (IL7R). IL7R is critical for T-cell development and its deficiency is a known cause of T-cell immunodeficiency. In conclusion, the gene expression alterations detected are known causes of disease and differ among the tissues studied. These findings suggest that SMARCAL1 deficiency may cause each disease feature by tissue-specific gene expression changes. Further studies are required to define the mechanism of how SMARCAL1 deficiency alters the expression of these genes.   iii Preface  Sections of Chapter 1 were previously published as a review article: Morimoto M, Lewis DB, Lücke T, Boerkoel CF. Schimke Immunoosseous Dysplasia. 2002 Oct 1 [Updated 2016 Feb 11]. In: Pagon RA, Adam MP, Ardinger HH, Wallace SE, Amemiya A, Bean LJH, Bird TD, Fong C-T, Mefford HC, Smith RJH, Stephens K, editors. GeneReviews® [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2016. Available from: http://www.ncbi.nlm.nih.gov/books/NBK1376/ Copyright © 1993-2016, University of Washington, Seattle. All rights reserved. I have updated and edited the review article since 2011 with co-authors to include relevant updates and new findings from my own research. Dr. Cornelius F. Boerkoel wrote the original draft of the review article in 2002.  A version of Chapter 2 was previously published: Morimoto M, Kérourédan O, Gendronneau M, Shuen C, Baradaran-Heravi A, Asakura Y, Basiratnia M, Bogdanovic R, Bonneau D, Buck A, Charrow J, Cochat P, DeHaai KA, Fenkci MS, Frange P, Fründ S, Fryssira H, Keller K, Kirmani S, Kobelka C, Kohler K, Lewis DB, Massella L, McLeod DR, Milford DV, Nobili F, Olney AH, Semerci CN, Stajic N, Stein A, Taque S, Zonana J, Lücke T, Hendson G, Bonnaure-Mallet M, Boerkoel CF. (2012) Dental abnormalities in Schimke immuno-osseous dysplasia. Journal of Dental Research. 91(7 Suppl): 29S-37S. Copyright © 2012 International & American Associations for Dental Research. I coordinated the collection of patient data from patients, parents or guardians of patients, and our collaborators; I performed all of the experiments excepting the contribution of Collin Shuen and Dr. Alireza Baradaran-Heravi mentioned below; I generated all of the figures and tables; and I designed the experiments, performed data analyses, and wrote and edited the manuscript with Dr. Cornelius F. Boerkoel. Contributions of co-authors:  Collin Shuen assisted in the analysis of the patient dental X-rays.  Dr. Alireza Baradaran-Heravi assessed the relative cell viability and proliferation of the cultured dermal fibroblasts used in the study.    iv  Olivia Kérourédan, Marion Gendronneau, Dr. Yumi Asakura, Dr. Mitra Basiratnia, Dr. Radovan Bogdanovic, Dr. Dominique Bonneau, Dr. Anna Buck, Dr. Joel Charrow, Dr. Pierre Cochat, Kristi A. DeHaai, Dr. M. Semin Fenkci, Dr. Pierre Frange, Dr. Stefan Fründ, Dr. Helen Fryssira, Kory Keller, Dr. Salman Kirmani, Christine Kobelka, Dr. Karen Kohler, Dr. David B. Lewis, Dr. Laura Massella, Dr. D. Ross McLeod, Dr. David V. Milford, Dr. François Nobili, Dr. Ann Haskins Olney, Dr. C. Nur Semerci, Dr. Natasa Stajic, Dr. Anja Stein, Dr. Sophie Taque, Dr. Jonathan Zonana, Dr. Thomas Lücke, and Dr. Martine Bonnaure-Mallet provided patient data.  Dr. Glenda Hendson provided fetal tissue sections for the analysis of SMARCAL1 expression in the developing human tooth.  In addition to the contributions mentioned above, Dr. Cornelius F. Boerkoel established the initial contact with patients or the parents or guardians of patients as well as the collaborations and supervised the research.  A version of Chapter 3 was previously published as two manuscripts, the first being: Morimoto M, Yu Z, Stenzel P, Clewing JM, Najafian B, Mayfield C, Hendson G, Weinkauf JG, Gormley AK, Parham DM, Ponniah U, André JL, Asakura Y, Basiratnia M, Bogdanovic R, Bokenkamp A, Bonneau D, Buck A, Charrow J, Cochat P, Cordeiro I, Deschenes G, M. Semin Fenkci, Frange P, Fründ S, Fryssira H, Guillen-Navarro E, Keller K, Kirmani S, Kobelka C, Lamfers P, Levtchenko E, Lewis DB, Massella L, McLeod DR, Milford DV, Nobili F, Saraiva JM, Semerci CN, Shoemaker L, Stajic N, Stein A, Taha D, Wand D, Zonana J, Lücke T, Boerkoel CF. (2012) Reduced elastogenesis: A clue to the arteriosclerosis and emphysematous changes in Schimke immuno-osseous dysplasia? Orphanet Journal of Rare Diseases. 7: 70. Copyright © 2012 Morimoto et al.; licensee BioMed Central Ltd. Dr. Cornelius F. Boerkoel and I coordinated the collection of clinical data and samples from patients, parents or guardians of patients, and our collaborators; I performed the wet-bench experiments and data analysis; I interpreted the data with Drs. Zhongxin Yu, Peter Stenzel, and Cornelius F. Boerkoel; I generated the tables and figures; and I wrote and edited the manuscript with Dr. Cornelius F. Boerkoel.    v Contributions of co-authors:  Drs. Zhongxin Yu, Peter Stenzel, J. Marietta Clewing, Christy Mayfield, Andrew K. Gormley, David M. Parham, and Thomas Lücke provided SIOD patient clinical data and aorta samples.   Dr. Behzad Najafian, Dr. Justin G. Weinkauf, Dr. Jean-Luc André, Dr. Yumi Asakura, Dr. Mitra Basiratnia, Dr. Radovan Bogdanovic, Dr. Arend Bokenkamp, Dr. Dominique Bonneau, Dr. Anna Buck, Dr. Joel Charrow, Dr. Pierre Cochat, Dr. Isabel Cordeiro, Dr. Georges Deschenes, Dr. M. Semin Fenkci, Dr. Pierre Frange, Dr. Stefan Fründ, Dr. Helen Fryssira, Dr. Encarna Guillen-Navarro, Kory Keller, Dr. Salman Kirmani, Christine Kobelka, Dr. Petra Lamfers, Dr. Elena Levtchenko, Dr. David B. Lewis, Dr. Laura Massella, Dr. D. Ross McLeod, Dr. David V. Milford, Dr. François Nobili, Dr. Jorge M. Saraiva, Dr. C. Nur Semerci, Dr. Lawrence Shoemaker, Dr. Natasa Stajic, Dr. Anja Stein, Dr. Doris Taha, Dorothea Wand, and Dr. Jonathan Zonana provided SIOD patient clinical data.  Dr. Umakumaran Ponniah performed and interpreted the echocardiogram of patient SD120 and wrote the methods for the echocardiogram.  Drs. Zhongxin Yu, Peter Stenzel, and Glenda Hendson provided unaffected control tissues and technical support for the histopathological studies.  Dr. Cornelius F. Boerkoel designed the study and supervised the research. In a second manuscript, I supervised summer student Karen Wang to further study the potential molecular mechanisms underlying the reduced elastin expression I previously observed in the aorta of one SIOD patient: Morimoto M, Wang KJ, Yu Z, Gormley AK, Bogdanovic R, Lücke T, Mayfield C, Weksberg R, Hendson G, Boerkoel CF. (2015) Transcriptional and posttranscriptional mechanisms contribute to the dysregulation of elastogenesis in Schimke immuno-osseous dysplasia. Pediatric Research. 78(6): 609-617. Copyright © 2015 International Pediatric Research Foundation, Inc.  I designed the experiments with Dr. Cornelius Boerkoel; I performed the experiments, analyzed the data, and generated the tables and figures with Karen Wang; and I interpreted the data and wrote and edited the manuscript with Dr. Cornelius F. Boerkoel.   vi Contributions of co-authors:  Karen Wang assisted in DNA and RNA extraction, data analysis, and figure generation.  Drs. Zhongxin Yu, Andrew K. Gormley, and Christy Mayfield provided SIOD patient autopsy tissue.  Drs. Radovan Bogdanovic, Thomas Lücke, and Rosanna Weksberg provided the SIOD patient dermal fibroblast cell lines used in the study.  Dr. Glenda Hendson provided unaffected fetal (third trimester) and postnatal human aortas.  Dr. Cornelius F. Boerkoel supervised the research.  A version of Chapter 4 is currently under revision: Morimoto M, Myung C, Beirnes K, Choi K, Asakura Y, Bokenkamp A, Bonneau D, Brugnara M, Charrow J, Colin E, Davis A, Deschenes G, Gentile M, Giordano M, Gormley AK, Govender R, Joseph M, Keller K, Lerut E, Levtchenko E, Massella M, Mayfield C, Najafian B, Parham D, Spranger J, Stenzel P, Yis U, Yu Z, Zonana J, Hendson G, Boerkoel CF. Increased Wnt and Notch signaling: A clue to the renal disease in Schimke immuno-osseous dysplasia? I coordinated the collection of clinical data and samples from patients, parents or guardians of patients, and our collaborators; I performed the majority of the experiments (90%); I generated all of the figures and tables; and I designed the experiments, performed data analyses, and wrote and edited the manuscript with Dr. Cornelius F. Boerkoel. Contributions of co-authors:  With my supervision, Clara Myung, Kimberly Beirnes, and Kunho Choi assisted me with the Drosophila genetic studies.  Dr. Yumi Asakura, Dr. Arend Bokenkamp, Dr. Dominique Bonneau, Dr. Milena Brugnara, Dr. Joel Charrow, Dr. Estelle Colin, Dr. Georges Deschenes, Dr. Mattia Gentile, Dr. Mario Giordano, Dr. Andrew K. Gormley, Dr. Rajeshree Govender, Dr. Mark Joseph, Kory Keller, Dr. Evelyne Lerut, Dr. Elena Levtchenko, Dr. Laura Massella, Dr. Christy Mayfield, Dr. Behzad Najafian, Dr. David Parham, Dr. Jurgen Spranger, Dr. Peter Stenzel, Dr. Uluc Yis, Dr. Zhongxin Yu, and Dr. Jonathan Zonana provided patient clinical data and samples.   vii  Amira Davis and Dr. Glenda Hendson provided unaffected control tissues and technical support for the histopathological studies.  Dr. Cornelius F. Boerkoel supervised the research.  A version of Chapter 5 was previously published: Sanyal M*, Morimoto M*, Baradaran-Heravi A*, Choi K*, Kambham N, Jensen K, Dutt S, Dionis-Petersen KY, Liu LX, Felix K, Mayfield C, Dekel B, Bokenkamp A, Fryssira H, Guillen-Navarro E, Lama G, Brugnara M, Lücke T, Olney AH, Hunley TE, Polat AI, Yis U, Bogdanovic R, Mitrovic K, Berry S, Najera L, Najafian B, Gentile M, Semerci CN, Tsimaratos M, Lewis DB, Boerkoel CF. (2015) Lack of IL7R expression in T cells is a hallmark of T-cell immunodeficiency in Schimke immuno-osseous dysplasia (SIOD). Clinical Immunology. 161(2): 355-365. Copyright © 2015 Elsevier Inc. All rights reserved. *These authors contributed equally in this study. This work was undertaken in the laboratories of Drs. Cornelius F. Boerkoel and David B. Lewis at Stanford University. I performed the sequencing of SMARCAL1 and IL7R in SIOD patients, I generated the tables, I performed the data analyses pertaining to the aforementioned experiments, and I wrote and edited the manuscript with Drs. Mrinmoy Sanyal, David B. Lewis, and Cornelius F. Boerkoel. I also coordinated the additional recruitment of SIOD patients and shipment of samples to Dr. Mrinmoy Sanyal. Contributions of co-authors:  Dr. Mrinmoy Sanyal designed the experiments with Drs. David B. Lewis and Cornelius F. Boerkoel and generated the figures. He performed the flow cytometry experiments and developed, optimized, and performed the cell proliferation assay with the help of Kira Y. Dionis-Petersen and Lan Xiang Liu.  Dr. Alireza Baradaran-Heravi performed the RNA extraction and quantitative PCR for IL7R expression in the T cells of an SIOD patient and an unaffected control.  With my supervision, Kunho Choi performed the DNA extraction from SIOD patient T cells and bisulfite pyrosequencing of the IL7R promoter, and the RNA extraction and quantitative PCR for IL7R expression in the T cells of additional SIOD patients.  Dr. Neeraja Kambham performed the histopathological analysis.   viii  Dr. Kent Jensen performed the Luminex assays for measuring serum immunoglobulin isotypes and IgG subclasses.  Dr. Suparna Dutt recruited SIOD patients and designed the Luminex assays for measuring serum immunoglobulin isotypes and IgG subclasses.  Kira Y. Dionis-Petersen and Lan Xiang Liu archived blood samples and performed experiments as noted above with Dr. Mrinmoy Sanyal.  Katie Felix obtained unaffected human thymus and spleen.  Drs. Christy Mayfield, Benjamin Dekel, Arend Bokenkamp, Helen Fryssira, Encarna Guillen-Navarro, Giuliana Lama, Milena Brugnara, Thomas Lücke, Ann Haskins Olney, Tracy E. Hunley, Ayse Ipek Polat, Uluc Yis, Radovan Bogdanovic, Katarina Mitrovic, Susan Berry, Lydia Najera, Behzad Najafian, Mattia Gentile, C. Nur Semerci, and Michel Tsimaratos provided the SIOD patient tissue samples.  Patients or parents or guardians of the patients referred to the studies presented in this thesis gave informed consent approved by the Institutional Review Board of the Baylor College of Medicine (H-9669, Houston, TX, USA), the Hospital for Sick Children (REB-0019970093, Toronto, ON, Canada), the Stanford University School of Medicine (Protocol ID: 419 and 27,191, Stanford, CA, USA), or the University of British Columbia (H06-70283, Vancouver, BC, Canada). Autopsy and biopsy tissues were obtained according to the protocol approved by the University of British Columbia. The clinical data for patients were obtained from questionnaires completed by the attending physician as well as from the medical records. Human fetal tissue was obtained through the Laboratory of Developmental Biology at the University of Washington (41577, Seattle, WA, USA), a National Institute of Child Health & Human Development supported program.   ix Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iii Table of Contents ......................................................................................................................... ix List of Tables .............................................................................................................................. xvi List of Figures ............................................................................................................................ xvii List of Abbreviations ................................................................................................................. xix List of Gene Names ................................................................................................................... xxii Acknowledgements .................................................................................................................. xxiv Dedication .................................................................................................................................. xxv Chapter 1: Introduction ............................................................................................................... 1 1.1 Rare disease ....................................................................................................................... 1 1.1.1 Definition .................................................................................................................... 1 1.1.2 Challenges of and strategies for studying rare diseases .............................................. 2 1.1.3 Strengths and limitations of studying human tissues .................................................. 3 1.2 Schimke immuno-osseous dysplasia.................................................................................. 3 1.2.1 Historical background ................................................................................................. 3 1.2.2 Clinical features of SIOD............................................................................................ 4 1.2.2.1 Physical features .................................................................................................. 6 1.2.2.2 Development ........................................................................................................ 7 1.2.2.3 Growth ................................................................................................................. 7 1.2.2.4 Skeletal features ................................................................................................... 7 1.2.2.5 Renal disease ........................................................................................................ 9 1.2.2.6 Hematologic abnormalities .................................................................................. 9 1.2.2.7 Central nervous system symptoms....................................................................... 9 1.2.2.8 Atherosclerosis and hypertension ...................................................................... 10 1.2.2.9 Gastrointestinal findings .................................................................................... 10 1.2.2.10 Hypothyroidism ............................................................................................... 10 1.2.2.11 Non-Hodgkin lymphoma ................................................................................. 10   x 1.2.3 Clinical course and outcome ..................................................................................... 10 1.2.4 Management and treatment of manifestations .......................................................... 11 1.2.4.1 Evaluations following initial diagnosis .............................................................. 11 1.2.4.2 Treatment of manifestations .............................................................................. 12 1.2.4.2.1 Renal manifestations ................................................................................... 12 1.2.4.2.2 Orthopedic manifestations .......................................................................... 12 1.2.4.2.3 Immunologic manifestations ....................................................................... 12 1.2.4.2.4 Infectious disease manifestations ................................................................ 13 1.2.4.2.5 Neurologic manifestations .......................................................................... 13 1.2.4.2.6 Hypothyroidism .......................................................................................... 13 1.3 Genetics of SIOD ............................................................................................................. 14 1.3.1 The SMARCAL1 gene ............................................................................................... 14 1.3.2 Genotype-phenotype correlation ............................................................................... 14 1.3.3 Age-dependent penetrance and variable expressivity ............................................... 15 1.4 SMARCAL1: A multifunctional protein ......................................................................... 15 1.4.1 Structure and function ............................................................................................... 15 1.4.2 Role in DNA replication ........................................................................................... 18 1.4.3 Role in DNA repair ................................................................................................... 18 1.4.4 Role in gene expression ............................................................................................ 18 1.5 Models of SMARCAL1 deficiency ................................................................................. 19 1.5.1 Smarcal1 deficiency in the mouse (Mus musculus) .................................................. 19 1.5.2 smarcal1 deficiency in the zebrafish (Danio rerio) .................................................. 19 1.5.3 Marcal1 deficiency in the fruit fly (Drosophila melanogaster)................................ 20 1.5.4 smrc-1 deficiency in the nematode (Caenorhabditis elegans) ................................. 20 1.6 Hypothesis and objectives................................................................................................ 20 Chapter 2: Phenotypic Characterization of the Dentition in SIOD ....................................... 21 2.1 Introduction ...................................................................................................................... 21 2.2 Methods............................................................................................................................ 22 2.2.1 Immunohistochemistry ............................................................................................. 22 2.2.2 Cell culture ................................................................................................................ 23   xi 2.2.3 Immunofluorescence ................................................................................................. 23 2.2.4 Immunoblot ............................................................................................................... 23 2.2.5 RNA extraction and reverse transcription ................................................................. 24 2.2.6 Polymerase chain reaction ........................................................................................ 24 2.2.7 Cell viability and proliferation assay ........................................................................ 24 2.2.8 Morphogen induction of patient dermal fibroblasts.................................................. 24 2.2.9 Quantitative PCR ...................................................................................................... 25 2.2.10 Statistics .................................................................................................................. 25 2.3 Results .............................................................................................................................. 26 2.3.1 Developmental tooth abnormalities are a common feature of SIOD ........................ 26 2.3.2 SMARCAL1 is highly expressed in the developing human tooth ............................ 29 2.3.3 SIOD tooth anomalies are distinct from other disorders of DNA repair .................. 32 2.3.4 Wnt3A, BMP4, and TGF1 signaling is altered in cultured SIOD fibroblasts ........ 34 2.4 Discussion ........................................................................................................................ 35 Chapter 3: Molecular Characterization and Pathogenesis of the Vascular Disease in SIOD....................................................................................................................................................... 39 3.1 Introduction ...................................................................................................................... 39 3.2 Methods............................................................................................................................ 41 3.2.1 Human samples ......................................................................................................... 41 3.2.2 Tissue immunohistochemistry and staining .............................................................. 41 3.2.3 RNA extraction and reverse transcription ................................................................. 42 3.2.4 Polymerase chain reaction ........................................................................................ 42 3.2.5 Gene expression array ............................................................................................... 43 3.2.6 ELN mutation analysis .............................................................................................. 43 3.2.7 Cell culture ................................................................................................................ 43 3.2.8 Indirect immunofluorescence .................................................................................... 44 3.2.9 Immunoblot analysis ................................................................................................. 44 3.2.10 Fastin elastin assay .................................................................................................. 45 3.2.11 Arterial thickness analysis ...................................................................................... 45 3.2.12 Bisulfite Sanger sequencing .................................................................................... 46   xii 3.2.13 miR-29 expression analysis .................................................................................... 46 3.2.14 Poly(A) tail length assay ......................................................................................... 47 3.2.15 Statistics .................................................................................................................. 47 3.3 Results .............................................................................................................................. 48 3.3.1 Vascular disease is common in SIOD ....................................................................... 48 3.3.2 Histopathology of the SIOD aorta shows fragmented elastic lamellae and hyperplasia of the tunica intima and tunica media................................................................ 53 3.3.3 Inflammation is not increased in the SIOD aorta...................................................... 54 3.3.4 Histopathology of the SMARCAL1-deficient umbilical cord shows a fragmented internal elastic lamina ........................................................................................................... 54 3.3.5 SMARCAL1 is expressed in the vascular smooth muscle, endothelial, and adventitial fibroblast cells of the arterial wall....................................................................... 56 3.3.6 Elastin binding protein is not decreased in the SIOD aorta ...................................... 58 3.3.7 Elastin mRNA and protein are markedly reduced in the SIOD aorta ....................... 58 3.3.8 ELN gene mutations are not the cause of the reduced elastogenesis in SIOD .......... 59 3.3.9 ELN mRNA expression decreases during human aorta development ...................... 60 3.3.10 ELN pre-mRNA levels decrease modestly with aortic development from fetus to adult and is further decreased in an SIOD aorta ................................................................... 61 3.3.11 SIOD-associated expression of transcriptional regulators of ELN parallel those observed with aorta development ......................................................................................... 62 3.3.12 DNA methylation of the ELN promoter is unaltered in the SIOD aorta ................. 64 3.3.13 Expression of miR-29 family members is increased in the SIOD aorta ................. 66 3.3.14 ELN mRNA poly(A) tail length is shortened in SIOD ........................................... 67 3.4 Discussion ........................................................................................................................ 69 Chapter 4: Molecular Characterization and Pathogenesis of the Renal Disease in SIOD .. 74 4.1 Introduction ...................................................................................................................... 74 4.2 Methods............................................................................................................................ 75 4.2.1 Human samples ......................................................................................................... 75 4.2.2 Cell culture ................................................................................................................ 77 4.2.3 Wnt3a treatment ........................................................................................................ 77   xiii 4.2.4 Drosophila melanogaster lines ................................................................................. 77 4.2.5 RNA extraction ......................................................................................................... 78 4.2.6 RNA-seq and KEGG pathway analysis .................................................................... 78 4.2.7 Gene expression arrays ............................................................................................. 79 4.2.8 Quantitative PCR ...................................................................................................... 79 4.2.9 Indirect immunofluorescence .................................................................................... 79 4.2.10 Quantification of -catenin immunofluorescence .................................................. 80 4.2.11 Microarray gene expression analysis ...................................................................... 80 4.2.12 Drosophila melanogaster genetic screen to determine the effect of Wnt and Notch mutant alleles on the Marcal1 overexpression wing phenotype ........................................... 81 4.2.13 Drosophila melanogaster genetic studies to determine the effect of Marcal1 loss and gain on Notch mutant phenotypes .................................................................................. 82 4.2.14 Statistics .................................................................................................................. 83 4.3 Results .............................................................................................................................. 84 4.3.1 Transcriptome analysis identifies increased mRNA levels of Wnt signaling pathway genes in an SIOD patient kidney .......................................................................................... 84 4.3.2 Quantitative PCR detects increased mRNA levels of Wnt and Notch signaling pathway genes in an SIOD patient kidney ............................................................................ 87 4.3.3 Unphosphorylated -catenin is increased in the glomerular cells of postnatal SIOD patient kidneys comparable to FSGS controls ...................................................................... 87 4.3.4 Nuclear NICD expression is increased in the glomerular cells in postnatal SIOD patient kidneys comparable to FSGS controls ...................................................................... 88 4.3.5 Cultured renal proximal tubular cells of an SIOD patient and an FSGS patient have similar gene expression profiles ............................................................................................ 93 4.3.6 Unphosphorylated -catenin and nuclear NICD are not increased in the developing SMARCAL1-deficient kidney .............................................................................................. 94 4.3.7 Unphosphorylated -catenin and nuclear NICD are not increased in the transplanted kidney of an SIOD patient .................................................................................................... 94 4.3.8 Drosophila Marcal1 genetically interacts with the Wnt and Notch signaling pathways ............................................................................................................................... 94   xiv 4.4 Discussion ........................................................................................................................ 97 Chapter 5: Molecular Characterization and Pathogenesis of the Immune Disease in SIOD..................................................................................................................................................... 100 5.1 Introduction .................................................................................................................... 100 5.2 Methods.......................................................................................................................... 101 5.2.1 Human samples ....................................................................................................... 101 5.2.2 Antibodies ............................................................................................................... 103 5.2.3 High-dimensional flow cytometry .......................................................................... 103 5.2.4 Immunohistochemistry ........................................................................................... 104 5.2.5 Lymphocyte proliferation assays ............................................................................ 104 5.2.6 RNA isolation and reverse transcription ................................................................. 104 5.2.7 Quantitative PCR .................................................................................................... 105 5.2.8 Sequencing of SMARCAL1 cDNA.......................................................................... 105 5.2.9 Sequencing of IL7R exons ...................................................................................... 105 5.2.10 Bisulfite pyrosequencing ...................................................................................... 106 5.2.11 Measurement of serum immunoglobulin isotypes and IgG subtypes ................... 106 5.2.12 Statistical analyses ................................................................................................ 107 5.3 Results ............................................................................................................................ 107 5.3.1 SIOD patients are T-cell lymphopenic and their T cells are deficient in the interleukin 7 receptor alpha chain ....................................................................................... 107 5.3.2 Reduction of IL7R is not restricted to any specific T-cell subset ........................ 109 5.3.3 SIOD splenic T cells also have reduced IL7R expression ................................... 109 5.3.4 SIOD T cells have reduced IL7R mRNA levels ..................................................... 111 5.3.5 T cells in SIOD patients are not responsive to interleukin 7 (IL-7)........................ 111 5.3.6 DNA changes in the IL7R gene in SIOD are not pathogenic ................................. 114 5.3.7 Reduced thymic output in SIOD patients ............................................................... 116 5.3.8 CpG sites in the IL7R promoter are hypermethylated in SIOD T cells .................. 118 5.4 Discussion ...................................................................................................................... 120 Chapter 6: Discussion ............................................................................................................... 123 6.1 Potential mechanisms of SMARCAL1 deficiency modulating gene expression .......... 123   xv 6.1.1 Effect on transcription............................................................................................. 124 6.1.2 Effect through unrepaired DNA lesions impeding transcription ............................ 125 6.1.3 Effect on gene promoter structure........................................................................... 125 6.1.4 Effect through replication stress-induced alterations of chromatin structure ......... 125 6.2 Strengths and limitations................................................................................................ 126 6.2.1 Strengths ................................................................................................................. 126 6.2.1.1 Use of patient tissue ......................................................................................... 126 6.2.2 Limitations .............................................................................................................. 127 6.2.2.1 Small sample size ............................................................................................. 127 6.2.2.2 Lack of control tissue ....................................................................................... 127 6.2.2.3 Cell type heterogeneity .................................................................................... 128 6.3 Future directions ............................................................................................................ 129 6.4 Conclusions and significance ......................................................................................... 130 Bibliography .............................................................................................................................. 131 Appendices ................................................................................................................................. 148 Appendix A: Supplementary Tables and Figures for Chapter 2 ............................................. 148 Appendix B: Supplementary Tables and Figures for Chapter 3 ............................................. 164 Appendix C: Supplementary Tables and Figures for Chapter 4 ............................................. 181 Appendix D: Supplementary Tables and Figures for Chapter 5 ............................................. 209    xvi List of Tables  Table 1.1 Annual cost of some of the most expensive therapeutic drugs for rare diseases. ........... 1 Table 1.2 Summary of clinical features in SIOD patients with SMARCAL1 mutations. ................ 5 Table 2.1 Summary of dental findings in SIOD patients with SMARCAL1 mutations. ............... 26 Table 4.1 The renal parameters of the SIOD patients included in this study. .............................. 76 Table 4.2 Summary of results for the indirect immunofluorescent analyses of glomerular unphosphorylated -catenin and nuclear NICD expression in SIOD and FSGS patient kidney tissue. ............................................................................................................................................ 93 Table 5.1 The SMARCAL1 mutations of the SIOD patients included in this study.................... 102 Table 5.2 Exon sequencing results of IL7R in SIOD families. ................................................... 115    xvii List of Figures  Figure 1.1 Characteristic physical features of SIOD. ..................................................................... 6 Figure 1.2 Characteristic skeletal features of SIOD. ...................................................................... 8 Figure 1.3 The SNF2 subfamilies and SMARCAL1 orthologues. ............................................... 17 Figure 2.1 Dental x-rays and photographs showing the dental pathology of patients with SMARCAL1 mutations. ................................................................................................................. 29 Figure 2.2 Analysis of SMARCAL1 protein expression during tooth morphogenesis. ............... 31 Figure 2.3 Expression of SMARCAL1 in cultured human dermal fibroblasts and dysregulated transcriptional responses of SIOD patient dermal fibroblasts upon induction with BMP4 or TGF1. .......................................................................................................................................... 33 Figure 3.1 Verhoeff-Van Gieson staining of aortas from SIOD patients and age-matched unaffected controls. ....................................................................................................................... 54 Figure 3.2 Elastin expression analysis of the umbilical cord from SMARCAL1-deficient and unaffected fetuses at 15 weeks gestation. ..................................................................................... 56 Figure 3.3 SMARCAL1 mRNA and protein are expressed in arterial tissue and cell types. ......... 57 Figure 3.4 Elastin expression is significantly decreased in the aorta of an SIOD patient. ........... 59 Figure 3.5 ELN mRNA expression during human aorta development and aging. ....................... 60 Figure 3.6 ELN pre-mRNA expression in fetal, adult, and SIOD aortas. ..................................... 61 Figure 3.7 Expression of transcriptional regulators of ELN during human aorta development and aging. ............................................................................................................................................. 63 Figure 3.8 DNA methylation analysis of the ELN promoter in fetal, postnatal, and SIOD aortas........................................................................................................................................................ 65 Figure 3.9 Expression of the miR-29 family in fetal, adult, and SIOD aortas. ............................. 67 Figure 3.10 Poly(A) tail length analysis of ELN mRNA in fetal, adult, and SIOD aortas. .......... 68 Figure 4.1 Genome-wide and targeted gene expression analyses in an SIOD patient kidney. ..... 86 Figure 4.2 Immunofluorescent detection of the expression and localization of unphosphorylated -catenin in the glomerular cells of unaffected control and SIOD patient kidneys. ..................... 89   xviii Figure 4.3 Immunofluorescent detection of the expression and localization of the Notch1 intracellular domain (NICD) in the glomerular cells of unaffected control and SIOD patient kidneys. ......................................................................................................................................... 92 Figure 4.4 Genetic interaction of Marcal1 loss and gain with Notch signaling pathway mutant alleles and model........................................................................................................................... 96 Figure 5.1 Phenotypic analyses of T cells from SIOD patients reveal high proportions of memory T cells and deficiency of IL7R expression. .............................................................................. 108 Figure 5.2 An SIOD spleen has very few T cells and reduced IL7R expression. .................... 110 Figure 5.3 IL7R mRNA expression is reduced in SIOD T cells. ................................................ 111 Figure 5.4 SIOD patient T cells fail to respond to IL-7. ............................................................. 113 Figure 5.5 Reduced thymic output in SIOD patients. ................................................................. 117 Figure 5.6 IL7R promoter CpG sites are hypermethylated in SIOD patients. ............................ 119 Figure 6.1 Potential mechanisms of SMARCAL1 deficiency modulating gene expression. ..... 124    xix List of Abbreviations 3´ UTR  3´ untranslated region ACV   Anterior crossvein AoAF   Aortic adventitial fibroblast AoSMC  Aortic smooth muscle cell ATM   Ataxia telangiectasia mutated ATR   ATM and Rad3-related BMP4   Bone morphogenetic protein 4 cDNA   Complementary DNA CFSE   Carboxyfluorescein succinimidyl ester ChIP-seq  Chromatin immunoprecipitation sequencing COSMIC  Catalogue Of Somatic Mutations In Cancer CTF   Corrected total fluorescence CVA   Cerebrovascular accident DAB   3, 3´-diaminobenzidine DamID  DNA adenine methyltransferase identification DAPI   4, 6´-diamidino-2-phenylindole DAVID  Database for Annotation, Visualization, and Integrated Discovery DMEM  Dulbecco’s Modified Eagle Medium DMSO   Dimethyl sulfoxide DNA-PK  DNA-dependent protein kinase EBP   Elastin binding protein EDTA   Ethylenediaminetetraacetic acid ELISA   Enzyme-linked immunosorbent assay ELN   Elastin ESRD   End-stage renal disease FACS   Fluorescence-activated cell sorting FBS   Fetal bovine serum FCS   Fetal calf serum FFPE   Formalin-fixed paraffin-embedded   xx FGF   Fibroblast growth factor FI   Fluorescence intensity FPKM   Fragments per kilobase per million mapped reads FSGS   Focal segmental glomerulosclerosis GAPDH   Glyceraldehyde 3-phosphate dehydrogenase GG-NER  Global genomic nucleotide excision repair HARP   HepA-related protein HIAEC  Human iliac artery endothelial cell Hi-C   High-resolution chromatin conformation capture HRP   Horseradish peroxidase H & E   Hematoxylin and eosin IL-2   Interleukin 2 IL-7   Interleukin 7 IL7R   Interleukin 7 receptor alpha chain KEGG   Kyoto Encyclopedia of Genes and Genomes LCD   Laser capture microdissection miRNA  MicroRNA mRNA   Messenger RNA MTT   3-(4, 5)-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide NICD   Notch1 intracellular domain OCT   Optimal cutting temperature PBMC   Peripheral blood mononuclear cell PBS   Phosphate-buffered saline PCR   Polymerase chain reaction PCV   Posterior crossvein PHA   Phytohemagglutinin pre-mRNA  Precursor messenger RNA qRT-PCR  Quantitative reverse transcription polymerase chain reaction RNA-seq  RNA sequencing RPA   Replication protein A   xxi rRNA   Ribsomal RNA SIOD   Schimke immuno-osseous dysplasia siRNA   Small interfering RNA SMARCAL1 SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A-like 1 SNF2 Sucrose non-fermenting 2 SNP Single nucleotide polymorphism TACh-seq Tissue accessible chromatin sequencing TC-NER Transcription-coupled nucleotide excision repair TGF1 Transforming growth factor  1 TBS Tris-buffered saline TIA Transient ischemic attack TNA Transient neurologic attack UAS Upstream activating sequence WBS Williams-Beuren syndrome Wnt3A Wnt family member 3A   xxii List of Gene Names The following genes are human genes unless otherwise noted. ACTA2  -smooth muscle actin CDH5   Vascular endothelial cadherin Dl   Delta (D. melanogaster) ELN   Elastin Eln   Elastin (M. musculus) FGF2   Fibroblast growth factor 2 fng   Fringe (D. melanogaster) FOSL1   FOS-like 1 GAPDH   Glyceraldehyde 3-phosphate dehydrogenase GREM2  Gremlin 2 H   Hairless (D. melanogaster) ID1   Inhibitor of DNA binding 1 IGF1   Insulin-like growth factor 1 Il7   Interleukin 7 (M. musculus) IL7R   Interleukin 7 receptor alpha chain Il7r   Interleukin 7 receptor alpha chain (M. musculus) Marcal1  CG3753 gene product from transcript CG3753-RA (D. melanogaster) MMP10  Matrix metallopeptidase 10 N   Notch (D. melanogaster) P4HA3  Prolyl 4-hydroxylase subunit alpha 3 PRDM6  PR domain 6 Ser Serrate (D. melanogaster) SMAD6 SMAD family member 6 SMAD7 SMAD family member 7 SMARCAL1 SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A-like 1 Smarcal1 SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A-like 1 (M. musculus)   xxiii smarcal1 SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A-like 1 (D. rerio) smrc-1 Putative SMARCAL1-like protein (C. elegans) SP1 Sp1 transcription factor SP3 Sp3 transcription factor TGFB1 Transforming growth factor  1 Ubx   Ultrabithorax (D. melanogaster)   xxiv Acknowledgements First and foremost, I would like to thank my supervisor, Dr. Cornelius Boerkoel. Your steadfast commitment to rare diseases and translational research, with the end goal of bridging the clinic and science to improve patient care, has been truly inspirational.  Neal, thank you for believing in me and supporting me throughout this journey.  I would also like to thank the members of my supervisory committee, Dr. Matthew Lorincz, Dr. Cheryl Wellington, and Dr. Matthew Farrer, for their invaluable input, support, and time over the years. I would also like to extend my sincere gratitude to Cheryl Bishop for her administrative help and guidance throughout my degree as well as UBC for the financial support I received through a Four Year Doctoral Fellowship.  This work is the result of an immense collaborative effort of many brilliant and dedicated individuals from around the world. I would like to especially thank Drs. Mrinmoy Sanyal and David Lewis at Stanford University as well as Drs. Zhongxin Yu, David Parham, and Andrew Gormley at the University of Oklahoma Health Sciences Center for their hard work and collaboration. I would also like to thank Dr. Glenda Hendson and Dr. Thomas Lücke for their ongoing collaboration, helpful discussions, and support. Thank you to all Boerkoel Lab members past and present. Specifically, I would like to thank Kunho Choi and Dr. Alireza Baradaran-Heravi for providing technical mentorship; all of the students that I had the pleasure of mentoring for their hard work and enthusiasm: Karen Wang, Joanne Trinh, Clara Myung, Kimberly Beirnes, Collin Shuen, Paul Atkins, and Jenny Huang; and all of the fellow graduate students that I had the pleasure of working alongside including Andrew Fam, Dr. Chris Dias, and Miraj Chowdhury. Thank you so much for your friendship. Finally, thank you to my parents and my brother, Michael, and my dear friends, Federica, Catherine, Katrina, and Filyani, for your endless support, love, and encouragement.   xxv Dedication     For Mitchell, Emily, and Mr. Flett, For all patients with Schimke immuno-osseous dysplasia, For all patients with a rare or undiagnosed disease. 1 Chapter 1: Introduction  1.1 Rare disease 1.1.1 Definition A rare disease is any disease that affects a small percentage of a given population. There is no universally accepted definition of a rare disease. In the United States, a rare disease is defined as a disease that affects less than 200,000 people (National Organization for Rare Disorders), whereas in the European Union, it is defined as a disease with a prevalence of less than 1 in 2,000 (European Organization for Rare Diseases). While, by definition, rare diseases affect few people, they affect approximately 10% of Americans (National Institutes of Health). It is estimated that there are approximately 7,000 rare diseases and many more undiagnosed diseases. Individuals affected by rare diseases often have a delayed diagnosis, difficulty in accessing care, and limited treatment options. The diagnostic odyssey that many patients with a rare or undiagnosed disease experience has a high financial, temporal, and emotional cost. Moreover, even when curative treatment is available, it can be exceedingly costly (Table 1.1) (Edwards 2015). More awareness and research on rare diseases is required to provide patients with a timely diagnosis, social support, and clinical care.  Table 1.1 Annual cost of some of the most expensive therapeutic drugs for rare diseases.  Drug Pharmaceutical company Rare disease Annual cost (USD) Elaprase Shire MPS II $375,000 Naglazyme BioMarin Pharmaceutical MPS VI $485,000 Soliris Alexion Pharmaceuticals Paroxysmal nocturnal hemoglobinuria and atypical hemolytic uremic syndrome $537,000  Abbreviations: MPS, mucopolysaccharidosis; USD, United States dollar.   The formation of organizations that advocate for individuals with rare diseases, such as the Canadian Organization for Rare Disorders and the National Organization for Rare Disorders, have not only increased awareness of rare disease but have also provided support for patients and  2 families and patient organizations as well as contributed to the treatment of rare diseases. In fact, the National Organization for Rare Disorders was instrumental in the passing of the Orphan Drug Act of 1983, which provides financial incentives for developing treatments for rare diseases (National Organization of Rare Disorders). The increased awareness of rare diseases and the recognition that further research is required to diagnose, treat, and ultimately cure rare disease patients has precipitated several collaborative research efforts. The Canadian-funded Finding of Rare Disease Genes (FORGE) and Enhanced CARE for RARE Genetic Diseases (CARE4RARE), as well as the American-funded Undiagnosed Diseases Program at the National Institutes of Health have facilitated and accelerated the discovery of new disease genes underlying previously known and new rare diseases. Further, the International Rare Diseases Research Consortium is an international effort that was initiated to accelerate rare disease research and aims to define 200 new therapies for rare diseases by 2020 through the collaboration of patient organizations, physicians, researchers, pharmaceutical industries, and regulatory bodies (International Rare Diseases Research Consortium). Given the low prevalence of individual rare diseases, including the childhood disease that is the topic of this thesis, I will detail some of the unique challenges of and strategies for studying rare diseases. I will also detail the strengths and limitations of studying human tissues since the studies presented in this thesis rely heavily on the molecular characterization of patient tissues.  1.1.2 Challenges of and strategies for studying rare diseases The study of rare diseases gives rise to several unique challenges. These challenges include the difficulty in recruiting patients due to the rarity of their disease, designing experiments for studying biological phenomena in a small number of patients, and obtaining funding for rare disease research (Griggs et al. 2009). Collaborations are required in order to recruit an adequate number of patients with a specific rare disease; these collaborations can be formed by proactively seeking other researchers or physicians that also study the rare disease or by establishing oneself as an expert on the rare disease through publications. The development and study of a model system is an ideal strategy to address the second challenge of verifying biological phenomena observed in a small number of patients by providing a complementary  3 approach, however the development of a model system is not always possible and study design should be undertaken on a case-by-case basis. Finally, the establishment of and support from rare disease organizations and patient advocacy organizations should be considered to obtain adequate funding for rare disease research.  1.1.3 Strengths and limitations of studying human tissues There are several strengths and limitations of studying human tissues. The primary strength of studying human tissues for understanding human disease is the ability to perform studies in the organism of interest. Studies of human tissue can either serve to validate findings from other model systems or to address questions of pathogenesis directly when model systems do not recapitulate the human disease. Limitations of studying human tissues include the difficulty in obtaining human tissues that can lead to small sample sizes and/or the lack of ideal control tissues. Further, human tissue samples requiring specialized processing are more difficult to obtain in the clinical setting. Despite these limitations, the study of human tissues is clearly a valuable component of translational research, particularly when used to complement other approaches.  1.2 Schimke immuno-osseous dysplasia Schimke immuno-osseous dysplasia (SIOD) is one example of a rare disease. Although the prevalence of SIOD is unknown, the incidence in North America is estimated to be 1:1,000,000 to 1:3,000,000 live births based on referrals and published birth rates.  1.2.1 Historical background SIOD was first described by Dr. R. Neil Schimke in 1971. It was initially characterized as a disorder of disproportionate short stature, nephrotic syndrome, lymphopenia, defective cellular immunity, and chondroitin-6-sulphaturia (Schimke et al. 1971). Nearly two decades later, SIOD was further characterized by Ehrich et al. (1990) in three unrelated patients and by Spranger et al. (1991) in five patients from four families as a disorder of disproportionate short stature due to spondyloepiphyseal dysplasia, nephrotic syndrome due to focal segmental glomerulosclerosis (FSGS), T-cell immunodeficiency, pigmentary skin changes, and characteristic facial features. Although SIOD was initially characterized as a mucopolysaccharidosis, the careful study and  4 examination of additional patients revealed that increased urinary chondroitin-6-sulphate is not a common feature of SIOD. The co-occurrence of spondyloepiphyseal dysplasia, renal failure, and T-cell immunodeficiency represents a pathognomonic triad of clinical features for SIOD. Spranger et al. (1991) proposed that this newly recognized multisystemic disease be known as “Schimke immuno-osseous dysplasia”. SIOD is also known as “morbus Ehrich” in parts of Germany due to the contributions of Ehrich et al. (1990) to the clinical description of the disorder.  1.2.2 Clinical features of SIOD SIOD is a progressive, multisystemic childhood disorder with several clinical features. These clinical features of SIOD are delineated in the following sections and the percentage of patients that manifest each clinical feature is summarized in Table 1.2.   5 Table 1.2 Summary of clinical features in SIOD patients with SMARCAL1 mutations.  Clinical feature Percentage of affected individuals (Affected individuals/Total reported) Physical features      Wide and depressed nasal bridge      Broad nasal tip      Protruding abdomen      Pigmented macules      Unusual hair      Abnormal dentition      Corneal opacities  65% (53/81) 78% (61/78) 77% (59/77) 70% (57/81) 63% (43/68) 66% (33/50) 17% (11/65) Development      Developmental delay      Academic delay  34% (26/77) 28% (10/36) Growth      Intrauterine growth retardation      Disproportionate short stature  70% (58/83) 99% (82/83) Skeletal features      Short neck      Short trunk      Lumbar lordosis      Ovoid flat vertebrae      Hypoplastic pelvis      Abnormal femoral heads  86% (66/77) 85% (68/80) 74% (57/77) 77% (54/70) 65% (44/68) 89% (64/72) Renal disease      Proteinuria or nephropathy      Focal segmental glomerulosclerosis  99% (84/85) 83% (43/52) Hematologic abnormalities      T-cell immunodeficiency      Lymphopenia      Neutropenia      Thrombocytopenia      Anemia  76% (47/62) 74% (58/78) 38% (27/71) 25% (19/77) 57% (43/75) Central nervous system symptoms      Headaches      Transient ischemic attacks      Strokes  47% (28/60) 41% (31/76) 43% (30/69) Other      Hypothyroidism      Non-Hodgkin lymphoma1  36% (24/66) 3% (3/86)  1Epstein-Bar virus-positive and -negative non-Hodgkin lymphoma.  Abbreviations: SIOD, Schimke immuno-osseous dysplasia; SMARCAL1, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A-like 1.   6 1.2.2.1 Physical features Characteristic facial features of most SIOD patients include a low nasal bridge and a bulbous nasal tip. They also have disproportionate short stature with a short neck and trunk with lumbar lordosis and a protruding abdomen. Most affected individuals have hyperpigmented macules on the trunk and occasionally on the extremities, neck, and face. Less common ectodermal abnormalities include fine and/or sparse hair, abnormal dentition, and corneal opacities. Characteristic physical features are represented in Figure 1.1.   Figure 1.1 Characteristic physical features of SIOD. (A) Case 1. Note the peculiar face with broad depressed nasal bridge, bulbous tip of nose, and weak fine hair. The patient has a short trunk and neck, long extremities, and large hands and feet. (B) Case 2. Note the wide nose with a bulbous tip, low-set large ears, and fine weak hair. The patient has a barrel chest, short neck, and relatively long extremities with large hands and feet.  Figure used with permission from Springer: Tylki-Szymanska A, et al. (2003) Schimke immuno-osseous dysplasia: Two cases. Pediatr Radiol. 33(3): 216-218.  Springer-Verlag 2002.   7 1.2.2.2 Development Most individuals with SIOD have normal intellectual and neurologic development until the onset of cerebral ischemic events. A few have developmental delay; however, for most of these, the delay can be ascribed to the deleterious consequences of chronic illness and/or early recurrent cerebral ischemic events.  1.2.2.3 Growth Most affected children have prenatal and postnatal disproportionate growth failure. A few have normal intrauterine growth followed by postnatal growth failure. The observed disproportionate growth deficiency is not a result of renal failure. Comparison of the anthropometric measurements of persons with SIOD to persons with non-SIOD chronic kidney disease found that in nearly all parameters assessed, persons with SIOD differed significantly from those with non-SIOD chronic renal disease. The most marked difference is that in non-SIOD chronic kidney disease, the median leg length is significantly more reduced than trunk length, while in persons with SIOD, the reduction in trunk length was significantly more than that for leg length. Therefore, a sitting height/leg length ratio of less than 0.83 is suggestive of SIOD in persons with chronic kidney disease (Lücke et al. 2006a).  The mean age of diagnosis with growth failure was 2 years (range: age 0 - 13 years) (Clewing et al. 2007b). Generally, affected individuals have a normal growth hormone axis and no response to growth hormone supplementation. Height in those who have survived to adulthood is 136 - 157 cm for men and 98.5 - 143 cm for women.  1.2.2.4 Skeletal features SIOD is characterized by prominent spondyloepiphyseal dysplasia. The commonly observed radiologic abnormalities are ovoid and mildly flattened vertebral bodies, small and deformed capital femoral epiphyses, and shallow dysplastic acetabular fossae (Ehrich et al. 1990; Spranger et al. 1991; Hunter et al. 2010). Less frequent skeletal problems include a widened sella turcica, thoracic kyphosis, scoliosis, and osteopenia (Spranger et al. 1991; Hunter et al. 2010). Affected individuals do not usually have joint pain until they develop degenerative hip disease. Characteristic skeletal features are represented in Figure 1.2.  8  Figure 1.2 Characteristic skeletal features of SIOD. (A) Lateral spine radiograph of a 5-year-old child showing dorsally flattened, pear-shaped vertebral bodies. (B) Lateral skull radiograph of a 5-year-old child showing the typical widening of the sella. (C) Posteroanterior hand radiograph of a 13-year-old adolescent showing the absence of abnormalities. (D) Anteroposterior hip radiograph of a 4-year-old child showing the small, laterally displaced capital femoral epiphyses, hypoplastic basilar ilia, and upslanting and poorly formed acetabula.   Figure used with permission from Springer: Hunter KB, et al. (2010) Schimke immunoosseous dysplasia: Defining skeletal features. Eur J Pediatr. 169(7): 801-811.  The Authors 2009.   9 1.2.2.5 Renal disease Nephropathy usually develops before age 12 years and progresses to end-stage renal disease (ESRD) within the subsequent 1 to 11 years. Usually the diagnosis of nephropathy is made concurrent with or within the 5 years following the diagnosis of growth failure. Focal segmental glomerulosclerosis (FSGS) is the predominant renal pathology in individuals with SIOD.   1.2.2.6 Hematologic abnormalities T-cell immunodeficiency causes lymphopenia in nearly 80% of affected individuals. The B-cell count is usually normal to slightly elevated. In addition to T-cell immunodeficiency, several individuals with SIOD have had deficiencies of other blood cell lineages. See Table 1.2 for types and the percentages of patients that manifest each type of blood cell lineage deficiency.  Immunodeficiency increases the risk of opportunistic infections such as Pneumocystis jirovecii pneumonia. More than half of individuals with SIOD have recurrent infections with various bacteria, viruses (e.g., herpes simplex virus, varicella-zoster virus, cytomegalovirus), and fungi (e.g., oral and/or cutaneous candida) (Boerkoel et al. 2000). Infection is a common cause of death.  About 20% of individuals with SIOD have features of autoimmune disease. These manifestations include immune thrombocytopenia, hemolytic anemia, enteropathy, pericarditis with anti-cardiolipin antibodies, and Evans syndrome (a combination of hemolytic anemia and immune thrombocytopenia) (Zieg et al. 2011).  1.2.2.7 Central nervous system symptoms Nearly half of affected individuals have severe migraine-like headaches, transient neurologic attacks (TNAs), or transient ischemic attacks (TIAs) (Kilic et al. 2005). The TNAs are usually focal and generally do not have an ischemic origin. Some affected individuals also have heat intolerance and develop central nervous system symptoms during hot weather (Baradaran-Heravi et al. 2012a). Generally, those with TIAs or strokes have diffuse, progressive cerebral arteriosclerosis, whereas those with only migraine-like headaches do not. Frequently, the cerebral ischemic events are precipitated by hypertension. The cause of the severe migraine-like headaches is unknown.  10  1.2.2.8 Atherosclerosis and hypertension Half of individuals with SIOD have symptoms suggestive of atherosclerosis. Vascular changes observed on postmortem tissue from three individuals included focal intimal lipid deposition, focal myointimal proliferation, macrophage invasion, foam cells, fibrous transformation, and calcium deposits (Spranger et al. 1991; Lücke et al. 2004; Clewing et al. 2007a). The pulmonary systemic hypertension that persisted despite renal transplantation described by Lücke et al. (2004) could be explained by myointimal hyperplasia (Clewing et al. 2007a).  1.2.2.9 Gastrointestinal findings A few individuals with SIOD have enteropathy. In most of these individuals, the enteropathy results from infection (e.g., Heliobacter pylori). However, one individual without evidence of infection had gastrointestinal villous atrophy that improved with corticosteroid therapy (Kaitila et al. 1998).  1.2.2.10 Hypothyroidism A third of affected individuals have subclinical hypothyroidism that persists after renal transplantation. The concentration of thyroid stimulating hormone is increased, and free and total T3 and T4 concentrations are reduced.  1.2.2.11 Non-Hodgkin lymphoma Three individuals have developed B-cell non-Hodgkin lymphoma in the first 10 years of their life (Baradaran-Heravi et al. 2012b).  1.2.3 Clinical course and outcome SIOD varies in severity, ranging from in utero onset of growth retardation with death in the first few years of life to a slowly progressive course with survival into adulthood. Classically, SIOD has been divided into an infantile- or severe early-onset form and a juvenile- or milder later-onset form. SIOD follows a continuum such that affected individuals with early-onset and severe symptoms usually die early in life, whereas those with mild symptoms survive into  11 adulthood if ESRD is treated with renal dialysis and/or renal transplantation. Severity and age of onset of symptoms do not, however, invariably predict survival; a few individuals have survived beyond age 20 years despite having relatively severe early-onset disease (Lou et al. 2002; Lücke et al. 2004).  Most affected individuals develop other symptoms within 1 to 5 years of the diagnosis of growth failure. Those with severe symptoms usually die within 4 to 8 years. The mean age of death is 11 years. Causes of death include infection (23%), stroke (13%), pulmonary hypertension and congestive heart failure (13%), renal failure (11%), complications of organ transplantation (9%), lymphoproliferative disease (4%), gastrointestinal complications (4%), respiratory failure (4%), bone marrow failure (2%), non-Hodgkin lymphoma (2%), pancreatitis (2%), and other causes not reported (13%).  Among those who have survived beyond puberty, none have reproduced. Women develop menses, although the menstrual cycle is usually irregular. Men develop secondary sexual characteristics, but histopathological examination of the testes identified azoospermia (Clewing et al. 2007a).  1.2.4 Management and treatment of manifestations 1.2.4.1 Evaluations following initial diagnosis To establish the extent of disease and needs in an individual diagnosed with SIOD, the following evaluations are recommended:  Measurement of growth and assessment of body proportions, with plotting on age-appropriate growth charts (Lücke et al. 2006a)  Evaluation of renal function by measurement of serum concentrations of creatinine and urea, protein excretion in urine, and creatinine clearance  Referral to a nephrologist for evaluation  Hematology evaluations to assess lymphopenia, anemia, neutropenia, and thrombocytopenia  Immunology evaluations to evaluate the numbers of memory and naïve CD4+ and CD8+ T cells and B cells and immunoglobulin levels  Assessment of developmental status with referral for formal evaluation if significant developmental delays or academic delays are identified  12  Dental evaluation  Ophthalmologic evaluation  Detailed history for headaches or neurologic abnormalities  Orthopedic evaluation for symptoms of joint pain or evidence of scoliosis or kyphosis  Assessment of osteopenia  Thyroid function studies  Consultation with a medical geneticist and/or genetic counsellor  1.2.4.2 Treatment of manifestations There is currently no cure for SIOD. Patients benefit from the following symptomatic treatments for each of the disease manifestations.  1.2.4.2.1 Renal manifestations  The renal disease progresses from proteinuria to ESRD at variable rates and is not prevented by any known drug therapies, although treatment with cyclosporin A, tacrolimus, or corticosteroids has led to the transient reduction in the rate of renal disease progression in a few affected individuals (Boerkoel et al. 2000).  Renal transplantation effectively treats the nephropathy and neither nephropathy nor arteriosclerosis recurs in the graft (Ehrich et al. 1990; Lücke et al. 2004; Elizondo et al. 2006; Clewing et al. 2007a). Mild immunosuppressive therapy, such as immunosuppressive monotherapy, appears to improve outcome after renal transplantation (Lücke et al. 2009).  1.2.4.2.2 Orthopedic manifestations  Hip replacement is required in some affected individuals who have survived beyond childhood.  Treatment of scoliosis and/or kyphosis is standard.  1.2.4.2.3 Immunologic manifestations  Granulocyte colony-stimulating factor or granulocyte-macrophage colony-stimulating factor supplementation usually improves neutropenia.  13  Bone marrow transplantation has had variable outcomes: One affected individual has been successfully treated by bone marrow transplantation (Petty et al. 2000), while four affected individuals have died after bone marrow transplantation (Baradaran-Heravi et al. 2013).  Immunosuppressive therapy, such as steroids, cyclophosphamide, or intravenous immunoglobulin, has successfully treated affected individuals with autoimmune disease (Ludman et al. 1993; Kaitila et al. 1998).  Splenectomy has been effective in treating the autoimmune thrombocytopenia in one affected individual (Zieg et al. 2011). In another individual, the autoimmune thrombocytopenia resolved spontaneously (Zieg et al. 2011).  1.2.4.2.4 Infectious disease manifestations Individuals with recurrent infections, opportunistic infections, or declining lymphocytes or T-cell counts frequently require the care of an immunologist.  Acyclovir benefits affected individuals with recurrent herpetic infections.  Imiquimod and cidofovir improve affected individuals with severe disseminated cutaneous papilloma virus infections.  1.2.4.2.5 Neurologic manifestations  Agents that improve blood flow or decrease coagulability, such as pentoxifylline, acetylsalicylic acid, dipyridamole, warfarin, and heparin, usually temporarily improve the transient ischemic attacks or strokes in affected individuals. To date, no curative or effective long term therapies have been identified.  Anti-migraine medications, such as ergotamine, sumatriptan, verapamil, and propranolol, have improved the migraine headaches of some affected individuals; however, the response to anti-migraine medication is highly variable.  1.2.4.2.6 Hypothyroidism  Levothyroxine supplementation can treat the hypothyroidism; however, supplementation does not have an ameliorative effect on the renal disease or T-cell immunodeficiency.   14 1.3 Genetics of SIOD 1.3.1 The SMARCAL1 gene  Biallelic loss-of-function mutations of the SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1 (SMARCAL1) gene were first identified as a genetic cause of SIOD by Boerkoel et al. (2002) using genome-wide linkage mapping and a positional candidate approach in four unrelated families. Of individuals clinically diagnosed with SIOD based on the co-occurrence of spondyloepiphyseal dysplasia, renal failure, T-cell immunodeficiency, and typical dysmorphic facial features, 90% have biallelic mutations in SMARCAL1 detected by Sanger sequencing of all SMARCAL1 coding exons. Other causative genes of SIOD have not been identified as of yet. The SMARCAL1 gene consists of 18 exons, 2 of which are non-coding and 16 of which are coding. Mutations are distributed across SMARCAL1 and include deletions, insertions, splice-site mutations, frameshift mutations, and missense and nonsense point mutations (Boerkoel et al. 2002; Clewing et al. 2007b). SMARCAL1 mutations affect protein expression, stability, subcellular localization, chromatin binding, and enzymatic activity, suggestive that these mutations are loss-of-function mutations (Elizondo et al. 2009). SMARCAL1 is often mutated in the context of cancer, as demonstrated by the Catalogue Of Somatic Mutations In Cancer (COSMIC) database version 77 available at http://cancer.sanger.ac.uk/cosmic (Forbes et al. 2015). A total of 171 unique mutations have been identified in 184 samples; these mutations include missense, nonsense, and frameshift mutations and are distributed throughout the gene. The high frequency of SMARCAL1 mutations in cancer underscores the role of SMARCAL1 in maintaining genomic integrity.  1.3.2 Genotype-phenotype correlation Ongoing correlations between genotype and phenotype have shown that genotype does not predict disease severity or outcome either within or among families (Lama et al. 1995; Lou et al. 2002; Lücke et al. 2005a; Clewing et al. 2007b; Dekel et al. 2008). In five multiplex families, the phenotype of siblings has been notably variable:  A boy succumbed to a stroke at age 3.7 years after developing ESRD; his sister succumbed to bone marrow failure at age 2.75 years before developing renal failure and without symptoms of cerebral ischemia (Lou et al. 2002).  15  Of two brothers, one had severe disease and the other had relatively mild disease (Lücke et al. 2005a).  Of two brothers, one presented with growth failure at age 6 years, while the other had normal growth at age 10 years (Bokenkamp et al. 2005).  Of three siblings, one died as a child with severe disease, one had normal stature and died of a pulmonary infection at 43 years, and one had survived into the fourth decade (Lama et al. 1995).  Of three siblings, the elder brother demonstrated severe disease from age 3.5 years and two younger non-identical twin brothers had relatively mild disease (Dekel et al. 2008).  1.3.3 Age-dependent penetrance and variable expressivity SMARCAL1 mutations exhibit age-dependent penetrance and variable expressivity with SIOD features ranging in frequency from 3 - 99%. Furthermore, mutations in SMARCAL1 orthologues in the mouse and fruit fly are insufficient to cause disease (Baradaran-Heravi et al. 2012a). These observations suggest that the molecular mechanism underlying SIOD is particularly sensitive to genetic, epigenetic or environmental, and stochastic influences.  1.4 SMARCAL1: A multifunctional protein 1.4.1 Structure and function SMARCAL1 is a 954 amino acid nuclear protein and a distant member of the sucrose non-fermenting 2 (SNF2) family of ATP-dependent chromatin remodeling proteins that is characterized by the presence of seven motifs (I, Ia, II, III, IV, V, VI) that function in DNA binding and ATPase activity (Coleman et al. 2000) (Figure 1.3A). SMARCAL1 and its orthologues possess one or two HepA-related protein (HARP) domains, a DEXDc domain, and a helicase (HELICc) domain (Figure 1.3B). Additional motifs include two nuclear localization signals, the Walker A (phosphate binding) and Walker B (magnesium binding) domains (Coleman et al. 2000), as well as a replication protein A (RPA) binding motif (Bansbach et al. 2009; Ciccia et al. 2009; Postow et al. 2009; Yuan et al. 2009; Yusufzai et al. 2009). However, the SMARCAL1 orthologues do not have a bromodomain, chromodomain, or zinc finger motfis that are characteristically found in other related SNF2 proteins (Eisen et al. 1995). SMARCAL1  16 orthologues are expressed ubiquitously and throughout development (Coleman et al. 2000; Elizondo et al. 2006; Deguchi et al. 2008; Baradaran-Heravi et al. 2012a). In vitro studies of the bovine and human orthologues of SMARCAL1 have demonstrated that the enzyme recognizes single-stranded to double-stranded DNA transitions (Hockensmith et al. 1986; Muthuswami et al. 2000; Yusufzai and Kadonaga 2008). SMARCAL1 also recognizes three-way and four-way Holliday junctions and model replication forks that lack a single-strand region (Bétous et al. 2012). Therefore, the SMARCAL1 orthologues recognize DNA structure and not DNA sequence. Although SMARCAL1 has been predicted to have helicase activity based on the presence of a conserved helicase domain, it does not exhibit DNA duplex unwinding activity in vitro. SMARCAL1 orthologues function as DNA-dependent ATPases (Hockensmith et al. 1986; Coleman et al. 2000; Muthuswami et al. 2000; Baradaran-Heravi et al. 2012a), and use the energy derived from ATP hydrolysis to anneal single-stranded DNA to double-stranded DNA in vitro (Yusufzai and Kadonaga 2008) and the HARP domains are required for the annealing helicase activity of SMARCAL1 (Ghosal et al. 2011).  17   Figure 1.3 The SNF2 subfamilies and SMARCAL1 orthologues. (A) Rooted tree diagram representing the relationship between the 24 SNF2 subfamilies. Hidden Markov model profiles for full-length alignments of the helicase regions were used to calculate the rooted tree. Groupings of subfamilies are indicated by colour. (B) Protein schematic of SMARCAL1 orthologues showing the relative length and position of the HARP, DEXDc, and HELICc domains. Abbreviation: aa, amino acid.  Figure panel A modified and used with permission from Oxford University Press: Flaus A, et al. (2006) Identification of multiple distinct Snf2 subfamilies with conserved structural motifs. Nucleic Acids Res. 34(10): 2887-2905.  The Authors 2006.  18 1.4.2 Role in DNA replication  SMARCAL1 is involved in the activation of stalled replication forks and is recruited to these sites through its interaction with RPA (Ciccia et al. 2009; Postow et al. 2009; Yuan et al. 2009; Yusufzai et al. 2009). Ataxia-telangiectasia mutated (ATM), ATM and Rad3-related (ATR), and DNA-dependent protein kinase (DNA-PK) phosphorylate SMARCAL1 in response to replication stress (Bansbach et al. 2009), and phosphorylation of SMARCAL1 by ATR prevents replication fork collapse (Couch et al. 2013). SMARCAL1 remodels stalled replications forks through fork regression and branch migration to activate stalled replication forks (Bétous et al. 2012). Recent reports have demonstrated that SMARCAL1 also has a role in maintaining telomere integrity during DNA replication (Poole et al. 2015; Cox et al. 2016).  1.4.3 Role in DNA repair  SMARCAL1 is involved in the DNA stress response and is recruited to double-strand DNA breaks through its interaction with RPA (Postow et al. 2009; Yusufzai et al. 2009). Further, SMARCAL1 facilitates double-strand break repair by non-homologous end joining (Keka et al. 2015). Interaction of SMARCAL1 with replication forks prevents MUS81 endonuclease-dependent double-strand DNA breaks (Bétous et al. 2012), and SMARCAL1-deficient cells are hypersensitive to DNA damaging agents and produce more H2AX foci, a marker of double-strand DNA breaks (Bansbach et al. 2009; Ciccia et al. 2009; Postow et al. 2009; Yuan et al. 2009; Yusufzai et al. 2009). Further, treatment of Smarcal1-deficient mice with the DNA damaging agents irinotecan, etoposide, and hydroxyurea recapitulates the growth defect of SIOD (Baradaran-Heravi et al. 2012b).  1.4.4 Role in gene expression In addition to its roles in DNA replication and DNA repair, there are several lines of evidence for SMARCAL1 and its orthologues having a role in modulating gene expression. First, human SMARCAL1 and Drosophila Marcal1 localize to transcriptionally active regions of chromatin and colocalize with RNA polymerase II both by polytene chromosome staining and DNA methyltransferase identification (DamID) (Baradaran-Heravi et al. 2012a). Second, SMARCAL1 and Marcal1 exhibit genetic interactions with components of the transcription factor machinery as well as RNA polymerase II (Baradaran-Heravi et al. 2012a). Third,  19 deficiency of SMARCAL1 orthologues alters global gene expression and dysregulates transcriptional responses to stimuli, such as heat stress, morphogens, and genotoxic agents (Baradaran-Heravi et al. 2012a). Fourth, treatment of Smarcal1-deficient mice with the RNA polymerase II inhibitor -amanitin partially recapitulates some of the features of SIOD (Baradaran-Heravi et al. 2012a). Fifth, bovine SMARCAL1 has been shown to negatively regulate the transcription of a gene by directly altering the conformation of the promoter of that gene (Sharma et al. 2015).  1.5 Models of SMARCAL1 deficiency Model organisms of the deficiency of SMARCAL1 orthologues have been described, however most of these models do not give rise to any developmental phenotypes.  1.5.1 Smarcal1 deficiency in the mouse (Mus musculus) A knockout mouse model was generated using standard homologous recombination knockout technology (Baradaran-Heravi et al. 2012a). This Smarcal1-deficient mouse model lacks the first two coding exons of Smarcal1 (NM_018817.2: c.172-989del) and therefore lacks the RPA binding site and the first HARP domain (Baradaran-Heravi et al. 2012a). The Smarcal1-deficient mice do not exhibit any developmental or growth abnormalities, although they have a dysregulated heat shock response similar to SIOD patients (Baradaran-Heravi et al. 2012a).  1.5.2 smarcal1 deficiency in the zebrafish (Danio rerio) In the zebrafish, morpholino knockdown of the SMARCAL1 orthologue smarcal1 leads to a developmental phenotype of reduced length, which is reminiscent of the skeletal phenotype in SIOD; a reduction in red blood cells, which is reminiscent of the anemia in SIOD; as well as features unrelated to SIOD such as reduced pigment, pericardial edema, and cartilage defects (Huang et al. 2010). A cell proliferation defect, namely G0/G1 cell cycle arrest, was also observed in the smarcal1-deficient zebrafish model (Huang et al. 2010), which is in contrast to studies of human SMARCAL1-deficient cells where the overall cell proliferation rate is comparable to control cells (Bansbach et al. 2009; Ciccia et al. 2009).   20 1.5.3 Marcal1 deficiency in the fruit fly (Drosophila melanogaster) In the fruit fly, a loss-of-function Marcal1 mutant was generated through the imprecise excision of the P element KG9850 and screening for deletion of the Marcal1 gene (Baradaran-Heravi et al. 2012a). This Marcal1-deficient fly model lacks 679 bp extending from the middle of the first exon into the second intron (NM_135039.1: c.673_1258delins ATGATGAAATAACATCATTATATCGATTAACACAG, p.G225MfsX3) and does not express Marcal1 mRNA or protein (Baradaran-Heravi et al. 2012a). Similar to the Smarcal1-deficient mouse model, Marcal1-deficient flies do not exhibit any morphological abnormalities and have a normal life span at 20ºC, although they have a dysregulated heat shock response similar to SIOD patients (Baradaran-Heravi et al. 2012a).  1.5.4 smrc-1 deficiency in the nematode (Caenorhabditis elegans) In the nematode, knockdown of the SMARCAL1 orthologue smrc-1 by RNA interference does not lead to any mutant phenotypes (Eki et al. 2007). Further, none of the 15 alleles that lead to missense or nonsense mutations that are described in WormBase lead to any remarkable phenotypes.  1.6 Hypothesis and objectives Although SMARCAL1 functions to maintain genomic integrity, it is not known how SMARCAL1 deficiency leads to the various clinical features of SIOD. Given that SMARCAL1 has a role in modulating gene expression, the overall hypothesis of my thesis is that SMARCAL1 deficiency leads to the specific clinical features of SIOD by pathologically altering gene expression. Therefore, the specific objectives of this thesis are to further characterize the dental phenotype of SIOD patients and to characterize the molecular pathogeneses of the vascular, renal, and immune diseases of SIOD.   21 Chapter 2: Phenotypic Characterization of the Dentition in SIOD  2.1 Introduction Tooth morphogenesis proceeds by a series of precisely orchestrated molecular and morphogenic events (Thesleff 2003). Reciprocal interactions between the ectoderm and mesenchyme regulate tooth morphogenesis and lead to the thickening and budding of the oral epithelium that subsequently grows and folds to form the tooth crown. Odontoblasts and ameloblasts differentiate at the epithelial-mesenchymal interface to deposit dentin and enamel, respectively, and cementoblasts deposit cementum to form the three hard-tissue matrices of the developing tooth (Thesleff 2003). A number of conserved signaling pathways are involved in tooth morphogenesis including the transforming growth factor beta (TGF), bone morphogenetic protein (BMP), fibroblast growth factor (FGF), hedgehog, and Wnt pathways (Thesleff 2003). Mutations of genes encoding key structural components and transcription factors related to the aforementioned signaling pathways can lead to dental abnormalities of number, size, shape, and structure in isolation or as part of a syndrome (Thesleff 2006; Cobourne and Sharpe 2013). With respect to number, individuals may have fewer (hypodontia, oligodontia, or anodontia depending on the severity) or more (hyperdontia) teeth. With respect to size, individuals may have smaller (microdontia) or larger (macrodontia) teeth. The shape and structure of the dentition can also be affected; amelogenesis imperfecta and dentinogenesis imperfecta are two examples that respectively lead to altered tooth shape and defective structural formation of enamel and dentin. Dental abnormalities have been previously reported in two individuals with SIOD: Ludman et al. (1993) reported one with microdontia, and da Fonseca et al. (2000) reported another with hypodontia, molar root hypoplasia, and dental discolouration consistent with dentinogenesis imperfecta type II. These prior case reports raise the question of whether dental abnormalities are a common feature of SIOD.  Previous studies suggest a cell autonomous mechanism for many features of SIOD. First, mouse Smarcal1 is expressed in all tissues analogous to those affected in SIOD (Elizondo et al. 2006). Second, SIOD-specific renal failure does not recur in the renal grafts of transplanted SIOD patients (Ehrich et al. 1990; Clewing et al. 2007a). Third, the arterial disease characteristic of SIOD does not affect the renal grafts of SIOD patients (Lücke et al. 2004; Clewing et al.  22 2007a). Fourth, renal transplantation does not prevent arterial disease among SIOD patients (Boerkoel et al. 2000; Clewing et al. 2007a). I hypothesized therefore that if the dental abnormalities occur by a cell autonomous mechanism, then SMARCAL1 should be expressed in the developing tooth. Since SMARCAL1 maintains genomic integrity through its role in DNA repair, DNA replication, and modulates gene expression (Bansbach et al. 2009; Ciccia et al. 2009; Postow et al. 2009; Yuan et al. 2009; Yusufzai et al. 2009; Baradaran-Heravi et al. 2012a), SMARCAL1 deficiency could lead to altered transcriptional responses to developmental morphogens, hormones, cytokines, and environmental stimuli. Consistent with this, standard treatment with hormones or cytokines are often ineffective among SIOD patients (Boerkoel et al. 2000). Given the prior findings and observations, the objectives of this study were to determine the prevalence of dental abnormalities in SIOD patients with biallelic SMARCAL1 mutations, to define the expression pattern of human SMARCAL1 in the developing tooth, and to test the consequences of SMARCAL1 deficiency on the transcriptional responses to the tooth developmental morphogens Wnt family member 3A (Wnt3A), bone morphogenetic protein 4 (BMP4), and transforming growth factor  1 (TGF1).  2.2 Methods 2.2.1 Immunohistochemistry Formalin-fixed paraffin-embedded tissue sections were de-paraffinized in xylene and hydrated in a series of graded alcohols. Heat-induced epitope retrieval was conducted with sodium citrate buffer (10 mM sodium citrate, 0.05% Tween 20, pH 6.0). Endogenous peroxidases were inactivated with 3% H2O2 for 30 minutes. Sections were first blocked with blocking buffer (20% normal goat serum, 10% bovine serum albumin, 1 casein (Vector Laboratories, Burlington, ON, Canada), 0.2% Triton X-100, 1 phosphate-buffered saline (PBS), pH 7.4), and then incubated with rabbit anti-SMARCAL1 antiserum (1:200) (Kilic et al. 2005) diluted in blocking buffer, each overnight at 4ºC. The sections were washed and then incubated with biotinylated anti-rabbit IgG (1:200, Vector Laboratories, Burlington, ON, Canada). Then the sections were washed and incubated with a preformed avidin:biotinylated enzyme complex (Vector Laboratories, Burlington, ON, Canada). Immune complexes were visualized with 3, 3´-diaminobenzidine (DAB) (Dako, Mississauga, ON, Canada), and sections  23 were counterstained with Mayer’s Hematoxylin (Sigma, Oakville, ON, Canada). The staining of adjacent sections with pre-immune serum was used to confirm antiserum specificity (Figure 2.2C, D, G, H, K, and L).  2.2.2 Cell culture Dermal fibroblasts from SIOD patients were isolated and cultured from skin biopsies of the forearm. The fibroblasts were grown in high glucose Dulbecco’s Modified Eagle Medium (DMEM) (Invitrogen, Burlington, ON, Canada) supplemented with 10% fetal bovine serum (FBS) (Invitrogen, Burlington, ON, Canada) and 1 antibiotic-antimycotic (Invitrogen, Burlington, ON, Canada).  2.2.3 Immunofluorescence 5  105 cells were grown overnight on a coverslip in a 6-well plate. Cells were fixed with 4% paraformaldehyde and then permeabilized with 0.5% Triton X-100 for 15 minutes each at room temperature. Non-specific binding sites were blocked with Blocker Casein in PBS (Pierce, Rockford, IL, USA) containing 10% normal horse serum overnight at 4C. The cells were then incubated with rabbit anti-SMARCAL1 antiserum (1:200) (Kilic et al. 2005) and mouse anti-prolyl 4-hydroxylase (1:50, clone 5B5, Abcam, Cambridge, MA, USA) diluted in blocking buffer overnight at 4C. Alexa Fluor-conjugated secondary antibodies (1:10,000, Molecular Probes, Burlington, ON, Canada) were used to detect the primary antibodies. Cells were mounted in Vectashield containing 4, 6´-diamidino-2-phenylindole (DAPI) (Vector Laboratories, Burlington, ON, Canada). Images were acquired using a 100/1.30 oil Plan-NEOFLUAR objective lens, an Axiovert 200 inverted microscope, an AxioCamMR camera, and the AxioVision software version 4.8 (Carl Ziess, Toronto, ON, Canada).  2.2.4 Immunoblot Cell lysates were fractionated by 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a polyvinylidene fluoride membrane. The membrane was blocked in 1 PBS containing 0.2% I-BLOCK (Applied Biosystems, Foster City, CA, USA) and 0.1% Tween 20 overnight at 4C. Anti-SMARCAL1 antiserum (1:2,000) (Kilic et al. 2005) and anti-glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (1:2,000, 6C5, Advanced  24 ImmunoChemical, Long Beach, CA, USA) were used as primary antibodies. Alkaline phosphatase-conjugated secondary antibodies (1:10,000, Bio-Rad Laboratories, Mississauga, ON, Canada) were used to detect the primary antibodies. The bound antibody was detected by chemiluminescence using CDP-Star (Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s specifications.  2.2.5 RNA extraction and reverse transcription Total RNA was extracted from cells with the RNeasy 96 Kit (Qiagen, Toronto, ON, Canada), and on-column DNase I digestion (Qiagen, Toronto, ON, Canada) was performed to remove genomic DNA. Reverse transcription was performed with the qScript cDNA SuperMix (Quanta BioSciences, Gaithersburg, ON, Canada).  2.2.6 Polymerase chain reaction Following reverse transcription, 1.25 l of cDNA (equivalent to approximately 50 ng total RNA) served as template for each reaction and was amplified with the HotStarTaq Plus Master Mix Kit (Qiagen, Toronto, ON, Canada). The following conditions were used for amplification: 1 cycle of 95C for 5 minutes, followed by 30 cycles of 94C for 30 seconds, 55C for 30 seconds, 72C for 1 minute, and a final extension at 72C for 10 minutes. Polymerase chain reaction (PCR) was performed using the primers listed in Supplementary Table 2.1.  2.2.7 Cell viability and proliferation assay 3  103 cells were cultured in triplicate for each sample in a 96-well plate and cell viability and proliferation were assessed after 24 and 48 hours using the MTT assay (M5655, Sigma-Aldrich, Oakville, ON, Canada) as previously described (Mosmann 1983). The relative viability and proliferation rates were calculated for the 24-hour interval, and each SIOD fibroblast cell line was compared to the unaffected control fibroblast cell line.  2.2.8 Morphogen induction of patient dermal fibroblasts Forty-eight hours prior to Wnt3A, BMP4, or TGF1 induction, 5  104 cells were seeded in each well of a 24-well plate. Twenty-four hours prior to morphogen addition, the  25 growth medium was replaced with serum-free medium. Cells were treated with 100 ng/ml Wnt3A, 50 ng/ml BMP4, or 4 ng/ml TGF1 (R&D Systems, Minneapolis, MN, USA) for 0, 2, 4, 8, 12, 16, 20, or 24 hours. These morphogens were chosen based on their involvement in tooth formation in mice (Vainio et al. 1993; Unda et al. 2001; Plikus et al. 2005; Hosoya et al. 2008; Liu et al. 2008; Ahn et al. 2010); the morphogen concentrations and time points were chosen based on prior studies defining the transcriptional responses of the aforementioned morphogens in human dermal fibroblasts (Afrakhte et al. 1998; Klapholz-Brown et al. 2007; Fessing et al. 2010; Ishikawa et al. 2010). For each morphogen and time point, three parallel cultures were analyzed for each cell line.  2.2.9 Quantitative PCR SsoFast EvaGreen Supermix (Bio-Rad Laboratories, Mississauga, ON, Canada) was used with the StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Expression of the housekeeping gene glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as the internal control. The primer sequences are listed in Supplementary Table 2.1.  2.2.10 Statistics Graphed quantitative data are presented as the mean ± standard deviation of a minimum of three technical replicates. The relative quantification of gene expression was calculated by the 2-Ct method (Livak and Schmittgen 2001). The Ct of the unaffected control cell line was used as the calibrator for relative basal gene expression, and the Ct of the cell line of interest at time 0 hours was used as the calibrator for relative gene expression over time. Standard deviations were calculated from the triplicate samples at each time point after the 2-Ct transformations were performed. Data were analyzed by one-way analysis of variance (ANOVA), followed by the Tukey post hoc test for multiple comparisons between cell lines or time points, with SPSS Statistics version 20 (IBM, Armonk, NY, USA). A p value of less than 0.05 was considered statistically significant.   26 2.3 Results 2.3.1 Developmental tooth abnormalities are a common feature of SIOD Among SIOD patients with biallelic SMARCAL1 mutations and for whom data was available, 66% of patients (33 of 50) presented with at least one dental anomaly (Supplementary Table 2.2). For those patients for whom records were obtained, 47% had microdontia and 52% had hypodontia or oligodontia (Table 2.1 and Supplementary Table 2.2). The number of missing teeth ranged from 0 to 15, and the premolars were most frequently absent (Supplementary Table 2.2).  Table 2.1 Summary of dental findings in SIOD patients with SMARCAL1 mutations. Disease severity score Percentage of affected individuals (Affected individuals/Total reported) Microdontia Hypodontia/ oligodontia Molar root hypoplasia Other (Frequency) 1 0% (0/2) 0% (0/2) 0% (0/1) None 2 0% (0/2) 50% (1/2) 0% (0/1) Retained deciduous molar (1) 3 38% (3/8) 50% (4/8) 60% (3/5) Increased caries (1) Missing permanent premolar (1) Retained deciduous molar (1) 4 44% (7/16) 50% (7/14) 100% (6/6) Abnormal enamel (2) Discolouration (1) 5 56% (5/9) 67% (6/9) 63% (5/8) Increased caries (2) Abnormal enamel (1) Abnormal dentin (1) Discolouration (1) 6 78% (7/9) 43% (3/7) 100% (2/2) Delayed dentition (1) Increased caries (1) Abnormal dentin (1) Abnormal enamel (1) 7 0% (0/1) 100% (2/2) 100% (2/2) Abnormal superior incisors (1) Delayed dentition (1) Total 47% (22/47) 52% (23/44) 72% (18/25)   1Patients were grouped according to disease severity as previously described (Clewing et al. 2007b).  Abbreviations: SIOD, Schimke immuno-osseous dysplasia; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.  27  Besides small or absent teeth, 72% of SIOD patients had molar root hypoplasia (Table 2.1). The disproportion between the molar crown and root ranged from severe in SD38, SD57, SD60, SD74, and SD119 to nearly normal in SD18c (Figure 2.1A-F). The permanent premolars and first molars were commonly malformed, while the incisors and canines were usually normally shaped (Figure 2.1 and Supplementary Figure 2.1).  For the one patient (SD60) for whom I received photographs and radiographs at multiple developmental ages, both the deciduous and permanent dentitions were affected (Figure 2.1G-L). As shown for SD60, most had teeth of normal colour and opacity (Figure 2.1G and J).   28   29 Figure 2.1 Dental x-rays and photographs showing the dental pathology of patients with SMARCAL1 mutations. (A-F) Radiographic appearance of the teeth of 6 SIOD patients. The small white arrows indicate a retained deciduous molar in SD18c (A) and SD27 (B); the white asterisk indicates a missing permanent premolar in SD74 (E). (G-I) Physical (G) and radiographic (H and I) appearance of the deciduous teeth of patient SD60.  (J-L) Physical (J) and radiographic (K and L) appearance of the permanent teeth of patient SD60. Note that the microdontia, thin molar roots, and bulbous molar crowns are evident in both the deciduous and permanent teeth. The large white arrows indicate the bulbous molar crown in SD60 at 7 years of age (I) and 13 years of age (L); the small grey arrows indicate the thin molar roots in SD60 at 7 years of age (I) and 13 years of age (L). Abbreviations: SIOD, Schimke immuno-osseous dysplasia; yr, years.  2.3.2 SMARCAL1 is highly expressed in the developing human tooth To determine if SMARCAL1 was expressed in the tooth anlagen, I assessed SMARCAL1 expression by immunohistochemistry in postmortem tissue from 59-, 98-, and 105-day-gestation fetuses. SMARCAL1 was expressed in most cell types throughout the bud, cap, and bell stages (Figure 2.2A, B, E, F, I, and J and Supplementary Table 2.3). Compared with the oral epithelium, SMARCAL1 expression was moderate to strong in the outer and inner dental epithelia and primary enamel knot, moderate in the stellate reticulum, and weak in the dental papilla and dental lamina (Figure 2.2A, B, E, F, I, and J and Supplementary Table 2.3).  Observing that the premolars and molars were generally more affected than the anterior teeth, I hypothesized that SMARCAL1 was not expressed in the anterior teeth. However, immunohistochemical analysis of postmortem tissue from a 98-day-gestation fetus showed that SMARCAL1 was strongly expressed in the incisor, canine, and premolar anlagen as well as in the tooth bud of the permanent premolar (Supplementary Figure 2.2). 30   31 Figure 2.2 Analysis of SMARCAL1 protein expression during tooth morphogenesis. (A and B) Photomicrographs of SMARCAL1 immunohistochemical staining of the bud stage of tooth development. SMARCAL1 is expressed in the cells of the oral epithelium, dental lamina, and the mesenchymal cells, which give rise to the dental papilla. (C and D) Photomicrographs of pre-immune staining of the bud stage of tooth development. The cells of the oral epithelium, dental lamina, and mesenchymal cells showed minimal non-specific staining. (E and F) Photomicrographs of SMARCAL1 immunohistochemical staining of the cap stage of tooth development. SMARCAL1 is expressed in the cells of the dental lamina, outer dental epithelium, stellate reticulum, inner dental epithelium, primary enamel knot, and dental papilla. (G and H) Photomicrographs of pre-immune staining of the cap stage of tooth development. The cells of the dental lamina, outer dental epithelium, stellate reticulum, inner dental epithelium, primary enamel knot, and dental papilla did not show non-specific staining. (I and J) Photomicrographs of SMARCAL1 immunohistochemical staining of the bell stage of tooth development. SMARCAL1 is expressed in the cells of the outer dental epithelium, stellate reticulum, stratum intermedium, inner dental epithelium, and dental papilla. (K and L) Photomicrographs of pre-immune staining of the bell stage of tooth development. The cells of the dental lamina, outer dental epithelium, stellate reticulum, stratum intermedium, inner dental epithelium, and dental papilla showed minimal non-specific staining. The boxed regions correspond to the higher magnification images. Abbreviations: DL, dental lamina; DP, dental papilla; EK, primary enamel knot; IDE, inner dental epithelium; MC, mesenchymal cells; ODE, outer dental epithelium; OE, oral epithelium; SI, stratum intermedium; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1; SR, stellate reticulum. Scale bars: 50 m.  32 2.3.3 SIOD tooth anomalies are distinct from other disorders of DNA repair The known function of SMARCAL1 in DNA repair and replication suggests that SMARCAL1 deficiency in the proliferating tooth could lead to cell death or reduced proliferation. To test this hypothesis, we checked the viability and proliferation of dermal fibroblasts cultured from two SIOD patients. Dermal fibroblasts expressed SMARCAL1 mRNA and protein (Figure 2.3A-C), and dermal fibroblasts from SD120 and SD123 exhibited viability and proliferation rates similar to those from an unaffected control (Figure 2.3D). Additionally, I profiled the reported dental features of DNA repair and genomic instability disorders. Although microdontia, hypodontia, and short molar roots have been reported for Rothmund-Thomson syndrome, Fanconi anemia, Seckel syndrome, and dyskeratosis congenita (Supplementary Table 2.4), those teeth are distinct from those of SIOD. Together, these observations indicate that the dental anomalies observed in SIOD might not arise predominantly from impaired DNA repair or slowing of the cell cycle.  33  Figure 2.3 Expression of SMARCAL1 in cultured human dermal fibroblasts and dysregulated transcriptional responses of SIOD patient dermal fibroblasts upon induction with BMP4 or TGF1. (A) Photomicrographs showing immunofluorescence of SMARCAL1, fibroblast marker prolyl 4-hydroxylase, and DAPI in cultured human dermal fibroblasts. (B) Photograph of an immunoblot showing expression of SMARCAL1 protein in cultured human dermal fibroblasts. (C) Photograph of an agarose gel of RT-PCR products showing expression of SMARCAL1 mRNA in human dermal fibroblasts (+RT). A ‘no reverse transcription’ negative control  34 (-RT) shows that there is no detectable genomic DNA contamination. (D) The relative cell viability and proliferation rates for SD120 and SD123 patient fibroblast cell lines are graphed relative to unaffected control fibroblasts. (E) The relative basal gene expression levels of fibroblasts from an unaffected control (white) and patients SD120 (light grey) and SD123 (dark grey) were measured by qRT-PCR. Expression of the housekeeping gene GAPDH was used as the internal control; expression of each gene was first normalized to GAPDH expression and then graphed relative to the expression of the unaffected control. Error bars represent one standard deviation. (F) The transcriptional responses of fibroblasts from an unaffected control (white) and patients SD120 (light grey) and SD123 (dark grey) were measured by qRT-PCR following induction with the indicated morphogens for 0, 2, 4, 8, 12, 16, 20, or 24 hours. Expression of the housekeeping gene GAPDH was used as the internal control; expression of each gene was first normalized to GAPDH expression and then graphed relative to its expression in the relevant cell lines at time = 0 hours. Error bars represent one standard deviation. Abbreviations: *, p < 0.05; bp, base pairs; BMP4, bone morphogenetic protein 4; DAPI, 4, 6´-diamidino-2-phenylindole; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; h, hours; ID1, inhibitor of DNA binding 1; kDa, kilodalton; MMP10, matrix metallopeptidase 10; RT, reverse transcription; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1; TGF1, transforming growth factor  1. Scale bars: 10 m.  2.3.4 Wnt3A, BMP4, and TGF1 signaling is altered in cultured SIOD fibroblasts Since SMARCAL1 interacts with transcriptionally active chromatin, modulates gene expression (Baradaran-Heravi et al. 2012a), and is expressed in major signaling centres coordinating tooth development (Figure 2.2), I hypothesized that SMARCAL1 deficiency alters transcriptional responses to morphogens acting on or secreted by these centres. However, dental cells derived from SIOD patients are not available and knockdown of SMARCAL1 does not readily recapitulate the features of SIOD (Baradaran-Heravi et al. 2012a). Therefore, in an initial attempt to address this question, I asked whether SMARCAL1 deficiency cell-autonomously altered the transcriptional response to three morphogens involved in tooth formation in mice (Vainio et al. 1993; Unda et al. 2001; Plikus et al. 2005; Hosoya et al. 2008; Liu et al. 2008) and for which transcriptional responses have been defined in dermal fibroblasts (Afrakhte et al. 1998; Klapholz-Brown et al. 2007; Fessing et al. 2010; Ishikawa et al. 2010): Wnt3A, BMP4, and TGF1. By qRT-PCR, these morphogens induced the expression of all target genes analyzed in the unaffected control fibroblasts (Figure 2.3E and F, and Supplementary Figure 2.3), and SMARCAL1 deficiency altered the basal expression and induction of several targets (Figure 2.3E and F, Supplementary Figures 2.3 and 2.4, and Supplementary Tables 2.5 and 2.6). Less dramatically, but nonetheless significant, the level of induction of PRDM6 expression by Wnt3A was less than that observed in the control, and induction of SMAD6 and SMAD7 expression by TGF1 was premature compared to that of the control (Supplementary Figure 2.4 and Supplementary Table 2.6).   35 2.4 Discussion This first comprehensive review of the dental abnormalities in SIOD shows that 66% of patients with biallelic mutations of SMARCAL1 have tooth anomalies and demonstrates that the SMARCAL1 protein is highly expressed in the developing human tooth. Furthermore, as a potential explanation for the cell autonomous nature of the pathology, this study shows that SMARCAL1 deficiency significantly altered some gene expression responses to the tooth morphogens Wnt3A, BMP4, and TGF1 in cultured SIOD dermal fibroblasts.  As suggested by da Fonseca (2000), the dental phenotype in SIOD resembles that of dentinogenesis imperfecta type II, which is characterized by opalescent or translucent teeth with discolouration, increased attrition, short constricted roots, and obliteration of the pulp chambers (Shields et al. 1973). Similar to SIOD, dentinogenesis imperfecta type II affects both the deciduous and permanent dentition (Sclare 1948). However, unlike dentinogenesis imperfecta type II, SIOD teeth infrequently have discolouration or soft dentin, and the teeth of all SIOD patients reported herein had normal opacity. To the best of our knowledge, therefore, the dental phenotype of SIOD is unique. Since these dental findings are distinct and are moderately frequent in SIOD, they can be used to facilitate the diagnosis of SIOD. Therefore, individuals suspected to have SIOD should be referred for a comprehensive radiographic oral examination.  The expression of SMARCAL1 in the dental papilla, inner and outer dental epithelia, and the signaling centre known as the primary enamel knot suggests that its deficiency could cell-autonomously cause the root and crown malformations of SIOD. Within the developing murine tooth root, the inner and outer dental epithelia extend apically to give rise to the cervical loop and ultimately to Hertwig’s epithelial root sheath, which contributes to root development (Zeichner-David et al. 2003). Within the developing crown, the primary and secondary enamel knots regulate the size and shape of the crown while the dental papilla and inner dental epithelium give rise to the dentin-forming odontoblasts and enamel-forming ameloblasts (Thesleff 2003).  In addition to its role in DNA repair and replication, SMARCAL1 modulates gene expression (Baradaran-Heravi et al. 2012a). Although the mechanism by which it does this is unknown, its annealing helicase activity might modulate the DNA architecture of gene promoters. Indeed, Sharma et al. (2015) have recently demonstrated that the bovine orthologue of SMARCAL1 can alter the transcription of a gene by altering the conformation of its promoter (Sharma et al. 2015). According to this model, the deficiency of SMARCAL1 can lead to the  36 altered conformation of DNA in regulatory regions and promoters, and this inappropriately impedes or fosters the binding of transcription factors regulating responses to stimuli such as morphogens.  In this model, the tooth malformations observed in SIOD would arise by cell autonomous alterations in the transcriptional responses to dental developmental morphogens such as Wnt3A, TGF1, and BMP4. These morphogens are expressed in developing dental structures where SMARCAL1 is also expressed: Wnt3A and TGF1 are expressed by the cervical loop (Vaahtokari et al. 1991; Suomalainen and Thesleff 2010), BMP4 is expressed in pre-odontoblasts (Yamashiro et al. 2003), and BMP4 and TGF1 are expressed by the enamel knot (Vaahtokari et al. 1991; Aberg et al. 2004). These morphogens are involved in regulating various aspects of tooth morphogenesis. Wnt signaling regulates tooth number, size, and shape (Liu et al. 2008; Ahn et al. 2010). TGF1 modulates odontoblast differentiation (Unda et al. 2001). BMP4 mediates inductive epithelial-mesenchymal interactions (Vainio et al. 1993), regulates the formation of the enamel knot and Hertwig’s epithelial root sheath (Thesleff et al. 2001; Hosoya et al. 2008), and modulates tooth number, size, and shape (Plikus et al. 2005). Substantiation of this model, however, requires extensive additional studies. A finding not explained by this model is why SMARCAL1 deficiency affects molars more severely than anterior teeth. One possible speculation is that the more complex development of molars, which require the induction of secondary and tertiary enamel knots (Jernvall and Thesleff 2000; Luukko et al. 2003), renders the molar more susceptible to the consequences of SMARCAL1 deficiency on transcriptional responses to tooth morphogens. Alternatively, SMARCAL1 deficiency may not affect the expression of required genes in the developing anterior teeth as much as it does in the developing molar root. Future studies are required to test these speculations and define the differential dependence of developing molars on SMARCAL1 function. The failure of several morphogens to appropriately induce the expression of some target genes in SIOD patient-derived dermal fibroblasts is consistent with other observations of SIOD. First, supplemental growth hormone fails to improve growth rate and stature for 93% of SIOD patients (Boerkoel et al. 2000). Second, 40 - 50% of patients have a decreased response to thyroid-stimulating hormone and require levothyroxine supplementation (Boerkoel et al. 2000).  37 Third, the bone marrow failure and anemia associated with SIOD frequently do not respond to treatment with stem cell factor and erythropoietin, respectively (Boerkoel et al. 2000). Interestingly, I also observed that the basal expression of some of the target genes tested was altered (Figure 2.3E and Supplementary Figure 2.4A). These findings agree with our prior observations of SMARCAL1 deficiency altering gene expression (Baradaran-Heravi 2012a), but also have implications for the interpretation of the morphogen induction experiments. Specifically, some of the target genes may have failed to respond to the morphogen of interest due to the gene being expressed at a higher level in the SIOD patient dermal fibroblasts. One of our objectives was to test the consequences of SMARCAL1 deficiency on the transcriptional responses to the tooth developmental morphogens Wnt3A, BMP4, and TGF1. Cultured dermal fibroblasts from SIOD patients were used for this study because the patient cell lines were available, and the transcriptional targets of the morphogens of interest have been characterized well in this model. Other systems were considered but were insufficient for the study for several reasons. First, although a small number of human studies have been undertaken to determine differentiation factors to differentiate dental pulp cells into specific dental cell types such as ameloblasts (DenBesten et al. 1999) and odontoblasts (Couble et al. 2000; Huang et al. 2006; Lee et al. 2011), teeth from SIOD patients extracted for orthodontic reasons have not been available so far and transcriptional targets are less defined for these cell types in the human. Second, the use of induced pluripotent stem cells would circumvent the requirement for SIOD dental tissue, however studies on induced pluripotent stem cells and mammalian tooth development in general are largely based on murine models (Arakaki et al. 2012; Liu et al. 2013; Ozeki et al. 2013; Seki et al. 2015; Yoshida et al. 2015); human dentition is distinct from murine dentition in the number, size, and shape of teeth, and thus the factors that differentiate induced pluripotent stem cells in the mouse must be first verified in the human. Third, the use of dental pulp cells from Smarcal1-deficient mice would allow us to leverage the extensive techniques and knowledge from prior studies, however Smarcal1-deficient mice do not exhibit dental abnormalities (data not shown). Fourth, knockdown of SMARCAL1 orthologues does not readily recapitulate the features of SIOD (Baradaran-Heravi et al. 2012a).  In summary, my findings show that dental abnormalities are common among SIOD patients and that SMARCAL1 is highly expressed in the developing tooth. Furthermore, the finding that SMARCAL1 deficiency alters transcriptional responses to morphogens in cultured  38 fibroblasts suggests a mechanism for the dental pathology of SIOD. These observations also provide a model for understanding how SMARCAL1 deficiency could give rise to other malformations characteristic of SIOD.    39 Chapter 3: Molecular Characterization and Pathogenesis of the Vascular Disease in SIOD  3.1 Introduction The vascular disease of SIOD manifests as migraine-like headaches, transient ischemic attacks (TIAs) (transient episodes of neurologic dysfunction due to insufficient blood flow), and cerebrovascular accidents (CVAs) (prolonged episodes of neurologic dysfunction due to insufficient blood flow) (Boerkoel et al. 2000). As renal transplantation and dialysis have prolonged the longevity of SIOD patients, cerebral ischemia from arteriosclerosis has increasingly contributed to morbidity and mortality (Boerkoel et al. 1998; Boerkoel et al. 2000). Although treatment with anticoagulant or hemorheological medications can transiently decrease the frequency and severity of cerebrovascular accidents and transient ischemic attacks, the vascular disease ultimately progresses (Boerkoel et al. 2000) and is not associated with detectable alterations in nitric oxide production or mitochondrial dysfunction (Lücke et al. 2005b; Lücke et al. 2006b). The arterial histopathology shows intimal and medial hyperplasia, smooth muscle cell hyperplasia, and fragmented and disorganized elastic lamellae (Clewing et al. 2007a). Potential contributors to the arteriosclerosis include immune dysfunction, hyperlipidemia, hypertension, and renal disease (Smith 1978; Ehrich et al. 1995; Boerkoel et al. 2000; Zieg et al. 2011). However, the arterial pathology observed by Clewing et al. (2007a) is most similar to that reported for elastin deficiency (Milewicz et al. 2000; Bateman et al. 2009; Shao et al. 2011). Elastin deficiency arises either from mutations of the elastin (ELN) gene or from impaired function or expression of enzymes that process or bind elastin, such as the chaperone elastin binding protein (EBP) (Sandberg et al. 1981). Mice heterozygous for elastin (Eln) gene deletions show many features in common with SIOD patients, including systemic hypertension, pulmonary hypertension, aortic valve disease and frequent inguinal hernias (Boerkoel et al. 2000; Dietz and Mecham 2000; Pezet et al. 2008). Further highlighting the possibility of elastin deficiency as a contributor to the arteriosclerosis, the postmortem lungs of two SIOD patients showed enlarged air spaces or emphysematous changes that are a common feature in disorders of elastogenesis (Ehrich et al. 1995; Dietz and Mecham 2000; Pezet et al. 2008).  40 Given these observations, I hypothesized that EBP deficiency and/or elastin deficiency were the primary causes of the vascular disease associated with SIOD. Therefore the objectives of the first study were to determine the prevalence of vascular disease in SIOD by reviewing the records of SIOD patients with identified SMARCAL1 mutations, to delineate further the arterial pathology of SIOD through histopathological analyses, and to profile gene expression in the postmortem aorta of an SIOD patient by quantitative reverse transcription PCR (qRT-PCR). I identified reduced ELN messenger RNA (mRNA) expression as a possible basis of the arteriosclerosis of SIOD. Elastin, which is critical for the development and maintenance of the arteries (Li et al. 1998a; Karnik et al. 2003), confers the elastic recoil properties required for the proper function of the arteries as well as other load-bearing tissues such as the skin, intestines, and lungs and has signaling capabilities in addition to being a structural protein (Karnik et al. 2003). Mouse models deficient for elastin die shortly after birth due to arterial obstruction (Li et al. 1998a), and mouse models haploinsufficient for Eln exhibit hypertension and alterations in arterial wall structure (Faury et al. 2003). Similarly, human haploinsufficiency for ELN causes supravalvular aortic stenosis, and elastin deficiency causes lax skin, vascular disease, and respiratory disease (Milewicz et al. 2000). To further investigate the potential molecular mechanisms underlying the reduced ELN mRNA expression in SIOD, I performed several targeted studies to assess known transcriptional and post-transcriptional mechanisms of ELN gene regulation in SIOD. Although ELN and its regulation have been studied for several decades, its transcriptional and post-transcriptional regulation during human aorta development is largely unknown. Therefore, the aim of this second study was, first, to establish the transcriptional and post-transcriptional mechanisms that regulate ELN expression in the developing human aorta and, second, to assess these mechanisms in SIOD. The transcriptional mechanisms studied include the expression of transcriptional regulators of ELN and promoter methylation of the ELN gene, as well as the expression of ELN precursor messenger RNA (pre-mRNA) as a general readout of ELN gene transcription. The post-transcriptional mechanisms studied included the expression of microRNA (miRNA) regulators and poly(A) tail shortening of ELN mRNA. Since elastin expression is vastly different between the fetal and adult aortas, I hypothesized that at least one or more of these mechanisms  41 contributes to the postnatal decrease in ELN mRNA expression and that dysregulation of one or more of these mechanisms contributes to the decreased ELN mRNA expression in SIOD.  3.2 Methods 3.2.1 Human samples SIOD patients referred to this study are presented in Table 3.1. Human adult aorta total RNA pooled from four unaffected individuals ranging in age from 27 to 45 years was purchased from Clontech (Mountain View, CA, USA). Sample characteristics and sample usage with respect to each figure are noted in Supplementary Table 3.1.  3.2.2 Tissue immunohistochemistry and staining Formalin-fixed, paraffin-embedded sections were cut at 5 m. Following deparaffinization and rehydration, heat-induced epitope retrieval was conducted with sodium citrate buffer (10 mM sodium citrate, 0.05% Tween 20, pH 6.0) or tris-ethylenediaminetetraacetic acid (EDTA) buffer (10 mM Tris base, 1 mM EDTA, 0.05% Tween 20, pH 9.0). For immunohistochemical detection of elastin, proteolytic induced epitope retrieval was conducted with 0.4% pepsin at 37C for 15 minutes. Endogenous peroxidases were inactivated by incubating the sections with 0.3% H2O2 in methanol. Non-specific protein binding was blocked by incubating the sections in blocking buffer (10% horse serum in Tris-buffered saline (TBS)-T (10 mM Tris-HCl, 150 mM NaCl, 0.01% Tween 20, pH 7.4)) at 4C overnight. Sections were then incubated with anti-SMARCAL1 antiserum (1:200) (Kilic et al. 2005), anti-CD3 (1:50, MRQ-39, Cell Marque, Rocklin, CA, USA), anti-CD20 (1:50, L26, Cell Marque, Rocklin, CA, USA), anti-CD68 (1:250, KP1, Dako, Mississauga, ON, Canada), anti--smooth muscle actin (1:500, 1A4, Dako, Mississauga, ON, Canada), or anti-elastin (1:50, BA-4, Abcam, Cambridge, MA, USA) diluted in blocking buffer at 4C overnight. Sections were then washed five times with TBS-T and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies (EnVision+ System, Dako, Mississauga, ON, Canada) at room temperature for 30 minutes. Sections were then washed three times with TBS-T, and 3, 3´-diaminobenzidine (DAB, EnVision+ System, Dako, Mississauga, ON, Canada) was subsequently used as an HRP substrate. Sections were counterstained in Mayer’s Hematoxylin (Sigma, Oakville, ON, Canada).  42  Histochemical stains used included a modified Verhoeff-Van Gieson elastic stain (HT25A, Sigma, Oakville, ON, Canada) for elastic lamellae and a periodic acid-Schiff stain (395B, Sigma, Oakville, ON, Canada) for neutral glycosaminoglycans. Images were acquired using a 5/0.15 Plan-NEOFLUAR, 10/0.45 Plan-APOCHROMAT, 20/0.75 Plan-APOCHROMAT, 63/1.4 oil Plan-APOCHROMAT, or 100/1.30 oil Plan-NEOFLUAR objective lens on an Axiovert 200 inverted microscope, an AxiocamHR camera, and the AxioVision software version 4.8 (Carl Zeiss, Toronto, ON, Canada).  3.2.3 RNA extraction and reverse transcription For cultured cells, RNA was extracted from 1  107 cells using the RNeasy Mini Kit (Qiagen, Toronto, ON, Canada). For tissues, RNA was extracted from flash frozen tissue pulverized with a Bessman Tissue Pulverizer (Spectrum Laboratories, Rancho Dominguez, CA, USA) and lysed with TRIzol (Invitrogen, Burlington, ON, Canada) according to the manufacturer’s specifications. Subsequently, the RNeasy Mini Kit (Qiagen, Toronto, ON, Canada) was used to extract the RNA. Residual genomic DNA was removed by DNase I digestion.  Control aorta RNA pooled from four unaffected individuals ranging in age from 27 - 45 years was purchased from Clontech (636546, Lot no. 9052725A, Mountain View, CA, USA).  RNA from formalin-fixed paraffin-embedded umbilical cord was isolated using the Ambion RecoverAll Total Nucleic Acid Isolation Kit (Life Technologies, Burlington, ON, Canada) according to the manufacturer’s specifications.  Reverse transcription was performed using the qScript cDNA SuperMix (Quanta Biosciences, Gaithersburg, MD, USA) or the RT2 First Strand Kit (SABiosciences, Mississauga, ON, Canada) using 500 ng of RNA per reaction according to the manufacturer’s specifications.  3.2.4 Polymerase chain reaction Following reverse transcription, 1.5 l of complementary DNA (cDNA) served as template for each reaction and was amplified with the HotStarTaq Master Mix Kit (Qiagen, Toronto, ON, Canada). The following conditions were used for amplification: 1 cycle of 95C for 15 minutes, followed by 30 cycles of 94C for 30 seconds, 55C for 30 seconds, 72C for 1  43 minute, and a final extension at 72C for 10 minutes. Polymerase chain reaction (PCR) was performed using the primers listed in Supplementary Table 3.2.  3.2.5 Gene expression array The Atherosclerosis RT2 Profiler PCR Array (PAHS-038) (SABiosciences, Mississauga, ON, Canada) was used to assess differences in gene expression between unaffected control and SIOD aorta samples according to the manufacturer’s instructions.  3.2.6 ELN mutation analysis Genomic DNA was extracted from the aorta of SD120 using the DNeasy Tissue Kit (Qiagen, Toronto, ON, Canada) according to the manufacturer’s specifications. The 34 exons of ELN were amplified with the HotStarTaq Plus Master Mix Kit (Qiagen, Toronto, ON, Canada). The following conditions were used for amplification: 1 cycle of 95C for 5 minutes, followed by 35 cycles of 94C for 30 seconds, 55C or 60C for 30 seconds, 72C for 45 seconds, and a final extension at 72C for 10 minutes. PCR was performed using the primers listed in Supplementary Table 3.3. Unincorporated primers and nucleotides were removed using USB ExoSAP-IT PCR Product Cleanup (Affymetrix, Cleveland, OH, USA).  Sanger sequencing was used to sequence the PCR products (Macrogen, Seoul, Korea), and the sequences were aligned and analyzed using Sequencher v.4.10.1 (Gene Codes, Ann Arbor, MI, USA). Mutation interpretation analysis was conducted using Alamut 2.0 (Interactive Biosoftware, San Diego, CA, USA).  3.2.7 Cell culture Aortic smooth muscle cells (AoSMCs, Lonza, Walkersville, MD, USA) were grown in smooth muscle basal medium supplemented with 5% fetal bovine serum (FBS), epidermal growth factor, basic fibroblast growth factor, insulin, gentamicin, and amphotericin B (Lonza, Walkersville, MD, USA).  Human iliac artery endothelial cells (HIAECs, Lonza, Walkersville, MD, USA) were grown in endothelial basal medium supplemented with 5% FBS, epidermal growth factor, basic fibroblast growth factor, vascular endothelial growth factor, R3 insulin-like growth factor 1, hydrocortisone, ascorbic acid, gentamicin, and amphotericin B (Lonza, Walkersville, MD, USA).  44  Aortic adventitial fibroblasts (AoAFs, Lonza, Walkersville, MD, USA) were grown in stromal cell basal medium supplemented with 5% FBS, basic fibroblast growth factor, insulin, gentamicin, and amphotericin B (Lonza, Walkersville, MD, USA).  3.2.8 Indirect immunofluorescence Immunostaining of cultured cells was performed as previously described (Deguchi et al. 2008). 5  105 cells were grown overnight on a coverslip in a 6-well plate. With the exception of the AoSMCs, all other cells were fixed with 4% paraformaldehyde for 15 minutes at room temperature and permeabilized with 0.5% Triton X-100 for 15 minutes at room temperature. AoSMCs were fixed with 4% PFA and 0.15% picric acid for 20 minutes at room temperature, and permeabilized with 0.1% Triton X-100, 1% bovine serum albumin, and 10% normal horse serum in 1 phosphate-buffered saline (PBS). All cells were blocked overnight with Blocker Casein in PBS (Pierce Biotechnology, Rockford, IL, USA) containing 10% normal horse serum at 4C. The cells were then incubated with rabbit anti-SMARCAL1 antiserum (1:200) (Kilic et al. 2005), anti--smooth muscle actin (1:20, clone 1A4, Dako, Mississauga, ON, Canada), anti-prolyl 4-hydroxylase (1:50, clone 5B5, Abcam, Cambridge, MA, USA), or anti--tubulin (1:400, DM 1A, Sigma-Aldrich, Oakville, ON, Canada) diluted in blocking buffer at 4C for 24 hours. Cells were then gently washed 4 times with 1 PBS and incubated with Alexa Fluor-conjugated secondary antibodies (1:1,000, Molecular Probes, Burlington, ON, Canada) for 1 hour at room temperature. Cells were then washed 4 times with 1 PBS and mounted in Vectashield containing 4, 6´-diamidino-2-phenylindole (DAPI, Vector Laboratories, Burlington, ON, Canada). Images were acquired using a 100/1.30 oil Plan-NEOFLUAR objective lens on an Axiovert 200 inverted microscope, an AxioCamMR camera, and the AxioVision software version 4.8 (Carl Zeiss, Toronto, ON, Canada).  3.2.9 Immunoblot analysis Immunoblot analysis on cell lysates was performed as previously described (Deguchi et al. 2008). Cell lysates were fractionated by 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a polyvinylidene fluoride membrane. The membrane was blocked overnight at 4C, using gentle agitation, in 1 PBS containing 0.2% I-Block (Applied Biosystems, Foster City, CA, USA) and 0.1% Tween 20 overnight. Anti-SMARCAL1 (1:2,000)  45 (Kilic et al. 2005) and anti-glyceraldehyde 3-phosphate dehydrogenase (GAPDH, 1:2,000, clone 6C5, Advanced ImmunoChemical, Long Beach, CA, USA) were used as primary antibodies. Alkaline phosphatase-conjugated secondary antibodies (1:10,000, Bio-Rad Laboratories, Mississauga, ON, Canada) were used to detect the primary antibodies. The bound antibody was detected by chemiluminescence using CDP-Star (Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s specifications. GAPDH was detected as a loading control.  Immunoblot analysis on human tissue was performed as previously described (Deguchi et al. 2008). Anti-elastin binding protein (EBP, 1:200, a kind gift from Dr. Amelia Morrone, University of Florence, Florence, Italy) (Malvagia et al. 2004; Caciotti et al. 2005) and anti-GAPDH were used as primary antibodies. EBP expression in the aortas of 2 SIOD patients was compared to that of an unaffected control aorta protein medley pooled from 49 unaffected individuals ranging in age from 15 - 65 years and purchased from Clontech (635310, Lot no. 5110079, Mountain View, CA, USA). EBP expression was normalized to the expression of GAPDH for each sample. Densitometry analysis of three technical replicates was conducted using the Kodak 1D Image Analysis software version 3.6.  3.2.10 Fastin elastin assay The elastin content of arterial tissue was quantified using the Fastin Elastin Assay Kit (F2000, Bicolor Life Science Assay, United Kingdom). Tissue samples were flash frozen and pulverized with a Bessman Tissue Pulverizer (Spectrum Laboratories, Rancho Dominguez, CA, USA), weighed, and digested with 0.25 M oxalic acid at 95C for 6  1 hour time periods. Elastin concentration in pooled supernatants was calculated from the elastin standard curve and the total elastin per wet weight of each sample was determined according to the manufacturer’s specifications.  3.2.11 Arterial thickness analysis Analysis of the aortic intimal and medial thickness was carried out using the AxioVision software version 4.8 (Carl Zeiss, Toronto, ON, Canada) to measure the width of the tunica intima and the tunica media. Four random images of each sample were taken and five random measures were taken for both the tunica intima and the tunica media for each image. Measures of the tunica intima were taken from the luminal edge of the endothelium to the internal elastic lamina  46 perpendicular to the internal elastic lamina. Ratios were calculated comparing the widths of the tunica intima and tunica media of SIOD patient aortas to that of age-matched unaffected control aortas.  3.2.12 Bisulfite Sanger sequencing Genomic DNA was extracted from the aorta samples, and 500 ng of DNA was bisulfite converted using the EZ DNA Methylation-Gold Kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s protocol. Five overlapping regions of the ELN gene promoter were amplified by PCR with the HotStarTaq Master Mix Kit (Qiagen, Toronto, ON, Canada) and subsequently cloned into the pCR2.1 vector using The Original TA Cloning Kit (Invitrogen, Carlsbad, CA, USA). A schematic of the five amplified regions is presented in Figure 3.8A. The primer sequences used to amplify the five regions were designed using the MethPrimer program (http://www.urogene.org/methprimer/) and are listed in Supplementary Table 3.4. Individual clones were isolated and PCR amplified with the following primers: pCR2.1-F (5´-ATGACCATGATTACGCCAAG-3´) and pCR2.1-R (5´-CGACTCACTATAGGGCGAATTG-3´). The PCR products were sequenced using Sanger sequencing; for each sample, ten clones of each fragment were analyzed. The sequence data were analyzed using the Sequencher version 5.1 software (Gene Codes, Ann Arbor, MI).  3.2.13 miR-29 expression analysis To analyze the expression of the miR-29 family of miRNAs in the aorta, I used the miScript PCR System (Qiagen, Toronto, ON, Canada) according to the manufacturer’s protocol. Briefly, 135 ng of total RNA was reverse transcribed using the HiSpec Buffer in a 10 l reaction. The cDNA was diluted 1:5, and 1 l of the diluted cDNA was used per 10 l reaction. All quantitative PCRs were performed in triplicate using the QuantiTect SYBR Green PCR Master Mix (Qiagen, Toronto, ON, Canada) and the miScript Primer Assays Hs_miR-29a_1, Hs_miR-29b_1, and Hs_miR-29c_1 (forward primers) (Qiagen, Toronto, ON, Canada) and the miScript Universal Primer (reverse primer). Results were normalized to the level of small nuclear RNA RNU6-2. Quantitative PCR was performed on the StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Expression analyses were performed with the StepOnePlus software version 2.1 (Applied Biosystems, Foster City, CA, USA).  47 3.2.14 Poly(A) tail length assay To determine the poly(A) tail length of ELN mRNA, the Poly(A) Tail Length Assay Kit (USB/Affymetrix, Cleveland, OH, USA) was used according to the manufacturer’s protocol. In brief, 250 ng of DNase I-treated RNA was used for the initial tailing reaction. Five microlitres of each tailed RNA sample was used for reverse transcription. The cDNA was then diluted 1:1 with water and 2 l was used for each PCR. ELN mRNA has 2 poly(A) sites as previously described (Hagmeister et al. 2012). To amplify the ELN mRNA poly(A) tails, the universal primer supplied by the manufacturer and forward primer I or forward primer II were used to amplify transcripts terminating at the proximal and distal or only distal polyadenylation site(s), respectively. To ensure that the forward primers were specifically binding to the ELN mRNA 3´ UTR, a gene-specific control PCR was performed using primer pair I and primer pair II (Figure 3.10A and Supplementary Table 3.5). DNA fragments were separated on a 2% tris-acetate EDTA agarose gel and visualized by ethidium bromide staining. The 1 Kb Plus DNA Ladder (Invitrogen, Carlsbad, CA, USA) was used as a molecular weight marker.  3.2.15 Statistics For the PCR expression arrays, data were analyzed by the 2-tailed Student’s t-test. A p value of less than 0.05 was considered statistically significant.  To evaluate the correlation between age and the expression of ELN mRNA and known transcriptional regulators of ELN, the non-parametric Spearman’s rank order correlation was used. Statistical significance was defined as a p value of less than 0.05 for a one-tailed test (ELN mRNA expression vs. age) or a two-tailed test (transcription factor expression vs. age). Statistical analyses were conducted using SPSS Statistics version 21 (IBM, Armonk, NY, USA).       48  3.3 Results 3.3.1  Vascular disease is common in SIOD Among SIOD patients with biallelic SMARCAL1 mutations, 51% of patients (32 of 63) had clinical symptoms of cerebral ischemia. Forty-three percent of patients (25 of 58) had cerebrovascular accidents (CVAs), 46% of patients (27 of 59) had transient ischemic attacks (TIAs), and 23% of patients (7 of 30) had moya moya phenomenon (Table 3.1). For 7 patients, the onset of cerebral ischemia preceded the development of renal disease or hypertension, and among patients who received a renal transplant, cerebral ischemia worsened despite renal transplantation (data not shown). Of the 65 patients with SMARCAL1 mutations, 47 have died, and 15% of these patients (7 of 65) died from vascular disease (Table 3.1). 49 Table 3.1 Summary of vascular findings in SIOD patients with SMARCAL1 mutations.  Patient ID SMARCAL1 mutations Sex Vascular findings Age at death Cause of death Age at onset CVA TIA Moya moya SD4a c.[410delA];[1930C>T] F NA  NR NR 8 Renal failure SD4b c.[410delA];[1930C>T] M NA  NR NR 8 Renal failure SD8 c.[1190delT];[?]1 F NA  NR NR 5.7 Pneumonia SD16 c.[1643T>A];[1933C>T] M NA      SD18a c.[1756C>T];[1756C>T] M NA NR  NR 43 Cryptococcus meningitis SD18c c.[1756C>T];[1756C>T] F NA      SD22 c.[2459G>A];[2459G>A] M 8  +  14.6 CMV infection SD23 c.[2542G>T];[2542G>T] M 4.1 + +  10.3 Unknown SD24 NT_005403.17: g.[67482574_67497178del]+ [67482574_67497178del] F 7.5 + + NR 9 CVA SD25 c.[49C>T];[100C>T] F 5 + + NR 10.1 Cerebrovascular event SD26 c.[1190delT];[2542G>T] M 5.3 + +  8 Renal and bone marrow failure SD27 c.[1940A>C];[1940A>C] F NA    25.6 Infectious pulmonary disease SD28 c.[1696A>T;1698G>C;1702delG]; [1696A>T;1698G>C;1702delG] M NA NR  NR 12 Pulmonary hypertension SD29 c.[862+1G>T];[1934delG] M < 3 + + NR 4 Infectious pulmonary disease2 SD30 c.[1132G>T];[1132G>T] F 5.7 +  + 10 HSV pneumonitis SD31 NT_005403.17: g.[67482574_67497178del]+ [67482574_67497178del] F 11 + + + 14 Lymphoproliferative disease (secondary) SD33a c.[1097-2A>G]; [1146_1147delAA;1147+1_2delGT] F NA   NR 2.8 Bone marrow failure  50 Patient ID SMARCAL1 mutations Sex Vascular findings Age at death Cause of death Age at onset CVA TIA Moya moya SD33b c.[1097-2A>G]; [1146_1147delAA;1147+1_2delGT] M < 1 +  NR 3.7 CVA SD35 c.[1736C>T];[2321C>A] M NA    8 Renal failure SD38 c.[1096+1G>A];[1096+1G>A] M NA NR NR  10.8 Complications of blood stem cell transplant SD39 c.[1402G>C];[2114C>T] M 11 + + NR 15 CVA SD44 c.[1191delG];[2321C>A] M 9 + + NR 11.9 Digestive bleeding SD47 c.[2459G>A];[?]1 M 7 + +    SD48 c.[1939A>C];[1939A>C] F 4 + + + 6.8 EBV pneumonia SD49 c.[1920_1921insG];[2321C>A] M NA    4.8 Unknown SD50 c.[2542G>T];[2542G>T] F 4.5 + + NR 8 Peritonitis and sepsis post transverse colon perforation SD51 c.[2459G>A];[2542G>T] F NA      SD53 c.[2291G>A];[2543G>T] M NA   NR   SD57 c.[955C>T];[955C>T] F 8 + +  28 Pancreatitis SD60 c.[2542G>T];[2542G>T] M 8 + + NR 13.7 CVA SD61 c.[1146_1147delAA;1147+1_2delGT]; [1146_1147delAA;1147+1_2delGT] M NA   NR 5 Lymphoproliferative disease (primary) SD65a c.[836T>C];[2542G>T] M NA      SD65b c.[836T>C];[2542G>T] M 14 + +    SD66 c.[1933C>T];[1933C>T] M 7 + + + 13 Congestive heart failure SD68 c.[1940A>C];[2462T>G] F 6 + +  7.1 Cerebrovascular event SD70 c.[340_341insAGTCCAC];[836T>C] F 6 + +  18 Recurrent ileus pathology SD71 c.[836T>C];[1000C>T] M 6 + +  9 Unknown SD74 c.[1736C>T];[?]1 M NA       51 Patient ID SMARCAL1 mutations Sex Vascular findings Age at death Cause of death Age at onset CVA TIA Moya moya SD78 c.[1439C>T];[2264T>G] F NA NR  NR 10 Pneumonia SD79 c.[2459G>A];[?]1 F NA    10 Complications of BMT SD84 c.[1248_1249insC];[2104T>G] M 10  + + 23 Pulmonary hypertension with heart failure SD86 c.[1129G>C];[2263_2282delATCGATGGCTCCACCTCATC]; F NA    5.7 Complications following BMT SD96 c.[1427G>A];[1427G>A] M NA    6 Infection3 SD99 c.[1402G>C];[1402G>C] F NA   NR 5.5 Pulmonary edema and left heart failure SD101 c.[2542G>T];[2542G>T] M 3  +    SD102 c.[2542G>T];[2542G>T] M NA NR NR NR 8 Renal failure SD106 c.[1682G>A];[1682G>A] M 5.5  + NR 8 Non-Hodgkin lymphoma SD107 c.[2542G>T];[2542G>T] F NA NR  NR 6 Thrombosis SD108a c.[1798C>T];[1798C>T] M NA      SD108b c.[1798C>T];[1798C>T] M NA   NR   SD111 c.[1129G>C];[1592T>C] M 15 + +  17.5 Respiratory failure SD112a c.[1934G>A];[2542G>T] F NA      SD112b c.[1934G>A];[2542G>T] F NA      SD114 c.[1898T>C];[1898T>C] M 4 +  + 9.5 Unknown SD115 c.[1437_1438insG];[1437_1438insG] F NA    1 Pneumonia with respiratory failure SD119 c.[2449C>T];[2542G>T] F NA NR  NR   SD120 c.[2291G>A];[2542G>T] M 3  +  5.5 Respiratory failure SD121 c.[1382G>A];[2542G>T] F 3.3 +   4.8 CVA SD123 c.[49C>T];[49C>T] F 4  +    SD124 c.[1920_1921insG];[1920_1921insG] M NA  NR    SD127 c.[1736C>T];[1736C>T] F 7 + + +    52 Patient ID SMARCAL1 mutations Sex Vascular findings Age at death Cause of death Age at onset CVA TIA Moya moya SD131 c.[1026C>A];[2264T>G] M NR +   4.6 Cerebral hemorrhage SD133a c.[863-2A>G ;2343_2347_delGCTGT]; [=;2343_2347_delGCTGT] F NA    3 Pulmonary embolism (secondary) SD133b c.[863-2A>G ;2343_2347_delGCTGT]; [=;2343_2347_delGCTGT] F      Terminated pregnancy SD138 c.[2542G>T];[2542G>T] M NA NR NR NR    Abbreviation: +, feature present; -, feature absent; BMT, bone marrow transplant; CVA, cerebrovascular accident; CMV, cytomegalovirus; EBV, Epstein-Barr virus; F, female; HSV, herpes simplex virus; ID, identification; M, male; NA, not applicable; NR, not reported, SIOD, Schimke immuno-osseous dysplasia; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1; TIA, transient ischemic attack.  1[?] represents alleles with non-coding SMARCAL1 mutations as described by Clewing et al. (2007b). 2No bacteriologic or viral proof. 3Infection of peritoneal dialysis fluid. 53 3.3.2 Histopathology of the SIOD aorta shows fragmented elastic lamellae and hyperplasia of the tunica intima and tunica media To better define the histopathology of SIOD arteries, I analyzed postmortem arterial tissue from three individuals with SIOD (SD60, SD84, and SD120). Verhoeff-Van Gieson staining showed fragmented elastic lamellae compared to age-matched unaffected controls (Figure 3.1 and Supplementary Figure 3.1). The aorta, common iliac, and pulmonary arteries of SD60, SD84, and SD120 had marked intimal and medial hyperplasia accompanied by an increased number of elastic lamellae compared to age-matched unaffected controls (Figure 3.1 and Supplementary Figure 3.1). The aortic tunica intima of SD60 and SD120 were respectively 2.6- and 1.4-fold thicker than age-matched unaffected controls; the tunica media of SD60 and SD120 were 1.3-fold thicker than age-matched unaffected controls. By echocardiogram, SD120 had increased diameter of the sinotubular junction compared to normal using the Halifax formula, although measures at other levels of the aortic root were within the normal range (Supplementary Table 3.6). Immunostaining for -smooth muscle actin showed an increased number of positive cells suggesting smooth muscle cell hyperplasia in the aortas of SD60 and SD120 (Supplementary Figure 3.2).    54  Figure 3.1 Verhoeff-Van Gieson staining of aortas from SIOD patients and age-matched unaffected controls. Compared to age-matched unaffected controls, note the decreased elastic lamellae staining, the fragmentation and splitting of the elastic lamellae, the marked hyperplasia of the tunica intima and the tunica media in the aorta from three individuals with SIOD. Arteries are oriented with the tunica adventitia on the left and the tunica intima on the right; the age at death is in parentheses. Scale bars = 50 m. Abbreviations: SIOD, Schimke immuno-osseous dysplasia; yr, years.  3.3.3 Inflammation is not increased in the SIOD aorta Since SIOD patients have an immune disorder and the inflammation of atherosclerosis causes smooth muscle cell hyperplasia (Ross 1999; Libby 2002), I also looked for evidence of arterial inflammation. Using CD3 as a T-cell marker, CD20 as a B-cell marker, and CD68 as a macrophage marker, immunostaining did not detect an inflammatory infiltrate within the arterial walls except for the CD68+ macrophages within the atherosclerotic lesions of SD84 (Supplementary Figure 3.3).  3.3.4 Histopathology of the SMARCAL1-deficient umbilical cord shows a fragmented internal elastic lamina As longstanding hypertension, renal failure, and hyperlipidemia of individuals SD60, SD84, and SD120 could be a cause of the arterial disease and are not present in individuals with SIOD at birth, I tested this hypothesis by studying the umbilical artery of a 15-week-gestation  55 fetus (SD133b) with biallelic SMARCAL1 mutations. Compared to age-matched unaffected controls, the fetal umbilical arteries of SD133b had interrupted circumferential expression of tropoelastin and elastin suggesting an intrinsic problem with elastogenesis (Figure 3.2). Moreover, analysis of ELN mRNA expression in the umbilical cord of SD133b and two age-matched unaffected controls showed that the umbilical cord of both controls had 1.8- to 3.0-fold higher ELN mRNA expression compared to that of SD133b (Supplementary Figure 3.4).     56   Figure 3.2 Elastin expression analysis of the umbilical cord from SMARCAL1-deficient and unaffected fetuses at 15 weeks gestation. Note that the immunohistochemical analysis shows marked discontinuity and reduced expression of elastin in the internal elastic lamina in SD133b compared to that of two age-matched unaffected controls. This difference in expression of elastin precedes the development of hypertension, hypercholesterolemia, and renal disease. Boxed regions correspond to the higher magnification images on the right. Scale bars = 50 m. Abbreviations: A, artery; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1; V, vein.  3.3.5 SMARCAL1 is expressed in the vascular smooth muscle, endothelial, and adventitial fibroblast cells of the arterial wall The above findings suggest a local or cell autonomous basis for the arteriosclerosis in SIOD, and consistent with this mechanism, arteriosclerosis does not recur in the transplanted kidneys of SIOD patients (Lücke et al. 2004; Clewing et al. 2007a). As a first requirement for a  57 cell autonomous mechanism, SMARCAL1 must be expressed within the affected tissues, and indeed, SMARCAL1 was expressed in the smooth muscle cells and endothelium of the unaffected human aorta, common iliac, and pulmonary arteries (Figure 3.3A-C). It was also expressed in the nuclei of cultured aortic smooth muscle cells (AoSMCs), human iliac artery endothelial cells (HIAECs), and aortic adventitial fibroblasts (AoAFs) (Figure 3.3D-K). Given these findings, I hypothesized that the cell autonomous mechanisms of elastin binding protein (EBP) deficiency or impaired elastogenesis could give rise to arteriosclerosis.   Figure 3.3 SMARCAL1 mRNA and protein are expressed in arterial tissue and cell types. (A-C) Immunohistochemical detection of SMARCAL1 protein in the aorta, common iliac, and pulmonary arteries. (D) Immunoblot showing expression of SMARCAL1 protein in AoSMCs, HIAECs, and AoAFs. (E) RT-PCR products showing expression of cell-specific markers and SMARCAL1 mRNA in AoSMCs, HIAECs, and AoAFs. Note that ACTA2 is a marker of smooth muscle cells that has also been detected in aortic endothelial cells (Azuma et al. 2009), CDH5 is a marker of endothelial cells, and P4HA3 is a reported fibroblast marker but also has been detected in other cell types (Goodpaster et al. 2008). (F-H) Immunofluorescent localization of SMARCAL1 (Alexa Fluor 555) and -tubulin (Alexa Fluor 488) in cultured AoSMCs, HIAECs, and AoAFs. (I-K) Immunofluorescent localization of SMARCAL1 (Alexa Fluor 555) and the cell-specific markers -smooth muscle actin (I), vascular endothelial cadherin (J), and prolyl 4-hydroxylase subunit alpha 3 (K) in AoSMCs, HIAECs, and AoAFs (Alexa Fluor 488), respectively. Scale bars: (A-C) 50 m, 25 m for insets; (F-K) 10 m. Abbreviations: ACTA2, -smooth muscle actin; AoAF, aortic adventitial fibroblast; AoSMC, aortic smooth muscle cell; CDH5, vascular endothelial cadherin; kDa, kilodalton; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; HIAEC, human iliac artery endothelial cell; mRNA, messenger RNA; P4HA3, prolyl 4-hydroxylase subunit alpha 3; RT-PCR, reverse transcription PCR; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.   58 3.3.6 Elastin binding protein is not decreased in the SIOD aorta Based on the preceding, I hypothesized that the arteriosclerosis primarily arose from a defect of elastogenesis and was accentuated by hypertension, hyperlipidemia, and renal failure. One mechanism for impaired elastin fibre assembly is a reduction in the protective chaperone elastin binding protein (EBP) (Hinek et al. 1991; Hinek and Rabinovitch 1994). Elevated levels of glycosaminoglycans, which are found in the mucopolysaccharidoses like Morquio syndrome and Hurler syndrome, induce premature shedding of EBP and lead to impaired elastin fibre assembly (Hinek and Wilson 2000; Hinek et al. 2000). Although later studies have not confirmed mucopolysacchariduria as a consistent feature of SIOD (Spranger et al. 1991), I tested EBP levels since chondroitin-6-sulphaturia was initially described as a feature of SIOD by Schimke et al. (1971). Immunoblotting showed that EBP levels in the aortic lysate from SD60 and SD120 were comparable to those of unaffected controls (Supplementary Figure 3.5A), and periodic acid-Schiff staining did not show evidence of the increased deposition of neutral glycosaminoglycans that is characteristic of the mucopolysaccharidoses (Supplementary Figure 3.5B).  3.3.7 Elastin mRNA and protein are markedly reduced in the SIOD aorta To test whether the elastic lamellae pathology directly arises from altered expression of ELN mRNA, I profiled its expression using the Atherosclerosis RT2 Profiler PCR Array. ELN mRNA levels were 121-fold reduced in the aorta of SD120 (p value = 0.0033; Figure 3.4A and Supplementary Table 3.7), and total elastin protein, including soluble and insoluble elastin, was reduced by 64% in the aortic tissue lysate of SD120 (Figure 3.4B).  59  Figure 3.4 Elastin expression is significantly decreased in the aorta of an SIOD patient. (A) Volcano plot comparing the expression of atherosclerosis-related genes in the aorta of SD120 to a pooled aorta sample from four unaffected adults. Note the markedly reduced expression of ELN mRNA. White, grey, and black dots respectively represent downregulated (log2 fold change < -1), unchanged, and upregulated (log2 fold change > 1) genes in the SIOD aorta versus the pooled unaffected control aorta sample. For genes above the dotted line, the differential expression has a p value of less than 0.05. (B) Relative elastin protein in the aortic wall of SD120 compared to an unaffected control. Total elastin protein was measured with the Fastin Elastin Assay. Data are presented as the mean  1 standard deviation calculated from a minimum of three technical replicates Abbreviations: ELN, elastin; mRNA, messenger RNA; SIOD, Schimke immuno-osseous dysplasia.  3.3.8 ELN gene mutations are not the cause of the reduced elastogenesis in SIOD To determine whether the decreased ELN mRNA in SD120 arises from mutations in ELN, I sequenced the ELN gene in patient SD120. Among the 34 exons of the ELN gene, none were found to have pathogenic mutations. A heterozygous non-synonymous change was found in exon 20 (c.1264G>A, p.Gly422Ser), however the nucleotide and amino acid of interest are weakly conserved; Align-GVGD and SIFT algorithms predict this variant unlikely to be pathogenic, and this variant has been reported as a single nucleotide polymorphism (SNP, rs2071307) in dbSNP XML build 135 with an average heterozygosity of 0.41. A homozygous intronic change was found in intron 20 (c.1315+17C>T), however this change was not predicted to alter splicing and has been reported as a SNP (rs2856728) with an average heterozygosity of 0.38.   60 3.3.9 ELN mRNA expression decreases during human aorta development To further investigate the potential molecular mechanisms underlying the reduced ELN mRNA expression in SIOD, I assessed known transcriptional and post-transcriptional mechanisms of ELN gene regulation in SIOD. Since ELN regulation during human aorta development is largely unknown, I first studied these mechanisms in the developing human aorta to establish a regulatory context to assess these mechanisms in the SIOD aorta.  Studies on the expression of ELN mRNA during human aorta development or aging have been limited (Godfrey et al. 1993; Fritze et al. 2012). Therefore, I performed qRT-PCR to determine ELN mRNA expression during human aorta development and I observed a moderate negative correlation between ELN mRNA expression and age (rS = -0.626, p = 0.004; Supplementary Figure 3.6). The relative ELN mRNA level was 102-fold lower in adult aortas than in unaffected fetal aortas (Figure 3.5).   Figure 3.5 ELN mRNA expression during human aorta development and aging. Expression data are presented as a scatterplot of the log10 fold change in relative ELN mRNA expression in unaffected fetal (black circle) and postnatal (black diamond) human aortas. The red diamond represents the SIOD aorta. The ELN mRNA levels of three technical replicates were normalized to mRNA levels of the housekeeping gene GAPDH, and the fold changes in ELN mRNA expression were calculated by dividing the normalized ELN mRNA expression levels of the sample of interest by that of the commercially obtained pooled human adult aorta sample (black star). For those fold change values of less than 1, the negative of the reciprocal was calculated as the fold change. The age is represented as the log10 age in weeks after conception with birth (i.e., 40 weeks gestation) indicated by the vertical line. Abbreviations: ELN, elastin; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; mRNA, messenger RNA; SIOD, Schimke immuno-osseous dysplasia.  61  3.3.10 ELN pre-mRNA levels decrease modestly with aortic development from fetus to adult and is further decreased in an SIOD aorta To determine the relative contribution of decreased ELN transcription to the decreased ELN mRNA levels in unaffected adult and the SIOD aorta, I used intronic primers and qRT-PCR to measure ELN pre-mRNA levels. This showed that ELN pre-mRNA of the adult aortas was 1.8-fold lower than that of the two second trimester fetal aortas, whereas the ELN pre-mRNA of the aorta from the 5-year-old SIOD patient was 18-fold lower than the two second trimester fetal aortas (Figure 3.6).   Figure 3.6 ELN pre-mRNA expression in fetal, adult, and SIOD aortas. Expression data are presented as a bar graph of the relative mean ELN pre-mRNA expression in fetal (n = 2), adult (n = 4, pooled), and SIOD (n = 1) aortas. Primers were designed against intron 4 of ELN to assay ELN pre-mRNA expression. The pre-mRNA levels of three technical replicates were normalized to the mRNA levels of the housekeeping gene GAPDH and plotted relative to that of the commercially obtained pooled adult aorta sample. Error bars represent one standard deviation. Abbreviations: ELN, elastin; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; pre-mRNA, precursor messenger RNA; SIOD, Schimke immuno-osseous dysplasia.    62 3.3.11 SIOD-associated expression of transcriptional regulators of ELN parallel those observed with aorta development Altered expression of transcriptional regulators is associated with the modulation of ELN expression (Conn et al. 1996; Carreras et al. 2002; Kuang et al. 2007). Since the expression of transcriptional regulators of ELN in the human aorta is unknown, I profiled the mRNA levels of known transcriptional regulators of ELN during human aorta development and in the SIOD aorta by qRT-PCR. The transcriptional regulators studied included SP1, IGF1, TGFB1, SP3, FOSL1, and FGF2 (Conn et al. 1996; Carreras et al. 2002; Kuang et al. 2007). SP1 mRNA levels declined with development (Figure 3.7A) and had a moderate negative correlation with age (rS = -0.586, p = 0.008; Supplementary Figure 3.6). IGF1 mRNA levels in the first and second trimesters were comparable to postnatal levels and peaked in the third trimester (Figure 3.7B). TGFB1 mRNA levels were relatively constant throughout development (Figure 3.7C) and did not have a significant correlation with age (rS = 0.016, p = 0.949; Supplementary Figure 3.6). SP3 mRNA levels decreased during development (Figure 3.7D) and had a moderate negative correlation with age (rS = -0.649, p = 0.003; Supplementary Figure 3.7). FOSL1 mRNA levels increased postnatally (Figure 3.7E) and had a moderate positive correlation with age (rS = 0.647, p = 0.003; Supplementary Figure 3.7). Finally, FGF2 mRNA levels were relatively constant throughout development (Figure 3.7F) and did not have a significant correlation with age (rS = -0.256, p = 0.290; Supplementary Figure 3.7). The expression of these ELN transcriptional regulators in the SIOD aorta are generally consistent with changes occurring during development (Figure 3.7).   63  Figure 3.7 Expression of transcriptional regulators of ELN during human aorta development and aging. Positive regulators (A-C) and negative regulators (D-F) were analyzed and presented as scatterplots of relative mRNA expression in unaffected fetal (black circle) and postnatal (black diamond) human aortas. The red diamond represents the relative mRNA expression in the SIOD aorta. The mRNA levels of three technical replicates were  64 normalized to the mRNA levels of the housekeeping gene GAPDH and plotted relative to that of the commercially obtained pooled adult human aorta sample (black star). The age is represented as the log10 age in weeks after conception with birth (i.e., 40 weeks gestation) indicated by the vertical line. Abbreviations: ELN, elastin; FGF2, fibroblast growth factor 2; FOSL1, FOS-like 1; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; IGF1, insulin-like growth factor 1; mRNA, messenger RNA; SIOD, Schimke immuno-osseous dysplasia; SP1, Sp1 transcription factor; SP3, Sp3 transcription factor; TGFB1, transforming growth factor  1.  3.3.12 DNA methylation of the ELN promoter is unaltered in the SIOD aorta Often DNA methylation is inversely proportionate to the accessibility of the DNA to transcription factors (Jaenisch and Bird 2003), and therefore promoter methylation frequently reflects transcriptional inhibition. To determine if DNA methylation contributes to the regulation of ELN, I assessed the methylation of the ELN promoter (-1743 to +500 bp relative to the transcription start site) of a fetal, postnatal, and SIOD aorta by bisulfite Sanger sequencing. I did not detect increased ELN promoter methylation with development or in the SIOD aorta (Figure 3.8). 65  Figure 3.8 DNA methylation analysis of the ELN promoter in fetal, postnatal, and SIOD aortas. After bisulfite conversion of genomic DNA, the ELN promoter was amplified as five regions and each was cloned and sequenced. (A) A schematic of the PCR fragments and the corresponding CpG sites within each fragment (arrowheads). The location of a CpG island, the first exon (white box), and the first intron (black line) of ELN are indicated in the schematic. (B) Methylated CpGs are represented as black dots and unmethylated CpGs as white dots. Ten single molecules of each region were sequenced for each sample. Each row represents a single molecule. Abbreviations: chr, chromosome; ELN, elastin; SIOD, Schimke immuno-osseous dysplasia; TSS, transcription start site. 66 3.3.13 Expression of miR-29 family members is increased in the SIOD aorta The relative ELN mRNA level in the SIOD aorta is 121-fold less than that of unaffected adult aortas; however, the above analyses of ELN transcription account for only part of the reduction in ELN mRNA levels. I hypothesized therefore that post-transcriptional mechanisms also contribute. Increased expression of the conserved miR-29 family has been shown to directly promote the degradation of ELN mRNA in development and disease (van Rooij et al. 2008; Ott et al. 2011; Zhang et al. 2012). The seed sequence that confers target specificity is identical among all family members, and three conserved miRNA response elements are present in the human ELN 3´ untranslated region (3´ UTR) (Figure 3.9A) (Ott et al. 2011; Zhang et al. 2012).  To determine whether there was increased expression of the miR-29 family in development or SIOD, I quantified each family member by qRT-PCR. Comparison of unaffected adult to fetal aortas detected modest changes; miR-29a-3p (accession number: MIMAT0000086) and miR-29c-3p (accession number: MIMAT0000100) levels were respectively increased 4-fold and 2-fold and miR-29b-3p (accession number: MIMAT0000681) was reduced 1.5-fold (Figure 3.9B-D). In contrast, miR-29a-3p, miR-29b-3p, miR-29c-3p were respectively 17-, 30-, and 15-fold higher in the SIOD aorta compared to the fetal aortas (Figure 3.9B-D).   67  Figure 3.9 Expression of the miR-29 family in fetal, adult, and SIOD aortas. (A) The miRNAs assayed in this study and their identical seed sequences highlighted in grey. The relative positions of the miR-29 MREs are indicated in the schematic. (B-D) Expression data are presented as bar graphs of the relative mean miRNA expression of miR-29a-3p (B), miR-29b-3p (C), and miR-29c-3p (D) in fetal (n = 2), adult (n = 4, pooled), and SIOD (n = 1) aortas. The miRNA levels of three technical replicates were normalized to the levels of the small nuclear RNA RNU6-2 and plotted relative to that of the commercially obtained pooled adult aorta sample. Error bars represent one standard deviation. Abbreviations: CDS, coding DNA sequence; ELN, elastin; miRNA, microRNA; MRE, miRNA response element; SIOD, Schimke immuno-osseous dysplasia; UTR, untranslated region.  3.3.14 ELN mRNA poly(A) tail length is shortened in SIOD A marker of post-transcriptional degradation or mRNA instability is a shorter poly(A) tail (Wilusz et al. 2001). Prior comparison of ELN mRNA in adult versus fetal human skin, uterus, and lung showed that the transcripts from adult tissue have shorter poly(A) tails (Hagmeister et al. 2012). To determine if this also occurred in the human aorta, I compared the mRNA poly(A) tail length of ELN transcripts terminating at the proximal and distal polyadenylation sites and observed that both adult ELN transcripts had shorter poly(A) tails than the respective fetal  68 transcripts (Figure 3.10). Consistent with the destabilization of ELN mRNA in SIOD, the transcripts extracted from the aorta of the 5-year-old boy with SIOD had a distal poly(A) tail length comparable to that of unaffected adult aortas and a proximal poly(A) tail length shorter than that of unaffected adult aortas (Figure 3.10).  Figure 3.10 Poly(A) tail length analysis of ELN mRNA in fetal, adult, and SIOD aortas. (A) Schematic of the primer design for the analysis of ELN mRNA transcripts terminating at its two polyadenylation sites. The forward primer I and universal reverse primer assay transcripts terminating at both the proximal and distal polyadenylation sites, whereas the forward primer II and universal reverse primer assay transcripts terminating at only the distal polyadenylation site. A gene-specific control PCR with each of the forward primers and corresponding reverse primers was conducted to ensure that the forward primer specifically bound the ELN mRNA 3´ UTR. The relative positions of the miR-29 MREs are also indicated in the schematic. (B) Poly(A) tail products (upper panels) and gene-specific controls (lower panels) for the proximal and distal (left panels) and distal only (right panels) poly(A) tail assays. Abbreviations: +, reverse transcription; –, no reverse transcription; bp, base pairs; CDS, coding DNA sequence; ELN, elastin; MRE, miRNA response element; mRNA, messenger RNA; SIOD; Schimke immuno-osseous dysplasia; UTR, untranslated region.  69 3.4 Discussion Herein I demonstrated that SIOD patients have clinical and histopathological features of impaired vascular elastogenesis and that this pathology correlates with decreased ELN gene expression. Furthermore, I have shown that both transcriptional and post-transcriptional mechanisms are associated with the decreased postnatal ELN mRNA expression during human aorta development and that the pathologically decreased ELN mRNA expression in an SIOD aorta arises, in part, from the dysregulation of these known mechanisms for regulating ELN gene expression.  The histopathology of the arteries from SIOD patients revealed increased elastic lamellae, increased aortic wall thickness, and fragmented elastic lamellae. Since SIOD is a multisystemic disease characterized by T-cell immunodeficiency, hyperlipidemia, hypertension, and renal disease (Boerkoel et al. 2000), I explored cell non-autonomous mechanisms for the basis of the vascular disease. I did not detect an inflammatory infiltrate in the aortic wall but I did find evidence for onset of vascular ischemia prior to the onset of hypertension and renal failure in some SIOD patients and altered distribution of tropoelastin and elastin in a 15-week-gestation SMARCAL1-deficient fetus. The lack of recurrence of vascular disease in transplanted kidneys (Lücke et al. 2004; Clewing et al. 2007a) and the expression of SMARCAL1 in the unaffected aorta, common iliac, and pulmonary arteries as well as in several arterial cell types further support a cell autonomous mechanism. I concluded therefore that a local or cell autonomous mechanism was the most likely cause of the arteriosclerosis.  Arteriosclerosis can arise cell-autonomously from impaired elastogenesis, due to increased elastic lamellae and vascular smooth muscle cell proliferation in humans and mice (Hinek et al. 1991; Li et al. 1998a; Li et al. 1998b; Hinek and Wilson 2000; Hinek et al. 2000; Urban et al. 2002; Spencer et al. 2005). Elastogenesis, also known as elastic fibre assembly, involves transcription of the ELN gene, translation of the mRNA to tropoelastin, association of tropoelastin with the EBP chaperone for secretion of tropoelastin from the cell, dissociation of tropoelastin from EBP, formation of crosslinks by lysyl oxidase to form elastin, and deposition of elastin onto microfibrils composed of fibrillin, microfibril associated glycoproteins, fibulins, and elastin microfibril interface-located protein 1 (Wagenseil and Mecham 2007). Although the deficiency of any component of this complex system can lead to impaired elastogenesis, I chose to assess EBP expression due to the initial observation of Schimke et al. (1971) of increased  70 glycosaminoglycans in an SIOD patient and elastin expression due to the highly similar arterial pathology of elastin deficiency. I found that EBP levels were unaltered in SIOD arteries, whereas elastin mRNA and protein were decreased in the aorta of one SIOD patient, who did not have pathogenic mutations in ELN. Based on these findings, I conclude that a primary defect in elastin expression is the most parsimonious mechanism for the vascular disease in SIOD.  Elastin is critical for arterial development and maintenance (Li et al. 1998a). It regulates the proliferation, migration, and maturation of vascular smooth muscle cells and confers the elastic recoil properties required for the proper function of the arteries as well as other load-bearing tissues such as the skin, intestines, and lungs (Urban et al. 2002; Karnik et al. 2003). Haploinsufficiency for ELN causes arterial stenosis and hypertension in supravalvular aortic stenosis and Williams-Beuren syndrome (WBS) (Williams et al. 1961; Beuren et al. 1962). Elastin deficiency also causes vascular disease in cutis laxa, a more severe defect of elastogenesis (Milewicz et al. 2000). Similarly, mice heterozygous for deletion of the Eln gene have aortic valve disease, systemic and pulmonary hypertension, and frequent inguinal hernias (Dietz and Mecham 2000; Pezet et al. 2008); all of which are observed with increased frequency in SIOD patients (Boerkoel et al. 2000). The increase in the number of elastic lamellae and the increased thickness of the tunica media observed in WBS and of mice heterozygous for deletion of the Eln gene were also seen in the SIOD arteries (Li et al. 1998b; Urban et al. 2002), although the pathology in the SIOD tissue is milder than that typically observed for WBS and is therefore consistent with the later onset of arterial disease in SIOD. Prior studies of elastin protein concentration (Myers and Lang 1946; Andreotti et al. 1985) and ELN mRNA levels (Fritze et al. 2012) in the human aorta found that both decrease postnatally with age. To define this developmental decline in elastin better, I characterized aortic ELN mRNA expression during fetal development and postnatally. The prenatal expression of ELN mRNA was stable (rs = -0.368, p = 0.161) from late first trimester through to the third trimester of gestation. This pattern is consistent with prior histological studies showing initial deposition of extracellular aortic elastin in the latter half of the first trimester (Frankel et al. 1963) and increasing total deposition throughout prenatal development (Berry et al. 1972). For the fetal aorta, therefore, the increasing deposition and concentration of insoluble elastin arises from the accumulation of elastin protein and the continued high expression of ELN mRNA.   71 Compared to the levels of aortic ELN mRNA during fetal development, I found markedly lower levels of ELN mRNA in the postnatal aorta. Similar to findings in other tissues (Hagmeister et al. 2012), our quantification of ELN pre-mRNA in human fetal and adult aortas demonstrated only a 1.8-fold decrease in adult ELN transcription. Consistent with this steady transcription of ELN, I did not detect marked changes in the mRNA levels of known ELN transcriptional regulators or in methylation of the proximal ELN promoter. I conclude, therefore, that as in other tissues, the postnatal reduction in aortic ELN mRNA levels arises via post-transcriptional mechanisms. Both miRNAs and a shortened poly(A) tail contribute to the decreased postnatal levels of ELN mRNA in other tissues or organisms (Chen and Rajewsky 2007). In contrast to mice (Ott et al. 2011), I did not observe a marked increase in the miR-29 family of miRNAs in adult human aortas; this might reflect a species-specific difference or be a result of the number of samples or time points I tested. On the other hand, consistent with increased ELN mRNA degradation, both the proximal and distal poly(A) tails of ELN mRNA were shorter in adult aortas than in fetal aortas; this agrees with findings in other elastin-rich human tissues (Hagmeister et al. 2012), and affirms decreased ELN mRNA stability as a factor underlying the reduced postnatal levels of ELN mRNA in the aorta. In contrast to ELN mRNA regulation during the development of unaffected aortas, both transcriptional and post-transcriptional mechanisms contribute to the markedly decreased levels of ELN mRNA in the SIOD aorta. The markedly decreased ELN pre-mRNA levels define a reduction in ELN transcription that is not attributable to the altered expression of known ELN transcriptional regulators studied or to the methylation of the proximal ELN promoter; consequently, other transcriptional regulatory mechanisms such as chromatin accessibility, histone modifications, and additional transcription factors should be tested. Regarding post-transcriptional mechanisms, both increased miR-29a-3p, miR-29b-3p, and miR-29c-3p expression and accelerated poly(A) tail shortening appear to contribute to the reduced levels of ELN mRNA in the SIOD aorta. Interestingly, other genes targeted by miR-29 in human elastin-rich tissues were not substantially altered, such as COL1A1 (fold change = 1.39, p value = 0.019) and COL3A1 (fold change = 1.35, p value = 0.0017), but other genes were decreased as expected, such as VEGFA (fold change = -2.48, p value = 0.019, Supplementary Table 3.7); these results highlight the complexity of miR-29 regulation of mRNA levels and the ambiguity  72 of a causal association with the reduced ELN mRNA levels. Though there was no apparent accelerated poly(A) tail shortening of the distal polyadenylation site transcript, there was substantial paucity of the transcript. This observed paucity, however, was not due to the increased miR-29 expression since all three miR-29 MREs are present in both proximal and distal polyadenylation site transcripts (Figure 3.10A). Comparison of the SIOD aorta to a developmental series of human aortas establishes the validity of the assays used in the study and allows me to hypothesize whether the processes underlying the reduced levels of ELN mRNA in SIOD aorta are dysregulated developmental mechanisms or pathologic mechanisms unique to SIOD. The reduced transcription of ELN and the increased expression of the miR-29 regulators of ELN mRNA appear to be pathologic processes unique to SIOD. In contrast, the more severe shortening of the poly(A) tails suggests dysregulation of a developmental process for regulating aortic ELN mRNA levels. In the context of dissecting the mechanisms by which SMARCAL1 deficiency alters transcriptional and post-transcriptional mechanisms of ELN mRNA regulation, I evaluated cultured skin fibroblasts and knockout mice as model systems. Although cultured dermal fibroblasts are frequently used as a model for elastogenesis (Giro et al. 1985; Sephel and Davidson 1986), cultured primary dermal fibroblasts of SIOD patients did not consistently manifest markedly lower ELN mRNA levels compared to age-matched controls (Supplementary Figure 3.8). Also, the ELN mRNA levels did not correlate with whether the SIOD patient had vascular disease. Similarly, knockout mice showed no consistent difference in ELN mRNA levels (data not shown). The Smarcal1 knockout mouse might not have manifested vascular disease or marked dysregulated elastogenesis for several reasons. First, SMARCAL1 orthologues recognize DNA structure rather than DNA sequence, and the altered chromatin landscape in mice might lead to different gene targets of Smarcal1 deficiency and therefore not lead to Eln gene dysregulation. Second, humans are exquisitely prone to vascular disease and sensitive to elastin deficiency, whereas wild type mice are resistant to vascular disease and only develop vascular disease with defined genetic modifications (Zhang et al. 1992; Powell-Braxton et al. 1998). These biological differences might explain why I was unable to detect a molecular phenotype in the mice. The cultured primary dermal fibroblasts of SIOD patients also did not exhibit dysregulated elastogenesis, which I hypothesize is secondary to cell type-related  73 differences since not all tissues are equally affected in SIOD; e.g., patients do not have any pronounced skin manifestations, other than hyperpigmented macules (Boerkoel et al. 2000). Key limitations of this study are the modest number of samples analyzed and the absence of age-matched samples for these analyses of ELN gene expression and regulation. SIOD is a rare disorder and human aorta is a difficult tissue to collect, particularly for the molecular characterization of RNA. However, I performed a number of hypothesis-driven targeted studies to fully utilize this limited resource and to provide as much insight as possible into the dysregulation of ELN in SIOD as well as the regulation of ELN during aorta development. Further studies on additional SIOD samples are required in order to verify these findings. 74 Chapter 4: Molecular Characterization and Pathogenesis of the Renal Disease in SIOD  4.1 Introduction The renal system is comprised of the kidneys, ureters, bladder, and urethra, and functions to filter waste and excess fluid from the blood and to produce, store, and eliminate the waste and excess fluid as urine. It also functions to regulate blood pressure, blood pH, and the balance of electrolytes. The kidneys are the principal organs of the renal system that are comprised of nephrons; these filtering units consist of a renal corpuscle that performs an initial filtration and a renal tubule that subsequently allows for the resorption of ions and minerals. The glomerulus of the renal corpuscle is an intricate globular network of capillary blood vessels that function with specialized cells called podocytes to effectively filter the blood. Focal segmental glomerulosclerosis (FSGS) is a renal pathology that involves the scarring of part (segmental) of some (focal) of the glomeruli and leads to nephrotic syndrome, which is characterized by proteinuria, hypoalbuminuria, and edema. Causes of FSGS include drugs such as heroin, viral infections such as human immunodeficiency virus, systemic diseases such as obesity and diabetes mellitus, and genetic disorders such as Schimke immuno-osseous dysplasia (SIOD). The renal disease in SIOD begins as proteinuria, progresses to steroid-resistant nephropathy, and ultimately advances to end-stage renal disease (Boerkoel et al. 2000; Sarin et al. 2015). FSGS is the predominant renal pathology and is refractory to treatment with glucocorticoids, cyclosporine A, and cyclophosphamide (Boerkoel et al. 2000; Sarin et al. 2015). Suggesting a cell autonomous mechanism for the renal disease, renal transplantation treats the disease, and the disease does not recur in the graft (Ehrich et al. 1995; Boerkoel et al. 2000; Clewing et al. 2007a). Genetic causes for FSGS include mutations in genes encoding for proteins required for podocyte development, differentiation, and function (Fogo 2015). Since SMARCAL1 deficiency alters gene expression (Baradaran-Heravi et al. 2012a), I hypothesized that SMARCAL1 deficiency causes the renal disease of SIOD by altering the expression of a gene or set of genes that are required for glomerular maintenance and function. The objectives of this study were to profile the gene expression of an SIOD kidney by comparative transcriptome analysis and to  75 follow up and validate these gene expression alterations using targeted gene expression analysis, indirect immunofluorescence, and genetic interaction studies. These analyses showed upregulation of the Wnt and Notch signaling pathways in the SIOD kidney and genetic interaction between the SMARCAL1 orthologue Marcal1 and genes encoding components of the Wnt and Notch pathways. Studies of other glomerulopathies have implicated dysregulated Wnt (Dai et al. 2009; Kato et al. 2011; He et al. 2012; Shkreli et al. 2012) and Notch signaling (Niranjan et al. 2008; Waters et al. 2008; Lasagni et al. 2010; Murea et al. 2010) as causes of podocyte dysfunction. I interpret these results as suggesting that the upregulation of the Wnt and Notch developmental pathways contribute to the renal disease in SIOD.  4.2 Methods 4.2.1 Human samples The SMARCAL1 mutations and the renal parameters of the SIOD patients referred to this study are presented in Supplementary Table 4.1 and Table 4.1, respectively. De-identified unaffected control specimens included renal biopsy sections from ten pediatric patients with FSGS, postmortem kidney tissue from six second trimester fetuses and four pediatric patients, postmortem skin tissue from a 16-year-old female, and adenoma tissue from a 17-year-old female with familial adenomatous polyposis. Sample characteristics and use are summarized in Supplementary Table 4.2. 76 Table 4.1 The renal parameters of the SIOD patients included in this study. Patient ID Age at onset (years) Age at death (years) Nephrotic syndrome Hypertension Proteinuria Hypercholesterolemia Renal dialysis Age at renal dialysis (years) Renal transplant Age at renal transplantation (years) Renal pathology SD4b 3 8 + + + ?  n/a  n/a FSGS SD26 < 4 8 + + + + + 5  n/a FSGS SD60 7 13.7 + + + ? + 12.5 + 13 FSGS SD79 < 4 10 +  +   n/a  n/a FSGS SD120 4.5 5.4 + + + +  n/a  n/a FSGS SD121 2.5 4.8 +  + +  n/a  n/a Diffuse podocytopathy with early features of FSGS SD131 3 4.6 + + + + + 3.8  n/a Global glomerulosclerosis likely secondary to FSGS SD146 2 4   + +  n/a  n/a FSGS  Abbreviations: +, present; , absent; ?, unknown; FSGS, focal segmental glomerulosclerosis; ID, identification; n/a, not applicable; SIOD, Schimke immuno-osseous dysplasia. 77 4.2.2 Cell culture Primary renal proximal tubular cells from an SIOD and FSGS patient were isolated and cultured from renal biopsies as previously described (Vesey et al. 2009). The cells were cultured in DMEM/F-12 supplemented with sodium bicarbonate, 5 μg/ml insulin, 5 μg/ml transferrin, 5 ng/ml selenium, 10 ng/ml epidermal growth factor, 50 nM hydrocortisone, 5 pM triiodothyronine, 50 μM prostaglandin E1, 100 U/ml penicillin, and 100 μg/ml streptomycin at 37°C and 5% CO2. Unaffected primary human dermal fibroblasts (GM00942, Coriell Institute for Medical Research, Camden, NJ, USA) and HeLa cells (American Type Culture Collection, Manassas, VA, USA) were cultured in high glucose DMEM (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) and 1 antibiotic-antimycotic (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) at 37C and 5% CO2. Cells were grown to approximately 90% confluence.  4.2.3 Wnt3a treatment Prior to the beginning of the time course, the medium was replaced with low serum medium containing 0.5% fetal bovine serum for 48 hours. The dermal fibroblasts and HeLa cells were treated with 100 ng/ml Wnt3a (R&D Systems, Minneapolis, MN, USA) for 4 or 6 hours, respectively. Untreated cells were treated with the same volume of the vehicle solution. The cells were then pelleted, formalin-fixed, and paraffin-embedded using standard procedures for use as controls for the -catenin immunofluorescence staining.  4.2.4 Drosophila melanogaster lines The generation of the loss-of-function mutant Marcal1del by imprecise P element excision has been previously described (Baradaran-Heravi et al. 2012a). All overexpression lines utilized the GAL4-UAS system in which tissue-specific expression of the yeast transcriptional activator GAL4 drives expression of a gene of interest through the upstream activating sequence (UAS) (Brand and Perrimon 1993). The Marcal1 overexpression transgenic line pUAST-Marcal1/CyO; tubulin-GAL4/TM3, Sb1 has been previously described (Baradaran-Heravi et al. 2012a); following injection of the pUAST construct into embryos by Genetic Services (Sudbury, MA, USA), we obtained independent transgenic lines, and crossed those to P{tubP-GAL4}LL7  78 transgenic flies to overexpress Marcal1 in the developing and mature fly (Supplementary Figure 4.1A). The C96-GAL4 UAS-Hrs/MKRS transgenic line used to control for non-specific interactions with the GAL4-UAS system was a gift from Dr. Hugo Bellen (Baylor College of Medicine, Houston, TX, USA). All other Drosophila stocks were obtained from the Bloomington Drosophila Stock Center (Bloomington, IN, USA).  4.2.5 RNA extraction Total RNA was extracted from flash frozen kidney pulverized with a Bessman tissue pulverizer  (Spectrum Laboratories, Rancho Dominguez, CA, USA) or from 8 Drosophila adult female flies of each genotype by using the RNeasy Mini Kit (Qiagen, Toronto, ON, Canada) according to the manufacturer’s specifications. Genomic DNA was removed by on-column DNase I digestion (Qiagen, Toronto, ON, Canada). Total RNA from formalin-fixed paraffin-embedded (FFPE) fetal kidney was isolated using the RNeasy FFPE Kit (Qiagen, Toronto, ON, Canada) according to the manufacturer’s specifications.  4.2.6 RNA-seq and KEGG pathway analysis Strand-specific, paired-end RNA-seq on poly(A) RNA was performed by Macrogen (Seoul, Korea) using the TruSeq Stranded Total RNA Library Prep Kit (Illumina, San Diego, CA) and the HiSeq 2000 System (Illumina, San Diego, CA). This kit depleted the ribosomal RNA (rRNA) using Ribo-Zero rRNA reduction chemistry. Quantification was performed by calculating fragments per kilobase per million mapped reads (FPKM). Prior to fold change calculation and log2 transformation, a pseudocount of 1 was added to each FPKM value to reduce the inherent bias of finding gene expression changes in those genes where one sample has very little or no gene expression (Warden et al. 2013). The threshold for differential gene expression between the kidney from the SIOD patient and sex-matched unaffected control was set at log2 fold change (i.e., log2 (FPKMSIOD + 1/FPKMUNAFFECTED + 1)) > 1 or < -1. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed with the online bioinformatic resource Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6.7 available at https://david.ncifcrf.gov (Huang et al. 2009).   79 4.2.7 Gene expression arrays The Wnt (PAHS-043Y) and Notch (PAHS-059Y) Signaling Pathway Plus PCR Arrays (Qiagen, Toronto, ON, Canada) and the RT2 Real-Time SYBR Green/Rox PCR Master Mix (Qiagen, Toronto, ON, Canada) were used to assess differential mRNA levels between the sex-matched unaffected control and the SIOD kidney according to the manufacturer’s specifications. The threshold for calling differential mRNA levels was a log2 fold change > 1 or < -1 and a p value of less than 0.05.  4.2.8 Quantitative PCR SsoFast EvaGreen Supermix (Bio-Rad Laboratories, Mississauga, ON, Canada) was used with the StepOnePlus Real-Time PCR System (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA) for quantitative PCR. Human GAPDH and Drosophila Gapdh2 housekeeping genes were used as internal controls. The primer sequences used in this study are listed in Supplementary Table 4.3.  4.2.9 Indirect immunofluorescence FFPE sections of tissue or cell pellets were cut at 5 microns. Following deparaffinization and rehydration, heat induced epitope retrieval was performed with sodium citrate buffer (10 mM sodium citrate, 0.05% Tween 20, pH 6.0). Endogenous peroxidases were inactivated by incubating the sections with peroxidase quenching buffer (3% hydrogen peroxide in 1 PBS, 0.1% Tween 20, pH 7.4 (PBSTw) for unphosphorylated -catenin immunofluorescent staining or 1 PBS, 0.2% Triton X-100, pH 7.4 (PBST) for the Notch1 intracellular domain (NICD) immunofluorescent staining) for 1 hour at room temperature. Non-specific protein binding was blocked by incubating the sections with blocking buffer (20% normal goat serum, 10% bovine serum albumin, 1 casein (Vector Laboratories, Burlington, ON, Canada) in PBSTw or PBST) overnight at 4C. Endogenous biotin, biotin receptors, and avidin binding sites were blocked with the Avidin/Biotin Blocking Kit (Vector Laboratories, Burlington, ON, Canada) according to the manufacturer’s specifications. Rabbit anti-unphosphorylated -catenin (clone D13A1, Cell Signaling Technology, Danvers, MA, USA) or rabbit anti-NICD (ab8925, Abcam, Toronto, ON, Canada) were used as primary antibodies. A biotinylated anti-rabbit IgG secondary antibody was used to detect the  80 primary antibodies. Horseradish peroxidase-conjugated streptavidin was then used to detect the biotinylated anti-rabbit IgG secondary antibody. Subsequently, tyramide labeling was performed using Alexa Fluor 594 tyramide (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). ProLong Gold Antifade Mountant with 4, 6-diamidino-2-phenylindole (DAPI) (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) was used to mount the sections and counterstain the DNA. Representative images were acquired using a 20/0.75 Plan-APOCHROMAT, 40/1.3 oil DIC Plan-NEOFLUAR, or 100/1.30 oil Plan-NEOFLUAR objective lens on an Axiovert 200 inverted microscope, an AxioCam MR microscope camera, and the AxioVision software version 4.8 (Carl Zeiss, Toronto, ON, Canada).  4.2.10 Quantification of -catenin immunofluorescence ImageJ (U.S. National Institutes of Health, Bethesda, MD, USA) was used to quantify and compare the glomerular -catenin signal between the various kidney tissue sections. Representative images were captured using a 20 Plan-APOCHROMAT objective and a 10/23 mm eyepiece (Carl Zeiss, Toronto, ON, Canada). Though this setup provides a 1,150 m circular optical field of view, the AxioCam MR microscope camera captures images of approximately 448 m  336 m. The analyzed images are presented as the overview images in Figure 4.2 and Supplementary Figure 4.3. Using ImageJ, the glomeruli were outlined and the area and integrated density were measured, as well as the mean fluorescence of three adjacent background regions. The corrected total fluorescence (CTF = integrated density - (glomerular area  mean fluorescence of background readings) was calculated for each glomerulus. Where there was more than one glomerulus present in the image, an average CTF of all glomeruli was calculated. Data were plotted as box and whisker plots and analyzed by the 2-tailed Student’s t-test. A p value of less than 0.05 was considered statistically significant. The Bonferroni correction was applied to correct for multiple comparisons.  4.2.11 Microarray gene expression analysis For microarray gene expression analysis, two samples of RNA were extracted from cultured primary renal proximal tubular cells from the renal biopsy of a patient with isolated FSGS and three samples of RNA were extracted from that of an SIOD patient. Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Toronto, ON, Canada) according to the  81 manufacturer’s specifications. Biotinylated complementary RNA (cRNA) was prepared according to the standard Affymetrix protocol from total RNA. Following fragmentation, the cRNA was hybridized to a GeneChip Human Genome U133 Plus 2.0 Array. GeneChips were washed and stained in an Affymetrix GeneChip Fluidics Station according to standard protocols. GeneChips were scanned using the GeneChip Scanner 3000. The data were analyzed with GeneSpring version 13.0 using default analysis settings and Robust Multi-array Average (RMA) (summarization method) for normalization. A probe set was considered to have detected significant differential expression if the Benjamini-Hochberg corrected p value was < 0.05 and the log2 fold change was > 1.  4.2.12 Drosophila melanogaster genetic screen to determine the effect of Wnt and Notch mutant alleles on the Marcal1 overexpression wing phenotype Drosophila wings have five longitudinal veins (L1, L2, L3, L4, and L5) plus anterior and posterior cross veins (ACV and PCV) (Supplementary Figure 4.1B). The overexpression of Marcal1 induces an ectopic vein parallel and anterior to L2, an ectopic vein extending laterally from the PCV, a partially missing or completely absent ACV or PCV, and distal bending or splitting of longitudinal veins L2, L4, and L5 (Supplementary Figure 4.1C) (Baradaran-Heravi et al. 2012a). These wing vein alterations are dependent on enzymatic activity since overexpression of the enzymatically inactive mutant Marcal1K275R does not alter wing venation (Baradaran-Heravi et al. 2012a). I screened for Wnt and Notch alleles that lead to the suppression or enhancement of the ectopic wing veins induced by the overexpression of Marcal1 (Baradaran-Heravi et al. 2012a). Crosses for analyzing the epistatic interaction between the Marcal1 gene with genes encoding for components of the Wnt and Notch signaling pathways were maintained at 25C for three days. The crosses were then transferred to 28C. Upon eclosion, the desired F1 progeny were selected and their wings were mounted, imaged, and assessed as previously described (Baradaran-Heravi et al. 2012a). Ectopic wing veins observed in the F1 progeny of the crosses were scored according to the following guidelines. For each of the features scored, a phenotype resembling the wild type wing was given a score of 0. Ectopic veins parallel and anterior to the L2 longitudinal vein were given a score of 0 to 2 based on the length of the ectopic vein. The distal portion of the L2 vein  82 was given a score of 0 or 1 based on the absence or presence of bending or splitting. The distal portions of the L4 and L5 veins were given a score of 0 to 4 proportionate to the degree of deviation from the wild type phenotype. Ectopic veins extending perpendicularly from the posterior cross vein (PCV) were given a score of 0 to 3 based on the length of the ectopic vein. A partially or completely absent anterior cross vein (ACV) or PCV were each given a score of 1. Representative images for these phenotypes and their respective scores are present in Supplementary Figure 4.1C. The reference wing vein phenotype was determined by crossing Marcal1 overexpression flies to w1118 mutants of three genetic backgrounds; the scores from these crosses were averaged to provide a reference score. To determine whether there was any non-specific interaction between the various mutant alleles and the GAL4-UAS system, all mutant lines were crossed to the C96-GAL4, UAS-Hrs/MKRS transgenic line and the degree of wing margin scalloping in the desired F1 progeny was scored (Pepple et al. 2007). Any mutant alleles that interacted with the GAL4-UAS system (i.e., those that enhanced or suppressed the wing margin scalloping phenotype of the C96-GAL4, UAS-Hrs/MKRS transgenic line) were excluded; all mutant alleles presented here had no detectable non-specific interactions with the GAL4-UAS system. Ten or more wings were analyzed for each cross and scored by two independent readers. Scores for each cross were compared to the reference scores to determine whether the wing vein phenotype was suppressed or enhanced. Where there was a discrepancy between the first two reads, a third read was completed by C. F. B.  4.2.13 Drosophila melanogaster genetic studies to determine the effect of Marcal1 loss and gain on Notch mutant phenotypes Several Notch mutant alleles cause phenotypes that manifest in the wing, eye, and bristle. I screened for the suppression or enhancement of these phenotypes in the context of Marcal1 loss or gain. For the loss-of-function screen, selected Notch pathway mutant alleles from the Bloomington Drosophila Stock Center (Bloomington, IN, USA) were crossed into the Marcal1 loss-of-function background. These crosses were carried out and maintained at 20C. For the overexpression screen, selected Notch pathway mutant alleles were crossed into the Marcal1 overexpression background as detailed above. Upon eclosion, the desired F1 progeny were  83 selected, and the relevant phenotype analyzed. Since several of the alleles are temperature-dependent, the Notch mutant phenotypes were also assessed at 28C.  A minimum of 40 wings was scored for each genotype where the suppression or enhancement of a wing phenotype was being assessed. Notch alleles Nnd-1 and Nnd-3 exhibit wing notching in homozygous females and hemizygous males; all Delta alleles in this study exhibit deltas, wing vein thickening, and ectopic veins in heterozygous flies; the Serrate allele Ser1 exhibits serrated wings in heterozygous flies; the fringe allele fng13 occasionally exhibits wing notching in heterozygous flies; and all Hairless alleles in this study exhibit shortened longitudinal veins in heterozygous flies as previously described (Belt 1971; Bang et al. 1991; Lindsley and Zimm 1992). The presence or absence of the phenotype of interest was scored for each wing.  Eighty bristles of the relevant type were scored for each genotype where the suppression or enhancement of a bristle phenotype was being assessed. Notch allele Nspl-1 exhibits missing, double, or ectopic anterior and posterior scutellar bristles in homozygous females and hemizygous males, while all Hairless alleles in this study exhibit missing bristles on the head and notum in heterozygous flies as previously described (Bang et al. 1991; Kahali et al. 2009). A total of 80 anterior and posterior scutellar bristles were scored for the Nspl-1 allele, while 80 bristles of each type on the notum were scored for the Hairless alleles. The presence or absence of the phenotype of interest was scored for each bristle type.  Eighty eyes were scored for each genotype where the suppression or enhancement of an eye phenotype was being assessed. The Notch allele Nspl-1 exhibits rough and reduced eyes in homozygous females and hemizygous males as previously described (Knust et al. 1987). Wings exhibiting a blistered phenotype and eyes exhibiting a rough and reduced eye phenotype were imaged using an MZ16 Stereomicroscope (Leica Microsystems Inc., Concord, ON, Canada).  4.2.14 Statistics For the KEGG pathway analysis, enrichment p values were corrected for multiple comparisons by the Bonferroni method. A p value of less than 0.05 was considered statistically significant. For the PCR expression arrays, data were analyzed by the 2-tailed Student’s t-test. A p value of less than 0.05 was considered statistically significant.  84 4.3 Results 4.3.1 Transcriptome analysis identifies increased mRNA levels of Wnt signaling pathway genes in an SIOD patient kidney I hypothesized that SMARCAL1 deficiency leads to gene expression changes that contribute to the pathogenesis of the renal disease in SIOD. To test this, I used RNA-seq to compare the transcriptomes of kidney tissue from a 5.4-year-old male SIOD patient and a 3-year-old unaffected male. This comparison detected 2,241 genes with increased mRNA levels (log2 fold change > 1) and 892 genes with decreased mRNA levels (log2 fold change < -1) in the SIOD kidney tissue. After Bonferroni correction, KEGG pathway analysis of the genes with decreased mRNA levels did not reveal any significant patterns. In contrast, KEGG pathway analysis of genes with increased mRNA levels revealed significant alterations of cellular adhesion (e.g., focal adhesion, cell adhesion molecules), immune function (e.g., leukocyte transendothelial migration, Fc gamma R-mediated phagocytosis), disease (e.g., systemic lupus erythematosus, pathways in cancer, colorectal cancer), and Wnt signaling (Figure 4.1A and Supplementary Table 4.4). 85   86 Figure 4.1 Genome-wide and targeted gene expression analyses in an SIOD patient kidney. (A) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of upregulated genes (log2 fold change > 1) in an SIOD kidney compared to an unaffected control kidney. A Bonferroni-corrected p value of < 0.05 was used as a threshold for determining significant KEGG pathways. The horizontal axis represents the -log10 (p value) of significant KEGG pathways. The number of unique DAVID gene IDs involved in a given term is indicated within the bar representing each pathway. (B and C) Volcano plots comparing the expression of Wnt (B) and Notch (C) pathway genes and targets in an SIOD patient kidney and in an unaffected control kidney. White, grey, and black dots respectively represent downregulated (log2 fold change < -1), unchanged, and upregulated (log2 fold change > 1) genes in the SIOD kidney versus the unaffected control kidney. For genes above the dotted line, the differential expression has a p value of less than 0.05. Abbreviations: ECM, extracellular matrix; SIOD, Schimke immuno-osseous dysplasia. 87 4.3.2 Quantitative PCR detects increased mRNA levels of Wnt and Notch signaling pathway genes in an SIOD patient kidney Given that upregulation of the Wnt pathway (Dai et al. 2009; Kato et al. 2011; He et al. 2012; Shkreli et al. 2012) or the Notch pathway (Niranjan et al. 2008; Waters et al. 2008; Lasagni et al. 2010; Murea et al. 2010) is a cause of glomerulopathy, I measured mRNA levels of Wnt and Notch signaling pathway genes using the RT2 ProfilerTM PCR Arrays. These analyses showed that of the 84 Wnt pathway-related genes tested, 30 were differentially expressed (Figure 4.1B and Supplementary Table 4.5) and that of the 84 Notch pathway-related genes tested, 14 were differentially expressed (Figure 4.1C and Supplementary Table 4.6). Wnt pathway-related genes with increased mRNA levels included ligands (e.g., WNT2B, WNT4, WNT6, WNT7A, WNT10A), components (e.g., AXIN2, FZD2, FZD7, SFRP1, SFRP4), and targets (e.g., AXIN2, CCND2, JUN, MMP7, MYC). Notch pathway-related genes with increased mRNA levels included components (e.g., DTX1) and targets (e.g., HEYL, IL2RA).  4.3.3 Unphosphorylated -catenin is increased in the glomerular cells of postnatal SIOD patient kidneys comparable to FSGS controls Having established that several Wnt pathway-related genes and targets have increased expression in an SIOD kidney, I hypothesized that increased Wnt pathway signaling within the glomeruli contributes to the pathogenesis of FSGS in SIOD. To verify the initial finding and to test this in additional SIOD patients, I used indirect immunofluorescence on 7 SIOD patient kidneys to profile unphosphorylated -catenin, a marker of canonical Wnt pathway activation (Clevers and Nusse 2012) (Supplementary Figure 4.2), and found that 6 out of 7 patients had increased glomerular staining for unphosphorylated -catenin (Figure 4.2 and Table 4.2). In pediatric patients with primary FSGS, I found that 8 out of 9 patients had increased glomerular staining for unphosphorylated -catenin (Supplementary Figure 4.3 and Table 4.2). Therefore, as a group, SIOD and FSGS patients had increased glomerular staining of -catenin compared to unaffected controls (Supplementary Figure 4.4).   88 4.3.4 Nuclear NICD expression is increased in the glomerular cells in postnatal SIOD patient kidneys comparable to FSGS controls As several Notch pathway-related genes and targets had increased expression in an SIOD kidney, I hypothesized that increased Notch pathway signaling within the glomeruli contributes to the pathogenesis of FSGS in SIOD. To verify the initial finding and to test this in additional SIOD patients, I used indirect immunofluorescence on 8 SIOD patient kidneys to profile the nuclear localization of NICD, a marker of Notch pathway activation (Bray 2006) (Figure 4.3), and found that 6 of 8 patients had increased nuclear staining for NICD in the glomerular cells (Figure 4.3 and Table 4.2). In pediatric patients with primary FSGS, I found that 8 out of 9 patients had increased nuclear staining for NICD in the glomerular cells (Supplementary Figure 4.5 and Table 4.2). Therefore, as a group, SIOD and FSGS patients had increased glomerular staining for nuclear NICD compared to unaffected controls.  89  Figure 4.2 Immunofluorescent detection of the expression and localization of unphosphorylated -catenin in the glomerular cells of unaffected control and SIOD patient kidneys. Immunostaining with anti-unphosphorylated -catenin (Alexa Fluor 594) in unaffected control kidney (A) and SIOD patient kidneys (B-H). The nuclei were counterstained with DAPI. The boxed regions correspond to the higher  90 magnification images on the right. The glomeruli have been outlined to aid in the visualization of -catenin expression. These are representative glomeruli. Scale bars: overview images (200) and higher magnification images (400) = 100 microns. Abbreviations: DAPI, 4, 6-diamidino-2-phenylindole; SIOD, Schimke immuno-osseous dysplasia. 91   92 Figure 4.3 Immunofluorescent detection of the expression and localization of the Notch1 intracellular domain (NICD) in the glomerular cells of unaffected control and SIOD patient kidneys.  Immunostaining with anti-NICD (Alexa Fluor 594) in positive control skin (A), unaffected control kidney (B), and SIOD patient kidneys (C-J). The nuclei were counterstained with 4, 6-diamidino-2-phenylindole (DAPI). The boxed regions on the left correspond to the higher magnification images on the right. Scale bars: overview images (400) = 100 microns; higher magnification images (1000) = 10 microns. Abbreviations: DAPI, 4, 6-diamidino-2-phenylindole; NICD, Notch1 intracellular domain. 93 Table 4.2 Summary of results for the indirect immunofluorescent analyses of glomerular unphosphorylated -catenin and nuclear NICD expression in SIOD and FSGS patient kidney tissue.  Patient ID Unphosphorylated -catenin expression Nuclear  NICD expression SIOD patients      SD4b = =      SD26        SD60        SD60 Tx = =      SD79  =      SD120        SD121        SD131 n/a1       SD146   FSGS patients      FSGS-1  =      FSGS-2        FSGS-3        FSGS-4        FSGS-5        FSGS-6        FSGS-8        FSGS-9        FSGS-10    1No more tissue sections were available for analysis. Abbreviation: =, staining comparable to unaffected control kidney; , increased staining compared to unaffected control kidney; FSGS, focal segmental glomerulosclerosis; ID, identification; n/a, not available; NICD, Notch1 intracellular domain; SIOD, Schimke immuno-osseous dysplasia; Tx, transplant.  4.3.5 Cultured renal proximal tubular cells of an SIOD patient and an FSGS patient have similar gene expression profiles To test whether the general gene expression pattern of SIOD renal cells was similar to that of primary FSGS, I obtained cultured primary proximal tubular cells from an SIOD patient and a patient with primary FSGS. Microarray analysis showed a similar gene expression profile and did not detect any significant gene expression differences (data not shown).   94 4.3.6 Unphosphorylated -catenin and nuclear NICD are not increased in the developing SMARCAL1-deficient kidney To determine whether pathologically increased Wnt and Notch pathway signaling in SIOD begins prenatally, I performed indirect immunofluorescence for unphosphorylated -catenin and NICD in a 15-week-gestation SMARCAL1-deficient kidney and age-matched unaffected kidneys. The SMARCAL1-deficient fetal kidney expressed comparable levels of unphosphorylated -catenin and NICD to the age-matched controls in both S-shaped bodies and developing glomeruli (Supplementary Figures 4.6 and 4.7). In agreement with these findings, expression analysis of several Wnt and Notch target genes in the SMARCAL1-deficient fetal kidney and age-matched controls demonstrated comparable expression levels (Supplementary Figure 4.8).  4.3.7 Unphosphorylated -catenin and nuclear NICD are not increased in the transplanted kidney of an SIOD patient To determine whether this increased Wnt and Notch pathway signaling is a cell autonomous consequence of SMARCAL1 deficiency, I performed indirect immunofluorescence for unphosphorylated -catenin and NICD in the transplanted kidney of an SIOD patient. Consistent with the prior observations that the renal disease of SIOD does not recur in the renal graft, the staining pattern and intensity for unphosphorylated -catenin and NICD in the renal graft were similar to that observed among unaffected controls (Supplementary Figures 4.3-4.5 and Table 4.2).  4.3.8 Drosophila Marcal1 genetically interacts with the Wnt and Notch signaling pathways To assess whether the upregulation of the Wnt and Notch signaling pathways is a genetic consequence of SMARCAL1 deficiency and not simply an end product of the tissue pathology, I performed an overexpression and loss-of-function genetic screen in Drosophila to determine whether the SMARCAL1 orthologue Marcal1 exhibits epistasis with Wnt and Notch pathway genes (Supplementary Figure 4.9). I initially performed an overexpression screen since Marcal1 overexpression leads to ectopic wing veins that can be scored for their suppression or enhancement (Baradaran-Heravi et  95 al. 2012a). I found that Marcal1 overexpression was epistatic to both Wnt (Supplementary Figure 4.10 and Supplementary Table 4.7) and Notch pathway genes (Supplementary Figure 4.11 and Supplementary Table 4.8) since mutant alleles of genes from these pathways generally suppressed the ectopic veins of Marcal1 overexpression. I next assessed whether Marcal1 loss-of-function or overexpression could suppress or enhance the mutant phenotypes of these alleles; I focused on Notch pathway genes because mutations of these genes have well-characterized and easily scored wing, eye, and bristle phenotypes. Marcal1 loss and gain enhanced the penetrance and severity of wing notches for two Notch (N) mutant alleles and enhanced the severity of the rough eye phenotype for the Nspl-1 allele (Figure 4.4A and B, Supplementary Figure 4.12, and Supplementary Table 4.9). Marcal1 loss generally enhanced the penetrance of the thickened and ectopic wing vein phenotype of Delta (Dl) mutants (Figure 4.4A, Supplementary Figure 4.12, and Supplementary Table 4.9); the effect of Marcal1 gain could not be assessed because the Marcal1 overexpression phenotype overlaps with that of the Delta mutants. Marcal1 loss and gain generally suppressed the shortened longitudinal veins and missing notal bristles observed with Hairless (H) mutants (Figure 4.4A, Supplementary Figure 4.12, and Supplementary Table 4.9). Marcal1 loss and gain suppressed the weakly penetrant wing notch phenotype of a fringe (fng) mutant (Supplementary Figure 4.12 and Supplementary Table 4.9). No genetic interaction was observed between Marcal1 loss or gain and a Serrate (Ser) mutant (Figure 4.4A and Supplementary Table 4.9).  96  Figure 4.4 Genetic interaction of Marcal1 loss and gain with Notch signaling pathway mutant alleles and model. (A) Representative wings of the mutant allele of interest (left column), the mutant allele in the Marcal1 loss-of-function background (middle column), and the mutant allele in the Marcal1 overexpression background (right column). Hairless (H), Delta (Dl), and Serrate (Ser) are dominant alleles on chromosome 3; heterozygous males and females were assessed. Wings from females are shown. The Nnd-1 allele is a homozygous viable allele of Notch on chromosome 1; homozygous females and hemizygous males were assessed. Wings from hemizygous males are shown. (B) Representative eyes of the mutant allele Nspl-1 (left), the mutant allele in the Marcal1 loss-of-function background (middle), and the mutant allele in the Marcal1 overexpression background (right). The Nspl-1 allele is a homozygous viable allele of Notch on chromosome 1; homozygous females and hemizygous males were assessed. Eyes from hemizygous males are shown. (C) Model of renal disease pathogenesis in SIOD. Normal SMARCAL1 activity leads to regulated signaling of pathways and normal kidney function, while loss of SMARCAL1 activity leads to dysregulated Wnt and/or Notch signaling or signaling of other pathways, which in turn lead to FSGS. Abbreviations: FSGS, focal segmental glomerulosclerosis; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1; UAS, upstream activating sequence.   97 4.4 Discussion Herein I identify increased signaling of the Wnt and Notch pathways as potential causes for the renal disease in SIOD. Most SIOD kidneys exhibited upregulation of both unphosphorylated -catenin and NICD respectively indicating increased Wnt and Notch pathway activity (Figures 4.2 and 4.3, Table 4.2, and Supplementary Figure 4.4). Similarly, most control FSGS kidneys had upregulated unphorsphorylated -catenin and nuclear NICD (Supplementary Figures 4.3-4.5 and Table 4.2). The failure to observe this upregulation in the renal graft of the SIOD patient suggests that these molecular findings are cell autonomous. Additionally, the epistatic interaction observed between Marcal1 and the Wnt and Notch pathway genes in Drosophila suggests that SMARCAL1 deficiency might directly or indirectly induce upregulation of unphosphorylated -catenin and nuclear NICD. Interestingly, some SIOD and FSGS control kidneys had upregulation of only one or neither of unphosphorylated -catenin and nuclear NICD. Although this variability might be due to the stage of renal disease at the time of biopsy, these findings might suggest that in some cases increased signaling of either pathway is sufficient to cause FSGS, whereas in other cases, a different mechanism causes FSGS (Figure 4.4C). Based on our preliminary observations in the fetal kidney, one might argue that the potentially pathological activation of Wnt and Notch signaling in the SIOD kidneys arises postnatally and disrupts tissue maintenance. More studies are needed however to substantiate this. Previous histological analysis of transplanted kidneys in two SIOD patients demonstrated that the transplanted kidneys had no findings suggestive of recurrent FSGS (Lücke et al. 2004; Clewing et al. 2007a). Now I show by indirect immunofluorescent analysis of the transplanted kidney of one of these SIOD patients that unphosphorylated -catenin and nuclear NICD are not increased in the transplanted kidney. This further supports cell autonomous activation of the Wnt and Notch pathways in the pathogenesis of the kidney disease in SIOD. Although the Notch pathway gene expression changes were not identified in the KEGG pathway analysis of the transcriptome, the high level of crosstalk between the Wnt and Notch signaling pathways (Collu et al. 2014), and their role in kidney development and disease prompted us also to investigate the upregulation of the Notch pathway as a potential cause for the FSGS in SIOD. Possible reasons for the transcriptome analysis not detecting enrichment of the  98 Notch pathway include pathway size bias inherent to KEGG pathway analysis (the Wnt signaling pathway includes 141 genes, whereas the Notch signaling pathway includes 48 genes) and tissue heterogeneity. A limitation of the study was the use of whole kidney to profile differential gene expression in an SIOD kidney. Given that the primary lesion is limited to the glomeruli, which represents a small proportion of the kidney and that, by definition, FSGS involves the scarring of only part (segmental) of some (focal) of the glomeruli, the affected tissue represents just a small fraction of the total tissue. Most human gene expression studies on FSGS have used isolated glomeruli (Bennett et al. 2007; Hodgin et al. 2010), although one study that reported finding a molecular gene expression signature used renal biopsies (Schwab et al. 2004). Given that Schwab et al. (2004) detected a molecular gene expression signature using renal biopsies, I performed the gene expression analyses on the SIOD kidney. Similar to other human gene expression studies of FSGS (Schwab et al. 2004; Bennett et al. 2007; Hodgin et al. 2010), the expression of podocyte-specific genes required for podocyte function including NPHS1, NPHS2, and WT1 were downregulated in the SIOD kidney. Further comparison of our study to these prior studies revealed that genes encoding for most of the KEGG pathways that were enriched in our list of upregulated genes were also enriched in the prior studies, including the Wnt signaling pathway in one study of FSGS (Bennett et al. 2007). I followed up these initial gene expression findings with the indirect immunofluorescence analysis and genetic interaction studies. A second limitation of the study was that only unphosphorylated -catenin and nuclear NICD were examined by immunofluorescence due to limited tissue. I selected these proteins because they are the primary effectors of the canonical Wnt and Notch signaling pathways. However, Wnt signaling is complex with canonical and non-canonical pathways as well as Wnt-independent -catenin activation and many Wnt ligands (Haq et al. 2003). Notch signaling also has canonical and non-canonical pathways as well as 3 additional Notch receptors other than Notch1 (Ayaz and Osborne 2014). Despite this limitation, our findings set a precedent for future gene expression studies that can examine the expression and localization of other components and targets of these pathways in additional SIOD patients. In summary, my findings show that the Wnt and Notch pathways are upregulated in the SIOD patient kidney and that Marcal1, the Drosophila SMARCAL1 orthologue, genetically interacts with Wnt and Notch pathway genes. These findings, together with our prior findings,  99 suggest that SMARCAL1 deficiency gives rise to the clinically distinctive features of SIOD through alterations of gene expression.  100 Chapter 5: Molecular Characterization and Pathogenesis of the Immune Disease in SIOD  5.1 Introduction The immune system protects an organism against pathogens such as viruses, bacteria, and parasites by recognizing and attacking these infectious agents, while distinguishing them from the healthy tissue of the organism. The immune system has been divided into the innate and adaptive immune responses. The innate immune response is a rapid but general response that functions as the first line of defense against infection and is executed by granulocytes, macrophages, natural killer cells, and complement proteins; the adaptive immune response is a slow but highly specific response that confers long-lasting immunity and is executed by CD4+ helper and CD8+ cytotoxic T cells (that mediate cellular immunity) and B cells (that mediate humoral immunity) (Dranoff 2004). The primary immunodeficiencies are a heterogeneous group of disorders comprising of nearly 300 distinct disorders that arise from intrinsic defects of the immune system (Bousfiha et al. 2015). They are frequently associated with repeated bacterial, fungal, or viral infections and cause much morbidity and mortality. The primary immunodeficiencies have been classified into 9 categories by the International Union of Immunological Societies and arise by deficiencies in the quantity or functionality of immune system components as well as the dysregulation of the immune system (Bousfiha et al. 2015). Primary immunodeficiencies can also occur as part of a syndrome, such as in Schimke immuno-osseous dysplasia (SIOD). While B cell and natural killer cell counts are typically normal, SIOD patients have primary T-cell immunodeficiency (Spranger et al. 1991; Boerkoel et al. 2000). Since recurrent infection, due to T-cell immunodeficiency, causes morbidity and mortality among SIOD patients, insight into the etiology of the T-cell immunodeficiency of SIOD is critical. Genetic causes for T-cell immunodeficiency include defects of T-cell development, function, and survival (Bousfiha et al. 2015). Since SMARCAL1 deficiency alters gene expression (Baradaran-Heravi et al. 2012a), we hypothesized that altered expression of a gene or a set of genes required for T-cell development, proliferation, maintenance, or function underlies the immunodeficiency of SIOD. The objective of this study was to identify key receptors and/or  101 markers of T cells that were differentially expressed in SIOD T cells. We found that T cells in SIOD patients are deficient in the expression of the interleukin 7 receptor alpha chain (IL7R) and are not responsive to stimulation with interleukin 7 (IL-7). As a potential cause for the reduced IL7R expression, we detected hypermethylation of the IL7R promoter in the T cells from SIOD patients.  5.2 Methods 5.2.1 Human samples SIOD patients referred to this study are presented in Table 5.1. Blood samples were obtained according to the protocol approved by the Stanford University School of Medicine (Stanford, CA, USA) or the University of British Columbia (Vancouver, BC, Canada). Cord blood samples were purchased from AllCells (Alameda, CA, USA). 102 Table 5.1 The SMARCAL1 mutations of the SIOD patients included in this study. Patient ID Genotype Type of mutation Effect Age1 SD18c c.[1756C>T];[1756C>T] Missense p.[(R586W)];[R586W)] 33 SD51 c.[2459C>A];[2542G>T] Missense/nonsense p.[(R820H)];[(E848X)] 17 SD60 c.[2542G>T];[2542G>T] Nonsense p.[(E848X)];[(E848X)] 12 SD74 c.[1736C>T];[?]2 Missense p.[(S579L)];[(?)] 10 SD82 c.[1931G>A];[?]3 Missense p.[(R644Q)];[(?)] 21 SD100 c.[836T>C];[?]3 Missense p.[(F279S)];[(?)] 26 SD107 c.[2542G>T];[2542G>T] Nonsense p.[(E848X)];[(E848X)] 3 SD108a c.[1681C>T];[1681C>T] Missense p.[(R561C)];[(R561)] 21 SD108b c.[1681C>T];[1681C>T] Missense p.[(R561C)];[(R561)] 7 SD112b c.[1934G>A];[2542G>T] Missense/nonsense p.[(R645H)];[(E848X)] 19 SD119 c.[2449C>T];[2542G>T] Missense/nonsense p.[(R817C)];[(E848X)] 12 SD120 c.[2291G>A];[2542G>T] Missense/nonsense p.[(R764Q)];[(E848X)] 6 SD124 c.[1920_1921insG];[1920_1921inseG] Nonsense p.[(V641fsX51)];[(V641fsX51)] 7 SD131 c.[1026C>A];[2264T>G] Missense/nonsense p.[(Y342X)];[(I755S)] 4 SD136a c.[863-2A>G];[863-2A>G] Splice site p.[(M288_D366del)];[(M288_D366del)] 9 SD136b c.[863-2A>G];[863-2A>G] Splice site p.[(M288_D366del)];[(M288_D366del)] 8 SD138 c.[2542G>T];[2542G>T] Nonsense p.[(E848X)];[(E848X)] 4 SD139 c.[1190delT];[2575T>C] Nonsense/missense p.[(L397fsX40)];[(S859P)] 8 SD146 c.[1642_1644delATT];[1642_1644delATT] In-frame deletion p.[(I548del)];[(I548del)] 3 SD148 c.[740G>C];[2542G>T] Missense/nonsense p.[(R247P)];[(E848X)] 8 SD150 c.[2459G>A];[2459G>A] Missense p.[(R820H)];[(R820H)] 4  1Age at sample collection. 2This patient has a non-coding SMARCAL1 mutation as previously described by Clewing et al. (2007b). 3A second SMARCAL1 mutation was not detected, however the patient has a clinical diagnosis of SIOD.  Abbreviations: ID, identification; mRNA, messenger ribonucleic acid; SIOD, Schimke immuno-osseous dysplasia. 103 5.2.2 Antibodies Fluorochrome-conjugated monoclonal antibodies against human CD3, CD4, CD8, CD19, CD25, CD45RA, CD45RO, CD127, and isotype controls were purchased from BD Biosciences (San Diego, CA, USA). Anti-human CD31 antibody was purchased from BioLegend (San Diego, CA, USA). The complete list of antibodies used for flow cytometry is presented in Supplementary Table 5.1.  5.2.3 High-dimensional flow cytometry Peripheral blood mononuclear cells (PBMCs) from SIOD patients, unaffected siblings, and their parents, and unaffected controls were isolated by Ficoll-Hypaque density gradient centrifugation. Cells were then washed twice with Hank’s balanced salt solution and either used immediately or frozen in fetal calf serum (FCS) containing 10% dimethyl sulfoxide (DMSO) until use. Single-cell suspensions obtained from a fresh preparation or from frozen aliquots were stained with a combination of fluorochrome-conjugated antibodies against CD3, CD4, CD8, CD19, CD25, CD31, CD45RA, CD45RO, CD127, and isotype controls (BD Biosciences, San Diego, CA, USA). “Fluorescence-minus-one” controls were included to determine the level of non-specific staining and autofluorescence associated with subsets of cells in each fluorescence channel. For surface staining, propidium iodide was added to all of the samples before data collection to identify dead cells. Hi-dimensional flow cytometry data were collected on an LSRII FACS instrument (BD Biosciences, San Jose, CA, USA). FlowJo software (Tree Star, Ashland, OR, USA) was used for fluorescence compensation and analysis. Data are depicted as contour plots or histograms displaying fluorescence intensity (FI) plotted against cell numbers/FI interval with a total of 245 intervals per parameter.  Spleen samples were washed twice with 1 PBS, then once with RPMI 1640 medium containing 10% FCS. Single-cell suspensions were prepared by passing the tissue through a 70 m nylon mesh and collected in 1 PBS. Mononuclear cells from the single-cell suspension were isolated by Ficoll-Hypaque density gradient centrifugation. Cells were then washed and either used immediately or frozen in FCS containing 10% DMSO until use. Cells were stained as described above.   104 5.2.4 Immunohistochemistry A fresh spleen sample from an SIOD patient was obtained after autopsy. Portions of the tissue were formalin-fixed and paraffin-embedded (FFPE) using standard procedures. FFPE unaffected spleen samples were obtained from the archives of the Department of Pathology, Stanford University Medical Center (Stanford, CA, USA) following Institutional Review Board approval. Four m-thick FFPE tissue sections were de-paraffinized in xylene and hydrated in a series of graded alcohols. Heat-induced epitope retrieval was carried out by microwave pretreatment in citric acid buffer (10 mM citric acid, pH 6.0) for 10 minutes. Sections were stained with an anti-human CD3 antibody (1:100, clone UCHT1, BD Biosciences, San Diego, CA, USA). Detection was carried out using the DAKO Envision method (Dako, Carpinteria, CA, USA). Additional sections were stained by hematoxylin and eosin (H & E).  5.2.5 Lymphocyte proliferation assays PMBCs were incubated with 1 m carboxyfluorescein succinimidyl ester (CFSE) (Invitrogen, Carlsbad, CA, USA) for 7 minutes in serum-free medium at room temperature. Cells were then washed twice with RPMI 1640 medium containing 10% FCS. CFSE-labeled cells were plated in round-bottom 96-well plates at a density of 5  105 cells/well. Some wells were incubated with 100 ng/ml recombinant human IL-7 (R&D Systems, Minneapolis, MN, USA). For the induction of proliferation, cells were incubated with anti-CD3/CD28-conjugated beads (Invitrogen, Carlsbad, CA, USA) at a bead to cell ratio of 1:100. Control cells were left unstimulated. After incubation for 96 hours, cells were washed once with 1 PBS, stained with antibodies against CD3, CD4, and CD8, and washed again. Propidium iodide was added to each sample to identify dead cells and samples were analyzed by flow cytometry. Cell divisions were quantified by the loss of CFSE fluorescence using FlowJo software (Tree Star, Ashland, OR, USA).  5.2.6 RNA isolation and reverse transcription RNA from purified T cells or PBMCs was extracted using the RNeasy Mini Kit (Qiagen, Toronto, ON, Canada), and on-column DNase I digestion (Qiagen, Toronto, ON, Canada) was performed to remove genomic DNA. Reverse transcription was performed with the qScript  105 cDNA Synthesis Kit (Quanta Biosciences, Gaithersburg, MD, USA) according to the manufacturer’s specifications.  5.2.7 Quantitative PCR SsoFast EvaGreen Supermix (Bio-Rad Laboratories, Mississauga, ON, Canada) was used with the 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) for quantitative PCR. The following conditions were used for amplification: 1 cycle of 95C for 30 seconds, followed by 40 cycles of 95C for 5 seconds and 60C for 30 seconds, followed by a melt curve analysis.  The relative quantification of gene expression was calculated using the Ct method (Bookout and Mangelsdorf 2003). Expression of the housekeeping gene GAPDH was used as the internal control. Graphed quantitative data are presented as the mean  1 standard deviation calculated from three technical replicates. The primer sequences used in this study are listed in Supplementary Table 5.2.  5.2.8 Sequencing of SMARCAL1 cDNA For those SIOD patients for whom I only detected one heterozygous SMARCAL1 mutation, I synthesized cDNA from patient PBMC RNA and sequenced the coding region of the SMARCAL1 cDNA following PCR amplification. The amplified fragments were sequenced by the Sanger method (Macrogen, Seoul, Republic of Korea), and the sequences were aligned and analyzed using Sequencher version 4.10.1 (Gene Codes, Ann Arbor, MI, USA). The sequences of all of the primer pairs used for PCR and sequencing are presented in Supplementary Table 5.3.  5.2.9 Sequencing of IL7R exons To determine whether SIOD patients have mutations in the IL7R gene, all coding exons of the gene were amplified by PCR and sequenced. DNA was extracted from peripheral whole blood using the Gentra Puregene Blood Kit (Qiagen, Toronto, ON, Canada) according to the manufacturer’s protocol. The amplified fragments were sequenced by the Sanger method (Macrogen, Seoul, Republic of Korea), and the sequences were aligned and analyzed using Sequencher version 4.10.1 (Gene Codes, Ann Arbor, MI, USA). The sequences of all of the primer pairs used for PCR and sequencing are presented in Supplementary Table 5.4.  106  5.2.10 Bisulfite pyrosequencing  CD3+ T cells were FACS-purified from the peripheral blood of seven SIOD patients and their age- and sex-matched unaffected controls. The average DNA methylation of 6 CpG sites in the IL7R gene promoter was analyzed by bisulfite pyrosequencing as previously described (Kim et al. 2007). For each sample, genomic DNA of 2  104 - 1 105 CD3+ T cells was bisulfite converted using the EZ DNA Methylation-Direct Kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s instructions. The efficiency of bisulfite treatment, measured by the conversion of cytosine not followed by guanosines into uracils, was > 97% in all experiments. The regions of interest in the IL7R promoter were then amplified by PCR and subsequently sequenced using the PyroMark Gold Q96 Reagents (Qiagen, Toronto, ON, Canada) and the PyroMark Q96 MD system (Qiagen, Toronto, ON, Canada). The forward, reverse, and sequencing primers and PCR conditions have been previously described (Kim et al. 2007) and are listed in Supplementary Table 5.5. The average DNA methylation for each CpG site in the IL7R promoter at positions -960, -552, -482, -459, -451, and -331 relative to the transcription start site was evaluated using the PyroMark CpG software (Qiagen, Toronto, ON, Canada). Human High and Low Methylated Genomic DNA (EpigenDX, Hopkinton, MA, USA) were respectively used as positive and negative DNA methylation controls for the bisulfite pyrosequencing assays.  5.2.11 Measurement of serum immunoglobulin isotypes and IgG subtypes The concentration of serum immunoglobulin isotypes and IgG subtypes were measured using the MILLIPLEX MAP Human Isotyping Magnetic Bead Panel (HGAMMAG-301K, Millipore, Billerica, MA, USA) that utilizes magnetic microsphere-conjugated capture antibodies. The manufacturer has validated the panel for serum immunoglobulin measurement. Archived serum samples from SIOD patients and unaffected controls were diluted 1:16,000 using dilution buffer according to the manufacturer’s protocol. Fluorescent intensities of the microspheres were analyzed and the final concentrations were calculated using the Bio-Plex 200 System (Bio-Rad Laboratories, Hercules, CA, USA). Serum concentration of total IgG was measured using the Human Immunoglobulin G Enzyme-linked Immunosorbent Assay (ELISA) Kit (Molecular Innovations, Novi, MI, USA) following the manufacturer’s instructions.  107 5.2.12 Statistical analysis For the analysis of the average DNA methylation of the IL7R promoter CpG sites, the one-tailed Mann-Whitney U test was used to test the null hypothesis that the average DNA methylation of a CpG site of the IL7R promoter is the same or reduced in SIOD patient T cells compared to unaffected control T cells. A p value of less than 0.05 was considered to be statistically significant.  5.3 Results 5.3.1 SIOD patients are T-cell lymphopenic and their T cells are deficient in the interleukin 7 receptor alpha chain SIOD patients are T-cell lymphopenic (Spranger et al. 1991; Boerkoel et al. 2000). However, T-cell subset distribution in these patients has not been analyzed in detail. Initially, we resolved different lymphocyte subsets in the SIOD patients by Hi-Dimensional flow cytometry. B and T cells were identified based on CD19 and CD3 expression, respectively. CD3+ T cells were subsequently resolved for helper CD4+ and cytotoxic CD8+ subsets. Based on CD45RA and CD45RO expression, CD3+ CD4+ CD8– and CD3+ CD4– CD8+ T cells were further resolved into naïve (CD45RA+ CD45RO–) or memory (CD45RA– CD45RO+) T-cell subsets. We then analyzed the expression of the interleukin 7 receptor alpha chain (IL7R, CD127) in the T cells of SIOD patients and unaffected individuals (Figure 5.1).  As previously reported (Spranger et al. 1991; Boerkoel et al. 2000), we observed that SIOD patients are T-cell lymphopenic compared to unaffected individuals (Figure 5.1A and B). In the T-cell compartments, their CD4 to CD8 ratios were usually normal (8 out of 10 patients analyzed). From an early age, SIOD patients had very high proportions of memory cells (CD45RA– CD45RO+) compared to naïve cells (CD45RA+ CD45RO–) in both CD4+ and CD8+ subsets (9 out of 10 patients analyzed). IL7R expression was nearly undetectable in the T cells from SIOD patients (21 out of 21 patients analyzed) (Figure 5.1A and B, and Supplementary Figure 5.1). The fluorescent signals for patient T cells nearly overlapped with the signal from isotype control staining suggesting a complete lack of IL7R expression.    108  Figure 5.1 Phenotypic analyses of T cells from SIOD patients reveal high proportions of memory T cells and deficiency of IL7R expression. (A and B) PBMCs from SIOD patients, parents, and unaffected controls were stained with fluorochrome-conjugated CD3, CD4, CD8, CD19, CD45RA, CD45RO, and CD127 (IL7R) antibodies. FACS data are presented as probability contour plots. Data points falling outside of the lowest contour are represented as dots. Numbers adjacent to the boxed regions indicate the percentage of cells within the boxed region. Initially, B and T cells were identified based on CD19 and CD3 expression, respectively (left column). T cells (CD3+) were subsequently resolved for CD4 and CD8 subsets (middle left column). Both of these subsets were further resolved for naïve (CD45RA+ CD45RO–) and memory (CD45RA– CD45RO+) T cells (middle right and right columns). Expression of IL7R in CD4+ and CD8+ cells is presented as histograms. Shaded regions in the histogram plots represent signals from isotype control  109 staining. Note that SIOD patients SD74 (panel A) and SD138 (panel B) have reduced IL7R expression that overlaps with isotype control staining. These are representative data. (C) Expression of IL7R (CD127) from gated T-cell subsets from the SIOD patient and unaffected control presented in B is shown. Shaded regions in the histogram plots represent signals from isotype control staining. Note that total CD4+, naïve CD4+ cells (CD4+ CD45RA+ CD45RO–), memory CD4+ cells (CD4+ CD45RA– CD45RO+), total CD8+ cells, naïve CD8+ cells (CD8+ CD45RA+ CD45RO–), and memory CD8+ cells (CD8+ CD45RA– CD45RO+) uniformly express reduced IL7R in SIOD patients. These are representative data. Abbreviations: CD, cluster of differentiation; FACS, fluorescence-activated cell sorting; IL7R, interleukin 7 receptor alpha chain; PBMC, peripheral blood mononuclear cell; SIOD, Schimke immuno-osseous dysplasia.  The data for this figure was generated by Dr. Mrinmoy Sanyal, Kira Y. Dionis-Petersen, and Lan Xiang Liu.  5.3.2 Reduction of IL7R is not restricted to any specific T-cell subset To address whether the reduction of IL7R expression was restricted to a particular subset of T cells we analyzed its expression in the following T-cell subsets including total, naïve (CD45RA+ CD45RO–) and memory (CD45RA– CD45RO+) subsets of CD4+ and CD8+ T cells. Our analysis revealed that all of the subsets have uniformly reduced IL7R expression indicating that the deficiency had likely arisen very early during intrathymic T-cell development (Figure 5.1C).  5.3.3 SIOD splenic T cells also have reduced IL7R expression The analysis of 21 patients and their unaffected family members revealed that SIOD patients have IL7R deficiency in their peripheral blood T cells and that those T cells mostly display a memory phenotype. Because this skewed representation of T cells in peripheral blood may have altered the expression of IL7R, we analyzed IL7R expression in splenic T cells from an SIOD patient SD120 (Figure 5.2). When compared with T cells from the spleen of an unaffected individual with a similar memory to naïve T-cell subset representation, we noticed a similar reduction of IL7R expression. Histopathological analysis showed normal tissue architecture in the SIOD spleen but very few T cells (Figure 5.2B).  110  Figure 5.2 An SIOD spleen has very few T cells and reduced IL7R expression. (A) Mononuclear cells prepared from unaffected and SIOD spleens were analyzed for different T-cell subsets as in Figure 5.1. FACS data are presented as probability contour plots. Data points falling outside of the lowest contour are represented as dots. Numbers adjacent to the boxed regions indicate the percentage of cells within the boxed region. Similar to the peripheral blood of SIOD patients (Figure 5.1), analysis of splenic tissue from a 5-year-old patient (SD120) showed very few CD3+ T cells. Although, it seems that the CD4 versus CD8 T-cell ratio is affected in this SIOD spleen, subset analysis of these T cells revealed that they mostly display a memory phenotype (CD45RA– CD45RO+) in both CD4 and CD8 compartments. Expression of IL7R in T cells was severely reduced in the SIOD spleen compared to an unaffected spleen from a 65-year-old donor with a similar memory to naïve cell subset distribution as observed in the SIOD spleen. (B) Histopathological analysis revealed that the SIOD spleen (400) was nearly devoid of T cells in the lymphoid follicles and in the intra-follicular space. Upper panels show hematoxylin and eosin staining (H & E) for the analysis of tissue architecture. Bottom panels show CD3 staining of T cells. Abbreviations: CD, cluster of differentiation; FACS, fluorescence-activated cell sorting; IL7R, interleukin 7 receptor alpha chain; SIOD, Schimke immuno-osseous dysplasia.  The data for this figure was generated by Dr. Mrinmoy Sanyal, Kira Y. Dionis-Petersen, Lan Xiang Liu, and Dr. Neeraja Kambham.   111 5.3.4 SIOD T cells have reduced IL7R mRNA levels To address whether the reduction of surface expression of IL7R had arisen from altered mRNA synthesis, we purified T cells from an unaffected control and four SIOD patients and measured relative IL7R mRNA levels (Figure 5.3). We observed that SIOD T cells had significantly reduced relative IL7R mRNA levels compared to an unaffected control (Figure 5.3B). IL7R protein expression therefore correlates with transcript level, and we conclude that this likely arises from reduced IL7R mRNA expression.   Figure 5.3 IL7R mRNA expression is reduced in SIOD T cells. (A) The scheme for sorting T cells from peripheral blood. FACS data are presented as probability contour plots. Data points falling outside of the lowest contour are represented as dots. Numbers adjacent to the boxed regions indicate the percentage of cells within the boxed region. Cells within the boxed region were used for subsequent gene expression analysis. (B) Relative IL7R mRNA levels of peripheral blood CD3+ T cells from an unaffected control and four SIOD patients (SD51, SD108a, SD108b, and SD138) measured by qRT-PCR. The IL7R mRNA levels of three technical replicates were standardized to the mRNA levels of the housekeeping gene GAPDH and graphed relative to the unaffected control. Error bars represent one standard deviation. Abbreviation: CD, cluster of differentiation; FACS, fluorescence-activated cell sorting; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; IL7R, interleukin 7 receptor; qRT-PCR, quantitative reverse transcription polymerase chain reaction; SIOD, Schimke immuno-osseous dysplasia.  The data for this figure was generated by Dr. Mrinmoy Sanyal, Dr. Alireza Baradaran-Heravi, and Kunho Choi.  5.3.5 T cells in SIOD patients are not responsive to interleukin 7 (IL-7) Interleukin 7 (IL-7), produced by stromal cells and thymic epithelial cells, is a ligand for IL7R. Signaling via the IL-7 and IL7R system is important for T-cell development, survival, and proliferation. Since SIOD T cells do not express IL7R, we verified functional loss of the receptor by measuring IL-7-induced enhancement of T-cell proliferation (Figure 5.4 and Supplementary Figure 5.2). To develop this assay, we stimulated unaffected PBMCs with  112 increasing concentrations of recombinant IL-7 and analyzed the proliferation of CD4+ or CD8+ T cells (Supplementary Figure 5.2). The combination of using a sub-optimal level of CD3/CD28-conjugated beads (i.e., a bead to cell ratio of 1:100), which induces moderate T-cell proliferation, with 100 ng/ml IL-7 induced a marked proliferation of CD4+ and CD8+ cells (Supplementary Figure 5.2). Measuring IL-7-induced enhancement of proliferation in SIOD T cells, we found that both SIOD CD4+ (Figure 5.4A) and CD8+ (Figure 5.4B) T cells failed to respond to IL-7, although when stimulated with an optimal level of CD3/CD28-conjugated beads (i.e., a bead to cell ratio of 1:1), they proliferated normally. Also, a normal response was noted when SIOD T cells were stimulated with phytohemagglutinin (PHA) or interleukin 2 (IL-2) (Supplementary Figure 5.3). These observations suggest that the lack of response to IL-7 is specific and likely due to the deficiency of IL7R expression. We conclude therefore that there is a functional loss of the IL-7 receptor in SIOD T cells.   113  Figure 5.4 SIOD patient T cells fail to respond to IL-7. PBMCs were labeled with CFSE and cultured for 96 hours. Anti-CD3/CD28-conjugated beads at a bead-to-cell ratio of 1:100 were used to induce T-cell proliferation. Cells were incubated with 100 ng/ml IL-7 to determine its effect on T cells with or without anti-CD3/CD28-conjugated beads. After incubation for 96 hours, T-cell subsets were resolved using fluorochrome-conjugated anti-human CD3, CD4, and CD8 antibodies. Cellular proliferation was measured by CFSE dilution. A similar reduction in IL-7 responsiveness was observed in both SIOD CD4+ (panel A) and CD8+ (panel B) T cells. These are representative data. Abbreviations: CD, cluster of differentiation; CFSE, carboxyfluorescein succinimidyl ester; IL-7, interleukin 7; PBMC, peripheral blood mononuclear cell; SIOD, Schimke immuno-osseous dysplasia.  The data for this figure was generated by Dr. Mrinmoy Sanyal, Kira Y. Dionis-Petersen, and Lan Xiang Liu.   114 5.3.6 DNA changes in the IL7R gene in SIOD are not pathogenic Mutations in IL7R have been reported to abolish the expression of IL7R leading to T-cell immunodeficiency (Puel et al. 1998; Roifman et al. 2000). To test this as a potential mechanism of IL7R deficiency in SIOD patients, I sequenced the exons of IL7R from three patients and their parents. Our sequencing results identified 5 common polymorphisms of IL7R in these SIOD patients (Table 5.2). 115 Table 5.2 Exon sequencing results of IL7R in SIOD families. Nucleotide change Amino acid change SNP Proband genotype Paternal genotype Maternal genotype Pedigree SD18      c.82+16G>C      c.197T>C      c.412G>A      c.1066A>G  NA p.Ile66Thr p.Val138Ile p.Ile356Val  rs1353252 rs1494558 rs1494555 rs3194051  Heterozygous Heterozygous Heterozygous Heterozygous  Homozygous Homozygous Homozygous Heterozygous  No change No change No change No change Pedigree SD74      c.82+16G>C      c.197T>C      c.412G>A      c.1066A>G  NA p.Ile66Thr p.Val138Ile p.Ile356Val  rs1353252 rs1494558 rs1494555 rs3194051  Heterozygous Heterozygous Heterozygous Heterozygous  Heterozygous Heterozygous Heterozygous Heterozygous  Heterozygous Heterozygous Heterozygous No change Pedigree SD120      c.82+16G>C      c.197T>C      c.412G>A      c.495C>T      c.1066A>G  NA p.Ile66Thr p.Val138Ile p.His165His p.Ile356Val  rs1353252 rs1494558 rs1494555 rs2228141 rs3194051  Homozygous Homozygous Homozygous Heterozygous Heterozygous  Heterozygous Heterozygous Heterozygous No change Heterozygous  Homozygous Homozygous Homozygous Heterozygous No change  Abbreviations: IL7R, interleukin 7 receptor; NA, not applicable; SIOD, Schimke immuno-osseous dysplasia; SNP, single nucleotide polymorphism. 116  5.3.7 Reduced thymic output in SIOD patients T-cell immunodeficiency in SIOD patients could arise from defective thymic function leading to decreased thymic output or from peripheral T-cell loss. Thymic output is correlated with the abundance of recent thymic emigrant cells (CD4+ CD45+ CD31+) in the peripheral blood (Junge et al. 2007). To differentiate these two possibilities, we analyzed the frequency of CD4+ CD45+ CD31+ cells in the peripheral blood of SIOD patients. Figure 5.5 shows the progressive decline of CD4+ CD45+ CD31+ T cells in the peripheral blood with increasing age (cord blood, 19- and 45-year-old) among unaffected individuals. In contrast, the peripheral blood of SIOD patients was nearly devoid of CD4+ CD45+ CD31+ cells at any age. This observation suggests that there is decreased thymic output among SIOD patients.   117  Figure 5.5 Reduced thymic output in SIOD patients. Cord blood mononuclear cells and PBMCs from SIOD patients along with unaffected controls at different ages were stained with fluorochrome-conjugated anti-human CD3, CD4, CD8, CD19, CD45RA, CD45RO, and CD127 (IL7R) antibodies. Initially, T cells were identified based on CD3 expression (left column). T cells were subsequently resolved from CD4 and CD8 subsets (middle left column). CD3+ CD4+ subsets were further analyzed for CD45RA and CD31 expression (middle right column). Recent thymic emigrant cells corresponding to CD4+ CD45RA+ CD31+ cells are marked by rectangular gates in the middle right column. Expression of IL7R in CD3+ CD4+ cells are presented as histograms in the right column. Shaded regions in the histogram plots represent signal from isotype control staining. Note that there is a progressive decline of CD4+ CD45RA+ CD31+ cells with age in the unaffected controls. Two cord blood samples and peripheral blood samples from unaffected individuals of ages 18, 19, 21, 25, 45, and 65 were analyzed; data from one cord blood sample and two peripheral blood samples of representative ages are shown. Also note that the SIOD patients are nearly devoid of CD4+ CD45RA+ CD31+ cells and have reduced IL7R expression that overlaps with isotype control staining. Peripheral blood samples from six SIOD patients were analyzed; representative data from four patient peripheral blood samples are shown. Abbreviations: CD, cluster of differentiation; IL7R, interleukin 7 receptor alpha chain; PBMC, peripheral blood mononuclear cell; SIOD, Schimke immuno-osseous dysplasia.  The data for this figure was generated by Dr. Mrinmoy Sanyal.   118 5.3.8 CpG sites in the IL7R promoter are hypermethylated in SIOD T cells Methylation of CpG sites in the IL7R promoter diminishes the expression of IL7R in CD8+ T cells (Kim et al. 2007). To explore the possibility of promoter hypermethylation as a potential mechanism for the diminished expression of IL7R in SIOD patients, we sorted T cells from a cohort of seven SIOD patients along with age- and sex-matched unaffected controls and analyzed the average DNA methylation status of the six available CpG sites in the IL7R promoter by bisulfite pyrosequencing (Figure 5.6). We observed that the CpG sites most proximal to the transcription start site (i.e., positions -552, -482, -459, -451, and -331 relative to the transcription start site) were significantly hypermethylated in SIOD patients, whereas the more distal CpG site (i.e., -960) was comparably methylated to the unaffected controls (Figure 5.6).   119  Figure 5.6 IL7R promoter CpG sites are hypermethylated in SIOD patients. (A) CD3+ T cells were sorted from seven SIOD patients and their age- and sex-matched unaffected controls. Genomic DNA isolated from the T cells was analyzed for the average methylation of six CpG sites in the IL7R promoter. The average percent methylation of all 6 CpG sites, numbered relative to the transcription start site of the IL7R promoter, for each patient and unaffected control is shown. (B) Comparison of each CpG site between SIOD patients and unaffected controls. The median average methylation values for all SIOD patients and unaffected controls for each site are indicated by a horizontal line. The calculated p values for each of the sites are indicated above the respective site; a p value of less than 0.05 was considered to be statistically significant. Note that, except for the CpG at position -960, all other sites are significantly hypermethylated. Abbreviations: F, female; M, male; ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001; IL7R, interleukin 7 receptor; SIOD, Schimke immuno-osseous dysplasia; yr, years.  The data for this figure was generated by Kunho Choi.   120 5.4 Discussion SIOD is a multisystemic disorder predominantly involving the skeletal, renal, and immune systems (Schimke et al. 1971; Spranger et al. 1991; Ehrich et al. 1995; Boerkoel et al. 2000). T-cell immunodeficiency is a key clinical feature of SIOD, and recurrent infection, due to T-cell immunodeficiency, accounts for significant morbidity and mortality in SIOD patients (Spranger et al. 1991; Boerkoel et al. 2000). Herein, we identify deficiency of IL7R expression as an etiology of this T-cell immunodeficiency. During human T-cell development, T-lineage committed progenitors, arising from the bone marrow, home to the thymus and differentiate through successive stages of development to produce immature CD4+ and CD8+ T cells (Germain 2002). These cells then leave the thymus and enter the periphery and further mature. Generation of T-cell clonal diversity also occurs within the thymus, and peripheral T cells in SIOD patients are mono- or oligoclonal (Lev et al. 2009), suggesting defective T-cell development.  The cytokine IL-7, produced by thymic stromal cells, is essential for T-cell development. Genetic ablation of the Il7 gene in the mouse severely impairs T-cell and B-cell development (von Freeden-Jeffry et al. 1995; Maeurer and Lotze 1998). This cytokine signals through the IL-7 receptor complex, which consists of the IL7R chain and the common gamma chain (Palmer et al. 2008). In humans, either complete (Puel et al. 1998) or partial (Roifman et al. 2000) deficiency of IL7R abrogates T-cell development and causes severe T-cell immunodeficiency. In mice, deletion of the Il7r gene that encodes for IL7R severely disrupts T-cell development and impairs B-cell development (Peschon et al. 1994). In contrast, oncogenic gain-of-function IL7R mutations lead to childhood T-cell acute lymphoblastic leukemia (Zenatti et al. 2011). Thus, IL-7 and the IL-7 receptor play a critical and non-redundant role for early T-cell development and homeostasis. Any aberration in the expression or function of these molecules severely alters T-cell homeostasis. In this context, our observations suggest that the lack of IL7R expression is the likely cause of the T-cell immunodeficiency in SIOD patients. The IL-7 receptor is composed of two chains: the alpha chain (IL7R) and the common gamma chain shared by several other cytokine receptors (IL-2, IL-9, IL-15, and IL-21). Among cytokine receptors that require the common gamma chain for their function, the IL-2 receptor is abundantly expressed in T cells. Our demonstration that SIOD T cells do not respond to IL-7 stimulation (Figure 5.4) but respond normally to IL-2 (Supplementary Figure 5.3) confirms that  121 the common gamma chain is functional, whereas the alpha chain of IL7R is not functional. We have shown that all subsets of T cells in SIOD patients lack IL7R expression including naïve T cells (CD45RA+ CD45RO–) (Figure 5.1C). This suggests that the defect in IL7R expression in T cells may have arisen intrathymically, that is, before the cells reached the periphery.  Although the T-cell immunodeficiency in SIOD patients could be the result of defective thymic output or peripheral T-cell loss, our demonstration that SIOD patients are devoid of CD4+ CD45+ CD31+ recent thymic emigrant cells (Figure 5.5), and the overall decrease of naïve T cells (CD45RA+ CD45RO–) in both CD4 and CD8 compartments from an early age indicates that defective thymic output is the likely cause of the T-cell immunodeficiency in SIOD patients. Additionally, the normal proliferative response of SIOD T cells upon stimulation with optimal anti-CD3/CD28-conjugated beads (Figure 5.4) and mitogen (PHA) (Supplementary Figure 5.3) in the presence of IL-2 suggest that the lack of peripheral proliferation or decreased survival after migration of T cells to the periphery are unlikely to be causes of the T-cell immunodeficiency.  We anticipated that although SIOD patients have normal numbers of B cells, they would have impaired B-cell function, particularly functions requiring T-cell help, because their very few T cells are predominantly memory T cells. To understand this, we quantified serum concentrations of different immunoglobulin isotypes and IgG subclasses from SIOD patients. These analyses revealed that IgA and IgM levels were normal in most patients and total IgG levels were normal in 67% of patients (Supplementary Table 5.6). SIOD patients have a selective reduction of IgG2. The variable concentration of IgG4 prevented conclusions about IgG4 levels in SIOD patients. After birth, blood IgG2 and IgG4 take approximately 8 - 10 years longer to reach adult levels than do IgG1 and IgG3 (Buckley 2015). Since most of the patients were very young, the reduced IgG2 and variable IgG4 levels might have been age-related.  The deficiency of IL7R could arise from either 1) mutation(s) of the IL7R gene (Puel et al. 1998; Roifman et al. 2000) or 2) reduced transcription of this gene. To assess the first possibility, we analyzed the coding regions of the IL7R gene in SIOD patients; however, we only detected common polymorphisms. To assess the second possibility, we analyzed the expression of IL7R mRNA and observed that SIOD T cells had reduced IL7R mRNA levels, suggesting that there is reduced transcription of IL7R. Hypermethylation of CpG sites in the IL7R promoter abolishes the expression of this gene in T cells (Kim et al. 2007). To determine whether this could account for the loss of transcription of IL7R in SIOD patients, we analyzed the average  122 percent methylation of the six available CpG sites in the IL7R promoter of T cells obtained from seven SIOD patients and found hypermethylation of 5 of those 6 CpG sites. We hypothesize that this hypermethylation of the IL7R promoter contributes to the reduced expression of IL7R and IL7R and thereby the T-cell immunodeficiency of SIOD.  IL7R deficiency is unlikely the sole cause of T-cell immunodeficiency in SIOD because SMARCAL1 deficiency, combined with other genetic and environmental factors, alters the expression of many genes to cause disease (Baradaran-Heravi et al. 2012a). Analysis of the transcriptome, therefore, is necessary to assess the altered expression of other genes involved in T-cell development, especially in the T cell and cells of the thymus, where T-cell development takes place. Additionally, global evaluation of genome methylation patterns might provide insights into the role of DNA methylation in contributing to the T-cell immunodeficiency and other features of SIOD. The findings of IL7R deficiency and methylation of the IL7R promoter do, nonetheless, provide one plausible explanation that is consistently observed in each SIOD patient with T-cell immunodeficiency.  In summary, the lack of IL7R expression and IL-7 responsiveness in T cells is a hallmark of T-cell immunodeficiency in SIOD and could serve as diagnostic adjuncts. Based on the hypermethylation of the IL7R promoter in SIOD T cells and the observations that DNA methyltransferase inhibitors such as 5-azacytidine increase T-cell counts and subsequent survival of patients with high-risk myelodysplastic syndrome (Fenaux et al. 2009), I hypothesize that a similar strategy might reactivate transcription of IL7R and thereby be a therapy for the T-cell immunodeficiency in SIOD. 123 Chapter 6: Discussion In this thesis, I demonstrated that SMARCAL1 deficiency pathologically alters gene expression to contribute to the vascular, renal, and immune diseases of SIOD. I identified reduced ELN expression as a potential contributor to the arteriosclerosis, activation of the Wnt and Notch signaling pathways as potential contributors to the FSGS, and reduced IL7R expression as a potential contributor to the T-cell immunodeficiency; however, the mechanism by which SMARCAL1 deficiency alters the expression of these genes is not known. Thus, I will discuss potential mechanisms by which SMARCAL1 deficiency might alter gene expression, as well as highlight the strengths and limitations of this work and discuss future directions of the study of SIOD.  6.1 Potential mechanisms of SMARCAL1 deficiency modulating gene expression There are at least four nonexclusive mechanisms by which SMARCAL1 deficiency could potentially modulate gene expression that I will detail in the following sections (Figure 6.1).    124  Figure 6.1 Potential mechanisms of SMARCAL1 deficiency modulating gene expression. Four nonexclusive mechanisms by which SMARCAL1 deficiency could potentially modulate gene expression include an effect on transcription, an effect on gene expression through unrepaired DNA lesions impeding transcription, an effect on gene promoter structure, and an effect on gene expression through replication stress-induced alterations of chromatin structure. Abbreviations: RNAPII, RNA polymerase II; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.  6.1.1 Effect on transcription Similar to the DNA excision repair proteins ERCC6 and ERCC8 that also participate in transcription, SMARCAL1 might also be part of the RNA polymerase complex, and thus its deficiency would directly alter transcription (Figure 6.1). We previously observed that Marcal1 preferentially binds promoters and transcriptionally active chromatin and genetically interacts with genes encoding components of the transcription factor machinery (Baradaran-Heravi et al. 2012a). Evidence against this model includes 1) the failure of SMARCAL1 orthologues to co-purify or co-immunoprecipitate with RNA polymerase II (Robert et al. 2002; Jeronimo et al. 2004; Aygun et al. 2008) and 2) the failure of RNA polymerase II to co-purify or co-immunoprecipitate with SMARCAL1 or Marcal1 (Baradaran-Heravi et al. 2012a).   125 6.1.2 Effect through unrepaired DNA lesions impeding transcription Unrepaired DNA damage due to SMARCAL1 deficiency could lead to gene expression changes by impairing RNA polymerase II progression (Figure 6.1). Such DNA lesions are repaired by transcription-coupled nucleotide excision repair (TC-NER) (Fousteri and Mullenders 2008). Although SMARCAL1 is recruited to sites of DNA damage and has roles in DNA repair (Ciccia et al. 2009; Yusufzai et al. 2009; Keka et al. 2015), evidence against this model includes 1) the insensitivity of SMARCAL1-deficient dermal fibroblasts to ultraviolet light-induced transcriptional inhibition, which induces DNA damage that is repaired by global-genome nucleotide excision repair (GG-NER) and TC-NER and 2) the insensitivity of SMARCAL1-deficient dermal fibroblasts to illudin S, which induces DNA damage that is repaired exclusively by TC-NER (Baradaran-Heravi et al. 2012b).  6.1.3 Effect on gene promoter structure As an ATP-dependent DNA annealing helicase, SMARCAL1 could modulate gene expression by altering the promoter structure of a gene (Figure 6.1). Indeed, Sharma et al. (2015) have demonstrated that bovine SMARCAL1 is able to negatively regulate the transcription of c-Myc by altering the conformation of its promoter in an ATP-dependent manner (Sharma et al. 2015). Further studies are, however, required to define the nature of the conformational change, the mechanism by which SMARCAL1 is recruited to the promoter, and additional targets of SMARCAL1.  6.1.4 Effect through replication stress-induced alterations of chromatin structure The function of SMARCAL1 as a DNA replication stress response protein required for replication fork restart has been clearly established (Bansbach et al. 2009; Ciccia et al. 2009; Postow et al. 2009; Yuan et al. 2009; Yusufzai et al. 2009). There is also increasing evidence that replication stress leads to both transient and permanent alterations in chromatin structure that can give rise to changes in gene expression (Schiavone et al. 2014; Khurana and Oberdoerffer 2015; Papadopoulou et al. 2015). Therefore, SMARCAL1 deficiency might indirectly alter gene expression through replication stress-induced alterations of chromatin structure (Figure 6.1). Sources of replication stress include DNA lesions, DNA secondary structures, misincorporation of ribonucleotides, collision of the replication and transcription machinery, as  126 well as the depletion of factors required for efficient DNA replication (Zeman and Cimprich 2014). Therefore, DNA damage associated with replication stress is not evenly distributed throughout the genome. This could explain the poor modeling SIOD in other organisms since different species have different genomic sequences. This model also supports our prior findings of the DNA topological structural changes that we have observed in the context of the deficiency of SMARCAL1 orthologues including 1) SMARCAL1-deficient cells show increased S1-nuclease sensitivity when pulsed with potassium permanganate, 2) ectopic expression of the Ultrabithorax (Ubx) gene in Drosophila is suppressed by Marcal1 deficiency and corresponds with a Ubx promoter that has reduced accessibility as measured by micrococcal nuclease-Southern blot, and 3) SIOD T cells show increased methylation of the IL7R promoter which corresponds with the reduced expression of IL7R. Further, Papadopoulou et al. (2015) showed that global replication stress interacts with local replication obstacles to stochastically perturb gene expression (Papadopoulou et al. 2015). This mechanism might provide partial explanations for our prior clinical observations including 1) the age-dependent penetrance of SIOD, 2) the variable expressivity of SIOD, and 3) the poor genotype-phenotype correlation.   6.2 Strengths and limitations 6.2.1 Strengths 6.2.1.1 Use of patient tissue Several models of the deficiency of SMARCAL1 orthologues were considered for studying the vascular, renal, and immune diseases of SIOD. The zebrafish model exhibits some phenotypes similar to SIOD; however, the presence of several additional phenotypes unrelated to SIOD raises questions about the specificity of this model (Huang et al. 2010). The -amanitin-treated Smarcal1-deficient mice also have some phenotypes similar and specific to SIOD, however these mice do not develop vascular disease or immune disease and only develop mild albuminuria (Baradaran-Heravi et al. 2012a). Finally, cultured patient cell lines are more accessible than patient tissues, however the cell lines available (dermal fibroblasts and lymphoblastoid cells) are cell types that are unaffected in SIOD. Dermal fibroblasts are often used as a model for studying elastogenesis (Giro et al. 1985; Sephel and Davidson 1986), but in contrast to the aorta, ELN expression in the SIOD dermal fibroblasts is not altered, a finding  127 consistent with the absence of a skin phenotype in SIOD. Lastly, transient knockdown of SMARCAL1 using small interfering RNA (siRNA) in elastin-producing cells does not alter ELN expression. The use of patient tissue is thus required when other systems fail to model the human disease. Since the deficiency of SMARCAL1 orthologues does not model SIOD well, a major strength of my work is the use of affected SIOD patient tissues obtained through long-standing international collaborations. I performed a number of thoughtful targeted experiments that generated new hypotheses testable with more patient samples in the future.  6.2.2 Limitations 6.2.2.1 Small sample size One major limitation of the work was the study of a single patient for the RNA studies in Chapters 3 and 4. Frozen tissues are typically not suitable for gene expression studies due to the rapid degradation of RNA, however tissue samples from this particular patient were obtained under extraordinary circumstances with the full cooperation and support of the pathologists, pediatric intensivists, and parents. Every effort was made to substantiate these findings in additional patients if feasible. Renal biopsies are generally performed on SIOD patients and therefore the expression of unphosphorylated -catenin and NICD were analyzed in additional patients. However, the selection of these proteins was based on transcriptome analysis of a single patient and unaffected control kidney and findings in the literature, and therefore biased. Transcriptome studies should be performed in additional patients to verify these preliminary findings. Although the arterial histopathology of fragmented elastin fibres was demonstrated in three patients, additional frozen arterial tissue was not available for further gene expression analysis. Nonetheless, I utilized these valuable samples along with the resources that were available to generate numerous testable hypotheses that can be investigated in future studies.  6.2.2.2 Lack of control tissue  The utilization of patient tissues also led to the requirement of appropriate unaffected human control tissues for comparison. These are often difficult to obtain and therefore were a limitation for some of the studies. Specifically, frozen aorta samples from healthy individuals from the pediatric population were unavailable; therefore, a commercially available pooled RNA  128 sample from the aortas of four adults and a developmental series of fetal aortas were obtained from a National Institutes of Health-funded program to facilitate studies such as this one. This allowed me to confirm the ELN regulatory findings in the literature in the human aorta and provide a context with which to interpret the findings of the SIOD aorta. In contrast, T cells from age-matched sex-matched unaffected individuals as well as parents and unaffected siblings were accessible as controls for the T cell study since obtaining blood is considered a minimally invasive procedure. Further, the routine archival of renal biopsies allowed me to test the expression of unphosphorylated -catenin and NICD in additional SIOD patients as well as pediatric patients with primary FSGS.  6.2.2.3 Cell type heterogeneity Measures of gene expression are affected by the relative proportion of the various cell types comprising the tissue harvested. Differences in cell type composition are, therefore, a potential confounding factor, particularly for the study of the gene expression changes in the SIOD kidney presented in Chapter 4. Standard techniques used to address the issue of differences in cell type composition include laser capture microdissection (LCD), fluorescence-activated cell sorting (FACS), and in silico computational approaches. LCD allows for the isolation of subpopulations of cells without the perturbation of their molecular state, however tissues must be embedded in optimal cutting temperature (OCT) medium for the analysis of gene expression in frozen tissue and the amplification or pooling of samples may be required (Espina et al. 2006; Bennett et al. 2007). The use of LCD would have been ideal to isolate the glomeruli of the kidney; however, I was unable to obtain patient or unaffected control tissue embedded in OCT. FACS allows for the isolation of specific cell populations and is an excellent technique for fresh tissues that can be dissociated to single cell suspensions such as blood and for which good cell surface markers are available, and we applied this technique for the analysis of the SIOD T cells. I did not have fresh human aorta or kidney tissue available and I also suspect that the disruption of intact tissues would have altered gene expression among the cells of interest. Finally, several in silico computational approaches have been developed for use when the collection and isolation of a specific cell type is not possible. These computational approaches to deconvolve gene expression data have demonstrated efficacy in studies analyzing gene expression in blood or blood cell types in whole tissue (Repsilber et al. 2010; Shen-Orr et al.  129 2010; Gong et al. 2011; Qi et al. 2014; Shannon et al. 2014). Comparable analysis of tissues other than blood have been hampered by the lack of knowledge of cell-type specific markers that are critical for determining cell type composition, although a few studies have been performed for the mammary gland and organisms such as Caenorhabditus elegans (Wang et al. 2006; Burdick and Murray 2013) in which the knowledge of cell-type specific markers and the invariant cell lineage are known. More knowledge of cell-type specific markers for renal tissue is required for a similar in silico deconvolution of gene expression data in the kidney.  Although the above are limitations of my study, the results presented here were validated by orthologous assays and in additional patient samples when possible. Additionally, my studies have generated hypotheses that can be tested with more rigorous gene expression analyses when additional samples become available.  6.3 Future directions Further studies are required to delineate the molecular mechanism of how SMARCAL1 deficiency leads to altered gene expression. In particular, insight into the direct effects of SMARCAL1 deficiency on gene expression through its ability to alter the conformation of promoters as well as the indirect effects of SMARCAL1 deficiency on gene expression through the generation of replication stress-induced chromatin structural changes would provide insight into this biological process and the pathogenesis of SIOD.  Genome-wide studies to assess the relationship between gene expression and chromatin structure and epigenetic marks could provide valuable insight into the mechanism by which SMARCAL1 deficiency alters transcription. These studies would require the study of a single cell type, and I propose that comparative studies of SIOD and control T cells would be the best tissue given its accessibility and our findings of the reduced IL7R expression in SIOD T cells. Epigenetic measures of interest include Methyl-seq to assess DNA methylation and chromatin immunoprecipitation sequencing (ChIP-seq) to assess histone modifications. Chromatin assays for the topological changes include Hi-C (high-resolution chromatin conformation capture) to assess chromatin conformation and tissue accessible chromatin sequencing (TACh-seq) to assess nuclease accessible chromatin (Belton et al. 2012; Grontved et al. 2012). I hypothesize that integrated analysis of these results with gene expression changes (RNA-seq) provides valuable insight into the basis of the pathogenic gene expression changes within SIOD T cells. Further,  130 Repli-seq to assess genome-wide replication timing of the T cells might also be informative, given the role of SMARCAL1 in the restart of stalled replication forks (Ciccia et al. 2009; Postow et al. 2009; Yuan et al. 2009; Yusufzai et al. 2009; Hansen et al. 2010). In summary, these studies would provide a more comprehensive understanding of the mechanism underlying this gene expression alteration as well as why some genes are more sensitive to SMARCAL1 deficiency than are others. Further, approximately 90% of patients that are clinically diagnosed with SIOD based on the co-occurrence of spondyloepiphyseal dysplasia, renal failure, T-cell immunodeficiency, and typical dysmorphic facial features have mutations in SMARCAL1; thus, the discovery of mutations at new loci in SIOD patients without SMARCAL1 mutations could provide further insight into the mechanism by which SMARCAL1 deficiency alters gene expression.  6.4 Conclusions and significance SIOD is a complex multisystemic rare disease and my work characterizing the clinical phenotype and molecular pathogenesis of SIOD has led to an increased understanding of the disease as well as several testable hypotheses that can be explored in future studies. 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Primer Sequence GAPDH-F 5´-CTTTTGCGTCGCCAGCCGAG-3´ GAPDH-R 5´-GGTGACCAGGCGCCCAATACG-3´ SMARCAL1-F  5´-CCTCTACAAGGACCCAAAGCAGCAG-3´ SMARCAL1-R 5´-TCCAGGGTGTCTCCCATGTTCTGG-3´ GREM2-F 5´-GGCGGCGGGAGACCAAACTTA-3´ GREM2-R 5´-CTTCCAGAACATCCTGCAATGACGT-3´ ID1-F 5´-GCTATGCGGGGGTGCCTAAGG-3´ ID1-R 5´-GGAGGCGCTTCAGCGACACAA-3´ MMP10-F 5´-TCGCCCAGTTCCGCCTTTCG-3´ MMP10-R 5´-AGAGGCAGGGGGAGGTCCGTA-3´ PRDM6-F 5´-AGGTTTCCGGGCGGCACAATC-3´ PRDM6-R 5´-CGGCGCCTCGAACTGAAAACT-3´ SMAD6-F 5´-CCGGGTGAATTCTCAGACGCC-3´ SMAD6-R 5´-AGCCGATCTTGCTGCGCGTT-3´ SMAD7-F 5´-ACGCGGGAGGTGGATGGTGT-3´ SMAD7-R 5´-ACCCCAGCCCTTCACAAAGCTG-3´  Abbreviations: F, forward; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GREM2, gremlin 2; ID1, inhibitor of DNA binding 1; MMP10, matrix metallopeptidase 10; PRDM6, PR domain 6; R, reverse; SMAD6, SMAD family member 6; SMAD7, SMAD family member 7; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1. 149 Supplementary Table 2.2 Dental findings in SIOD patients with biallelic SMARCAL1 mutations.  Patient ID SMARCAL1 mutations Disease severity score1 Dental findings Microdontia Hypodontia or oligodontia2 Molar root hypoplasia Other2 SD8 c.[1190delT];[?]3 6 +  NR  SD16 c.[1643T>A];[1933C>T] 3 + + + Retained deciduous molar SD18a c.[1756C>T];[1756C>T] 3  + NR  SD18c c.[1756C>T];[1756C>T] 2  + Missing: 15, 16, 18, 28, 35, 37, 38, 42, 47, 48  Retained: 75 SD22 c.[2459G>A];[2459G>A] 5  NR NR NR SD23 c.[2542G>T];[2542G>T] 4   NR NR SD27 c.[1940A>C];[1940A>C] 3 + + Missing: 15, 17, 18, 24, 25, 27, 28, 34, 35, 37, 38, 44, 45, 47, 48 + Increased caries, retained: 64, 75 SD28 c.[1696A>T;1698G>C;1702delG]; [1696A>T;1698G>C;1702delG] 5     SD29 c.[862+1G>T];[1934delG] 7  + + Abn superior incisors, delayed dentition SD33a c.[1097-2A>G]; [1146_1147delAA;1147+1_2delGT] 4  + + NR SD33b c.[1097-2A>G]; [1146_1147delAA;1147+1_2delGT] 6   + NR SD35 c.[1736C>T];[2321C>A] 6   NR NR  SD38 c.[1096+1G>A];[1096+1G>A] 6 + + Missing: 14, 15, 18, 24, 25, 28, 35, 38, 45, 47, 48 + Delayed dentition, retained: 54, 55, 64, 65, 74, 75, 84, 85 SD44 c.[1191delG];[2321C>A] 5  + + NR SD47 c.[2459G>A];[?]3 4 + NR NR NR SD48 c.[1939A>C];[1939A>C] 6 + + NR NR  150 Patient ID SMARCAL1 mutations Disease severity score1 Dental findings Microdontia Hypodontia or oligodontia2 Molar root hypoplasia Other2 SD49 c.[1920_1921insG];[2321C>A] 6 + NR NR NR SD50 c.[2542G>T];[2542G>T] 4 + + + Abn enamel SD51 c.[2459G>A];[2542G>T] 4 + + NR  SD57 c.[955C>T];[955C>T] 5 + + Missing: 14, 15, 18, 24, 25, 28, 34, 35, 36, 44, 45, 46 + Retained: 54, 55, 64, 65, 73, 74, 75, 83, 84, 85 SD60 c.[2542G>T];[2542G>T] 5 + + 38 +  SD61 c.[1146_1147delAA;1147+1_2delGT];[1146_1147delAA;1147+1_2delGT] 5    NR SD65a c.[836T>C];[2542G>T] 1     SD65b c.[836T>C];[2542G>T] 3     SD66 c.[1933C>T];[1933C>T] 5 + + NR Increased caries SD70 c.[340_341insAGTCCAC];[836T>C] 6 + + NR Abn dentin SD74 c.[1736C>T];[?]3 3 + + Missing: 14, 15, 18, 25, 28, 38, 35, 45, 48 + NR SD78 c.[1439C>T];[2264T>G] 4 NR NR + NR SD79 c.[2459G>A];[?]3 4  + + NR SD84 c.[1248_1249insC];[2104T>G] 6 + NR NR NR SD96 c.[1427G>A];[1427G>A] 4 + NR NR NR SD99 c.[1402G>C];[1402G>C] 4 +  NR NR SD106 c.[1682G>A];[1682G>A] 4   NR NR SD107 c.[2542G>T];[2542G>T] 4  + NR Abn enamel SD108a c.[1798C>T];[1798C>T] 3   NR NR SD108b c.[1798C>T];[1798C>T] 1   NR NR SD111 c.[1129G>C];[1592T>C] 6 +  NR Abn enamel SD112a c.[1934G>A];[2542G>T] 4   NR NR SD112b c.[1934G>A];[2542G>T] 3   NR NR SD114 c.[1898T>C];[1898T>C] 4 + + + Discolouration SD115 c.[1437_1438insG];[1437_1438insG] 5 NR   NR SD119 c.[2449C>T];[2542G>T] 4  + +   151 Patient ID SMARCAL1 mutations Disease severity score1 Dental findings Microdontia Hypodontia or oligodontia2 Molar root hypoplasia Other2 SD120 c.[2291G>A];[2542G>T] 5 + + + NR SD121 c.[1382G>A];[2542G>T] 4   NR NR SD123 c.[49C>T];[49C>T] 4 +  NR NR SD124 c.[1920_1921insG];[1920_1921insG] 2   NR NR SD127 c.[1736C>T];[1736C>T] 5 + + + Increased caries, abn enamel and dentin, discolouration SD131 c.[1026C>A];[2264T>G] 7 NR + + NR  SD133a c.[1097-2A>G]; [2343_2347delGCTGT] 4   NR  SD138 c.[2542G>T];[2542G>T] 3      1To group patients according to disease severity, each patient’s signs and symptoms were scored as previously described by Clewing et al. (2007b). 2The International Standards Organizational System/Fédération Dentaire Internationale System was used to refer to the affected tooth. 3[?] represents alleles with non-coding SMARCAL1 mutations as described by Clewing et al. (2007b).  Abbreviations: +, feature present; , feature not present; abn, abnormal; ID, identification; NR, not reported; SIOD, Schimke immuno-osseous dysplasia; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1. 152 Supplementary Table 2.3 Summary of the immunohistochemical analysis of SMARCAL1 expression in the developing human tooth.  Developmental stage and cell type Expression level1 Bud stage2      Oral epithelium      Dental lamina      Mesenchymal cell  + + + + + + Cap stage3      Oral epithelium      Dental lamina      Outer dental epithelium      Stellate reticulum      Inner dental epithelium      Primary enamel knot      Dental papilla  + + + + + + + + + + + + + + + + + + Bell stage4      Oral epithelium      Dental lamina      Outer dental epithelium      Stellate reticulum      Stratum intermedium      Inner dental epithelium      Dental papilla  + + +  + + + +  / + + + +  / +  1Expression levels were all assessed relative to the oral epithelium of the developmental stage of interest, which was scored as + + +. 2Bud stage cell types were assessed in a 59-day-gestation fetus. 3Cap stage cell types were assessed in a 98-day-gestation fetus. 4Bell stage cell types were assessed in a 105-day-gestation fetus.  Abbreviations: , no detectable expression; +, weak expression; + +, moderate expression; + + +, strong expression; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.   153 Supplementary Table 2.4 Summary of tooth abnormalities associated with disorders of DNA repair or genomic instability.  Disorder Gene(s) Dental phenotype Reference Cockayne syndrome ERCC6 ERCC8 Increased dental caries (Tan et al. 2005) Dyskeratosis congenita DKC1 TERC TERT TINF2 NHP2 NOP10 Short roots, mild taurodontism (Atkinson et al. 2008; Abdel-Karim et al. 2009) Fanconi anemia BRCA2 BRIP1 FANCA FANCB FANCC FANCD2 FANCE FANCF FANCG FANCI FANCL FANCM PALB2 RAD51C SLX4 Microdontia, hypodontia, increased dental caries, gingivitis, periodontitis, transposition, supernumerary teeth (Acikgoz et al. 2005; Tekcicek et al. 2007) Rothmund-Thomson syndrome RECQL4 Hypodontia, short roots, sensitive gingivia (Haytac et al. 2002; Roinioti and Stefanopoulos 2007) Seckel syndrome ATR Microdontia, hypodontia, short roots, malocclusion, taurodontism, dentinal dysplasia, enamel hypoplasia (Kjaer et al. 2001; Regen et al. 2010) (Seymen et al. 2002)  Abbreviations: ATR, ATR serine/threonine kinase; BRCA2, breast cancer 2; BRIP1, BRCA1 interacting protein C-terminal helicase 1; DKC1, dyskerin pseudouridine synthase 1; ERCC6, excision repair cross-complementation group 6; ERCC8, excision repair cross-complementation group 8; FANCA, Fanconi anemia complementation group A; FANCB, Fanconi anemia complementation group B; FANCC, Fanconi anemia complementation group C; FANCD2, Fanconi anemia complementation group D2; FANCE, Fanconi anemia complementation group E; FANCF, Fanconi anemia complementation group F; FANCG, Fanconi anemia complementation group G; FANCI, Fanconi anemia complementation group I; FANCL, Fanconi anemia complementation group L; FANCM, Fanconi anemia complementation group M; NHP2, NHP2 ribonucleoprotein; NOP10, NOP10 ribonucleoprotein; PALB2, partner and localizer of BRCA2; RAD51C, RAD51 paralogue C; RECQL4, RecQ like helicase 4; SLX4, SLX4 structure-specific endonuclease subunit; TERC, telomerase RNA component; TERT, telomerase reverse transcriptase; TINF2, TERF1 interacting nuclear factor 2.   154 Supplementary Table 2.5 Relative basal gene expression levels of the SIOD patient dermal fibroblasts (SD120 and SD123) used in this study.  Morphogen and target gene Cell line Relative basal gene expression1 p value2 Wnt3A         GREM2 SD120 0.6 **      GREM2 SD123 1.5 ** Wnt3A         PRDM6 SD120 18.0 **      PRDM6 SD123 14.6 ** BMP4         ID1 SD120 15.5 **      ID1 SD123 9.7 * BMP4         SMAD6 SD120 1.2 n.s.      SMAD6 SD123 1.8 ** TGF1         SMAD6 SD120 3.0 n.s.      SMAD6 SD123 3.9 n.s. TGF1         SMAD7 SD120 1.2 n.s.      SMAD7 SD123 1.0 n.s. TGF1         MMP10 SD120 1.8 n.s.      MMP10 SD123 1.4 n.s.  1For each SIOD patient dermal fibroblast cell line, the expression of each gene was first normalized to the expression of GAPDH and then normalized to the expression level of the unaffected control dermal fibroblast cell line. 2p values were calculated using the Tukey post hoc test following one-way ANOVA and represent the statistical significance between the relative gene expression of the SIOD patient cell line of interest and the unaffected control cell line.  Abbreviations: *, p < 0.05; **, p < 0.01; ANOVA, analysis of variance; BMP4, bone morphogenetic protein 4; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GREM2, gremlin 2; ID1, inhibitor of DNA binding 1; MMP10, matrix metallopeptidase 10; n.s., not significant; PRDM6, PR domain 6; SIOD, Schimke immuno-osseous dysplasia; SMAD6, SMAD family member 6; SMAD7, SMAD family member 7; TGF1, transforming growth factor  1; Wnt3A, Wnt family member 3A.   155 Supplementary Table 2.6 Relative gene expression changes in SIOD patient dermal fibroblasts in response to the morphogens Wnt3A, BMP4, or TGF1 over 24 hours.  Morphogen and target gene Cell line Time point (hours after induction) Relative gene expression1 p value2 Wnt3A          GREM2 Control 2 1.10       GREM2 SD120 2 1.45 n.s.      GREM2 SD123 2 1.02 n.s.      GREM2 Control 4 1.71       GREM2 SD120 4 2.97 **      GREM2 SD123 4 1.72 n.s.      GREM2 Control 8 3.20       GREM2 SD120 8 3.29 n.s.      GREM2 SD123 8 2.08 **      GREM2 Control 12 3.22       GREM2 SD120 12 3.23 n.s.      GREM2 SD123 12 1.69 *      GREM2 Control 16 3.97       GREM2 SD120 16 2.62 **      GREM2 SD123 16 1.79 ***      GREM2 Control 20 2.80       GREM2 SD120 20 1.45 **      GREM2 SD123 20 1.16 ***      GREM2 Control 24 1.89       GREM2 SD120 24 1.33 **      GREM2 SD123 24 0.95 ** Wnt3A          PRDM6 Control 2 0.74       PRDM6 SD120 2 0.68 n.s.      PRDM6 SD123 2 0.72 n.s.      PRDM6 Control 4 4.32       PRDM6 SD120 4 1.84 *      PRDM6 SD123 4 1.59 **      PRDM6 Control 8 3.11       PRDM6 SD120 8 0.90 ***      PRDM6 SD123 8 1.36 **      PRDM6 Control 12 2.99       PRDM6 SD120 12 1.05 *      PRDM6 SD123 12 3.18 n.s.      PRDM6 Control 16 4.93       PRDM6 SD120 16 0.67 **      PRDM6 SD123 16 2.06 *      PRDM6 Control 20 2.32       PRDM6 SD120 20 0.70 ***  156 Morphogen and target gene Cell line Time point (hours after induction) Relative gene expression1 p value2      PRDM6 SD123 20 1.26 **      PRDM6 Control 24 1.78       PRDM6 SD120 24 0.83 *      PRDM6 SD123 24 1.63 n.s. BMP4          ID1 Control 2 233.98       ID1 SD120 2 27.17 ***      ID1 SD123 2 30.06 ***      ID1 Control 4 168.62       ID1 SD120 4 17.89 ***      ID1 SD123 4 20.37 ***      ID1 Control 8 146.54       ID1 SD120 8 20.06 ***      ID1 SD123 8 19.59 ***      ID1 Control 12 60.99       ID1 SD120 12 5.41 ***      ID1 SD123 12 7.34 ***      ID1 Control 16 58.07       ID1 SD120 16 4.73 ***      ID1 SD123 16 6.84 ***      ID1 Control 20 77.8       ID1 SD120 20 5.25 ***      ID1 SD123 20 5.86 ***      ID1 Control 24 54.24       ID1 SD120 24 4.87 ***      ID1 SD123 24 5.43 *** BMP4          SMAD6 Control 2 7.13       SMAD6 SD120 2 6.80 n.s.      SMAD6 SD123 2 4.67 *      SMAD6 Control 4 3.71       SMAD6 SD120 4 4.35 n.s.      SMAD6 SD123 4 3.47 n.s.      SMAD6 Control 8 6.51       SMAD6 SD120 8 9.91 *      SMAD6 SD123 8 5.14 n.s.      SMAD6 Control 12 8.96       SMAD6 SD120 12 6.65 *      SMAD6 SD123 12 6.60 *      SMAD6 Control 16 7.71       SMAD6 SD120 16 3.87 *      SMAD6 SD123 16 6.54 n.s.      SMAD6 Control 20 6.83   157 Morphogen and target gene Cell line Time point (hours after induction) Relative gene expression1 p value2      SMAD6 SD120 20 4.97 n.s.      SMAD6 SD123 20 5.05 n.s.      SMAD6 Control 24 8.29       SMAD6 SD120 24 7.34 *      SMAD6 SD123 24 5.27 ** TGF1          SMAD6 Control 2 2.96       SMAD6 SD120 2 9.49 ***      SMAD6 SD123 2 5.16 *      SMAD6 Control 4 6.12       SMAD6 SD120 4 1.83 ***      SMAD6 SD123 4 1.36 ***      SMAD6 Control 8 6.67       SMAD6 SD120 8 1.81 ***      SMAD6 SD123 8 0.90 ***      SMAD6 Control 12 2.94       SMAD6 SD120 12 2.69 n.s.      SMAD6 SD123 12 1.49 **      SMAD6 Control 16 1.12       SMAD6 SD120 16 2.39 *      SMAD6 SD123 16 0.96 n.s.      SMAD6 Control 20 0.51       SMAD6 SD120 20 1.07 n.s.      SMAD6 SD123 20 0.94 n.s.      SMAD6 Control 24 2.10       SMAD6 SD120 24 2.20 n.s.      SMAD6 SD123 24 1.62 n.s. TGF1          SMAD7 Control 2 1.52       SMAD7 SD120 2 6.58 ***      SMAD7 SD123 2 6.95 ***      SMAD7 Control 4 8.19       SMAD7 SD120 4 4.55 **      SMAD7 SD123 4 4.83 **      SMAD7 Control 8 9.92       SMAD7 SD120 8 7.67 *      SMAD7 SD123 8 8.11 *      SMAD7 Control 12 10.54       SMAD7 SD120 12 5.20 **      SMAD7 SD123 12 9.53 n.s.      SMAD7 Control 16 9.11       SMAD7 SD120 16 8.42 n.s.      SMAD7 SD123 16 9.45 n.s.  158 Morphogen and target gene Cell line Time point (hours after induction) Relative gene expression1 p value2      SMAD7 Control 20 9.47       SMAD7 SD120 20 7.60 n.s.      SMAD7 SD123 20 8.90 n.s.      SMAD7 Control 24 9.47       SMAD7 SD120 24 9.95 n.s.      SMAD7 SD123 24 9.64 n.s. TGF1          MMP10 Control 2 1.75       MMP10 SD120 2 1.34 n.s.      MMP10 SD123 2 1.29 n.s.      MMP10 Control 4 2.14       MMP10 SD120 4 1.36 *      MMP10 SD123 4 1.49 *      MMP10 Control 8 2.17       MMP10 SD120 8 1.52 *      MMP10 SD123 8 1.54 *      MMP10 Control 12 2.42       MMP10 SD120 12 0.83 **      MMP10 SD123 12 0.92 **      MMP10 Control 16 5.05       MMP10 SD120 16 0.80 ***      MMP10 SD123 16 0.87 ***      MMP10 Control 20 10.91       MMP10 SD120 20 0.93 ***      MMP10 SD123 20 0.95 ***      MMP10 Control 24 12.86       MMP10 SD120 24 1.17 ***      MMP10 SD123 24 1.21 ***  1The expression of each gene was first normalized to GAPDH expression and then graphed relative to its expression in the relevant cell line at time = 0 hours. 2p values were calculated using the Tukey post hoc test following one-way ANOVA and represent the statistical significance between the relative gene expression of the patient cell line of interest and the unaffected control cell line at each time point.  Abbreviations: *, p < 0.05; **, p < 0.01; ***, p < 0.001; ANOVA, analysis of variance; BMP4, bone morphogenetic protein 4; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GREM2, gremlin 2; ID1, inhibitor of DNA binding 1; MMP10, matrix metallopeptidase 10; n.s., not significant; PRDM6, PR domain 6; SIOD, Schimke immuno-osseous dysplasia; SMAD6, SMAD family member 6; SMAD7, SMAD family member 7; TGF1, transforming growth factor  1; Wnt3A, Wnt family member 3A.   159  Supplementary Figure 2.1 Additional dental radiographs showing the tooth pathology of patients with biallelic SMARCAL1 mutations. (A) Bitewing radiograph of SD16 showing a mild dental phenotype with normal molar roots and crowns. The white arrow indicates a retained deciduous molar. (B-F) Lateral skull radiographs of SD33a, SD33b, SD50, SD78, and SD131 demonstrating the distinctive bulbous crowns and thin molar roots. (G) Occlusion radiograph of SD120. Abbreviation: SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.   160  Supplementary Figure 2.2 Analysis of SMARCAL1 protein expression in the developing incisor, canine, and premolar. (A-C) Photomicrographs of SMARCAL1 immunohistochemical staining of the incisor, canine, and premolar. (A) Overview of the cross-section of the jawbone of a 98-day-gestation fetus. Twenty tooth buds give rise to the deciduous teeth, and each half jaw consists of 2 incisors, 1 canine, and 2 premolars at this stage of development. Four of the 5 tooth buds present in a developing half jaw can be observed in this section. (B) SMARCAL1 is expressed in the incisor and the canine. (C) SMARCAL1 is expressed in the premolar. Note that the bud of the permanent premolar also showed expression of SMARCAL1. (D-F) Photomicrographs of pre-immune staining of an adjacent section showed minimal non-specific staining. The boxed regions correspond to the higher magnification images. Abbreviations: C, canine; I, incisor; PPM, permanent premolar; PM, premolar; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.   161  Supplementary Figure 2.3 Transcriptional responses of SIOD patient dermal fibroblasts induced with Wnt3A, BMP4, or TGF1, comparing relative gene expression between time points. The transcriptional responses of fibroblasts from an unaffected control (white bars) and patients SD120 (light grey bars) and SD123 (dark grey bars) were measured by qRT-PCR following induction with Wnt3A, BMP4, or TGF1 for 0, 2, 4, 8, 12, 16, 20, or 24 hours. Expression of the housekeeping gene GAPDH was used as the internal control; expression of each gene was first normalized to GAPDH expression and then graphed relative to its expression in the relevant cell line at time = 0 hours. Asterisks denote significant gene expression changes within a cell line between the time point of interest and time = 0 hours. Abbreviations: *, p < 0.05; BMP4, bone morphogenetic protein 4; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GREM2, gremlin 2; h, hours; ID1, inhibitor of DNA binding 1; MMP10, matrix metallopeptidase 10; qRT-PCR, quantitative reverse transcription polymerase chain reaction; PRDM6, PR domain 6; SIOD, Schimke immuno-osseous dysplasia; SMAD6, SMAD family member 6; SMAD7, SMAD family member 7; TGF1, transforming growth factor  1; Wnt3A, Wnt family member 3A.   162  Supplementary Figure 2.4 Unaltered or minimally altered transcriptional responses of SIOD patient dermal fibroblasts induced with Wnt3A, BMP4, or TGF1, comparing relative gene expression between patient and unaffected control fibroblasts at each time point. (A) Relative basal gene expression levels of fibroblasts from an unaffected control (white bars) and patients SD120 (light grey bars) and SD123 (dark grey bars) were measured by qRT-PCR. Expression of the housekeeping gene GAPDH was used as the internal control; expression of each gene was first normalized to GAPDH expression and then graphed relative to the expression of the unaffected control. Error bars represent one standard deviation. (B) Transcriptional responses of fibroblasts from an unaffected control (white bars) and patients SD120 (light grey bars)  163 and SD123 (dark grey bars) were measured by qRT-PCR following induction with Wnt3A, BMP4, or TGF1 for 0, 2, 4, 8, 12, 16, 20, or 24 hrs. Expression of the housekeeping gene GAPDH was used as the internal control; expression of each gene was first normalized to GAPDH expression and then graphed relative to its expression in the relevant cell line at time = 0 hrs. Abbreviations: *, p < 0.05; BMP4, bone morphogenetic protein 4; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GREM2, gremlin 2; h, hours; qRT-PCR, quantitative reverse transcription polymerase chain reaction; PRDM6, PR domain 6; SIOD, Schimke immuno-osseous dysplasia; SMAD6, SMAD family member 6; SMAD7, SMAD family member 7; TGF1, transforming growth factor  1; Wnt3A, Wnt family member 3A.  164 Appendix B: Supplementary Tables and Figures for Chapter 3 Supplementary Table 3.1 Aorta RNA samples used in the study.  Sex Age Developmental stage Figures Comments M 11 weeks gestation 1st trimester Figures 3.5 and 3.7  F 12 weeks gestation 1st trimester Figures 3.5 and 3.7  F 14 weeks gestation 2nd trimester Figures 3.5 and 3.7  F 15 weeks gestation 2nd trimester Figures 3.5 and 3.7  M 15 weeks gestation 2nd trimester Figures 3.5-3.7, 3.9, and 3.10  F 16 weeks gestation 2nd trimester Figures 3.5-3.7, 3.9, and 3.10  F 16 weeks gestation 2nd trimester Figures 3.5 and 3.7  M 16 weeks gestation 2nd trimester Figures 3.5 and 3.7  F 17 weeks gestation 2nd trimester Figures 3.5 and 3.7  M 19 weeks gestation 2nd trimester Figures 3.5 and 3.7  M 20 weeks gestation 2nd trimester Figures 3.5 and 3.7  M 22 weeks gestation 2nd trimester Figures 3.5 and 3.7  F 23 weeks gestation 2nd trimester Figures 3.5 and 3.7  F 26 weeks gestation 2nd trimester Figures 3.5 and 3.7  F 31 weeks gestation 3rd trimester Figures 3.5 and 3.7  F Term 3rd trimester Figures 3.5 and 3.7  F 16 years Postnatal Figures 3.5 and 3.7  F 17 years Postnatal Figures 3.5 and 3.7  M/F 27-45 years Postnatal Figures 3.4-3.7, 3.9, and 3.10 Commercially available sample pooled from 4 Caucasian individuals M 5.4 years Postnatal Figures 3.4-3.7, 3.9, and 3.10 SIOD patient SD120  Abbreviations: F, female; M, male; SIOD, Schimke immuno-osseous dysplasia. 165 Supplementary Table 3.2 Primers used for the analysis of gene expression.  Primer Sequence SMARCAL1 and cell-specific markers      ACTA2-F 5´-CGGGAATCCTGTGAAGCAGCTCC-3´      ACTA2-R 5´-ATCACCCCCTGATGTCTGGGACG-3´      CDH5-F 5´-TCGTCGGCTGTGGGGACCTC-3´      CDH5-R 5´-GTCGCCCCGCAAGATGCTGT-3´      P4HA3-F 5´-CCCTGGGTTCCCAGCCCACT-3´      P4HA3-R 5´-GAGGGCAGCAATGCGGTGGT-3´      SMARCAL1-F  5´-CCTCTACAAGGACCCAAAGCAGCAG-3´      SMARCAL1-R 5´-TCCAGGGTGTCTCCCATGTTCTGG-3´      GAPDH-F 5´-CTTTTGCGTCGCCAGCCGAG-3´      GAPDH-R 5´-GGTGACCAGGCGCCCAATACG-3´ ELN mRNA       ELN-F 5´-AAATACGGTGCTGCTGGCCTT-3´      ELN-R 5´-ACAATCCGAAGCCAGGTCTTG-3´ ELN pre-mRNA        ELN pre-mRNA intron 4-F 5´-ACCTGGCATGGTTGTGAACT-3´      ELN pre-mRNA intron 4-R 5´-CAATCGCTTCGTCTCCCACT-3´ ELN positive regulators        SP1 mRNA-F 5´-CTCGTCAGCGTCCGCGTTTTTC-3´      SP1 mRNA-R 5´-GGAGTGGACTCATCCTTACCGCTC-3´      IGF1 mRNA-F 5´-TGTGACATTGCTCTCAACATCTCCC-3´      IGF1 mRNA-R 5´-GACATGGTGTGCATCTTCACCTTCA-3´      TGFB1 mRNA-F 5´-CTGCAAGTGGACATCAACGGGTT-3´      TGFB1 mRNA-R 5´-GCACGCAGCAGTTCTTCTCCGT-3´ ELN negative regulators       SP3 mRNA-F 5´-ACCTACTTTCCTTGGCAGGAAGCT-3´      SP3 mRNA-R 5´-TCACCAGAGTTGGGAAGAAGGCA-3´      FOSL1 mRNA-F 5´-CGCCTCCAGGGGTACGTCGAA-3´      FOSL1 mRNA-R 5´-TTCCAGTTTGTCAGTCTCCGCCTGC-3´      FGF2 mRNA-F 5´-GACCCCAAGCGGCTGTACTGC-3´      FGF2 mRNA-R 5´-TTGTAGCTTGATGTGAGGGTCGC-3´  Abbreviations: ACTA2, -smooth muscle actin; CDH5, vascular endothelial cadherin; ELN, elastin; F, forward; FGF2, fibroblast growth factor 2; FOSL1, FOS-like 1; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; IGF1, insulin-like growth factor 1; mRNA, messenger RNA; P4HA3, prolyl 4-hydroxylase subunit alpha 3; pre-mRNA, precursor messenger RNA; R, reverse; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1; SP1, Sp1 transcription factor; SP3, Sp3 transcription factor; TGFB1, transforming growth factor  1. 166 Supplementary Table 3.3 Primers used for the analysis of ELN gene coding mutations by Sanger sequencing.  Primer Sequence ELN-1F 5´-TTTGGGGATAAAACGAGGTG-3´ ELN-1R 5´-GTCTAGTCACCTGGCCCAAA-3´ ELN-2F 5´-TGGGTTTTGCCATTGAAAGT-3´ ELN-2R 5´-GGTAGGCGTGTCAATGTTCC-3´ ELN-3F 5´-GCTTGCAGAGAGCAGGTCTT-3´ ELN-3R 5´-CCTGGATGCTGTCTGTGAAA-3´ ELN-4F 5´-ATTCCACACTGCCCACACTT-3´ ELN-4R 5´-TTGGCTTCCTTGTCCTGTTC-3´ ELN-5F 5´-CTGATCACAGCACTGCCCTA-3´ ELN-5R 5´-GGCAGTTGGTATCAGCATCA-3´ ELN-6F 5´-TCCAATGTGCTTCCTGAGTG-3´ ELN-6R 5´-CAAGCCTGAGTTGAGGGAAG-3´ ELN-7&8F 5´-GCCCAGTCTGAAGGTGAGTT-3´ ELN-7&8R 5´-GTTTCTCTGCCCGACTGAAG-3´ ELN-9F 5´-ACAGAGGCTGTGGGTTTGAG-3´ ELN-9R 5´-CCTCAGTCTCCCAAAGCAAT-3´ ELN-10F 5´-AGTCAGTCCCAAGGGAGGTC-3´ ELN-10R 5´-CCAGGGCTGGAACATAAAAA-3´ ELN-11F 5´-AGAACTGGCCATTCCTTGG-3´ ELN-11R 5´-TACAGCCACTGCAAGACCTG-3´ ELN-12F 5´-GAAGCAGGATGGTTTCTGGA-3´ ELN-12R 5´-AGTGATCTGCCCGCCTTAG-3´ ELN-13F 5´-TTTAGCACCTGTGGGGGTAG-3´ ELN-13R 5´-CACAGCACCCTTGCTAGGAC-3´ ELN-14F 5´-TGCAGACTCAGGACAGCTTG-3´ ELN-14R 5´-CTGCTTTCAGGAGGGAACCT-3´ ELN-15F 5´-CCATCAGCCTCTGCCTACTC-3´ ELN-15R 5´-GGGTATGTAGGGGCCACTTT-3´ ELN-16&17F 5´-GCCCCATTCCTGGATAAGAT-3´ ELN-16&17R 5´-AGAAGGGGCAAAGGTAAGGA-3´ ELN-18F 5´-AGGAAATGTCAACCCACCTG-3´ ELN-18R 5´-AGATGAGGGGATGAGACGTG-3´ ELN-19F 5´-TGTTGGCATGAAAGGAGATG-3´ ELN-19R 5´-TTCCCTTCTCTGGGCAAGTA-3´ ELN-20&21F 5´-AATCCATCAGCATCCCTCAG-3´ ELN-20&21R 5´-AGTTTGCCCTGAGGTTGGAC-3´ ELN-22&23F 5´-GAATTGAAGGTGCCAGGAAG-3´ ELN-22&23R 5´-GGGCCAAAAGGTCTAAGGAG-3´ ELN-24A&24&25F 5´-CTCCAGCCCTCTTTCCATAA-3´ ELN-24A&24&25R 5´-TGAGCCTAGTCCCAGAGGAA-3´  167 Primer Sequence ELN-26&26AF 5´-TCTGGGACTAGGCTCAGCTC-3´ ELN-26&26AR 5´-CCCTCTACTGAGGAGGCAGA-3´ ELN-27F 5´-CCCTAAAGCTCTGTGCCTGT-3´ ELN-27R 5´-GCCGAAACTGCCTTTTTCTA-3´ ELN-28&29F 5´-CTTCCTGTCCACTGCTCCTC-3´ ELN-28&29R 5´-TACAGTGCCTGGTCAGAAGC-3´ ELN-30F 5´-GACACCTCCTGGCTCCACT-3´ ELN-30R 5´-GGTGGGAACGAAAGTCTCTG-3´ ELN-31&32F 5´-GGCGAAGGAGTGAGACTCTG-3´ ELN-31&32R 5´-GCAATCCTAAAGTGCCCTCA-3´ ELN-33F 5´-GTGCAGGCAGAAAGTGATGA-3´ ELN-33R 5´-GAGATGGCACAGGAGAGGAG-3´ ELN-36F 5´-GCTTCTCTTGGCTTCTTGGA-3´ ELN-36R 5´-CATGGGATGGGGTTACAAAG-3´  Abbreviations: ELN, elastin; F, forward; R, reverse.  168 Supplementary Table 3.4 Primers used for the analysis of DNA methylation by bisulfite Sanger sequencing.  Primer Sequence ELN bisulfite sequencing-1F 5´-GGTTTGGGAGGTTTGTGAGT-3´ ELN bisulfite sequencing-1R 5´-AAACAACAAAACCTAAATACTAAACTAAAA-3´ ELN bisulfite sequencing-2F 5´-TTTTTAAATTTTTAGATTTGTTTAATGTTT-3´ ELN bisulfite sequencing-2R 5´-ACTCACAAACCTCCCAAACCC-3´ ELN bisulfite sequencing-3F 5´-TTAGTGATAATGGGAAGTTGGGTTGT-3´ ELN bisulfite sequencing-3R 5´-CACCCCCAAATCTAACTAAAAACAAAT-3´ ELN bisulfite sequencing-4F 5´-GTGGTGTAGGGAAAAGTTTATAGGG-3´ ELN bisulfite sequencing-4R 5´-AAAATCAAACTAAATCCCCAAATACC-3´ ELN bisulfite sequencing-5F 5´-AAGGAAGGGTTTGTTTAGGGTTTT-3´ ELN bisulfite sequencing-5R 5´-CCCTATAAACTTTTCCCTACACCAC-3´  Abbreviations: ELN, elastin; F, forward; mRNA, messenger RNA; R, reverse. 169 Supplementary Table 3.5 Primers used for the analysis of poly(A) tail length of ELN mRNA by the Poly(A) Tail Length Assay Kit.  Primer Sequence ELN forward primer I 5´-ATCTTTTTGTGTCTCGCTGTGATAGAT-3´ ELN reverse primer I 5´-GGTGTGTTTCATCCAGAGTTATATTAGAG-3´ ELN forward primer II  5´-CTCTAATATAACTCTGGATGAAACACACC-3´ ELN reverse primer II 5´-TTTCATTTATTAGTCATTTCTGAAGCAGTT-3´  Abbreviations: ELN, elastin; mRNA, messenger RNA.    170 Supplementary Table 3.6 Summary of the echocardiogram data for SD120.  Level of the aortic root1 Diameter (mm) Z score2 Aortic valve 11.1 0.27 Sinus of Valsalva 14.8 0.41 Sinotubular junction  14.7 2.09 Ascending aorta 15.3 1.97  1Measurements of the aorta were obtained in accordance with the American Society of Echocardiogram guidelines in real time and confirmed postmortem (Lopez et al. 2010). 2Body surface area and Z scores were calculated using the Haycock and Halifax formulae, respectively. The normal range is considered to be within -2 to 2 standard deviations from the mean diameter (Haycock et al. 1978; Warren et al. 2006). 171 Supplementary Table 3.7 Significant gene expression differences (fold change > 2) between an SIOD aorta sample and a commercially available pooled unaffected adult aorta sample measured by the Atherosclerosis PCR Array (PAHS-038).  Gene Fold change p value Upregulated genes        FGA 96.4 *      ABCA1 25.9 ***      MSR1 22.3 ***      IL5 8.4 ***      CSF1 8.4 ***      IL1R1 8.1 ***      APOE 7.5 ***      CCR1 6.7 ***      FAS 4.8 **      PPARA 3.8 **      HBEGF 3.8 **      BCL2A1 3.6 *      BAX 3.4 ***      PPARG 3.4 **      TNFAIP3 3.4 **      BIRC3 3.3 **      CFLAR 3.2 ***      NR1H3 3.1 *      ACE 2.7 **      KDR 2.7 *      SPP1 2.6 **      FGF2 2.6 ***      PPARD 2.6 *      ITGB2 2.5 **      BCL2 2.5 ***      LAMA1 2.4 *      SELPLG 2.4 **      IFNAR2 2.1 * Downregulated genes        ELN -121.5 **      MMP3 -30.1 ***      SERPINE1 -14.2 ***      EGR1 -7.8 ***      IFNG -5.1 *      CCL5 -4.4 ***      PDGFA -4.3 ***      MMP1 -4.1 *      ITGA5 -4.0 **  172 Gene Fold change p value      NPY -3.8 **      CTGF -2.6 ***      THBS4 -2.5 **      VEGFA -2.5 ***      TNC -2.1 **  Abbreviations: *, p < 0.05, **, < 0.01; ***, p < 0.001.    173  Supplementary Figure 3.1 Verhoeff-Van Gieson staining of the common iliac and pulmonary arteries from 2 SIOD patients and an unaffected control. Verhoeff-Van Gieson staining of these arteries reveals fragmented and reduced elastin fibres. Arteries are oriented with the tunica adventitia on the left and the tunica intima on the right; the age of death is in parentheses. Scale bars: 50 m. Abbreviations: SIOD, Schimke immuno-osseous dysplasia; yr, years.    174  Supplementary Figure 3.2 Immunohistochemical detection of alpha smooth muscle actin in the aorta of 3 SIOD patients. Alpha smooth muscle actin is a marker of smooth muscle cells. Smooth muscle cell hyperplasia was observed in the aortas of SD60 and SD120. Arteries are oriented with the tunica adventitia on the left and the tunica intima on the right; the age of death is in parentheses. Scale bars: 50 m. Abbreviations: IgG, immunoglobulin G; SIOD, Schimke immuno-osseous dysplasia; yr, years.   175  Supplementary Figure 3.3 Immunohistochemical detection of CD3+, CD20+, and CD68+ cells in the aortas of 3 SIOD patients. CD3, CD20, and CD68 are markers of T cells, B cells, and macrophages, respectively. Inflammatory infiltrates were not observed in the 3 patients with the exception of macrophages within an atherosclerotic plaque of the aorta of SD84. Arteries are oriented with the tunica adventitia on the left and the tunica intima on the right; the age of death is in parentheses. Lymph node tissue sections were used as a positive control. Scale bars: 50 m. Abbreviations: SIOD, Schimke immuno-osseous dysplasia; yr, years.    176  Supplementary Figure 3.4 ELN mRNA expression analysis of the umbilical cord from SMARCAL1-deficient and unaffected fetuses at 15 weeks gestation. Bar graph showing relative ELN mRNA expression of the umbilical cord of two age-matched unaffected controls compared to that of SD133b by qRT-PCR. The mRNA levels of three technical replicates were standardized to mRNA levels of the housekeeping gene GAPDH and plotted relative to the ELN mRNA expression of the umbilical cord of SD133b. Error bars represent one standard deviation. Abbreviations: ELN, elastin; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; mRNA, messenger RNA.   177  Supplementary Figure 3.5 Molecular and histopathological analysis of elastin binding protein expression and periodic acid-Schiff staining of SIOD aortas. (A) Immunoblot showing unaltered EBP expression in the aortic lysates of SD60 and SD120 compared to a pooled lysate of 49 unaffected individuals. GAPDH was used as a loading control. (B) Periodic acid-Schiff staining of the aorta of two patients did not show altered staining compared to age-matched controls. Arteries are oriented with the tunica adventitia on the left and the tunica intima on the right; the age of death is in parentheses. Scale bars: 50 m. Abbreviations: EBP, elastin binding protein; kDa, kilodalton; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; SIOD, Schimke immuno-osseous dysplasia; yr, years.   178  Supplementary Figure 3.6 Spearman rank order correlations between the relative mRNA expression of known positive transcriptional regulators of ELN or ELN and age. Spearman rank order correlation tests were performed to determine the correlation between these variables. The Spearman rank order correlation (rS) and the p value (p) are shown in the panels below the diagonal. A scatterplot of ranked values are shown in the panels above the diagonal. Bolded boxes highlight significant correlations (p < 0.05). The Spearman rank order correlation for comparing IGF1 mRNA expression and age was not performed since this data set did not meet the assumption of monotonicity. Abbreviations: ELN, elastin; IGF1, insulin-like growth factor 1; mRNA, messenger RNA; SP1, Sp1 transcription factor; TGFB1, transforming growth factor  1.   179  Supplementary Figure 3.7 Spearman rank order correlations between the relative mRNA expression of known negative transcriptional regulators of ELN or ELN and age. Spearman rank order correlation tests were performed to determine the correlation between these variables. The Spearman rank order correlation (rS) and the p value (p) are shown in the panels below the diagonal. A scatterplot of ranked values are shown in the panels above the diagonal. Bolded boxes highlight significant correlations (p < 0.05). Abbreviations: ELN, elastin; FGF2, fibroblast growth factor 2; FOSL1, FOS-like 1; mRNA, messenger RNA; SP3, Sp3 transcription factor.   180  Supplementary Figure 3.8 Quantitative PCR analysis of ELN mRNA expression in cultured SIOD patient dermal fibroblasts. Expression data are presented as bar graphs of the relative mean ELN mRNA expression of fibroblasts from 5-, 9-, and 15-year-old unaffected controls (white bars) and patients (grey bars). The ELN mRNA levels of three technical replicates were normalized to levels of the housekeeping gene GAPDH and plotted relative to that of the unaffected control fibroblasts. Red dots signify patients who had vascular disease at the time of skin biopsy. Abbreviations: ELN, elastin; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; mRNA, messenger RNA; SIOD, Schimke immuno-osseous dysplasia.    181 Appendix C: Supplementary Tables and Figures for Chapter 4 Supplementary Table 4.1 SMARCAL1 mutations identified in SIOD patients included in this study.  Patient ID Sex SMARCAL1 mutations Nucleotide change Predicted amino acid change SD4b M c.[410delA];[1930C>T] p.[(Q137Rfs*3)];[(R644W)] SD26 M c.[1190delT];[2542G>T] p.[(L397Rfs*40)];[(E848X)] SD51 F c.[2459G>A];[2542G>T] p.[(R820H)];[(E848X)] SD60 M c.[2542G>T];[2542G>T] p.[(E848X)];[(E848X)] SD79 F c.[2459G>A];[?]1 p.[(R820H)];[(?)] SD120 M c.[2291G>A];[2542G>T] p.[(R764Q)];[(E848X)] SD121 F c.[1382G>A];[2542G>T] p.[(G461D)];[(E848X)] SD131 M c.[1026C>A];[2264T>G] p.[(Y342X)];[(I755S)] SD133b F c.[863-2A>G]; [2343_2347delGCTGT] p.[(M288_D366delinsN)]; [(L782Hfs*14)] SD146 F c.[1642_1644delATT]; [1642_1644delATT] p.[(I548del)];[(I548del)]  1[?] represents an allele with a non-coding SMARCAL1 mutation as previously described by Clewing et al. (2007b).   Abbreviations: F, female; ID, identification; M, male; SIOD, Schimke immuno-osseous dysplasia; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1. 182 Supplementary Table 4.2 Human tissue samples used in the study.  Sex Age at sampling Tissue Sample type Studies Comments M 3 years Kidney RNA from frozen tissue Fig. 1 Unaffected control M 5.4 years Kidney RNA from frozen tissue Fig. 1 SIOD patient SD120 F 7 years Kidney FFPE Figs. 2, 3, S. Fig. 2 Unaffected control M 8 years Kidney FFPE Figs. 2, 3, S. Fig. 2 SIOD patient SD4b M 4 years Kidney FFPE Figs. 2, 3, S. Fig. 2 SIOD patient SD26 F < 10 years Kidney RNA from cultured cells n/a (microarray) FSGS patient F 4.8 years Kidney RNA from cultured cells n/a (microarray) SIOD patient SD51 M 13.7 years Kidney FFPE Figs. 2, 3, S. Fig. 2 SIOD patient SD60 F 10 years Kidney FFPE Figs. 2, 3, S. Fig. 2 SIOD patient SD79 M 5.4 years Kidney FFPE Figs. 2, 3, S. Fig. 2 SIOD patient SD120 F 3 years Kidney FFPE Figs. 2, 3, S. Fig. 2 SIOD patient SD121 M 3.8 years Kidney FFPE Fig. 3 SIOD patient SD131 F 2 years Kidney FFPE Figs. 2, 3, S. Fig. 2 SIOD patient SD146 F 16 years Skin FFPE Fig. 3 Positive control for Notch1 IF F 17 years Adenoma FFPE S. Fig. 1 Positive control for -catenin IF F 3 years Kidney FFPE S. Figs. 2-4 Unaffected control F 14 years Kidney FFPE S. Figs. 2-4 Unaffected control M 13.7 years Tx Kidney FFPE S. Figs. 2-4 SIOD patient SD60 ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-1 ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-2 ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-3 ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-4 ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-5 ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-6 ? ? Kidney FFPE n/a FSGS patient FSGS-7,  no glomeruli in this biopsy ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-8 ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-9 ? ? Kidney FFPE S. Figs. 2-4 FSGS patient FSGS-10  183 Sex Age at sampling Tissue Sample type Studies Comments M 15 weeks gestation Kidney FFPE S. Figs. 5, 6 Unaffected control H-25194 F 15 weeks gestation Kidney FFPE S. Figs. 5, 6 Unaffected control H-25217 F 15 weeks gestation Kidney FFPE S. Figs. 5, 6 SMARCAL1-deficient fetus SD133b M 15 weeks gestation Kidney RNA from FFPE tissue S. Fig. 7 Unaffected control H-25194 M 15 weeks gestation Kidney RNA from FFPE tissue S. Fig. 7 Unaffected control AE14 3118 M 15 weeks gestation Kidney RNA from FFPE tissue S. Fig. 7 Unaffected control AE14 3138 F 15 weeks gestation Kidney RNA from FFPE tissue S. Fig. 7 Unaffected control AE14 3158 M 15 weeks gestation Kidney RNA from FFPE tissue S. Fig. 7 Unaffected control AE15 3040 F 15 weeks gestation Kidney RNA from FFPE tissue S. Fig. 7 SMARCAL1-deficient fetus SD133b  Abbreviations: ?, unknown; F, female; Fig., Figure; Figs., Figures; FFPE, formalin-fixed paraffin-embedded; FSGS, focal segmental glomerulosclerosis; IF, immunofluorescence; M, male; n/a, not applicable; RNA, ribonucleic acid; SIOD, Schimke immuno-osseous dysplasia; S.; Supplementary; Tx, transplant. 184 Supplementary Table 4.3 Primer sequences used in this study.  Primer Sequence Human Wnt target gene expression     GAPDH-F 5´-CTTTTGCGTCGCCAGCCGAG-3´     GAPDH-R 5´-GGTGACCAGGCGCCCAATACG-3´     AXIN2-F 5´-TAACCCCTCAGAGCGATGGA-3´     AXIN2-R 5´-AACCTCCTCTCTTTTACAGCAGG-3´     CCND1-F 5´-CTTCAAATGTGTGCAGAAGGAGG-3´     CCND1-R 5´-CTCGCAGACCTCCAGCATC-3´     CCND2-F 5´-AGCAGGATGAGGAAGTGAGC-3´     CCND2-R 5´-GACAATCCACGTCTGTGTTGG-3´     JUN-F 5´-AGGGTCCGCACTGATCCGCT-3´     JUN-R 5´-CTCGGAGTCCGCAGGCGAAC-3´ Human Notch target gene expression     GAPDH-F 5´-CTTTTGCGTCGCCAGCCGAG-3´     GAPDH-R 5´-GGTGACCAGGCGCCCAATACG-3´     HES1-F 5´-AGAATAAATGAAAGTCTGAGCCAGC-3´     HES1-R 5´-ATGCCGCGAGCTATCTTTCT-3´     HES2-F 5´-GACCTCGGTTTCCCTTTGCG-3´     HES2-R 5´-CTTCAGGCTCTTGCGCAGC-3´     HEY1-F 5´-GGATCTGCTAAGCTAGAAAAAGCC-3´     HEY1-R 5´-AAGTAACCTTTCCCTCCTGCC-3´     HEY2-F 5´-AGAACAATTACTCGGGGCAAAGT-3´     HEY2-R 5´-TCCCTCTCCTTTTCTTTCTTGCC-3´ Drosophila Marcal1 overexpression     Gapdh2-F 5´-ATCGTCGAGGGTCTGATGAC-3´     Gapdh2-R 5´-TCAGCTTCACGAACTTGTCG-3´     Marcal1-2F 5´-AAGTGCTACGATGGCCAAAC-3´     Marcal1-2R 5´-GCCTGATCCGTGAGTCTTTT-3´  Abbreviations: AXIN2, axin 2; CCND1, cyclin D1; CCND2, cyclin D2; F, forward; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; HES1, hes family bHLH transcription factor 1; HES2, hes family bHLH transcription factor 2; HEY1, hes related family bHLH transcription factor with YRPW motif 1; HEY2, hes related family bHLH transcription factor with YRPW motif 2; JUN, Jun proto-oncogene; R, reverse. 185 Supplementary Table 4.4 Significantly enriched KEGG pathway terms of upregulated genes (log2 fold change > 1) in an SIOD kidney compared to an unaffected control kidney.  Term Genes1 %2 Fold enrichment1 p value4 hsa04512: ECM-receptor interaction AGRN, CD36, CD44, CD47, COL1A1, COL3A1, COL4A1, COL4A2, COL5A1, COL5A2, COL6A1, COL6A2, COL6A3, COMP, FN1, HSPG2, ITGA1, ITGA2, ITGA4, ITGA5, ITGA11, ITGB1, ITGB4, ITGB6, LAMA2, LAMB1, LAMBC1, LAMC2, LAMC3, SDC3, THBS1, THBS2, TNC, VWF 1.5 2.9 1.5E-06 hsa04510: Focal adhesion ACTN1, AKT2, AKT3, BCL2, CCND2, COL1A1, COL3A1, COL4A1, COL4A2, COL5A1, COL5A2, COL6A1, COL6A2, COL6A3, COMP, FLNA, FLNB, FN1, HGF, IGF1, IGF1R, ITGA1, ITGA2, ITGA4, ITGA5, ITGA11, ITGB1, ITGB4, ITGB6, JUN, LAMA2, LAMB1, LAMC1, LAMC2, LAMC3, MAPK10, MYL9, MYLK, PDGFA, PDGFC, PDGFRA, PDGFRB, PIK3CD, PIK3R1, PPP1R12A, PTEN, PXN, RAC1, RAC2, RAPGEF1, SHC1, SRC, THBS1, THBS2, TNC, VAV1, VEGFC, VWF 2.6 2.0 1.1E-05 hsa05322: Systemic lupus erythematosus ACTN1, C1QA, C1QB, C1QC, C1S, C1R, C3, C7, CD40, CD86, CTSG, FCGR1A, FCGR2A, FCGR2C, FCGR3A, FCGR2B, H2AFX, H2AFY, HIST1H2AB, HIST1H2AE, HIST1H2AG, HIST1H2AH, HIST1H2AI, HIST1H2AJ, HIST1H2AL, HIST1H2AM, HIST1H2BB, HIST1H2BE, HIST1H2BH, HIST1H2BJ, HIST1H2BL, HIST1H2BM, HIST1H2BN, HIST1H2BO, HIST1H3B, HIST1H3C, HIST1H3D, HIST1H3F, HIST1H3G, HIST1H3H, HIST1H3I, HIST1H3J, HIST1H4A, HIST1H4B, HIST1H4D, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K, HIST1H4L, HIST2H2AB, HIST2H2AC, HIST2H3A, HIST2H3D, HIST2H4B, HIST3H2A, HIST3H2BB, HLA-DQA1, HLA-DRB55 1.6 2.6 1.3E-05 hsa05200: Pathways in cancer ABL1, AKT2, AKT3, APC, AXIN1, BCL2, BMP4, CASP3, CDKN1B, CDK6, CEBPA, CKS1B, COL4A1, COL4A2, CREBBP, CSF1R, CTBP1, FADD, FGFR3, FGF7, FN1, FOS, FZD2, FZD6, FZD7, FZD8, HGF, HIF1A, HSP90AA1, HSP90AB1, IGF1, IGF1R, ITGA2, ITGB1, JAK1, JUN, LAMA2, LAMB1, LAMC1, LAMC2, LAMC3, LEF1, MAPK10, MECOM, MITF, MMP2, MYC, NCOA4, PDGFA, PDGFRA, PDGFRB, PIK3CD, PIK3R1, PLCG2, PML, PPARD, PPARG, PTCH1, PTEN, RAC1, RAC2, RALBP1, RALGDS, RARA, 3.6 1.7 3.6E-05  186 Term Genes1 %2 Fold enrichment1 p value4 RASSF5, RUNX1, SMAD4, SPI1, STAT1, STAT3, STK4, TCF7L2, TFG, TGFB1, TGFB2, TGFBR2, VEGFC, WNT4, WNT5A, WNT9B, WNT10A hsa04670: Leukocyte transendothelial migration ACTN1, CD99, CLDN3, CLDN5, CLDN7, CLDN10, CLDN11, CLDN14, CLDN15, CLDN16, CLDN19, CTNND1, CXCL12, CXCR4, CYBA, CYBB, EZR, GNAI2, ITGA4, ITGAM, ITGB1, ITGB2, JAM2, JAM3, MMP2, MYL9, NCF2, NCF4, PIK3CD, PIK3R1, PLCG2, PXN, RAC1, RAC2, RASSF5, VAV1, VCAM1 1.7 2.2 5.2E-04 hsa04310: Wnt signaling pathway APC, AXIN1, CCND2, CREBBP, CSNK1E, CTBP1, DAAM1, FZD2, FZD6, FZD7, FZD8, JUN, LEF1, MAP3K7, MAPK10, MMP7, MYC, NFAT5, NFATC3, NFATC4, NKD2, PPARD, PPP2R5A, PPP2R5C, PPP2R5D, PPP3CA, PRICKLE1, PRKACA, PRKACB, PSEN1, RAC1, RAC2, SFRP1, SFRP2, SMAD4, TBL1X, TBL1XR1, TCF7L2, VANGL2, WNT4, WNT5A, WNT9B, WNT10A 1.9 2.0 1.1E-03 hsa05210: Colorectal cancer AKT2, AKT3, APC, AXIN1, BCL2, CASP3, FOS, FZD2, FZD6, FZD7, FZD8, JUN, IGF1R, LEF1, MAPK10, MYC, PDGFRA, PDGFRB, PIK3CD, PIK3R1, RAC1, RAC2, RALGDS, SMAD4, TCF7L2, TGFB1, TGFB2, TGFBR2 1.2 2.4 3.2E-03 hsa04666: Fc gamma  R-mediated phagocytosis AKT2, AKT3, ASAP1, DNM1, DNM1L, DNM2, DOCK2, FCGR1A, FCGR2A, FCGR2B, FCGR2C, FCGR3A, GAB2, GSN, HCK, INPP5D, MARCKS, MARCKSL1, PIK3CD, PIK3R1, PIP4K2B, PIP5K1A, PLA2G4A, PLA2G4B, PLCG2, PLPP2, RAC1, RAC2, SYK, VAV1, WASF26 1.3 2.2 4.7E-03 hsa04514: Cell adhesion molecules CD4, CD40, CD58, CD86, CD99, CD276, CDH3, CLDN3, CLDN5, CLDN7, CLDN10, CLDN11, CLDN14, CLDN15, CLDN16, CLDN19, CNTNAP1, HLA-DRB5, HLA-DQA1, ICAM2, ICOSLG, ITGA4, ITGAM, ITGB1, ITGB2, JAM2, JAM3, NCAM1, NEGR1, NFASC, NRXN2, PVR, SDC3, VCAM1, VCAN 1.6 1.9 4.4E-02  1The genes that are involved in the individual KEGG pathway term. 2The number of genes involved in a given term divided by the total number of input genes (i.e., 2,241 genes). 3The magnitude of enrichment of a given term represented by the ratio of the proportion of genes involved in the term within the input genes and of the proportion of genes involved in the term within the human genome. 4Modified Fisher’s Exact p values, corrected for multiple comparisons by the Bonferroni method, representing the significance of enrichment of a given term. 5These 59 genes correspond to 60 RefSeq IDs and are represented by 36 unique DAVID gene IDs. 6These 31 genes correspond to 32 RefSeq IDs and are represented by 30 unique DAVID gene IDs.   187 Abbreviations: DAVID, Database for Annotation, Visualization, and Integrated Discovery; ECM, extracellular matrix; ID, identification; KEGG, Kyoto Encyclopedia of Genes and Genomes; SIOD, Schimke immuno-osseous dysplasia. 188 Supplementary Table 4.5 Significant gene expression differences (fold change > 2) between an SIOD and an unaffected control kidney measured by the Wnt Signaling Pathway Plus PCR Array (PAHS-043Y).  Gene Fold change p value Upregulated genes         WNT7A 22.3 **       SFRP4 19.0 **       WNT10A 14.0 ***       FRZB 12.3 **       WNT4 8.7 ***       CCND2 7.9 ***       WNT5A 7.6 ***       VANGL2 6.9 **       MYC 6.5 ***       DKK3 6.2 ***       AXIN2 5.9 **       MMP7 5.6 ***       WIF1 4.9 *       SFRP1 3.9 ***       PRICKLE1 3.9 *       FZD7 3.5 **       FZD2 3.3 **       NFATC1 2.9 **       WNT9A 2.8 **       WNT5B 2.7 ***       WNT6 2.7 *       LEF1 2.5 *       WNT2B 2.4 ***       DAAM1 2.4 **       JUN 2.3 *       DKK1 2.2 *       TCF7 2.2 *** Downregulated genes         MTFP1 -4.0 ***       PITX2 -3.3 **       FZD5 -2.4 ***  Abbreviations: *, p < 0.05; **, p < 0.01; ***, p < 0.001; SIOD, Schimke immuno-osseous dysplasia.   189 Supplementary Table 4.6 Significant gene expression differences (fold change > 2) between an SIOD and an unaffected control kidney measured by the Notch Signaling Pathway Plus PCR Array (PAHS-059Y).  Gene Fold change p value Upregulated genes         RUNX1 6.4 ***       FIGF 3.8 ***       UBD 3.5 **       CD44 3.2 **       MMP7 3.1 **       SHH 2.7 *       DTX1 2.6 *       IL2RA 2.6 **       HEYL 2.2 ** Downregulated genes         SLC6A12 -13.8 **       SERPINA3 -7.3 *       PSENEN -3.1 *       TFF1 -2.9 ***       CCND1 -2.1 *  Abbreviations: *, p < 0.05; **, p < 0.01; ***, p < 0.001; SIOD, Schimke immuno-osseous dysplasia. 190 Supplementary Table 4.7 Effect of Wnt pathway mutants on the ectopic wing veins induced by the overexpression of Drosophila Marcal1.  Drosophila gene Allele Effect on Drosophila Marcal1 overexpression phenotype wg Sp-1 0-S1 wg spd-1 E1 wg l-16 S1 wg 1 S2 wg l-17 0-E1 wg l-12 S2 dsh 6 S2 dsh 1 S2 dsh 3 S2 fz 1 Lethal fz EY13696 S2 sgg 1 E2-E3 sgg EP1576 Lethal Axn EY10228 Lethal Apc2 d40 0-S1 Apc2 N175K S1-S2 Apc2 Apc N175K Q8 S1-S2 pan 13a S1 pan 2 0-S1 pan 3 S1-S2 arm 4 S2 arm 1 S2 arm 2 S2 arm 8 S2 arm G0192 S2  Abbreviations: S2, strong suppression; S1-S2, moderate suppression; S1, weak suppression; 0-S1, very weak suppression; 0, no enhancement or suppression; 0-E1, very weak enhancement; E1, weak enhancement; E1-E2, moderate suppression; E2, strong suppression; E2-E3, very strong enhancement. 191 Supplementary Table 4.8 Effect of Notch pathway mutants on the ectopic wing veins induced by the overexpression of Drosophila Marcal1.  Drosophila gene Allele Effect on Drosophila Marcal1 overexpression phenotype Dl 3 n/a1 Dl 9 n/a1 Dl 11 n/a1 Dl 12 n/a1 Dl 13 n/a1 Dl X n/a1 Dl 14 n/a1 Dl B2 n/a1 Ser 1 S2 fng 13 S2 fng 52 S2 fng M69 S1 N nd-1 S2 N nd-3 S2 N spl-1 S1 Psn 9 S2 Psn 143 S2 Su(H) 1 S2 Su(H) 2 S2 Su(H) IB115 S2 mam 2 S2 mam 8 S2 nej Q7 S2 H 1 S1 H 2 S2 H 3 S2 CtBP 03463 0 CtBP 87De-10 S2 gro 1 S2 gro C105 0-S1 HDAC1 04556 S1-S2 HDAC1 303 S2 HDAC1 328 S2 HDAC1 def24 S2  1Since several features of the Dl mutant phenotype and the Marcal1 overexpression phenotype overlap, it was not possible to assess these wings.   Abbreviations: n/a, not applicable; S2, strong suppression; S1-S2, moderate suppression; S1, weak suppression; 0-S1, very weak suppression; 0, no enhancement or suppression. 192 Supplementary Table 9. Summary of the effect of Marcal1 loss and gain on Notch pathway mutant allele phenotypes.  Gene Allele Mutant phenotype Control penetrance Marcal1 loss-of-function penetrance1 Marcal1  overexpression penetrance1 Male Female Male Female Male Female N spl-1 Missing a sc bristle Missing p sc bristle Double a sc bristle Double p sc bristle Ectopic a sc bristle Ectopic p sc bristle Rough and reduced eyes2 3% 51% 25% 1% 3% 0% 100% 1% 55% 23% 0% 15% 0% 100% 1% 34% 6% 3% 0% 0% 100% 0% 40% 1% 0% 0% 0% 100% 18% 21% 18% 3% 1% 1% 100% NA3 N nd-1 Notched wings 12% 15% 100% 91% 100% NA3 N nd-3 Notched wings 26% 29% 72% 45% 100% NA3 Dl 3 Delta wing veins Thickened and/or ectopic L2 Thickened and/or ectopic L3 Thickened L4 posterior to ACV Thickened and/or ectopic PCV Ectopic vein posterior to L5 98% 40% 13% 27% 21% 21% 100% 75% 17% 56% 81% 19% 100% 63% 10% 83% 68% 33% 100% 96% 33% 100% 100% 83% Blistered wings: 11%4 Blistered wings: 30%4 Dl 11 Delta wing veins Thickened and/or ectopic L2 Thickened L4 posterior to ACV Thickened and/or ectopic PCV 64% 100% 75% 7% 38% 100% 48% 48% 79% 86% 77% 28% 100% 100% 95% 93% Blistered wings: 2%4 Blistered wings: 14%4 Dl 12 Delta wing veins Thickened and/or ectopic L2 Thickened and/or ectopic L3 Thickened L4 posterior to ACV Thickened and/or ectopic PCV Ectopic vein posterior to L5 Ectopic crossvein anterior to ACV 98% 95% 0% 39% 16% 0% 0% 63% 93% 0% 65% 48% 7% 2% 100% 92% 6% 25% 73% 6% 2% 100% 96% 50% 71% 96% 52% 29% Blistered wings: 8%4 Blistered wings: 8%4 Dl 13 Delta wing veins 24% 45% 75% 93% Blistered Blistered  193 Gene Allele Mutant phenotype Control penetrance Marcal1 loss-of-function penetrance1 Marcal1  overexpression penetrance1 Male Female Male Female Male Female Thickened and/or ectopic L2 Thickened L4 posterior to ACV Thickened and/or ectopic PCV Ectopic vein posterior to L5 81% 2% 2% 4% 79% 9% 11% 38% 13% 0% 13% 3% 37% 0% 37% 17% wings: 0%4 wings: 2%4 Dl 14 Delta wing veins Thickened and/or ectopic L2 Thickened and/or ectopic L3 Thickened L4 posterior to ACV Thickened and/or ectopic PCV Ectopic vein posterior to L5 Ectopic crossvein anterior to ACV 0% 39% 0% 0% 0% 0% 0% 28% 69% 6% 0% 11% 22% 0% 100% 56% 7% 28% 70% 0% 0% 98% 76% 50% 36% 100% 14% 43% Blistered wings: 0%4 Blistered wings: 5%4 Dl B2 Delta wing veins Thickened and/or ectopic L2 Thickened and/or ectopic L3 Thickened L4 posterior to ACV Thickened and/or ectopic PCV Ectopic vein posterior to L5 84% 82% 10% 0% 23% 3% 92% 81% 29% 32% 86% 44% 100% 65% 10% 23% 25% 38% 100% 86% 57% 55% 76% 62% Blistered wings: 0%4 Blistered wings: 7%4 Dl X Delta wing veins Thickened and/or ectopic L2 Thickened and/or ectopic L3 Thickened L4 posterior to ACV Thickened and/or ectopic PCV Ectopic vein posterior to L5 93% 89% 7% 18% 39% 7% 100% 96% 13% 51% 82% 11% 100% 71% 5% 80% 85% 32% 100% 98% 50% 98% 94% 96% Blistered wings: 26%4 Blistered wings: 27%4 Ser 1 Serrated wing margin 100% 100% 100% 100% 100% 100% fng 13 Loss of tissue from distal tip of wing 16% 6% 1% 1% 0% 0% H 1 Shortened L4 Shortened L5 Missing u h bristle Missing l h bristle 1% 100% 73% 14% 0% 100% 71% 25% lethal lethal 0% 18% 86% 9% 0% 0% 39% 4%  194 Gene Allele Mutant phenotype Control penetrance Marcal1 loss-of-function penetrance1 Marcal1  overexpression penetrance1 Male Female Male Female Male Female Missing p s bristle Missing a np bristle Missing p np bristle Missing a sa bristle Missing p sa bristle Missing a pa bristle Missing p pa bristle Missing a dc bristle Missing p dc bristle Missing a sc bristle Missing p sc bristle 16% 0% 0% 3% 68% 0% 31% 11% 4% 11% 1% 29% 0% 0% 1% 70% 0% 23% 30% 5% 6% 0% 75% 5% 1% 14% 30% 3% 88% 20% 1% 16% 3% 31% 0% 0% 19% 24% 0% 44% 20% 0% 9% 0% H 2 Shortened L2 Shortened L5 Missing u h bristle Missing l h bristle Missing p s bristle Missing a np bristle Missing p np bristle Missing a sa bristle Missing p sa bristle Missing a pa bristle Missing p pa bristle Missing a dc bristle Missing p dc bristle Missing a sc bristle Missing p sc bristle 0% 91% 89% 6% 38% 0% 0% 1% 44% 0% 45% 80% 5% 0% 0% 0% 92% 94% 6% 41% 0% 0% 0% 44% 1% 41% 89% 0% 5% 1% 20% 99% 83% 0% 14% 0% 0% 0% 3% 0% 8% 10% 0% 0% 0% 21% 95% 75% 3% 15% 0% 0% 0% 3% 0% 6% 14% 1% 0% 0% 0% 52% 59% 0% 25% 0% 3% 20% 4% 1% 31% 29% 4% 13% 0% 0% 26% 41% 4% 16% 0% 0% 25% 20% 0% 20% 54% 5% 6% 0% H 3 Shortened L2 Shortened L5 Missing u h bristle 0% 99% 74% 0% 98% 74% 3% 97% 70% 0% 84% 64% 0% 50% 41% 0% 10% 34%  195 Gene Allele Mutant phenotype Control penetrance Marcal1 loss-of-function penetrance1 Marcal1  overexpression penetrance1 Male Female Male Female Male Female Missing l h bristle Missing p s bristle Missing a np bristle Missing p np bristle Missing a sa bristle Missing p sa bristle Missing a pa bristle Missing p pa bristle Missing a dc bristle Missing p dc bristle Missing a sc bristle Missing p sc bristle 1% 31% 1% 0% 8% 45% 0% 59% 28% 5% 6% 0% 3% 30% 0% 0% 1% 40% 0% 39% 58% 1% 0% 0% 0% 19% 0% 0% 0% 3% 0% 11% 4% 0% 0% 0% 0% 10% 0% 0% 0% 1% 0% 4% 6% 0% 0% 0% 5% 95% 0% 0% 24% 3% 0% 59% 19% 3% 5% 0% 0% 71% 0% 0% 26% 8% 0% 23% 29% 0% 4% 0%  1Percentages that are in red reflect a decrease in penetrance of ≥10% compared to the controls of the relevant sex; percentages that are in green reflect an increase in penetrance of ≥10% compared to the controls of the relevant sex. 2Although the penetrance of the rough and reduced eye phenotype was 100% for all genotypes and sexes, the severity of the phenotype was enhanced in the Marcal1 loss-of-function and overexpression backgrounds. 3Only hemizygous males were assessed since the phenotype of interest is only present in homozygous females and hemizygous males and the Marcal1 overexpression cross did not give rise to homozygous female progeny. 4Since several features of the Delta mutant phenotype and the Marcal1 overexpression phenotype overlap, it was not possible to assess these wings. However, as evidence of a genetic interaction between Dl and Marcal1, a proportion of flies presented with the new phenotype of blistered wings. Percentages of blistered wings observed are shown.  Abbreviations: ACV, anterior crossvein; a dc, anterior dorsocentral; a np; anterior notopleural; a pa; anterior postalar; a sa, anterior supraalar; a sc, anterior scutellar; L, longitudinal vein; l h, lower humeral; NA, not applicable; PCV, posterior crossvein; p dc, posterior dorsocentral; p np; posterior notopleural; p pa, posterior postalar; p s, presutural; p sa, posterior supraalar; p sc, posterior scutellar; u h, upper humeral. 196  Supplementary Figure 4.1. Gene expression analysis of Marcal1 mRNA in the Marcal1 overexpression transgenic line and the scoring reference used for scoring ectopic wing veins in the genetic screen. (A) Relative Marcal1 mRNA expression measured by quantitative PCR in yw; pUAST-Marcal1; tubulin-GAL4/MKRS; and pUAST-Marcal1, tubulin-GAL4/MKRS adult flies. The Marcal1 mRNA levels of three technical replicates were normalized to the mRNA levels of the housekeeping gene Gapdh2 and the mean Marcal1 mRNA level was plotted relative to yw. Error bars represent one standard deviation. (B) A representation of a wild type Drosophila wing showing the 5 longitudinal veins (L1-L5) and the 2 crossveins (ACV and PCV). (C) The scoring reference showing the region of the wing analyzed (red box) and the representative phenotypes for each feature and the corresponding score that would be assessed. An ectopic vein parallel to the L2 vein; veins at the L2, L4, and L5 wing margins; an ectopic vein perpendicular to the PCV; and the absence of the ACV and PCV were assessed. Abbreviations: ACV, anterior cross vein; L, longitudinal; mRNA, messenger RNA; PCV, posterior cross vein.   197  Supplementary Figure 4.2. Positive controls for the unphosphorylated -catenin immunofluorescent staining. Immunostaining with anti-unphosphorylated -catenin (Alexa 594) of an adenoma from a familial adenomatous polyposis patient (A), untreated and Wnt3a-treated dermal fibroblast cells (B and C, respectively), and untreated and Wnt3a-treated HeLa cells (D and E, respectively). The nuclei were counterstained with DAPI. The boxed regions correspond to the higher magnification images. Scale bars: overview images (200) = 100 microns; higher magnification images (400) = 100 microns. Abbreviation: DAPI, 4, 6-diamidino-2-phenylindole. 198  Supplementary Figure 4.3. Immunofluorescent detection of the expression and localization of unphosphorylated -catenin in the glomerular cells of additional unaffected control kidneys, FSGS patient kidneys, and a transplanted kidney in an SIOD patient.  Immunostaining with anti-unphosphorylated -catenin (Alexa Fluor 594) in unaffected control kidneys (A and B), FSGS patient kidneys (C-K), and a transplanted kidney in an SIOD patient (L). The nuclei were counterstained with DAPI. The boxed regions correspond to the higher magnification images on the right. The glomeruli have been outlined to aid in the visualization of -catenin expression. These are representative glomeruli. Scale bars: overview images (200) and higher magnification images (400) = 100 microns. Abbreviations: DAPI, 4, 6-diamidino-2-phenylindole; FSGS, focal segmental glomerulosclerosis; SIOD, Schimke immuno-osseous dysplasia; Tx, transplant; yr, years. 199  Supplementary Figure 4.4. Quantification of -catenin immunofluorescent staining in SIOD and FSGS kidneys. Expression data are presented as box and whisker plots of relative -catenin immunofluorescence in unaffected control (n = 3), SIOD (n = 7), and FSGS (n = 9) kidneys. A single transplanted kidney in an SIOD patient was also available for analysis (SIOD Tx). Boxes represent the interquartile range (25th - 75th percentile), horizontal lines within boxes represent the median, and whiskers represent the range. Adjacent to the box and whisker plots are the individual data points from which the box and whisker plots are derived. -catenin immunofluorescence values were normalized to the median unaffected control value set at 1 (dotted line). Abbreviations: **, p < 0.01; FSGS, focal segmental glomerulosclerosis; SIOD, Schimke immuno-osseous dysplasia; Tx, transplant.   200  Supplementary Figure 4.5. Immunofluorescent detection of the expression and localization of the Notch1 intracellular domain (NICD) in the glomerular cells of additional unaffected control kidneys, FSGS patient kidneys, and a transplanted kidney in an SIOD patient.  Immunostaining with anti-NICD (Alexa Fluor 594) in unaffected control kidneys (A and B), FSGS patient kidneys (C-K), and a transplanted kidney in an SIOD patient (L). The nuclei were counterstained with DAPI. The boxed regions on the left correspond to the higher magnification images on the right. These are representative glomeruli. Scale bars: overview images (400) = 100 microns; higher magnification images (1000) = 10 microns. Abbreviations: DAPI, 4, 6-diamidino-2-phenylindole; FSGS, focal segmental glomerulosclerosis; NICD, Notch1 intracellular domain; SIOD, Schimke immuno-osseous dysplasia; Tx, transplant; yr, years.   201  Supplementary Figure 4.6. Immunofluorescent detection of the expression and localization of unphosphorylated -catenin in the developing unaffected and SMARCAL1-deficient kidney.  Immunostaining with anti-unphosphorylated -catenin (Alexa 594) in the S-shaped bodies and glomeruli of an unaffected 15-week-gestation male (A and B, respectively) and female (C and D, respectively) and of a SMARCAL1-deficient 15-week-gestation female (E and F, respectively). The nuclei were counterstained with DAPI. The boxed regions correspond to the higher magnification images. These are representative S-shaped bodies and glomeruli. Scale bars: overview images (200) = 100 microns; higher magnification images (400) = 100 microns. Abbreviations: DAPI, 4, 6-diamidino-2-phenylindole; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.   202  Supplementary Figure 4.7. Immunofluorescent detection of the expression and localization of the Notch1 intracellular domain (NICD) in the developing unaffected and SMARCAL1-deficient kidney.  Immunostaining with anti-NICD (Alexa Fluor 594) in the S-shaped bodies and glomeruli of an unaffected 15-week-gestation male (A and B, respectively) and female (C and D, respectively) and of a SMARCAL1-deficient 15-week-gestation female (E and F, respectively). The nuclei were counterstained with DAPI. The boxed regions correspond to the higher magnification images. These are representative S-shaped bodies and glomeruli. Scale bars: overview images (200) = 100 microns; higher magnification images (400) = 100 microns. Abbreviations: DAPI, 4, 6-diamidino-2-phenylindole; NICD; Notch1 intracellular domain; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.   203  Supplementary Figure 4.8. Graph showing the relative expression of target genes of the Wnt and Notch signaling pathways in the developing unaffected versus SMARCAL1-deficient kidney.  Relative expression of Wnt pathway target genes (AXIN2, CCND1, CCND2, and CTNNB1) and Notch pathway target genes (HES1, HES2, HEY1, and HEY2) were measured by quantitative PCR in unaffected 15-week-gestation kidneys (n = 5) and in a SMARCAL1-deficient 15-week-gestation kidney (n = 1, SD133b). For each sample, the mRNA levels of three technical replicates were normalized to the mRNA levels of the housekeeping gene GAPDH and plotted relative to SD133b set at 1 (dotted line). Boxes represent the interquartile range (25th - 75th percentile), horizontal lines within boxes represent the median, whiskers represent the range, and individual points represent outliers. Adjacent to the box and whisker plots are the individual data points from which the box and whisker plots are derived. Abbreviations: GAPDH, glyceraldehyde 3-phosphate dehydrogenase; mRNA, messenger RNA; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.   204   205 Supplementary Figure 4.9. A schematic of the Drosophila Wnt and Notch signaling pathways.  (A) Wnt pathway schematic: In the absence of the wingless (wg) ligand, armadillo (arm) is phosphorylated by shaggy (sgg), ubiquitinated, and targeted for degradation via the proteasome. In the presence of wg, the complex associates with phosphorylated arrow (arr). Although arm is still phosphorylated by sgg, ubiquitination of arm is inhibited. The complex becomes saturated with phosphorylated arm, and newly synthesized arm accumulates and translocates to the nucleus to activate target gene expression (Li et al. 2012). (B) Notch pathway schematic: In the absence of ligand, the Notch pathway is inactive. Suppressor of hairless (Su(H)) binds Hairless (H) to recruit co-repressors such as C-terminal binding protein (CtBP), groucho (gro), and histone deacetylase (HDAC1) to repress transcription. In the presence of the ligand Delta (Dl) or Serrate (Ser), the ligand binds Notch (N) to trigger consecutive S2 and S3 proteolytic cleavages of N; Presenilin (Psn) is part of the -secretase complex which is involved in the S3 cleavage. The Notch intracellular domain (NICD) is then released into the cytoplasm and translocates to the nucleus where it binds Su(H) and recruits co-activators such as mastermind (mam) and nejire (nej) to activate transcription. 206  Supplementary Figure 4.10. Genetic interaction of Marcal1 with the Wnt signaling pathway mutants. Representative F1 wings of crosses between the Marcal1 overexpression line and the Wnt pathway mutants are shown on the right. Representative wings of each of the Wnt pathway mutants are shown on the left. Representative F1 wings of crosses between the C96-GAL4 UAS-Hrs/MKRS line and the Wnt pathway mutants are shown in the centre. The mutant alleles and chromosomal location of the gene of interest are shown. All wings shown are from female flies. Abbreviations: Chr, chromosome; UAS, upstream activating sequence.   207  Supplementary Figure 4.11. Genetic interaction of Marcal1 with the Notch signaling pathway mutants. Representative F1 wings of crosses between the Marcal1 overexpression line and the Notch pathway mutants are shown on the right. Representative wings of each of the Notch pathway mutants are shown on the left. Representative F1 wings of crosses between the C96-GAL4 UAS-Hrs/MKRS line and the Notch pathway mutants are shown in the centre. The mutant alleles and chromosomal location of the gene of interest are shown. All wings shown are from female flies. Abbreviations: Chr, chromosome; UAS, upstream activating sequence.   208  Supplementary Figure 4.12. Genetic interaction of Marcal1 loss and gain with Notch signaling pathway mutant alleles. Representative wings of the mutant of interest (left columns), the mutant allele in the Marcal1 loss-of-function background (middle and middle left columns), and the mutant allele in the Marcal1 overexpression background (right and middle right columns). Since several features of the Delta mutant phenotype and the Marcal1 overexpression phenotype overlap, it was not possible to assess these wings (middle right column), however a proportion of flies presented with the new phenotype of blistered wings (right column), indicative of a genetic interaction between Marcal1 gain and Delta. Percentages of blistered wings observed are shown. All wings shown are from female flies, excepting the Nnd-1 allele since the N phenotype of interest is only present in homozygous females and hemizygous males and the Marcal1 overexpression cross did not give rise to homozygous female progeny. 209 Appendix D: Supplementary Tables and Figures for Chapter 5 Supplementary Table 5.1 Antibodies used for flow cytometry.  Antigen Clone Conjugated fluorochrome CD3 UCHT1 Alexa Fluor 700 CD4 SK3 PE-Cy7 CD8 SK1 APC-Cy7 CD19 H1B19 V450 CD25 M-A251 FITC CD31 WM59 FITC CD45RA HI100 PE CD45RO UCHL1 PerCP-Cy5.5 CD127 HIL-7R-M21 APC  Abbreviations: APC, allophycocyanin; APC-Cy7, allophycocyanin-Cy7; CD, cluster of differentiation; FITC, fluorescein isothiocyanate; PE, phycoerythrin; PE-Cy7; phycoerythrin-Cy7; PerCP-Cy5.5, peridinin-chlorophyll protein-Cy5.5.   210 Supplementary Table 5.2 Primer sequences for the analysis of IL7R gene expression by quantitative PCR.  Primer Sequence GAPDH-F 5´-CTTTTGCGTCGCCAGCCGAG-3´ GAPDH-R 5´-GGTGACCAGGCGCCCAATACG-3´ IL7R-F 5´-ATGGATCGCAGCACTCACTG-3´ IL7R-R 5´-ACGAGGGCCCCACATATTTC-3´  Abbreviations: F, forward; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; IL7R, interleukin 7 receptor alpha chain; PCR, polymerase chain reaction; R, reverse.   211 Supplementary Table 5.3 Primer sequences for the analysis of SMARCAL1 cDNA mutations by Sanger sequencing.  Primer Sequence SMARCAL1-cDNA-1F 5´-TGCTTTTGCCAACTTTCCAATTAAA-3´ SMARCAL1-cDNA-1R 5´-TCTTCTGGCTTTTTCCATATTCCC-3´ SMARCAL1-cDNA-2F 5´-AGCCATGGTGTCATTTTCAAGC-3´ SMARCAL1-cDNA-2R 5´-GAGGCTTTTGCTGTCTTGGC-3´ SMARCAL1-cDNA-3F 5´-AACCAAAGAGTTCCCAAGAGACAC-3´ SMARCAL1-cDNA-3R 5´-TGCCATAGGCCCATTCCAGA-3´ SMARCAL1-cDNA-4F 5´-CCTGACACCAAGACGTGGAA-3´ SMARCAL1-cDNA-4R 5´-CAAACGCCAGGGTGAGAGT-3´ SMARCAL1-cDNA-5F 5´-AGCACAGTAAACTAATTGCAAAGGT-3´ SMARCAL1-cDNA-5R 5´-CGACGTTGATGCAATCTGGG-3´ SMARCAL1-cDNA-6F 5´-CAGCCTTTTACCGGAAGGAGT-3´ SMARCAL1-cDNA-6R 5´-GGCATGAAACTGGGGGAAGA-3´ SMARCAL1-cDNA-7F 5´-GGGTGATCCTGTTGTCGGG-3´ SMARCAL1-cDNA-7R 5´-GGGATTTTAGCTTCAGCTGTTCTG-3´ SMARCAL1-cDNA-8F 5´-AACTAAACAGCAGCAGAAAGATGC-3´ SMARCAL1-cDNA-8R 5´-TGGTCTGTCCAATGCGGTG-3´ SMARCAL1-cDNA-9F 5´-GTGTTTGCTGAGCTGTTTTGGAA-3´ SMARCAL1-cDNA-9R 5´-ATCTTCTGCTGCTTTGGGTCC-3´ SMARCAL1-cDNA-10F 5´-CCGGGCTTTCTGAGACCAAT-3´ SMARCAL1-cDNA-10R 5´-CCCTTTTACAGGGGAGACGTAAA-3´  Abbreviations: cDNA, complementary DNA; F, forward; R, reverse; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.   212 Supplementary Table 5.4 Primer sequences for the analysis of IL7R coding sequence mutations by Sanger sequencing.  Primer Sequence IL7R-Exon-1F 5´-CCTCCCTCCCTTCCTCTTAC-3´ IL7R-Exon-1R 5´-ATTCAAACCCAGTGCCTGAC-3´ IL7R-Exon-2F 5´-TTGGGCTTTTCTTCCTTGAA-3´ IL7R-Exon-2R 5´-AGGAGTTTCAGGAGGCCTTT-3´ IL7R-Exon-3F 5´-ATTCCTGAACATGCCTCCAC-3´ IL7R-Exon-3R 5´-TGCCAGTTGTTTCCACTATTTG-3´ IL7R-Exon-4F 5´-GGTCAAAGTGACTTGCAGAGG-3´ IL7R-Exon-4R 5´-CCCAGGGGAAATGCACTACT-3´ IL7R-Exon-5F 5´-TGGGACTAAAGGAATCCCAAT-3´ IL7R-Exon-5R 5´-CCCTCCAAGGGTGTCCTATT-3´ IL7R-Exon-6F 5´-CAAAGCACCCTGAGACCCTA-3´ IL7R-Exon-6R 5´-GCCTTAATCCCCTTTGTGGT-3´ IL7R-Exon-7F 5´-TGGTCACCCACCTAATTGTG-3´ IL7R-Exon-7R 5´-CCAAAAGGTGAGGTTCAACTG-3´ IL7R-Exon-8aF 5´-ACATGCTGGCAATTCTGTGA-3´ IL7R-Exon-8aR 5´-CCTGAGCAACTGGGTTCAAT-3´ IL7R-Exon-8bF 5´-CTGGGAATGTCAGTGCATGT-3´ IL7R-Exon-8bR 5´-TCTCTGTGCTGTGAGGGAGA-3´  Abbreviations: F, forward; IL7R, interleukin 7 receptor alpha chain; R, reverse.   213 Supplementary Table 5.5 Primer sequences for the analysis of IL7R promoter methylation by bisulfite pyrosequencing.  CpG sites(s) assessed1 Primer Sequence1 -960 Forward Reverse Sequencing2 5´-TGATATATAAATGGGTGAGGTTGT-3´ Biotin-5´-CTTTTTTTTTCCCAAATAAACCTT-3´ 5´-TGATATATAAATGGGTGAGGTTGT-3´ -552 Forward Reverse Sequencing 5´-GTGAAATTTGGAAGTTGGAGGTAA-3´ Biotin-5´-CCCAAATTCAAACAATTCTCCT-3´ 5´-TAGATTTTTTTAAAGTGGGT-3´ -482, -459, -451 Forward Reverse Sequencing 5´-GTGAAATTTGGAAGTTGGAGGTAA-3´ Biotin-5´-CCCAAATTCAAACAATTCTCCT-3´ 5´-AGGTAGATTATTTGAGGTTA-3´ -331 Forward Reverse Sequencing 5´-TTGGGAGGTGAAAATTGTAGTGAG-3´ Biotin-5´-TAAATATTCCCTACAACCCCCACA-3´ 5´-GGAGGTGAAAATTGTAGTG-3´  1The CpG site positions are relative to the transcription start site of IL7R.  2All bisulfite pyrosequencing primers have been previously described by Kim et al. (2007) excepting this sequencing primer we designed to assess CpG -960.  Abbreviation: IL7R, interleukin 7 receptor alpha chain.   214 Supplementary Table 5.6 Serum immunoglobulin levels in SIOD patients with biallelic SMARCAL1 mutations.  Patient ID IgA 17/191 IgG 12/201 IgG1 14/201 IgG2 4/211 IgG3 17/201 IgG4 13/211 IgM 17/191 SD9 nl nl ND  ND  nl SD18c nl nl nl  nl  nl SD22 nl nl nl  nl nl nl SD24 nl  nl  nl nl nl SD25 nl    nl nl nl SD27 nl    nl   SD38 ND ND nl  nl nl ND SD41 nl nl nl nl nl nl nl SD44 nl nl nl  nl nl nl SD47 nl nl nl nl nl nl nl SD60 nl nl nl  nl  nl SD74 nl nl nl  nl nl  SD94b   nl  nl  nl SD107   nl  nl  nl SD108a nl    nl nl nl SD108b nl nl nl nl nl nl nl SD109 nl nl nl  nl nl nl SD114 ND   nl  nl ND SD136a nl nl    nl nl SD136b nl nl nl  nl  nl SD138 nl      nl  1Number of patients that have a normal level of the indicated immunoglobulin isotype or subclass out of the total number of patients analyzed.  Abbreviations: ID, identification; IgA, immunoglobulin A; IgG, immunoglobulin G; IgG1, immunoglobulin G subclass 1; IgG2, immunoglobulin G subclass 2; IgG3, immunoglobulin G subclass 3; IgG4, immunoglobulin G subclass 4; IgM, immunoglobulin M; nl, normal; , lower than normal values; , higher than normal values; ND, not done; SIOD, Schimke immuno-osseous dysplasia; SMARCAL1, SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily A-like 1.    215  Supplementary Figure 5.1 FACS analyses for CD3 and IL7R expression in the T cells of SIOD patients. PBMCs isolated from patient blood were stained with anti-human CD3 and IL7R (CD127) antibodies. Gated live cells are shown here for the expression of T-cell marker CD3 and IL7R. Note that SIOD patients do not express IL7R on their T cells. Abbreviations: CD, cluster of differentiation; FACS, fluorescence-activated cell sorting; IL7R, interleukin 7 receptor alpha chain; PBMC, peripheral blood mononuclear cell; SIOD, Schimke immuno-osseous dysplasia.  The data for this figure was generated by Dr. Mrinmoy Sanyal, Kira Y. Dionis-Petersen, and Lan Xiang Liu.    216  Supplementary Figure 5.2 Establishment of the IL-7-induced proliferation assay. PBMCs from an unaffected donor were labeled with CFSE and cultured for 96 hours. After incubation for 96 hours, T-cell subsets were resolved using fluorochrome-conjugated anti-human CD3, CD4, and CD8 antibodies. Cellular proliferation was measured by CFSE dilution. (A) Cells were stimulated with increasing concentrations of IL-7 (1 - 1000 ng/ml). Note that IL-7 alone did not induce proliferation of T cells at any concentration between 1 - 1000 ng/ml either in CD4+ (left column) or CD8+ (right column) cells. (B) Cells were stimulated with anti-CD3/CD28-conjugated beads at a 1:1 or 1:100 bead-to-cell ratio to induce T-cell proliferation. Note that at a 1:1 bead-to-cell ratio, the majority of the T cells (both CD4+ and CD8+, upper panels) proliferated. Middle panels show the proliferation of T cells at a sub-optimal bead-to-cell ratio of 1:100 and cells showed moderate proliferation. Lower panels show the proliferation of T cells at a 1:100 bead-to-cell ratio in the presence of 100 ng/ml IL-7. Note that the addition of IL-7 increased proliferation of T cells (both CD4+ and CD8+) above the values for stimulation with a 1:100 bead-to-cell ratio alone. Subsequently, we used this assay for IL-7-induced enhancement of proliferation to interrogate the responsiveness of SIOD T cells to IL-7. Abbreviations: CD, cluster of differentiation; CFSE, carboxyfluorescein succinimidyl ester; IL-7, interleukin 7; PBMC, peripheral blood mononuclear cell.  The data for this figure was generated by Dr. Mrinmoy Sanyal, Kira Y. Dionis-Petersen, and Lan Xiang Liu.    217  Supplementary Figure 5.3 Effect of IL-2 on the proliferation of SIOD T cells. PBMCs were labeled with CFSE and cultured for 96 hours. Anti-CD3/CD28-conjugated beads at a bead-to-cell ratio of 1:100 were used to induce T-cell proliferation. Cells were incubated with 10 IU/ml IL-2 to determine its effect on T cells with or without anti-CD3/CD28-conjugated beads. After incubation for 96 hours, T-cell subsets were resolved using fluorochrome-conjugated anti-human CD3, CD4, and CD8 antibodies. Cellular proliferation was measured by CFSE dilution. A representative analysis from an SIOD patient and an unaffected control is shown. Note that the CD4+ T cells (A) respond to IL-2-induced enhancement of T-cell proliferation. A similar IL-2 response was observed in SIOD CD8+ T cells (B). SIOD T cells also responded normally to optimal anti-CD3/CD28-conjugated beads (i.e., 1:1 bead-to-cell ratio) and PHA. Abbreviations: CD, cluster of differentiation; CFSE; carboxyfluorescein succinimidyl ester; IL-2, interleukin 2; IU, international unit; PBMC, peripheral blood mononuclear cell; PHA, phytohemagglutinin; SIOD, Schimke immuno-osseous dysplasia.  The data for this figure was generated by Dr. Mrinmoy Sanyal, Kira Y. Dionis-Petersen, and Lan Xiang Liu.     

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