Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Schimke immuno-osseous dysplasia : association of SMARCAL1 mutations with genetic and environmental disturbances… Baradaran-Heravi, Alireza 2013

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2013_fall_baradaranheravi_alireza.pdf [ 6.34MB ]
Metadata
JSON: 24-1.0073847.json
JSON-LD: 24-1.0073847-ld.json
RDF/XML (Pretty): 24-1.0073847-rdf.xml
RDF/JSON: 24-1.0073847-rdf.json
Turtle: 24-1.0073847-turtle.txt
N-Triples: 24-1.0073847-rdf-ntriples.txt
Original Record: 24-1.0073847-source.json
Full Text
24-1.0073847-fulltext.txt
Citation
24-1.0073847.ris

Full Text

SCHIMKE IMMUNO-OSSEOUS DYSPLASIA: ASSOCIATION OF SMARCAL1 MUTATIONS WITH GENETIC AND ENVIRONMENTAL DISTURBANCES OF GENE EXPRESSION  by  Alireza Baradaran-Heravi M.D., MASHHAD UNIVERSITY OF MEDICAL SCIENCES, 2004 	
   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2013   © Alireza Baradaran-Heravi, 2013  ii Abstract Schimke immuno-osseous dysplasia (SIOD, MIM 242900) is an incompletely penetrant autosomal recessive multisystem disorder characterized by dysmorphic facies, short stature, renal failure, and T-cell immunodeficiency. Biallelic loss-of-function mutations in SMARCAL1 (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily a-like 1), which encodes a DNA stress response enzyme with annealing helicase activity, are associated with SIOD. My studies focused on further delineation of the molecular basis of SIOD by assessing the contribution of defective DNA repair to the pathophysiology of SIOD and a mechanism by which SMARCAL1 can modulate trait penetrance. Through collaborative analyses of the clinical data and molecular studies of SIOD patients, my coworkers and I observed that SIOD patients have a high frequency of non- Hodgkin lymphoma and showed that despite similarities among SIOD and other DNA repair diseases, SMARCAL1-deficient cells do not have increased baseline DNA breaks detectable by comet assay. Additionally, SIOD patients do not have a detectable defect of nucleotide excision repair (NER), homologous recombination (HR) or nonhomologous end joining (NHEJ) to explain their ectodermal or immunological features, although Smarcal1del/del mice are hypersensitive to several DNA damaging agents. Furthermore, my colleagues and I found that SMARCAL1 orthologues localize to transcriptionally active chromatin and modulate gene expression, that deficiency of the SMARCAL1 orthologues alone is insufficient to cause disease in fruit flies and mice, that SMARCAL1 deficiency causes modest diffuse alterations in gene expression, and that SMARCAL1 deficiency causes disease via interaction with genetic and environmental factors that further alter gene expression.  iii In summary, our findings provide guidance for clinical management of SIOD and suggest that alterations in the variance of gene expression levels contribute to the penetrance of SIOD.   iv Preface Collaborators: Anja Raams, Dr. Nicolaas G. J. Jaspers Department of Genetics, Erasmus Medical Center, Rotterdam, The Netherlands Contribution: Analysis of cell survival assays in SIOD cells following UV or illudin S exposures.  Dr. Kyoung Sang Cho Department of Biological Sciences, Konkuk University, Seoul, Republic of Korea Contribution: Drosophila genetic studies and experiments.  Dr. Bas Tolhuis, Dr. Maarten van Lohuizen Division of Molecular Genetics and the Centre for Biomedical Genetics, The Netherlands Cancer Institute, Amsterdam, The Netherlands Contribution: DNA adenine methyltransferase analysis of Marcal1 binding.  Dr. Mrinmoy Sanyal, Dr. David B. Lewis, Kira Y. Dionis Department of Pediatrics and Institute for Immunology, Transplantation, and Infectious Disease, Stanford University School of Medicine, Stanford, CA, USA Contribution: Immunological studies in Smarcal1-deficient mice.  Dr. Olena Morozova, Dr. Marco A. Marra  v Michael Smith Genome Sciences Centre, British Columbia Cancer Agency and University of British Columbia, Vancouver, British Columbia, Canada Contribution: Data analysis of mouse RNA sequencing.  Dr. Darren Bridgewater Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada Contribution: Urinary protein analysis in Smarcal1-deficient mice.  Dr. Thomas Lücke Department of Neuropediatrics, University Children's Hospital, Ruhr University Bochum, Bochum, Germany Contribution: Clinical analysis of SIOD patients.  Dr. Arend Bokenkamp Department of Pediatrics, VU University Medical Center, Amsterdam, The Netherlands Contribution: Clinical analysis of SIOD patients.  Dr. Chad Shaw Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA Contribution: Data analysis of mouse RNA sequencing and of human cell and Drosophila microarray gene expression data.  vi Marie Morimoto, Dr. Leah I. Elizondo, Dr. Joanna Lubieniecka Department of Medical Genetics, Child and Family Research Institute, University of British Columbia, Vancouver, British Columbia, Canada Contribution: Marie Morimoto, Drosophila genetic studies and experiments; Leah Elizondo, analysis of ATPase activity of SMARCAL1; Joanna Lubieniecka, heat stress experiment in Drosophila.  Portions of chapter 1 have been published in: - Baradaran-Heravi A, Elizondo LI, Boerkoel CF: Gene clusters, molecular evolution and disease: a speculation. In Advances in Genome Science: Changing Views on Living Organisms (Volume 1). Bentham Science (ebook) 2013: 101-145. and - Baradaran-Heravi A, Morimoto M, Lücke T, Boerkoel CF: Schimke Immunoosseous Dysplasia (March 2011) in: GeneReviews at GeneTests: Medical Genetics Information Resource [database online]. Copyright, University of Washington, Seattle, 1997-2013. Available at http://www.genetests.org. I updated these two reviews under the guidance of Dr. Cornelius Boerkoel.  Portions of chapter 2 have been published in: - Baradaran-Heravi A, Raams A, Lubieniecka J, Cho KS, Dehaai KA, Basiratnia M, Mari PO, Xue Y, Rauth M, Olney AH, et al: SMARCAL1 deficiency predisposes to non-Hodgkin lymphoma and hypersensitivity to genotoxic agents in vivo. American Journal of Medical Genetics Part A 2012, 158A:2204-2213.  vii I conducted the majority of the experiments and wrote most of the manuscript. These experiments included the comet assay, analysis of immunoglobulin switch junctions, treatment of mice with genotoxic agents, urinary protein analysis in mice, cell culture, and histopathology. Analysis of sister chromatid exchange was performed by Mary Shago (Hospital for Sick Children). Culturing of normal human fibroblasts and Cockayne syndrome B fibroblasts as well as cell survival assays following UV or illudin S were performed by Anja Raams (Jaspers Lab, Erasmus Medical Center, the Netherlands).  Portions of chapter 3 and 4 have been published in: - Baradaran-Heravi A, Cho KS, Tolhuis B, Sanyal M, Morozova O, Morimoto M, Elizondo LI, Bridgewater D, Lubieniecka J, Beirnes K, et al: Penetrance of biallelic SMARCAL1 mutations is associated with environmental and genetic disturbances of gene expression. Human Molecular Genetics 2012, 21:2572-2587. I conducted most of the experiments on human cells and mice and wrote most of the manuscript. These experiments include ATPase activity of Smarcal1, cell culture, heat stress of human fibroblasts and mice, α-amanitin treatment of human fibroblasts and mice, phenotypic and histopathological analyses of mice, RpII knock-down analysis, and gene ontology analysis. Most of the Drosophila genetic studies and experiments were performed by Kyoung Sang Cho and Marie Morimoto. Heat stress experiments in Drosophila were performed by Kyoung Sang Cho, Joanna Lubieniecka, and Marie Morimoto. DNA adenine methyltransferase analysis of Marcal1 binding was performed by Bas Tolhuis (the Netherlands Cancer Institute). Analyses of ATPase activity of Marcal1 and SMARCAL1 were performed by Kyoung Sang Cho and Leah Elizondo. High-dimensional flow cytometry  viii on mouse lymphocytes was performed by Mrinmoy Sanyal (Lewis Lab, Stanford University). Urinary protein analysis in mice was performed by Darren Bridgewater (McMaster University). Data analysis of RNA sequencing, microarray gene expression of human cells and Drosophila was performed by Chad Shaw (Baylor College of Medicine).  Patients referred to this study gave informed consent approved by the Institutional Review Board of Baylor College of Medicine (H-9669, Houston, TX, USA), the Hospital for Sick Children (REB-0019970093, Toronto, ON, Canada), or the University of British Columbia (H06-70283, Vancouver, BC, Canada). Also, the institutional review board of Stanford University School of Medicine (IRB-419, Stanford, CA, USA) approved the use of human tissue. The clinical data for patients were obtained from questionnaires completed by the attending physician as well as from the medical records.  Mice used in this study were housed, bred, and euthanized in accordance with accepted ethical guidelines. These procedures were approved by the Institutional Review Board of Baylor College of Medicine (Houston, TX, USA, IRB protocol: AN-2983) or the University of British Columbia (Vancouver, BC, Canada, Animal Care Certificate: A10- 0296).      ix Table of Contents  Abstract .................................................................................................................................... ii	
   Preface ..................................................................................................................................... iv	
   Table of Contents ................................................................................................................... ix	
   List of Tables ........................................................................................................................ xvi	
   List of Figures ...................................................................................................................... xvii	
   List of Abbreviations ............................................................................................................ xx	
   Acknowledgements ........................................................................................................... xxvii	
   Dedication ......................................................................................................................... xxviii	
   1.	
   Introduction ...................................................................................................................... 1	
   1.1	
   Higher order regulation of gene expression ................................................................. 2	
   1.1.1	
   Three-dimensional nuclear organization and regulation of gene expression ........ 3	
   1.1.1.1	
   Chromosomal spatial orientation ................................................................... 4	
   1.1.1.2	
   Nuclear envelope ........................................................................................... 5	
   1.1.1.3	
   Boundary elements ......................................................................................... 6	
   1.1.2	
   DNA methylation .................................................................................................. 8	
   1.1.3	
   Histone modifications ........................................................................................... 9	
   1.1.4	
   Chromatin remodeling ........................................................................................ 11	
   1.2	
   SMARCAL1 .............................................................................................................. 12	
   1.3	
   Schimke immuno-osseous dysplasia (SIOD) ............................................................. 14	
   1.3.1	
   Characteristics and clinical features .................................................................... 15	
   1.3.2	
   Clinical course and outcome ............................................................................... 21	
    x 1.3.3	
   Management and treatment of manifestations .................................................... 22	
   1.4	
   Thesis objectives and significance ............................................................................. 25	
   2.	
   Contribution of DNA repair defects to pathophysiology of SIOD ............................ 26	
   2.1	
   Introduction ................................................................................................................ 26	
   2.2	
   Materials and methods ............................................................................................... 27	
   2.2.1	
   Comet assay ........................................................................................................ 27	
   2.2.2	
   Analysis of sister chromatid exchange ............................................................... 27	
   2.2.3	
   Immunoglobulin switch junction amplification and sequencing ........................ 28	
   2.2.4	
   Smarcal1del/del mice and treatment with CPT-11, etoposide, and HU ................. 29	
   2.2.5	
   Urinary creatinine and albumin assays ............................................................... 30	
   2.2.6	
   Histopathology and TUNEL assay ..................................................................... 30	
   2.2.7	
   Cell culture .......................................................................................................... 31	
   2.2.8	
   Cell survival assays ............................................................................................. 31	
   2.3	
   Results ........................................................................................................................ 32	
   2.3.1	
   SIOD patients have features observed in other disorders of DNA repair ........... 32	
   2.3.2	
   SIOD patient dermal fibroblasts and Smarcal1-deficient mouse embryonic fibroblasts do not have increased DNA breaks detectable by comet assay .................... 34	
   2.3.3	
   SMARCAL1 and DNA nucleotide excision repair .............................................. 35	
   2.3.4	
   SMARCAL1 and DNA repair by homologous recombination ............................ 36	
   2.3.5	
   SMARCAL1 and DNA repair by non-homologous end joining .......................... 36	
   2.3.6	
   Smarcal1-deficient mice are hypersensitive to genotoxic agents ....................... 40	
   2.3.7	
   Irinotecan, etoposide, and hydroxyurea exposure partially recapitulate the bone phenotype of SIOD in Smarcal1-deficient mice ............................................................. 44	
    xi 2.3.8	
   Irinotecan, etoposide, and hydroxyurea exposure do not recapitulate the renal phenotype of SIOD in Smarcal1-deficient mice ............................................................. 46	
   2.4	
   Discussion .................................................................................................................. 47	
   3.	
   Contribution of gene expression alterations to the penetrance of SMARCAL1 mutations ............................................................................................................................... 51	
   3.1	
   Introduction ................................................................................................................ 51	
   3.2	
   Materials and methods ............................................................................................... 53	
   3.2.1	
   Drosophila genetic studies .................................................................................. 53	
   3.2.2	
   ATPase activity assay ......................................................................................... 54	
   3.2.3	
   Immunofluorescence ........................................................................................... 55	
   3.2.4	
   High-dimensional (11-Color) flow cytometry .................................................... 55	
   3.2.5	
   Cell cultures ........................................................................................................ 56	
   3.2.6	
   Heat stress ........................................................................................................... 57	
   3.2.7	
   α-amanitin treatment ........................................................................................... 57	
   3.2.8	
   RNA extraction ................................................................................................... 58	
   3.2.9	
   RT-PCR .............................................................................................................. 58	
   3.2.10	
   Urinary protein and creatinine measurement .................................................... 61	
   3.2.11	
   Histopathology .................................................................................................. 62	
   3.2.12	
   Apoptotic analysis ............................................................................................. 62	
   3.2.13	
   RpII knock-down .............................................................................................. 63	
   3.2.14	
   RNA sequencing and data analysis ................................................................... 64	
   3.2.15	
   Microarray gene expression analysis in human cells ........................................ 65	
   3.2.16	
   Microarray gene expression analysis in flies .................................................... 66	
    xii 3.2.17	
   Gene Ontology (GO) analysis ........................................................................... 67	
   3.2.18	
   DNA adenine methyltransferase analysis of Marcal1 binding ......................... 67	
   3.3	
   Results ........................................................................................................................ 69	
   3.3.1	
   Deficiency of Marcal1 is insufficient for disease manifestations in Drosophila .................................................................................................................. 69	
   3.3.2	
   Deficiency of murine Smarcal1 is insufficient to cause disease ......................... 73	
   3.3.3	
   SMARCAL1 and Marcal1 bind open chromatin ................................................ 75	
   3.3.4	
   Marcal1 and SMARCAL1 genetically interact with transcriptional components ..................................................................................................................... 79	
   3.3.5	
   SMARCAL1 deficiency alters gene expression in SIOD skin fibroblasts ........... 80	
   3.3.6	
   Marcal1 contributes to heat tolerance and modulates heat stress gene expression in Drosophila .................................................................................................................. 83	
   3.3.7	
   Smarcal1 contributes to heat tolerance and modulates heat stress gene expression in mice. ............................................................................................................................ 88 3.3.8	
   RpII inhibition decreases proliferation in SMARCAL1-deficient human fibroblasts ........................................................................................................................ 90	
   3.3.9	
   RpII mutations decrease the viability and increase gene expression changes in Marcal1del/del flies ............................................................................................................ 94	
   3.3.10	
   RpII inhibition decreases proliferation in Smarcal1-deficient mouse embryonic fibroblasts ........................................................................................................................ 95	
   3.3.11	
   RpII inhibition modifies the penetrance of Smarcal1 deficiency and partially recapitulates SIOD in Smarcal1-deficient mice ............................................................. 96	
   3.4	
   Discussion ................................................................................................................ 100	
    xiii 4.	
   Discussion and concluding remarks ........................................................................... 102	
   4.1	
   Improved molecular understanding of SIOD ........................................................... 102	
   4.1.1	
   SMARCAL1 is a multifunctional protein facilitating the cross-talk among DNA repair, replication, recombination and transcription ..................................................... 103	
   4.1.2	
   SIOD does not arise from defective DNA replication, repair or recombination ............................................................................................................... 105	
   4.1.3	
   SIOD is associated with transcriptional alterations beyond a threshold ........... 106	
   4.1.4	
   Possible mechanisms by which SMARCAL1 modulates gene expression ...... 111	
   4.2	
   Overall significance ................................................................................................. 113	
   4.3	
   Future directions ...................................................................................................... 113	
   Bibliography ........................................................................................................................ 115	
   Appendices ........................................................................................................................... 133	
   Appendix 1- Figure showing microhomology usage at Sµ-Sα1 junctions in the immunoglobulins of SIOD patients. ................................................................................. 134	
   Appendix 2- Figure showing microhomology usage at Sµ-Sγ1 junctions in the immunoglobulins of SIOD patients. ................................................................................. 136	
   Appendix 3- Table showing suppressors and enhancers of ectopic wing veins induced by the overexpression of Drosophila Marcal1 and human SMARCAL1. ............................. 138	
   Appendix 4- Table showing enrichment in biological process GO-terms (all levels) of upregulated expressed genes (q<0.05 and fold change >2) in SIOD compared to control human skin fibroblasts. ..................................................................................................... 141	
    xiv Appendix 5- Table showing enrichment in biological process GO-terms (all levels) of downregulated expressed genes (q<0.05 and fold change >2) in SIOD compared to control human skin fibroblasts. ..................................................................................................... 146	
   Appendix 6- Table showing qRT-PCR measurement of mRNA levels of stress response genes in skin fibroblasts of three SIOD patients (SD31, SD120, and SD123) compared to control fibroblasts after 1 hour incubation at 43°C followed by 1 hour of recovery at 37°C. ........................................................................................................................................... 152	
   Appendix 7- Table showing enrichment in biological process GO-terms (all levels) of upregulated expressed genes (fold change >2) between Smarcal1del/del and Smarcal1+/+ mouse livers at 20°C. ........................................................................................................ 154	
   Appendix 8- Table showing enrichment in biological process GO-terms (all levels) of downregulated expressed genes (fold change >2) between Smarcal1del/del and Smarcal1+/+ mouse livers at 20°C. ........................................................................................................ 158	
   Appendix 9- Table showing enrichment in biological process GO-terms (all levels) of upregulated expressed genes (fold change >2) between Smarcal1del/del and Smarcal1+/+ mouse livers at 39.5°C. ..................................................................................................... 162	
   Appendix 10- Table showing enrichment in biological process GO-terms (all levels) of downregulated expressed genes (fold change >2) between Smarcal1del/del and Smarcal1+/+ mouse livers at 39.5°C. ..................................................................................................... 163	
   Appendix 11- Table showing enrichment in biological process GO-terms (all levels) of differentially expressed genes (q<0.05) between RpII2154/FM7;Marcal1del/del and yw fly ovaries. .............................................................................................................................. 169	
    xv Appendix 12- Table showing enrichment in biological process GO-terms (all levels) of differentially expressed genes (q<0.05) between RpII2154/FM7 and yw fly ovaries. ...... 175	
     xvi List of Tables Table 1-1- Frequency of disease features in individuals with SIOD with biallelic SMARCAL1 mutations. ........................................................................................................................ 19	
   Table 2-1- Oligonucleotides used for amplification and sequencing of immunoglobulin switch regions. ................................................................................................................ 29	
   Table 2-2- Comparison of the prominent clinical features of SIOD to those of some DNA repair disorders. ............................................................................................................... 33	
   Table 2-3- Response of Smarcal1+/+ and Smarcal1del/del mice to exposure to CPT-11, etoposide and hydroxyurea. ............................................................................................ 40	
   Table 3-1- Antibodies used for flowcytometry. ..................................................................... 56	
   Table 3-2- Oligonucleotide primers. ...................................................................................... 60	
   Table 3-3- Enrichment in biological process GO-terms (all levels) of differentially expressed genes (q<0.05) between Marcal1del/del and yw ovaries at 20oC. ..................................... 87	
   Table 3-4- Enrichment in biological process GO-terms (all levels) of differentially expressed genes (q<0.05) between Marcal1del/del and yw ovaries at 25oC. ...................................... 87	
   Table 3-5- qRT-PCR measurement of mRNA levels of stress genes in the brain and liver of mice following 3 hours of heat shock at 39.5ºC. ............................................................ 89	
     xvii List of Figures Figure 1-1- Processes involved in higher order regulation of gene expression. ...................... 3	
   Figure 1-2- Distribution of CpG islands and their contribution to gene expression. ............... 9	
   Figure 1-3- Schematic representation of covalent histone modifications. ............................. 10	
   Figure 1-4- Diagram of the structure of the SMARCAL1 gene and SMARCAL1 protein. .... 14	
   Figure 1-5- Typical bony features of SIOD. .......................................................................... 20	
   Figure 2-1- Analyses of DNA breaks in cultured SMARCAL1-deficient skin fibroblasts (N859) and Smarcal1-deficient mouse embryonic fibroblasts (MEFs) by alkaline comet assay. ............................................................................................................................... 34	
   Figure 2-2- Analysis of SMARCAL1 participation in nucleotide excision repair. ............... 35	
   Figure 2-3- SMARCAL1 participation in homologous recombination. ................................ 36	
   Figure 2-4- Examples of microhomology usage at Sµ-Sα1 and Sµ-Sγ1 junctions in the immunoglobulins of SIOD patients. ............................................................................... 39	
   Figure 2-5- Hypersensitivity of Smarcal1del/del mice to CPT-11. ........................................... 42	
   Figure 2-6- Hypersensitivity of Smarcal1del/del mice to etoposide and HU. .......................... 44	
   Figure 2-7- CPT-11, etoposide and HU exposure partially recapitulate SIOD phenotypes in Smarcal1del/del mice. ........................................................................................................ 46	
   Figure 3-1- DNA specificity and chromatin binding of Marcal1, SMARCAL1 and Smarcal1. ......................................................................................................................................... 71	
   Figure 3-2- Temporospatial expression of Marcal1 in Drosophila cells and tissues. ............ 72	
   Figure 3-3- Generation of a fly model for the study of Marcal1 function. ............................ 72	
   Figure 3-4- Phenotypic comparison of Smarcal1+/+ and Smarcal1del/del mice. ...................... 74	
    xviii Figure 3-5- Photographs showing immunofluorescent co-localization of Marcal1 (green) with panacetyl-histone H4 (AcH4; red) on Drosophila polytene chromosomes. ........... 76	
   Figure 3-6- Expression of Marcal1 tagged with DNA adenine methyltransferase (Dam) on its amino terminus methylates adenine in genomic regions of Kc167 cells with hallmarks of active transcription. ......................................................................................................... 77	
   Figure 3-7- Photographs of Drosophila wings showing the extra wing veins induced by expression of Marcal1 and SMARCAL1. ........................................................................ 78	
   Figure 3-8- SMARCAL1 deficiency alters gene expression. .................................................. 81	
   Figure 3-9- SMARCAL1 deficiency causes abnormal expression of heat stress genes. ......... 82	
   Figure 3-10- Marcal1 and Smarcal1 deficiency increase susceptibility to heat stress. ......... 85	
   Figure 3-11- Effect of Marcal1 deficiency on expression of heat stress genes and proteins in 1-3 day old Drosophila females. ..................................................................................... 86	
   Figure 3-12- Inhibition of RpII function causes penetrance of SMARCAL1, Marcal1 and Smarcal1 deficiency. ....................................................................................................... 92	
   Figure 3-13- Effect of knocking down RpII components in SMARCAL1 and  Smarcal1 deficient fibroblasts. ........................................................................................................ 93	
   Figure 3-14- Treatment of Smarcal1del/del mice with α-amanitin does not recapitulate T-cell deficiency in SIOD. ........................................................................................................ 98	
   Figure 3-15- Treatment of Smarcal1del/del mice with α-amanitin partially recapitulates SIOD. ......................................................................................................................................... 99	
   Figure 4-1- Schematic model showing cross-talk between basic DNA metabolic functions and maintenance of genomic structure. ........................................................................ 105	
    xix Figure 4-2- Polygenic model for SIOD depicting the contribution of many dysregulated genes each with small effect size to the penetrance of SIOD. ...................................... 109	
   Figure 4-3- Oligogenic model for SIOD depicting the contribution of a few dysregulated genes to the penetrance of SIOD. ................................................................................. 110	
      xx List of Abbreviations AcH4   Panacetyl-histone H4 ANOVA Analysis of variance AT   Ataxia telangiectasia ATM    Ataxia-telangiectasia mutated ATR    ATM and Rad3-related ATRX   α-thalassemia X-linked mental retardation; BER   Base excision repair BMT    Bone marrow transplantation BrdU   Bromo-deoxyuridine BRG1    Brahma-related gene 1 BRM    Brahma BS    Bloom syndrome C. elegans   Caenorhabditis elegans CDK9   Cyclin-dependent kinase 9 CFTR Cystic fibrosis transmembrane conductance regulator CHD7   Chromodomain helicase DNA binding protein 7 CMV   Cytomegalovirus CNS    Central nervous system CPT-11  Camptothecin-11 CS    Cockayne syndrome CsA    Cyclosporine A CTCF    CCCTC-binding factor  xxi CyO Curly of Oster DamID  DNA adenine methyltransferase identification DAVID Database for annotation, visualization and integrated discovery DFNB26 Deafness, autosomal recessive 26 DFNM1  Deafness (recessive, nonsyndromic) modifier 1 DMEM  Dulbecco's modified eagle media DNA   Deoxyribonucleic acid DNA-PK   DNA-dependent protein kinase ds   Double-stranded DSB   Double-strand break dTopors   Drosophila Topoisomerase I-interacting RS proteins EBV   Epstein-Barr virus EdU 5-ethynyl-2´-deoxyuridine ELISA   Enzyme-linked immunosorbent assay ELN   Elastin ERCC2 Excision repair cross-complementing rodent repair deficiency, complementation group 2 ERCC3 Excision repair cross-complementing rodent repair deficiency, complementation group 3 ERCC6  Excision repair cross-complementing rodent repair deficiency, complementation group 6 ESRD    End stage renal disease F    Female  xxii FA   Fanconi anemia FACS Fluorescence activated cell sorting FBP FUSE binding protein FBS   Fetal bovine serum FCS Fetal calf serum FDR False discovery rate FIR FBP-interacting repressor FSGS    Focal segmental glomerulosclerosis FUSE Far upstream element GC   Guanosine-cytosine G-CSF   Granulocyte-colony stimulating factor GDP    Guanosine diphosphate GEO Gene expression omnibus GFP Green fluorescent protein GI   Gastrointestinal GMP   Guanosine monophosphate GO Gene ontology GTF2H5  General transcription factor IIH, polypeptide 5 GTP   Guanosine triphosphate GVHD   Graft versus host disease H3K27me3 Histone 3 lysine 27 tri-methylation H3K4me3  Histone 3 lysine 4 tri-methylation HARP   HepA related protein  xxiii HCT-CI  Hematopoietic cell transplant-comorbidity index HEPES  4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HFE   Hemochromatosis HLA   Human leukocyte antigen HR   Homologous recombination HU   Hydroxyurea HZ Hypertrophic zone ICL   Interstrand crosslink IL7RA   Interleukin 7 receptor ip   Intra-peritoneal IUGR    Intrauterine growth retardation IVIG   Intravenous immunoglobulin LIMMA Linear models for microarray data M   Male MEFs   Mouse embryonic fibroblasts MEM   Minimum essential media mg HA/ccm   Milligrams hydroxyapatite per cubic centimeter MMF    Mycophenolate mofetil MMR Mismatch repair MRA   Magnetic resonance angiography mRNA   Messenger ribonucleic acid NBS   Nijmegen breakage syndrome NER   Nucleotide excision repair  xxiv NHEJ   Non-homologous end joining NHL   Non-Hodgkin lymphoma NLS   Nuclear localization signal NR   Not reported PAH   Phenylalanine hydroxylase PBS   Phosphate buffered saline Pc Polycomb PCR   polymerase chain reaction PFA Paraformaldehyde PZ Proliferative zone qRT-PCR  Quantitative reverse-transcriptase PCR R-CHOP Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone RMA Robust multi-array average RMRP   RNase mitochondrial RNA processing RNA   Ribonucleic acid RPA    Replication protein A RpII RNA polymerase II RPMI medium Roswell Park Memorial Institute medium RS   Rapadilino syndrome RTS   Rothmund-Thomson syndrome SCE   Sister chromatid exchange SED    Spondyloepiphyseal dysplasia  xxv SF2    Super family 2 shRNA Short hairpin RNA SIOD    Schimke immuno-osseous dysplasia siRNA Small interfering RNA SMARCA4 SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4 SMARCAL1  SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a-like 1 SMARCB1  SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily b, member 1 SMARCC1 SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily c, member 1 SMARCC2 SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily c, member 2 ss   Single-stranded SWI2/SNF2   SWItching defective 2 or sucrose non-fermenting 2 T3   Triiodothyronine T4   Thyroxine TC-NER  Transcription-coupled nucleotide excision repair TCR T cell receptor TIA    Transient ischemic attack TNA   Transient neurological attack TSH    Thyroid stimulating hormone  xxvi TUNEL Terminal deoxynucleotidyl transferase dUTP nick end labeling UAS Upstream activating sequence Ubx Ultrabithorax UV   Ultraviolet light WS   Werner syndrome XP   Xeroderma pigmentosum   xxvii Acknowledgements My sincere thanks to my supervisor, Dr. Cornelius Boerkoel, from whom I learned thoughtful thinking in science. I am so grateful for his guidance, encouragement, and friendship throughout my graduate training over the past 6 years. He gave me the opportunity to get involved in a myriad of experiences focused on improving patient care as the ultimate goal. His generosity in teaching, his commitment to rare disorders and translational research, and his curiosity for learning have been a great source of inspiration for me. Neal! Thank you for all your help! This dissertation is the culmination of a collaborative endeavor of many talented and dedicated individuals from around the world and I would like to express my gratitude to all of them. I am thankful to my supervisory committee members, Drs. Jan Friedman, Lorne Clarke, and Millan Patel for their valuable advice and support. In particular, I am very grateful to Millan Patel for many fascinating and fruitful discussions over the years. I would also like to express my gratitude to all current and previous Boerkoel Lab members particularly Kunho Choi, Marie Morimoto, Andrew Fam, Miraj Kobad Chowdhury, and Chris Dias for their friendship, help, and discussions. I am also very grateful to Cheryl Bishop for her valuable advice and help. I thank my parents and sisters for their encouragement and support. Lastly, I would like to express my gratitude to my loving wife Sara for her unending support, motivation, patience and devotion. I am greatly indebted to her and words cannot describe my gratitude.  xxviii Dedication  To Mitchell, Wouter, Ashley, Liana, Emily, Ali, Tasin, Robert, Maria and all other SIOD patients.      1 1. Introduction One objective of medical genetics is understanding the correlation between genotype and phenotype. Traditionally, genetics has done this by segregation of abnormal phenotypes to uncover their genetic basis. While this approach has been highly successful, the reverse process of predicting phenotype from genotype has been rather unsatisfying. This challenge is quite intriguing for Mendelian disorders since the phenotype is often fully explained by mutations at a single locus 1-3. As illustrated by phenylketonuria and the ciliopathies, however, prediction of disease severity is frequently the exception rather than the norm 3,4. The spectrum of clinical differences in Mendelian disorders ranges from no expression of disease in genetically predisposed individuals (a property known as incomplete penetrance) to variations in the severity of clinical symptoms (variable expressivity). Variations in expressivity as well as incomplete penetrance have been attributed to genetic and environmental factors as well as to developmental stochastics 5-8. Although incomplete penetrance has been described in a few autosomal recessive conditions 5,9-12, the underlying molecular mechanism for this in most autosomal recessive disorders has yet to be discovered. One hypothesis is that the variable expressivity and incomplete penetrance observed in Mendelian disorders arise from the impingement of environmental and genetic factors on a biological process sensitive to quantitative modulation 13. Gene expression (transcription) is a quantitative trait, and recently it has been shown that variations in mRNA expression contribute to the incomplete penetrance of mutant alleles in model organisms 8. Whether dysregulation of gene expression would also lead to variability in trait penetrance in human  2 diseases is not well understood. Testing this hypothesis requires not only a suitable disease model but also a good understanding of mechanisms modulating gene expression. Processes involved in the regulation of gene expression include basal and gene- specific transcription factors, as well as genetic and epigenetic mechanisms. Examples of epigenetic regulators of gene expression include three dimensional nuclear organization, DNA methylation, histone modifications, and chromatin remodeling 14-19. Understanding these mechanisms and the diseases associated with them gives us insight into how mutations in a single gene lead to pleiotropy. To this end, therefore, I review higher order regulatory mechanisms of gene expression in this chapter and then focus on the pleiotropic disorder Schimke immuno-osseous dysplasia (SIOD), which is associated with mutations of the chromatin remodeling ATPase SMARCAL1 (SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a-like 1). Finally, I lay out the objectives of my dissertation and their significance.  1.1 Higher order regulation of gene expression Processes involved in the regulation of gene expression beyond the transcription factors include three-dimensional nuclear organization, DNA methylation, covalent histone modifications, and chromatin remodeling 15,20-25 (Figure 1-1). Through alteration in chromatin structure these processes modify the accessibility of promoter regions to transcription factors and regulate gene expression. I review in this section the mechanisms contributing to higher order regulation of gene expression in order to place the function of SMARCAL1 in context.  3  Figure 1-1- Processes involved in higher order regulation of gene expression. Adapted and modified by permission from Elsevier: Biochimica et Biophysica Acta (BBA), 1819, 401-410, copyright 2012, and Macmillan Publishers Ltd: Nature Reviews Cancer, 4, 133-142, copyright 2004.   1.1.1 Three-dimensional nuclear organization and regulation of gene expression Gene expression at the highest level is regulated by three-dimensional nuclear organization. Consequently gene expression is associated with location of chromosome territories within the nucleus and with interaction of chromatin with the nuclear envelope as well as with boundary elements. In this section I will further discuss these and reported contributions to the regulation of gene expression.  4 1.1.1.1 Chromosomal spatial orientation Rabl first proposed a territorial organization of interphase chromosomes in 1885 26 and in 1909 Boveri introduced the term “chromosome territory” 27. Within the interphase nucleus, the DNA from the various chromosomes is not randomly intertwined, rather chromosomes occupy non-overlapping territories of irregular shape 28-32. Of note, chromatin from separate territories minimally intermingles 30,33-35.  Electron microscopy and polymerization of probes such as vimentin clearly define an interchromosomal domain or space between chromosomal territories 36-40. Additionally, supporting separation of the chromosomal territories, higher resolution analysis of nuclear genomic organization using chromosome conformation capture techniques has identified a predominance of intra- chromosomal physical interactions compared to inter-chromosomal interactions 41,42. A model for the distribution of chromosome territories suggests that within the nucleus, gene-dense chromosomes are located more internally than gene-poor ones 25,43,44 and that this localization also correlates with transcriptional activity, replication timing, and GC content 45-48.  Although not consistently observed 49, this arrangement has been frequently reported for mammalian and chicken cells 30,50-54 as well as for homologous chromosomes in higher primates 55 and syntenic chromosomes in humans and mice 56-58. Furthermore, genes are generally distributed along the periphery of chromosome territories and invaginating channels where they can loop out into interchromosomal domains upon the induction of expression 56,57,59-67. Thus, transcription may predominate along the periphery of a chromosome territory to allow access for transcription factors and to facilitate processing and transport of mRNA 30,39,40,60.   5 1.1.1.2 Nuclear envelope One mechanism by which chromosome territories could be fixed within the nucleus is by specific attachment of chromatin to the nuclear matrix. The best-characterized component of the nuclear matrix is the nuclear envelope that consists of the outer and inner nuclear membranes, nuclear pore complexes and the nuclear lamina. The lamins and associated proteins are the major components of the nuclear lamina that underlie the inner membrane of the nuclear envelope. At lower concentrations, lamins are also distributed throughout the nucleoplasm. Lamins and their associated proteins maintain the integrity of the nuclear envelope 68, provide a structural attachment point for chromatin 69, help define DNA replication sites 68,70-72, localize nuclear bodies 73, regulate epigenetic modifications of chromatin 74, and facilitate DNA repair and transcription 75-81. Suggesting that the nuclear matrix and in particular nuclear lamina are key to intranuclear chromosomal positions and gene expression, profiling of gene expression changes in cells with a dominant LMNA mutation showed alterations in both of these properties 81. The nuclear matrix also modulates gene expression directly and not only by maintenance of chromatin territories. This is substantiated by interaction of lamin-associated proteins with the transcriptional apparatus. For example, emerin, an inner nuclear envelope protein, not only binds lamins A and B but also interacts with the transcriptional factors GCL, Btf, Lmo7, BAF, and YT521-B 75,78,82-85. Interactions between the nuclear lamina and chromatin have been mapped by DNA adenine methyltransferase identification (DamID) in Drosophila, mouse and human cell lines 86-88. The lamina-associated domains are generally gene-poor and transcriptionally inactive,  6 and when those domains dissociate from the lamina, the genes within them often become transcriptionally active or poised for transcription 86-88.  1.1.1.3 Boundary elements  Observations from yeast, Drosophila, chicken, mouse and human studies show that insulators compartmentalize the genome into separate regions of gene expression through interactions with DNA, the nuclear matrix, and other protein components 89-91. Insulator complexes are defined by two activities: 1) they can inhibit the spread of heterochromatin, and/or 2) they can block a transcriptional enhancer from activating a promoter when located between the two 91. One of the best-studied insulators is the gypsy element found in the Drosophila gypsy retrotransposon. Proteins that directly or indirectly bind the gypsy insulator include suppressor of hairy wing, modifier of mdg4, Centrosomal protein 190 kD, Trithorax-like, and dTopors (Drosophila Topoisomerase I-interacting RS proteins); the last binds the gypsy insulator to the nuclear lamina 92-96. Modulation of the interaction between these proteins and the gypsy insulator sequences defines higher order chromatin structures, such as chromatin loops, and the functionality of each gypsy sequence 92,93,95. In vertebrates, our understanding of insulator function primarily comes from studies of imprinted genomic domains such as 11p15.5 and the beta-globin gene cluster 97,98. CCCTC-binding factor (CTCF), which binds the insulator sequences in these regions as well as many other sites, is the best-characterized vertebrate insulator-binding protein. The 5ʹ′ chicken HS4 insulator sequence, which is located upstream of the beta-globin cluster, has two separable functions: 1) it inhibits the adjacent heterochromatin from spreading into the  7 beta-globin locus, and 2) it blocks enhancer-promoter interactions 99. In addition to binding sequences such as HS4, CTCF binds nucleophosmin, a nucleolar protein and a component of the nuclear matrix 100-102. Binding of CTCF to the HS4 and other insulators is proposed to facilitate the formation of chromatin loops and to tether these loops to subnuclear structures, such as the nucleolus and the nuclear matrix 102. In cultured human lung fibroblasts the lamin B1-associated domains are flanked by CTCF 87, and association of CTCF with lamin A is required for localization of telomeres to the nuclear periphery 103. Lastly, CTCF is not only involved in chromosome-matrix interactions but also in intra- and inter-chromosomal interactions 104-107. Therefore, by maintaining three-dimensional nuclear organization, insulators modulate various basic cellular functions including transcription (for a review see 108,109). Another chromatin structural motif that is involved in higher order regulation of gene expression is the cohesin complex. Cohesin complexes were originally identified for their role in maintaining sister chromatid cohesion prior to their separation during anaphase 110. The Drosophila protein Nipped-B loads the cohesin complex onto the chromosomes and is also required for facilitating enhancer-promoter communication 111-113 and regulating gene expression 111,114. These findings as well as the association of cohesin complexes with CTCF target sequences in mammalian cells suggest that cohesin complexes are structural elements defining and regulating gene expression. Indeed, impaired or errant localization of cohesin complexes affect RNA transcription and putatively do so across large genomic regions 115-124.   8 1.1.2 DNA methylation DNA methylation is one of the major epigenetic modifications present in organisms from bacteria to human. In eukaryotes DNA methylation occurs at cytosine residues through transferring of the methyl groups by DNA methyltransferases 125; however, this covalent modification is absent or rare in some of the commonly used model organisms including yeast (Saccharomyces cerevisiae, Saccharomyces pombe), fly (Drosophila melanogaster) and nematode (Caenorhabditis elegans). Based on the position along the transcriptional unit DNA methylation modifies gene expression (Figure 1-2) 24. In mammals, DNA methylation of CpG dinucleotide sequences at the promoters is generally associated with gene expression suppression while DNA methylation in the gene body might even stimulate transcription elongation and affect splicing 24. Whether DNA methylation at CpG islands of the promoters precedes transcriptional silencing or functions as a reinforcing lock for previously silenced genes is yet to be fully delineated 24,126.   9  Figure 1-2- Distribution of CpG islands and their contribution to gene expression. More than half of human gene promoters have CpG islands. Methylation of these sequences are associated with gene expression silencing. Gene bodies and repetitive elements are mostly CpG-depleted and are predominantly methylated. Enhancers are also CpG-poor and have variable CpG methylation. At insulator regions DNA methylation can block the binding of CTCF and alter gene expression. LMR, low-methylated region; NDR, nucleosome- depleted region; TET, ten-eleven translocation; TSS, transcription start site. Adapted by permission from Macmillan Publishers Ltd: Nature Reviews Genetics, 13, 484-492, copyright 2012.   1.1.3 Histone modifications Another major epigenetic modification that has a key role in regulating gene expression is the covalent modification of histones (Figure 1-3). These modifications modulate transcription either by directly changing chromatin structure or by altering the binding of other proteins 22. Examples of modifications that directly change DNA structure  10 include histone acetylation and phosphorylation. These modifications reduce the positive charge of histones and thereby their affinity for DNA and in turn allow the DNA to assume a more open conformation that facilitates transcription factor binding. An example of a modification that alters the binding of other proteins to DNA is tri-methylation of H3K4 (H3K4me3), a histone mark associated with active transcription. This modification leads to the recruitment of chromatin modifying enzymes such as CHD1 to reposition nucleosomes 22,127,128.  Figure 1-3- Schematic representation of covalent histone modifications. The major modifications include acetylation, ubiquitylation, methylation, and phosphorylation. Positions of the modified amino acids are labeled. Adapted by permission from Macmillan Publishers Ltd: Nature, 421(6921):448-53, copyright 2003.    11 1.1.4 Chromatin remodeling In addition to three-dimensional nuclear organization and covalent modifications of DNA and histones, gene expression is modulated by ATP-dependent chromatin remodeling complexes. These proteins bind DNA and expend energy from ATP hydrolysis to reorganize nucleosomes or DNA structures 129-131. SWI2/SNF2 (SWItching defective 2 or Sucrose Non- Fermenting 2) or SNF2 family of ATP-dependent chromatin remodeling proteins belongs to the SF2 (Super Family 2) class of helicases 23. Subunits of this family share a SNF2 domain containing seven motifs (I, Ia, II, III, IV, V, VI) that form the nucleotide binding site and participate in nucleotide triphosphate hydrolysis and coupling of DNA binding with nucleotide triphosphate hydrolysis 132. However, these enzymes do not have helicase activity but remodel chromatin by rearranging nucleosomes 133,134, translocating along DNA 135,136, disrupting protein-DNA interactions 137, generating superhelical torsions 133,134, or annealing helicase activity 130,131. Through chromatin remodeling SNF2 proteins contribute to a variety of cellular processes including DNA replication (e.g. SWI/SNF, INO80, ISWI, SMARCAL1 subfamilies), DNA repair and recombination (e.g. SWI/SNF, INO80, SWR1, and SMARCAL1 subfamilies), gene expression (e.g. SWI/SNF, ERCC6, and CHD7 subfamilies), and chromosome segregation (e.g. ATRX subfamily) 138-144. Focusing on their role in transcription, genome-wide mapping of the interactions of several members of the SWI/SNF subfamily including Ini1 (SMARCB1), Brg1 (SMARCA4), BAF155 (SMARCC1) and BAF170 (SMARCC2) showed that they associate with nuclear matrix proteins and transcription factors and frequently occupy transcription start sites and enhancers, CTCF- binding domains and regions occupied by RNA polymerase II 145. SMARCAL1, the enzyme defective in SIOD, is involved in DNA replication, recombination, repair and transcription.  12 1.2 SMARCAL1 Human SMARCAL1 (OMIM 606622) consists of 18 exons, 2 non-coding and 16 coding exons, and encodes a 954- amino acid nuclear protein, SMARCAL1 or HARP (HepA related protein) that is a distinct member of the SNF2 subfamily of chromatin remodeling ATPases 146. In addition to a SNF2 domain at the C-terminus, SMARCAL1 contains an N- terminal Replication Protein A (RPA) binding site, two nuclear localization signals (NLS1 at N-terminus and NLS2 within SNF2 domain), and two tandem HARP motifs (HARP1 and HARP2) at the N-terminal site with sequence similarity to regions of prokaryotic HepA (Figure 1-4). SMARCAL1 recognizes DNA structures that contain single-stranded (ss)-to- double-stranded (ds) transitions including fork DNA, DNA bubbles, DNA substrates with either a 5′ or 3′ recessed end, three- and four-way DNA junctions, and gapped DNA substrates 130,140,147. Binding of ss-to-ds transitions stimulates ATP hydrolysis by SMARCAL1 as it rewinds ss-DNA 130,140,147. This annealing helicase activity and in part the DNA binding and ATPase activity of SMARCAL1 are mediated by the HARP2 domain but not by the HARP1 domain 140,148. In contrast to several previous reports that did not detect any helicase activity for SMARCAL1 130,147, Ciccia et al. showed that SMARCAL1 disrupts D loop structures in the presence of magnesium but not calcium containing buffers 149. At the cellular level SMARCAL1 is localized to its target DNA structures through an interaction with the ssDNA-binding protein RPA 139,141-144. Through this interaction, which is critical for SMARCAL1 in vivo function, SMARCAL1 travels with elongating replication forks and also localizes to stalled replication forks and sites of DNA ds-breaks 139-144. In response to replication stress SMARCAL1 becomes phosphorylated at multiple sites by ATM (Ataxia-telangiectasia mutated), ATR (ATM and Rad3-related), and DNA-PK (DNA-  13 dependent protein kinase) 139. Therefore, SMARCAL1 is a DNA stress response protein and both silencing and overexpression of this enzyme activates the DNA damage response during S phase 139. Consistently, interaction of SMARCAL1 with replication forks prevents MUS81 endonuclease-dependent ds-breaks 140. Moreover, SMARCAL1-deficient cells are hypersensitive to DNA damaging agents and produce more γH2AX foci compared to control cells 139,141-144,150. Localizing to stalled replication forks, SMARCAL1 is involved in replication fork restart 141. This can be attributed to the newly discovered role of SMARCAL1 in remodeling replication forks by catalyzing branch migration 140. Although cell cycle analyses have shown slightly delayed transition from G2 to M phase in SMARCAL1 deficient cells, the overall proliferation rate of SMARCAL1-deficient cells is not detectably perturbed relative to control cells 139,141. In contrast to SMARCAL1 deficiency in human cells, morpholino knockdown of smarcal1 in zebrafish has shown a defect in cell cycle progression, namely cell cycle arrest at the G0/G1 stage, which was associated with minor dysregulation of expression of several cell cycle genes 151. Furthermore, SMARCAL1 can bind four-way Holliday junctions and promote branch migration of these structures 140. Since these structures frequently occur during homologous recombination, the ability to remodel Holliday junctions suggests a role for SMARCAL1 in homologous recombination. In summary, SMARCAL1 is a chromatin remodeling enzyme with annealing helicase activity that remodels DNA structures at ss-to-ds transitions. Its roles in the DNA stress response, cell cycle progression, and remodeling of stalled replication forks and of Holliday junctions indicate that this enzyme is involved in several basic cellular functions.  14   Figure 1-4- Diagram of the structure of the SMARCAL1 gene and SMARCAL1 protein. The top panel represents the SMARCAL1 gene with its 18 exons in which the first two exons (blue bars) are noncoding and the rest of the exons are coding (purple bars). The lower panel represents the SMARCAL1 protein with its functional domains: RPA binding domain (yellow box), two HARP domains (blue boxes), SNF2 domain (purple box), and two nuclear localization signals (NLS1 and NLS2).  1.3 Schimke immuno-osseous dysplasia (SIOD) Biallelic mutations of SMARCAL1 gene are associated with a fatal childhood disorder called Schimke immuno-osseous dysplasia (SIOD, MIM 242900). SIOD was first described in 1971 by Dr. R. Neil Schimke. He reported a new disease with the cardinal signs of nephritic syndrome, defective cellular immunity, and possibly chondroitin-6-sulphaturia 152. About twenty years later Ehrich et al. (1990) contributed to the clinical description of the disease and Spranger et al. (1991) designated the co-occurrence of growth retardation, immunopathy, and nephritic syndrome as “Schimke immuno-osseous dysplasia”153,154. About ten years later Boerkoel et al. (2002) discovered that 50-60% of SIOD patients have biallelic mutations in their SMARCAL1 gene 155. Mutations are distributed across SMARCAL1 and include deletions, insertions, splice site mutations, and missense and nonsense point mutations, suggesting loss of function 155. Biallelic mutations of SMARCAL1 cell autonomously lead to SIOD disease features 156-158. So far, mutations in other genes have not been associated with SIOD.  15 1.3.1 Characteristics and clinical features Schimke immuno-osseous dysplasia (SIOD) is a panethnic, autosomal recessive, highly pleiotropic, multisystem childhood disorder with variable expressivity and incomplete penetrance 13,130,155,157,159. The most prominent features of the disease are growth failure from spondyloepiphyseal dysplasia, renal failure from focal segmental glomerulosclerosis (FSGS), and T-cell immunodeficiency (Table 1-1). Combined with the facial dysmorphism, these form a diagnostic triad unique to SIOD 152,154,160-162. The prevalence of SIOD is unknown. However, based on referrals and published birth rates, the incidence in North America is estimated to be one in 1-3 million live births. The clinical features of SIOD are described as follows, and their frequencies are summarized in Table 1-1: • Physical features. Most SIOD patients have facial dysmorphism including a broad, low nasal bridge and bulbous nasal tip. They also have disproportionate short stature with a short neck and trunk as well as lumbar lordosis and protruding abdomen. They also commonly have hyperpigmented macules on the trunk and occasionally on the extremities, neck, and face. Less common ectodermal abnormalities include fine or sparse hair, microdontia or adontia, and corneal opacities. • Development. Most individuals with SIOD have normal intellectual and neurologic development until the onset of cerebral ischemic events. Although a few have had developmental delay, the delay can be ascribed for most to the deleterious consequences of chronic illness. • Growth and endocrine findings. Most affected children have prenatal and postnatal disproportionate growth failure. A few have normal intrauterine growth followed by  16 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, 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 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 163. The mean age of diagnosis with growth failure was two years (range: age 0-13 years) 13. Generally, affected individuals have a normal growth hormone axis and no response to growth hormone supplementation. Heights of those who have survived to adulthood are 136-157 cm for men and 98.5-143 cm for women. Nearly half of affected individuals have subclinical hypothyroidism that persists after renal transplantation. The concentration of thyroid stimulating hormone (TSH) is increased, and free and total T3 and T4 concentrations are reduced. • Skeletal findings. SIOD is characterized by prominent spondyloepiphyseal dysplasia (SED) of the spine, pelvis and capital femoral epiphyses 164. Less frequent skeletal problems include a widened sella turcica, thoracic kyphosis, scoliosis, and osteopenia (Figure 1-5). Affected individuals do not usually have joint pain until they develop degenerative hip disease. • Hematologic findings and infection. T-cell deficiency causes lymphopenia in approximately 80% of affected individuals. The B-cell count is usually normal to  17 slightly elevated. In addition to T-cell deficiency, several individuals with SIOD have had deficiencies of other blood cell lineages. See Table 1 for types and frequency. Because of immunodeficiency, affected individuals have an increased risk for opportunistic infections such as Pneumocystis carinii pneumonia and more than half have recurrent infections with various bacteria, viruses (including Herpes simplex, Herpes zoster, cytomegalovirus), and fungi (oral and/or cutaneous candida) 155,161. Infection is a common cause of death. • Autoimmune findings. About 20% of SIOD patients have features of autoimmune disease. These manifestations include thrombocytopenia, hemolytic anemia, enteropathy, pericarditis with anti-cardiolipin antibodies, and Evans syndrome. In one patient with thrombocytopenia, the autoimmune features resolved spontaneously; in one they resolved after steroid and IVIG treatments, and in one they cleared after splenectomy. All other patients excluding the one with Evans syndrome, were successfully treated with immunosuppressive therapy such as steroids, cyclophosphamide or IVIG. The patient with Evans syndrome was resistant to treatment with steroids, cyclosporine A and rituximab 165. • Renal findings. Nephropathy usually develops before age 12 years and progresses to end stage renal disease (ESRD) within the subsequent one to 11 years. Usually the diagnosis of nephropathy is made concurrent with or within the five years following the diagnosis of growth failure. Focal segmental glomerulosclerosis (FSGS) is the predominant renal pathology among individuals with SIOD. • Gastrointestinal findings. A few individuals with SIOD have enteropathy. For most of these individuals, the enteropathy results from infection, e.g., Heliobacter pylori.  18 However, one individual without evidence of infection had gastrointestinal villous atrophy that improved with corticosteroid therapy 166. • 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 154,156,167. The pulmonary and systemic hypertension that persisted despite renal transplantation described by Lücke et al. (2004) could be explained by myointimal hyperplasia 156. Also, gene expression studies have identified a significant decrease in the expression of ELN in individuals with SIOD 168. This gene encodes for elastin protein, which is critical for maintaining the integrity of the arterial wall. Histopathologic analysis of postmortem arterial tissue from three individuals with SIOD showed splitting and fragmentation of elastin fibers 156,168. Reduction in the elastin protein results in the increased proliferation of smooth muscle cells in arterial walls and leads to intimal hyperplasia 169. • Central nervous system (CNS) symptoms. Nearly half of affected individuals have severe migraine-like headaches, transient neurological attacks (TNAs), or strokes 162. Frequently, the cerebral ischemic events are precipitated by hypertension. The TNAs are usually focal and are frequently assumed to be transient ischemic attacks (TIAs). Interestingly, however, using magnetic resonance angiography (MRA), these TIAs are often not associated with vascular abnormalities and can be precipitated by fatigue or stress conditions such as hot weather. The cause of the severe migraine-like headaches is unknown.  19 Table 1-1- Frequency of disease features in individuals with SIOD with biallelic SMARCAL1 mutations.  Feature Number of Affected Individuals with Feature (%) Total Individuals with SIOD2 Physical features Broad and low nasal bridge 37 (65) 57 Bulbous nasal tip 43 (80) 54 Microdontia 11 (39) 28 Pigmented macules 44 (76) 58 Unusual hair 30 (63) 48 Short neck 45 (83) 54 Short trunk 47 (82) 57 Lumbar lordosis 41 (75) 55 Protruded abdomen 44 (80) 55 Corneal opacities 9 (16) 57 Development Schooling delay 6 (23) 26 Developmental delay 14 (26) 54 Growth IUGR  31 (72) 43 Decreased postnatal growth rate/short stature 59 (98) 60 Endocrine Abnormal TFTs  21 (44) 48 Skeleton Ovoid flat vertebrae 42 (79) 53 Hypoplastic pelvis 34 (68) 50 Abnormal femoral heads 43 (84) 51 Hematology T-cell deficiency 38 (83) 46 Lymphopenia 46 (81) 57 Neutropenia 21 (41) 51 Thrombocytopenia 17 (31) 55 Anemia 30 (59) 51 Kidney Proteinuria or nephropathy 57 (98) 58 FSGS  34 (79) 43 Vasculature Headaches 22 (49) 45 TIAs  24 (45) 53 Strokes 22 (45) 49 Miscellaneous Autoimmune disease 8 (20) 41 Non-Hodgkin lymphoma 1 3 (5) 60  IUGR, intrauterine growth retardation; TFT, thyroid function test; FSGS, focal segmental glomerulosclerosis; TIA, transient ischemic attack. 1 EBV-positive and negative non-Hodgkin lymphoma 2 Each row represents the total number of SIOD patients with biallelic SMARCAL1 mutations in whom the specific clinical feature has been reported. The data for this table was compiled from SIOD patient registry maintained by Cornelius F. Boerkoel (C.F.B., SIOD patient registry).  20  Figure 1-5- Typical bony 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 widening of the sella. (C) Posteroanterior hand radiograph of a 13-year-old adolescent showing the absence of bony 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. Adapted with kind permission from Springer Sceince and Business Media: Eur J Pediatr (2010) 169:801–811, Schimke immunoosseous dysplasia: defining skeletal features, Hunter KB et al., figure 1.      21 1.3.2 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 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 167,170. Growth failure is usually the primary clinical feature of SIOD and most affected individuals develop other symptoms within one to five years of the diagnosis of growth failure. Those with severe symptoms usually die within four to eight years. The mean age of death is 10.8 years. Causes of death include stroke (17%), renal failure (15%), infection (23%), pulmonary hypertension and congestive heart failure (15%), bone marrow failure (3%), complications of organ transplantation (9%), gastrointestinal bleeding (6%), complications of lymphoproliferative disease (9%), and unspecified acute restrictive lung disease (3%) (C.F.B., SIOD patient registry). Ongoing correlations of genotype to phenotype have shown that genotype does not predict disease severity or outcome, either within or among families 13,159,171,172. In five multiplex families, the phenotype of siblings has been variable:  22 • 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 170. • Of two brothers, one had severe disease and the other had relatively mild disease 172. • Of two brothers with homozygous SMARCAL1 mutations, one presented with growth failure at age six years and the other had no symptoms at age seven years 159. • Of three siblings reported by Lama et al. (1995), one died as a child, one died at 43 years and the other survived into her fourth decade 173. • Of three siblings reported by Dekel et al. (2008) the elder brother had severe disease beginning at age 3.5 years and two younger non-identical twin brothers had relatively mild disease 171. Among those who have survived beyond puberty, none has reproduced yet. Women and men develop secondary sexual characteristics. Women have menses, although the menstrual cycle is usually irregular. Men have small testes with histopathologically identified azoospermia 156.  1.3.3 Management and treatment of manifestations To establish the extent of disease in an individual diagnosed with SIOD, the following evaluations are considered: • Detailed history for headaches or neurologic abnormalities • Assessment of developmental status with referral for formal evaluation if significant developmental delays or schooling delays are identified  23 • Measurement of growth and assessment of body proportions, with plotting on age- appropriate growth charts • Evaluation of renal function by measurement of serum concentrations of creatinine and urea, protein excretion in urine, and creatinine clearance and referral to a nephrologist for evaluation • Hematology evaluations to assess lymphopenia, anemia, neutropenia, and thrombocytopenia • Orthopedic evaluation for symptoms of joint pain or evidence of scoliosis or kyphosis • Assessment for osteopenia • Thyroid function studies • Ophthalmologic evaluation • Dental evaluation after teeth are present Although there is no curative therapy available for SIOD, patients benefit from symptomatic treatments: • Renal transplantation effectively treats the nephropathy, and neither nephropathy nor arteriosclerosis recurs in the graft 156,158,167. Immunosuppressive monotherapy seems to improve the outcome after renal transplantation 174. • Some affected individuals who have survived beyond childhood have required hip replacement. Treatment of scoliosis and/or kyphosis is standard. • Affected individuals with recurrent herpetic infections benefit from treatment with acyclovir. A few patients have developed severe disseminated cutaneous papilloma  24 virus infections that have improved with imiquimod and cidofovir (C.F.B., SIOD patient registry). • Neutropenia usually responds well to supplementation with granulocyte colony- stimulating factor or granulocyte-macrophage colony-stimulating factor 161. One affected individual has been successfully treated by bone marrow transplantation (BMT), however, four other patients died within the first few months after BMT 175,176. A few individuals have been transfusion dependent because of anemia or thrombocytopenia 161. • Individuals with transient ischemic attacks or strokes usually have temporary improvement upon treatment with agents that improve blood flow or decrease coagulability (pentoxifylline, acetylsalicylic acid, dipyridamole, warfarin, heparin) 161. To date, no curative or effective long-term therapies have been identified. • The migraine headaches are often difficult to treat since response to anti-migraine medication is variable 162. Medications that have helped some individuals include ergotamine, sumatriptan, verapamil, and propranolol 162. • TSH concentrations are corrected with levothyroxine supplementation; however, supplementation does not have an ameliorative effect on the renal disease or T-cell deficiency 161. • Individuals with recurrent infections, opportunistic infections, or declining lymphocytes or T-cell counts frequently require prophylactic antibiotics and the care of an immunologist 161.   25 1.4 Thesis objectives and significance The aim of my doctoral study was to dissect further the molecular pathophysiology of SIOD. Since SMARCAL1 annealing helicase activity can potentially modulate transcription through altering DNA structure and variations in gene expression can account for alterations in disease expression, I hypothesized that SIOD arises from dysregulation of gene expression.  In the last three years, several reports from the Cortez Lab, Elledge Lab, Kadonaga Lab, Chen Lab and Funabiki Lab have demonstrated that SMARCAL1 participates in the DNA stress response and also in reactivation of stalled replication forks 139,141-144. However, it was not clear whether these roles of SMARCAL1 were relevant to the molecular pathophysiology of SIOD. In chapter 2, I addressed this question by looking for dysfunction of various DNA repair pathways in SIOD patients and cells. In chapter 3, I determined that SMARCAL1 also participates in transcription and tested whether this role contributed to the pathobiology of SIOD. Using human tissues, fruit flies and mice, my colleagues and I showed that SMARCAL1 modulates transcription but that SMARCAL1 deficiency is insufficient to cause the disease in model organisms. However, disease manifests when SMARCAL1 deficiency interacts with genetic and environmental factors that further alter gene expression.  26 2. Contribution of DNA repair defects to pathophysiology of SIOD 2.1 Introduction In this and the next chapter, I focus on delineating further the molecular mechanism of SIOD. Herein I evaluate whether defects in the DNA repair pathways are major contributors to the SIOD phenotype. As mentioned earlier in chapter 1, SIOD is associated with biallelic mutations of SMARCAL1, which encodes an enzyme with annealing helicase activity 130,155. This enzyme participates in the DNA stress response to genotoxic agents such as hydroxyurea, camptothecin and aphidicolin 139,141-144. These observations raise the question of whether loss of SMARCAL1 function causes disease by impeding DNA repair. To test the potential contribution of defective DNA repair to the phenotype of SIOD, we compared SIOD to other disorders of DNA repair, profiled the cancer prevalence in SIOD, tested for defects in the DNA repair processes including nucleotide excision repair (NER), homologous recombination (HR), and non-homologous end joining (NHEJ) and measured the sensitivity of Smarcal1-deficient mice to genotoxic agents. We find that SIOD patients have a high frequency of non-Hodgkin lymphoma (NHL) and that Smarcal1-deficient mice are hypersensitive to several genotoxic agents.   27 2.2 Materials and methods 2.2.1 Comet assay Control and SIOD (N859) fibroblasts as well as Smarcal1del/del and Smarcal1+/+ mouse embryonic fibroblasts (MEFs) were analyzed by alkaline comet assay as previously described 177. In brief, cultured cells were trypsinized, washed with medium, resuspended in low melting point agarose, and layered on agarose-coated slides cooling on ice. After 10 minutes the slides were immersed in lysis solution (2.5 M NaCl, 100 mM EDTA, 10 mM Trizma base, 1% Triton X-100, 10% dimethyl sulfoxide, pH 10) for 5 hours. Slides were rinsed in deionized water and immersed in a 4°C alkaline solution (50 mM NaOH, 1 mM EDTA, 1% dimethyl sulfoxide, pH>13) for 25 minutes followed by electrophoresis at a constant voltage of 25 V for 25 minutes at 4°C. Finally, the slides were neutralized in 0.4 M Tris–HCl pH 7.5 and the DNA was stained with SYBR Green I. A total of 150 comets were scored for each sample using CometScore Version 1.5 software (TriTek corporation) and statistically significant differences in the distribution of comet olive moments were determined using the two-tailed Student’s t-test.  2.2.2 Analysis of sister chromatid exchange Mary Shago from the Hospital for Sick Children performed this experiment. Patient and control peripheral blood samples were cultured in MEM/FBS for a total of 72 hours. Thirty-six hours prior to harvesting, 30 µg/mL bromo-deoxyuridine (BrdU) (Sigma) was added to the cultures. Harvesting of cultures and slidemaking were performed according to standard cytogenetic techniques. Slides were aged at room temperature for 5 days and then  28 immersed in Hoechst 33342 Dye (final concentration 3 µg/mL) (Sigma) for 1.5 hours while protected from the light. After rinsing with PBS and coverslipping, the slides were exposed to ultraviolet light (365 nm) for 2 hours. Coverslips were removed, and the slides were rinsed again with PBS and stained with Giemsa (EMD). Sister chromatid exchanges were scored in 20 metaphase cells from the patient and control.  2.2.3 Immunoglobulin switch junction amplification and sequencing Genomic DNA was extracted from peripheral blood cells of 7 SIOD patients and 4 unaffected controls. Amplification, sequencing and analysis of the switch Sµ-Sα1 and Sµ- Sγ1 junctions were performed as previously described with minor modifications 178. In brief, a nested polymerase chain reaction (PCR) strategy was used to amplify Sµ-Sα1 and Sµ-Sγ1 junctions. In the first round of PCR reactions, 50 ng or 150 ng of genomic DNA from each individual was amplified in 8 parallel reactions using either Sµ1 and Sα-common-1 primers for a total of 35 cycles of 94°C for 1 minute, 60°C for 1 minute, and 72°C for 90 seconds, or Sµ1 and Sγ-common primers for a total of 35 cycles of 94°C for 1 minute, 66°C for 1 minute, and 72°C for 30 seconds, respectively. In order to amplify Sµ-Sα1 junctions, 1 µl of each Sµ1-Sα-common-1 reaction was amplified using Sµ5 and Sα1-specific primers in reactions consisting of 35 cycles of 94°C for 1 minute, 65°C for 1 minute, and 72°C for 1 minute. Also, in order to amplify Sµ-Sγ1 junctions, 3 µl of each Sµ1- Sγ-common reaction was amplified using Sµ5 and Sγ1-specific primers in reactions consisting of 40 cycles of 94°C for 1 minute, 66°C for 1 minute, and 72°C for 1 minute. PCR products from the second round of reactions were gel purified with QIAEX II Gel Extraction Kit (Qiagen) and sequenced using the Sµ5,  29 Sα1-specific, and Sγ1-specific primers. The switch fragment sequences were aligned with Sµ (X54713) /Sα1 (L19121) or Sµ /Sγ1 (U39737) reference sequences and microhomology at each switch junction was defined as the longest region of identity with both the Sµ and Sα1 or Sµ and Sγ1 reference sequences. The primers are listed in Table 2-1.  Table 2-1- Oligonucleotides used for amplification and sequencing of immunoglobulin switch regions. Switch region Sequence Reference Sµ1 GACCATGGGGACCTGCTCATTTTTATC 179 Sα-common-1 ACGTCGACGCCCTCAGAACCCCTAAGAA 180 Sγ-common GTCTGCAGTGCCCCTGCCTGAGAGC 179 Sµ5 ACGCATGCGGCAATGAGATGGCTTTAG 179 Sα1-specific ACGTCGACCAGTCCAGCCCAAGTCATC 180 Sγ1-specific ACGTCGACGCCCTCAGCTGTCTGTT 178   2.2.4 Smarcal1del/del mice and treatment with CPT-11, etoposide, and HU The Smarcal1del/del mice have a deletion of the first two coding exons of Smarcal1 (NM_018817.2:c.172_989del), which includes the replication protein A (RPA) binding site, nuclear localization signal and the first HARP domain. They were generated as described elsewhere 181. Smarcal1-deficient (Smarcal1del/del) and wild-type (Smarcal1+/+) male mice were divided into 4 groups (3-5 mice in each group) at the age of 4-weeks. Mice in each group were given intra-peritoneal (ip) injections of CPT-11 (40 mg/kg, Sandoz), etoposide (20 mg/kg, Sigma), HU (100 mg/kg, Sigma), or carrier (PBS, Gibco). After 5 consecutive daily injections, the mice were followed for 3 days, and then their spleens were harvested. To analyze the long-term effect of these drugs, mice were injected daily for 8 weeks. To assess the recovery of growth retardation, CPT-injected mice were observed for another 12 weeks  30 without further injections. At the end of the treatment, radiography of mice was performed using the Faxitron X-ray cabinet. The density of the distal femur was measured using a Scanco µCT35 scanner (Scanco Medical, Bassersdorf, Switzerland) as previously described 182. To compare the toxicity of each drug among Smarcal1del/del and Smarcal1+/+ mice, 3- 4 mice of each genotype were given daily ip injections with CPT-11 (80 mg/kg), etoposide (40 mg/kg), HU (500 mg/kg) or PBS for 5 consecutive days and followed for two weeks unless sacrificed earlier.  2.2.5 Urinary creatinine and albumin assays Spot urine was collected from Smarcal1del/del and Smarcal1+/+ mice injected with PBS, CPT-11, etoposide or HU. Urinary creatinine was measured using the creatinine assay kit from Cayman Chemical Company (Ann Arbor, MI, USA) following the manufacturer’s instructions. Urinary albumin was measured using the mouse albumin ELISA kit from Immunology Consultants Laboratory (Portland, OR, USA) following the manufacturer’s instructions. Urinary albumin excretion was normalized to urinary creatinine for each sample.  2.2.6 Histopathology and TUNEL assay Mouse tissues for histopathology and TUNEL assay were fixed in 4% paraformaldehyde (PFA) in PBS, processed, paraffin embedded, and cut into 5 µm sections according to standard protocols as previously described 177. TUNEL detection of apoptotic cells in the spleens was carried out using the ApopTag Peroxidase In Situ Apoptosis Detection Kit (S7101, Chemicon), and the tissue was counterstained with Mayer’s  31 hematoxylin. Harris hematoxylin and eosin (H&E) staining of the femurs and Masson trichrome staining (Sigma) of the kidneys were performed following the manufacturers’ instructions. Images of the processed tissues were captured using a Zeiss Axiovert 200 microscope. For the distal femoral growth plate analysis, we counted all chondrocytes within the proliferative and hypertrophic zones and within 250 µm of the vertical midline.  2.2.7 Cell culture The isolation and culturing of human dermal fibroblast cell lines were performed as described by Clewing et al. 13. The SIOD patient fibroblasts (N859) were derived from patient SD31, who had a homozygous deletion of the first 5 exons of SMARCAL1 (NT_005403.17:g.[67482574_67497178del]+[67482574_67497178del]), and cultured in DMEM (Gibco) containing 15% fetal bovine serum (FBS; Gibco) and 1% antibiotic/antimycotic (penicillin, streptomycin and amphotericin B; Gibco) at 37°C and 5% CO2. Other cell lines including normal human fibroblasts (C5RO) and Cockayne syndrome B fibroblasts (CS188TOR and CS163TOR) were also cultured in DMEM containing 15% FBS and 1% antibiotic/antimycotic at 37°C and 5% CO2.  2.2.8 Cell survival assays Anja Raams from Erasmus Medical Center, the Netherlands performed this experiment. Cell survival following UV or illudin S treatment was performed using a modified assay based on tritiated thymidine incorporation 183. In brief, triplicate sparse cultures (5 x 103 to 1 x 104 cells/6-cm dish) of primary C5RO, N859, CS188TOR, and CS163TOR fibroblasts  32 were grown for 48 hours and exposed to illudin S (0, 0.2, 0.4, 0.6, 0.8, and 1 ng/ml) or UV (0, 5, 10, and 15 J/m2). After 3–5 days additional culture, the cells were pulse-labelled with tritiated thymidine (40–60 Ci/mmol, 5 µCi/ml) for 3 hours in presence of 20 mM HEPES pH 7.3. Following 30 minutes incubation in unlabelled medium, the cells were harvested and the incorporated radioactivity was measured in a liquid scintillation counter.  2.3 Results 2.3.1 SIOD patients have features observed in other disorders of DNA repair Comparison of SIOD patients to individuals with Bloom syndrome, Fanconi anemia, Cockayne syndrome, ataxia telangiectasia, Nijmegen breakage syndrome, xeroderma pigmentosum, Werner syndrome, Rothmund-Thomson syndrome, and RAPADILINO syndrome showed that SIOD shares many features with these disorders, including poor postnatal growth, immune defects, and premature atherosclerosis (Table 2-2) 156,161. Unlike these disorders, however, SIOD is not reported to predispose to cancer, although there are anecdotal reports of patients with non-Hodgkin lymphoma (NHL) 161,184,185. To ascertain better the prevalence of cancer in SIOD, we reviewed the medical records of 71 SIOD patients with identified biallelic SMARCAL1 mutations. Three individuals developed B-cell NHL in the first ten years of their life, and two had EBV positive tumors. One individual developed osteosarcoma. No other cancers were reported for this cohort.        33 Table 2-2- Comparison of the prominent clinical features of SIOD to those of some DNA repair disorders 186-189.  Clinical Feature BS FA CS AT NBS XP WS RTS RS SIOD Growth deficiency + + + +/- + + + + + + Radial ray defects - + - - - - - + + - Cognitive deficits - + + - + + - - - - Ataxia - - + + - +/- - - - - Corneal or lens defects + + + - - + + + - + Immunodeficiency + - - + + - - +/- - + Single cytopenias + + - + + - - +/- - + Pancytopenia - + - - - - - +/- - + Malignancy predisposition + + - + + + + + + - Photosensitivity + - + - - + - + + - Radiosensitivity - +/- - + + - - + NR - Abnormal pigmentation + + + +/- + + + + + + Telangiectasias + - - + - +/- - + - - Thin hair -  + - - - + + - + Premature atherosclerosis - - + + - - + - NR + Hypogonadism + + + + + + + + NR + Chromosomal instability + + - + + - + + NR - Poor lymphocyte mitogenic response + - - + + - - +/- - + DNA repair defect HR ICL repair, HR TC- NER DSB repair DSB reapair, replication fork repair NER HR, BER, telomere maintenance BER, HR? Resolution of stalled replication/ transcription intermediates Replication fork repair Abbreviations: BS, Bloom syndrome; FA, Fanconi anemia; CS, Cockayne syndrome; AT, Ataxia telangectasia; NBS, Nijmegen breakage syndrome; XP, Xeroderma pigmentosum; WS, Werner syndrome; RTS, Rothmund-Thomson syndrome; RS, RAPADILINO syndrome; +, common feature; -, uncommon feature; +/-, presented as case reports; NR, not reported; HR, homologous recombination; ICL, interstrand crosslink; TC-NER, transcription-coupled nucleotide excision repair; DSB, double- strand break; BER, base excision repair.   34 2.3.2 SIOD patient dermal fibroblasts and Smarcal1-deficient mouse embryonic fibroblasts do not have increased DNA breaks detectable by comet assay To determine if defects of DNA repair contribute to this increased prevalence of NHL, we used the alkaline comet assay to test for an increase in DNA breaks in fibroblasts deficient for SMARCAL1 or Smarcal1, the murine homologue. Neither SIOD dermal fibroblasts (N859) nor Smarcal1-deficient MEFs had an increased comet olive moment compared to control fibroblasts (Figure 2-1).   Figure 2-1- Analyses of DNA breaks in cultured SMARCAL1-deficient skin fibroblasts (N859) and Smarcal1-deficient mouse embryonic fibroblasts (MEFs) by alkaline comet assay. (A) Representative photographs of SYBR Green stained comet tails of control and SMARCAL1-deficient (N859) human skin fibroblasts. (B) Graph of the distribution of 150 alkaline comet olive moments for control and N859 fibroblasts (p = 0.27). (C) Representative photographs of SYBR Green stained comet tails of Smarcal1+/+ and Smarcal1del/del MEFs. (D) Graph of the distribution of 150 alkaline comet olive moments for Smarcal1+/+ and Smarcal1del/del MEFs (p = 0.22).    35 2.3.3 SMARCAL1 and DNA nucleotide excision repair Reminiscent of disorders of NER such as xeroderma pigmentosum and Cockayne syndrome, prominent ectodermal features of SIOD include hyperpigmented macules as well as fine sparse hair. Therefore, to test if a defect of NER might contribute to SIOD, we measured hypersensitivity of dermal fibroblasts from a patient with a homozygous deletion of SMARCAL1 promoter (SD31) 157 to ultraviolet light (UV) and to the anti-tumor antibiotic illudin S. The latter agent causes damage that is repaired exclusively by transcription coupled (TC)-NER. In contrast to Cockayne syndrome (CS) cells, which have a defect of TC-NER 183, SMARCAL1-deficient fibroblasts were not hypersensitive to either UV or to illudin S treatment (Figure 2-2).  Figure 2-2- Analysis of SMARCAL1 participation in nucleotide excision repair. (A) Survival curve comparing the sensitivity of SIOD (N859), unaffected (C5RO), and Cockayne syndrome B (CS-B, CS188TOR) skin fibroblasts to killing by UV radiation (254 nm). This is a measure of both TC-NER and global NER. (B) Survival curve comparing the sensitivity of SIOD (N859), unaffected (C5RO), and CS-B (CS163TOR) skin fibroblasts to killing by illudin-S. This is a measure of TC-NER. The data for this figure was provided by Anja Raams.    36 2.3.4 SMARCAL1 and DNA repair by homologous recombination To determine if SMARCAL1 participates in homologous recombination, we looked for the increased sister chromatid exchange (SCE) that arises from a failure to suppress ectopic recombination and crossing-over between homologous chromosomes 190. As detected by bromodeoxyuridine incorporation and Giemsa staining 190, the frequency of SCE in SIOD cells was within the normal range (Figure 2-3).  Figure 2-3- SMARCAL1 participation in homologous recombination. Photomicrograph of a metaphase spread from BrdU labelled SIOD lymphocytes showing no increase in sister chromatid exchange. The data for this figure was provided by Mary Shago.  2.3.5 SMARCAL1 and DNA repair by non-homologous end joining The efficient generation of antibodies and T-cell receptors requires competency for double strand break repair, particularly NHEJ 191,192. Therefore, to test if SIOD patients had defects of NHEJ that could account for their prominent T-cell immunodeficiency and occasional immunoglobulin deficits 161, we checked for a NHEJ defect altering immunoglobulin switching in SIOD patients. We analyzed 25 Sµ-Sα1 and 22 Sµ-Sγ1  37 immunoglobulin switch recombination junctions amplified from the peripheral blood DNA of 7 SIOD patients including one who died of NHL and had hypersensitivity to chemotherapy 185. Comparison of the junctions from these patients to those from 4 control individuals did not identify abnormalities such as increased switch junction homology or breakpoint mutations (Figure 2-4 and Appendices 1 and 2).  38   39 Figure 2-4- Examples of microhomology usage at Sµ-Sα1 and Sµ-Sγ1 junctions in the immunoglobulins of SIOD patients. (A) The Sµ and Sα1 reference sequences are aligned above and below the switch junction sequences from a SIOD patient (SD107) and a control (SD107.1). (B) The Sµ and Sγ1 reference sequences are aligned above and below the switch junction sequences from a SIOD patient (SD107) and a control (SD107.1). Microhomology (solid-line boxes) at each switch junction is the longest region of identity between the Sµ and Sα1 or Sγ1 reference sequences, whereas imperfect microhomology (dashed-line boxes) at each switch junction is the longest region of near identity (1 mismatch on either side of the breakpoint).                  40 2.3.6 Smarcal1-deficient mice are hypersensitive to genotoxic agents Recent findings have indicated SMARCAL1 is a component of the DNA stress response and that SMARCAL1-deficient cells are hypersensitive to genotoxic agents hydroxyurea and camptothecin 139,141-144. However, so far we have been unable to find a definable defect of nucleotide excision repair, homologous recombination, or non- homologous end joining in SMARCAL1-deficient fibroblasts. Therefore to clarify better the contribution of SMARCAL1-deficiency to hypersensitivity to genotoxic agents, we asked if Smarcal1del/del mice were hypersensitive to genotoxic agents (Table 2-3). Using growth and splenic cell apoptosis as outcome measures, Smarcal1del/del mice were more sensitive to CPT- 11, etoposide, or HU than were Smarcal1+/+ mice (Figure 2-5A-L and Figure 2-6A-J).  Table 2-3- Response of Smarcal1+/+ and Smarcal1del/del mice to exposure to CPT-11, etoposide and hydroxyurea.  Compound Dose (mg/kg) Frequency                        Phenotype Smarcal1+/+ Smarcal1del/del CPT-11 40 daily x 56 days Intermittent diarrhea Chronic diarrhea, weakness, severe growth arrest 80 daily x 5 days (1 week rest) daily x 10 days Weight loss (2/3), mild diarrhea (2/3) Weight loss (3/3), diarrhea (3/3), severe weakness and death (2/3) Etoposide 20 daily x 56 days Normal Severe growth arrest 40 daily x 5 days Weight loss (3/3), severe weakness (2/3), death (1/3) Weight loss and severe weakness (4/4), death (2/4) Hydroxyurea 100 daily x 56 days Normal Mild growth arrest 500 daily x 10 days Weight loss (4/4), death (1/4) Weight loss (4/4), severe weakness and death (3/4)   41      42 Figure 2-5- Hypersensitivity of Smarcal1del/del mice to CPT-11. (A-D) Photomicrographs of TUNEL-positive nuclei in splenic tissue from postnatal day (P) 36 Smarcal1del/del and Smarcal1+/+ mice following 5 daily ip-injections with PBS. Panels C and D are higher magnification photomicrographs of the boxed areas in panels A and B, respectively. (E) Graph showing the average weight gain of Smarcal1del/del (n = 4) and Smarcal1+/+ (n = 4) mice receiving daily ip-injections of PBS from P28 through P84 and during 90 days following treatment (shaded area). (F) Representative skeletal radiographs of Smarcal1del/del and Smarcal1+/+ mice at P174 following 8 weeks of daily injections with PBS. (G-J) Photomicrographs of TUNEL-positive nuclei in splenic tissue from P36 Smarcal1del/del and Smarcal1+/+ mice following 5 daily ip-injections with CPT-11. Panels I and J are higher magnification photomicrographs of the boxed areas in panels g and h, respectively. (K) Graph showing the average weight gain of Smarcal1del/del (n = 3) and Smarcal1+/+ (n = 4) mice receiving daily ip-injections of CPT-11 from P28 through P84 and during 90 days following treatment (shaded area). (L) Representative skeletal radiographs of Smarcal1del/del and Smarcal1+/+ mice at P173 following 8 weeks of daily injections with CPT-11. (M-T) Photomicrographs of representative sections of the distal femoral growth plate of Smarcal1+/+ and Smarcal1del/del male mice treated with PBS or CPT-11 for 8 weeks. The tissue was stained with H&E. Panels Q-T are higher magnification photomicrographs of the boxed areas in panels M-P, respectively. Note the poorly organized columns of chondrocytes in the smaller hypocellular growth plates of the CPT-11 treated Smarcal1del/del mice. (U) Graph showing the mean bone density of the distal femur of Smarcal1+/+ and Smarcal1del/del mice treated either with PBS or CPT-11. Scale bars = 100 µm. Error bars represent one standard deviation. For statistically significant differences between Smarcal1del/del and Smarcal1+/+ mice, a * indicates p<0.05 and a ** indicates p<0.01. Abbreviations: CPT-11, irinotecan; ip, intraperitoneal; mg HA/ccm, milligrams hydroxyapatite per cubic centimeter.           43    44 Figure 2-6- Hypersensitivity of Smarcal1del/del mice to etoposide and HU. (A-D) Photomicrographs of TUNEL-positive nuclei in splenic tissue from P36 Smarcal1del/del and Smarcal1+/+ mice following 5 daily ip-injections with etoposide. Panels C and D are higher magnification photomicrographs of the boxed areas in panels A and B, respectively. (E) Graph showing the average weight gain of Smarcal1del/del (n = 4) and Smarcal1+/+ (n=3) mice receiving daily ip-injections of etoposide from P28 through P84. (F-I) Photomicrographs of TUNEL-positive nuclei in splenic tissue from P36 Smarcal1del/del and Smarcal1+/+ mice following 5 daily ip-injections with hydroxyurea (HU). Panels H and I are higher magnification photomicrographs of the boxed areas in panels F and G, respectively. (J) Graph showing the average weight gain of Smarcal1del/del (n = 5) and Smarcal1+/+ (n = 4) mice receiving daily ip-injections of HU from P28 through P84. (K-V) Photomicrographs of representative sections of the distal femoral growth plate of Smarcal1+/+ and Smarcal1del/del male mice treated with PBS, etoposide, or HU for 8 weeks. The tissue was stained with H&E. Panels Q-V are higher magnification photomicrographs of the boxed areas in panels K-P, respectively. Note the poorly organized columns of chondrocytes in the hypocellular growth plates of the etoposide and HU treated Smarcal1del/del mice. Also note the near absence of trabeculae in the etoposide treated Smarcal1del/del mice. (W) Graph showing the bone mean density of the distal femur of Smarcal1+/+ and Smarcal1del/del mice treated with PBS, etoposide or HU. Scale bars = 100 µm. Error bars represent one standard deviation. For statistically significant differences between Smarcal1del/del and Smarcal1+/+ mice, a * indicates p<0.05 and a ** indicates p<0.01. Abbreviations: HU, hydroxyurea; ip, intraperitoneal; mg HA/ccm, milligrams hydroxyapatite per cubic centimeter.    2.3.7 Irinotecan, etoposide, and hydroxyurea exposure partially recapitulate the bone phenotype of SIOD in Smarcal1-deficient mice Following detection of hypersensitivity of Smarcal1del/del mice to genotoxic agents, we asked whether these exposures would partially or fully recapitulate SIOD in the Smarcal1del/del mice. Reminiscent of the growth plates of SIOD patients 154,156, the distal femoral growth plates of Smarcal1del/del mice treated with genotoxic agents were hypocellular, and chondrocytes in the proliferative and hypertrophic zones formed less organized columns (Figure 2-5M-T, Figure 2-6K-V, and Figure 2-7A). Also, like the reduced bone density observed in some SIOD patients 164,193, micro-computed tomography scanning of the distal femurs revealed significantly reduced bone density in Smarcal1del/del mice treated with CPT-  45 11 (Figure 2-5U); the bone density was not significantly different from controls after treatment with etoposide or HU (Figure 2-6W). Additionally, we hypothesized that if CPT-11 treatment moves chondrocytes past a state-changed threshold required for expression of skeletal dysplasia in Smarcal1del/del mice, then the growth rate of Smarcal1del/del mice should not return to normal when CPT exposure was removed. To test this, we monitored CPT-11- injected mice for another 12 weeks after discontinuation of CPT-11 injections; interestingly, the growth rates of the treated Smarcal1del/del mice approximated that of the Smarcal1+/+ mice negating this hypothesis (Figures 2-5E and K).       46  Figure 2-7- CPT-11, etoposide and HU exposure partially recapitulate SIOD phenotypes in Smarcal1del/del mice. (A) Plot of fold change in chondrocyte number in the proliferative (PZ) and hypertrophic (HZ) zones in the distal femoral growth plate of CPT-11, etoposide and HU -treated Smarcal1del/del (n=3-4) and Smarcal1+/+ (n=3-4) mice, relative to PBS-treated Smarcal1del/del (n=3) and Smarcal1+/+ (n=3) mice. (B) Graph showing urine albumin excretion by Smarcal1del/del mice relative to Smarcal1+/+ mice following PBS or CPT-11, etoposide and HU treatments. (C) Photomicrographs of representative Masson trichrome staining of the kidneys of Smarcal1+/+ and Smarcal1del/del male mice treated with PBS or CPT-11, etoposide and HU for 8 weeks. Scale bar = 100 µm. Error bars in panels A and B represent one standard deviation. For statistically significant differences between Smarcal1del/del and Smarcal1+/+ mice, a * indicates p<0.05.  2.3.8 Irinotecan, etoposide, and hydroxyurea exposure do not recapitulate the renal phenotype of SIOD in Smarcal1-deficient mice Besides skeletal dysplasia, renal failure is another cardinal feature of SIOD and one  47 SIOD patient developed renal failure following treatment with genotoxic agents 175. We hypothesized therefore that the Smarcal1del/del mice treated with genotoxic agents might also develop renal failure. Measurement of urine albumin excretion as well as Masson trichrome staining of the kidneys after 8 weeks of exposure to CPT-11, etoposide, and HU did not detect increased albuminuria in Smarcal1del/del mice or signs of sclerosis compared to controls (Figure 2-7B-C).  2.4 Discussion Despite superficial similarities to classical disorders of DNA repair, SIOD is a distinct disorder. The ectodermal defects cannot be ascribed to an obvious defect of NER as in Cockayne syndrome or xeroderma pigmentosum, and the immune defects cannot be ascribed to molecular defects of NHEJ as in ataxia telangiectasia and Nijmegen breakage syndrome. SIOD is not a classical cancer predisposition syndrome in which there is increased occurrence of multiple cancers although there is an increased prevalence of NHL and possibly osteosarcoma. The detection of EBV in 2 of the 3 NHL tumors suggests that the increased prevalence of NHL is attributable to the immunodeficiency. NHL is a heterogeneous group of malignancies with variable etiologic, morphologic, immune, genetic, and clinical features 194. Risk factors for NHL include congenital and acquired immunodeficiency, chronic antigen stimulation, autoimmune disorders, environmental factors, and genetic deficiency of NHEJ enzymes 195,196. Since the prevalence of NHL in the general population during the first ten years of life is 0.0026% 197, the relative risk for NHL in our cohort of SIOD patients is 1625 (95% confidence interval, 1567 to 1683). Although  48 defective DNA repair might in an undefined manner contribute to this, the absence of recurrent cancers during the lifetime of SIOD patients suggests that the immunodeficiency is likely the major contributor to the increased risk of NHL in SIOD. Whether the single SIOD patient with osteosarcoma represents an increased risk for osteosarcoma or is a coincidental co-occurrence is unclear. Assuming that this is not coincidental co-occurrence, the relative risk for osteosarcoma in our cohort of SIOD patients is 150 (95% confidence interval, 142 to 161) since the prevalence of osteosarcoma in the general population during the first twenty years of life is 0.0093% 197. An increased risk for osteosarcoma is also observed in the DNA repair disorders Rothmund-Thompson syndrome, RAPADILINO syndrome and Werner syndrome 198-200. Again, the absence of recurrent cancers during the lifetime of SIOD patients questions whether SIOD is a cancer predisposition disorder unless one assumes the patients die too early to manifest recurrent tumors. As an annealing helicase, SMARCAL1 resolves single to double-stranded DNA transitions 130 and contributes to reactivation of stalled replication forks, associates with double-strand DNA breaks, and facilitates cell cycle progression from the G2 to the M phase 139,141-144. Four domains within the SMARCAL1 enzyme are critical to these functions: a nuclear localization signal (NLS), a replication protein A (RPA) binding domain, two tandem HARP domains with annealing helicase activity, and a SWI/SNF ATPase and DNA binding domain 139,141-144,146,148,157. The deletion of the NLS, RPA and HARP domains is associated with SIOD in humans 13,155, and herein we show that mice with deletion of the homologous domains are hypersensitive to genotoxic agents. Extension of these observations to humans found that one SIOD patient was hypersensitive to anticancer agents 185, another with NHL  49 tolerated standard doses of R-CHOP chemotherapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) but then developed severe infection and delayed neutrophil recovery when treated with one cycle of ifosfamide (76.5 mL/hr x 24 hours) and etoposide (61 mg) for relapse and another with osteosarcoma tolerated and survived chemotherapeutic regimens adjusted for immunodeficiency. Definitive evidence of whether SIOD patients are hypersensitive to genotoxic agents will therefore only come as more patients are treated with anticancer agents; however, in the interim, caution in the use of such agents in SIOD patients seems advisable. Treatment of Smarcal1del/del mice with genotoxic agents induced growth retardation and skeletal features similar to those observed in SIOD. Additionally, exposure to CPT-11 did not induce a continued loss of growth potential after its removal suggesting that the growth of SIOD patients will be retarded by exposure to genotoxic agents during bone marrow ablation or treatment for cancer but that their growth rate will likely return to baseline when the genotoxic therapy is removed. In the management of SIOD, bone marrow transplantation is often considered for the immunodeficiency. Of concern, however, is the one SIOD patient who developed renal failure shortly after bone marrow transplantation 175. The question arises therefore as to whether this was the natural progression of SIOD or prematurely induced by the exposure to the genotoxic agents. The failure of genotoxic agents to induce albuminuria and renal sclerosis in Smarcal1del/del mice suggests that despite the potential hypersensitivity of SIOD patients to chemotherapy or bone marrow ablation, short-term treatment with genotoxic agents is unlikely to hasten renal failure. Nonetheless, since the four other SIOD patients treated with bone marrow transplantation died from viral infection, cerebral hemorrhage,  50 pulmonary embolism and pulmonary edema shortly after transplant, definitive evidence as to whether genotoxic agents cause long term sequelae in SIOD will only come as more patients survive cancer therapy and bone marrow transplantation. In the interim, the hypersensitivity of the Smarcal1del/del mice suggests caution in the use of such agents in SIOD patients. Regarding screening of SIOD patients for cancer, extra hematologic surveillance or imaging appear unnecessary. Patients should maintain close contact with their physicians, and symptoms that cannot be accounted for otherwise should be evaluated promptly as potential early indicators of cancer. In summary, SIOD patients have an increased risk for NHL that is likely attributable to their T-cell immunodeficiency and possibly for osteosarcoma; therefore patients should be evaluated promptly for cancer when they have unexplained symptoms. SIOD patients do not have a detectable defect of NER or NHEJ to explain their ectodermal or immunological features, but as shown by the hypersensitivity of Smarcal1del/del mice to some DNA damaging agents, some SIOD patients are likely to be hypersensitive to genotoxic drugs. We conclude therefore that extra caution should be observed during bone marrow ablation and administration of genotoxic agents to SIOD patients.   51 3. Contribution of gene expression alterations to the penetrance of SMARCAL1 mutations 3.1 Introduction In the previous chapter, I concluded that defective DNA repair is not a major contributor to the pathophysiology of SIOD. In this chapter I further analyze the molecular basis of SIOD and evaluate the role of SMARCAL1 in transcriptional regulation with the aim to characterize the underlying mechanism for the incomplete penetrance of SIOD. With the discovery of incomplete penetrance in 1925 201-203, it became apparent that not all individuals with identical mutant alleles necessarily manifest a trait. Incomplete penetrance has been attributed to genetic and environmental factors as well as to stochastic factors acting in development 5-8. In its strictest sense, incomplete penetrance applies to the entire lifetime of the individual 204, although some traits initially considered incompletely penetrant probably are better described as having age-dependent penetrance 204,205. Incomplete penetrance for autosomal recessive disorders is less commonly described than for autosomal dominant disorders, but it does occur 5,9-12. Examples of genetic and environmental modulators of incomplete penetrance in autosomal recessive disorders are illustrated by the following. The penetrance of the deafness phenotype in individuals with biallelic mutations of DFNB26 can be suppressed by a dominant allele of the DFNM1 locus 9. The penetrance of Bardet-Biedl syndrome can require the triallelic inheritance of mutations compromising BBSome complex assembly and vesicle trafficking to the ciliary membrane 10,206,207. In cystic fibrosis, the T7 polypyrimidine (poly-T) track variant in intron 8 of CFTR leads to more efficient splicing of exon 9 and reduces penetrance of the p.R117H mutation  52 and cystic fibrosis to 0.03%  for individuals with the compound heterozygous mutations p.[R117H; T7] + [∆F508] 11,208,209. Suppression of phenylketonuria in individuals with biallelic mutations of PAH is possible through restriction of dietary phenylalanine 187. Lastly, the penetrance of hemochromatosis in individuals with biallelic mutations of HFE depends on a combination of genetic factors such as polymorphisms in iron metabolism genes and environmental factors affecting the iron load such as alcohol, dietary iron intake, age, and gender 12,210. Despite delineation of the underlying mechanism for incomplete penetrance in some of these disorders, however, the basis of incomplete penetrance remains undefined for most autosomal recessive disorders including Schimke immuno-osseous dysplasia (SIOD). Recently two families have been reported in which siblings of affected individuals have incomplete penetrance of SIOD 159,171. As mentioned earlier in chapter 1, SIOD is associated with biallelic mutations in SMARCAL1 that cause loss of SMARCAL1 enzymatic function 130,155,157. The SMARCAL1 enzyme is a DNA annealing helicase 130 and serves as a DNA stress response protein that participates in the maintenance of genomic integrity at stalled replication forks 139,141-144. Since components of DNA repair pathways frequently participate in transcription 211-218 and since variations in transcription can modulate trait penetrance 8, we hypothesized that SMARCAL1 also modulates transcription and that penetrance of SMARCAL1 deficiency results from further alteration of gene expression. To test these hypotheses, we explored the effect of deficiency of the human, Drosophila, and mouse SMARCAL1 orthologues on gene expression and the interaction of such deficiency with environmental and genetic modifiers of transcription. We find that in humans, flies and mice, deficiency of the respective SMARCAL1 orthologue alters expression  53 of many genes and that in flies and mice deficiency of the respective orthologue is insufficient to cause disease in the absence of additional environmental or genetic insults.  3.2 Materials and methods 3.2.1 Drosophila genetic studies Kyoung Sang Cho and Marie Morimoto performed the Drosophila genetic studies. y1;P{SUPorP}Marcal1KG9850/CyO flies, which have a P element insertion in the Drosophila homologue of SMARCAL1 (Marcal1) at 25B4, were obtained from the Bloomington Drosophila Stock Center (Bloomington). The loss-of-function mutant was obtained by imprecise P element excision 219; this deleted 679-bp nucleotides extending from the middle of the first exon into the second intron. The Marcal1del flies were crossed to w1118 flies for 8 generations. For expression of tagged and untagged Marcal1 cDNAs, we generated 5 transgenic lines in w1118 flies using the pUAST and pUASP vectors: 1) UAST-Marcal1, 2) UAST-HA- GFP-Marcal1, 3) UAST-SMARCAL1, 4) UASP-HA-GFP-Marcal1 and 5) UAST-N-Dam- Marcal1. All the other UAS lines, insertions, and GAL4 lines used in this study were obtained from the Bloomington Drosophila Stock Center. Using the SMARCAL1 and Marcal1 overexpression lines MS1096-GAL4/MS1096- GAL4; +/+; pUAST-SMARCAL1/pUAST-SMARCAL1 and pUAST-Marcal1/CyO; tubulin- GAL4/TM3, Sb1, the F1 genetic screen was carried out at 28°C; all other crosses were performed at 20°C unless indicated otherwise in the text. For the wing phenotype analysis, images from ten wings for each genotype were acquired using a Zeiss Axiovert 200  54 microscope and scored by two independent readers. The reference for enhancement or suppression of the wing veins was the average of SMARCAL1 or Marcal1 overexpression flies crossed to w1118 mutants of three different genetic backgrounds (Bloomington stocks 3605, 5905, 6326). For egg hatching efficiency, three groups of 100 yw, Marcal1del/del, RpII2153 or 4 or 8 or K1/FM7 and RpII2153 or 4 or 8 or K1/FM7;Marcal1del/del embryos were collected at 20ºC. Hatching rates were calculated 45-48 hours after egg deposition.  3.2.2 ATPase activity assay Analyses of ATPase activity of Marcal1 and SMARCAL1 were performed by Kyoung Sang Cho and Leah Elizondo. The ATPase assay was performed with the purified Marcal1, SMARCAL1, and Smarcal1 proteins as previously described 157,220. To assay ATPase activity, HA-GFP-Marcal1 was immunoprecipitated from protein extracts of tubulin- GAL4, UAST-HA-GFP-Marcal1/TM6B flies using the monoclonal anti-HA agarose conjugate (Sigma). His-tagged human SMARCAL1 and mouse Smarcal1 were purified from protein extracts of a stably transfected Flp-In T-Rex-293 cell lines (Invitrogen) using a His- Bind Purification Kit (Novagen/EMD). ATPase activity of the SMARCAL1 mutants was measured using the Kinase-Glo Luminescent Kinase Assay (Promega). The ATPase reaction was performed using 20 ng/µl of SMARCAL1, ATPase Buffer (20 mM KPO4, 2 mM MgCl2, 40 mM KCl, 1 mM DTT, 100 µg/ml BSA, 100 µM ATP), and 75 nM hairpin loop DNA or ss-DNA or ds-DNA or total RNA or mRNA or tRNA. The DNA or RNA substrates were prepared by heating to 70°C for 3 min, followed by slow cooling to room temperature over time. Reactions were incubated at 37°C for 1 h. Kinase-Glo buffer was added to each  55 reaction mixture and incubated for 10 min at room temperature. ATPase activity was measured by luminescence.  3.2.3 Immunofluorescence Kyoung Sang Cho performed the immunofluorescence studies of Drosophila tissues. Polytene chromosomes of third instar larva were prepared and processed as described 221 and immunostaining of the polytene chromosomes, wing imaginal discs, and embryos were performed according to standard procedures 222. We used mouse anti-HA (1:200, Sigma), rabbit anti-acetyl-histone H4 (1:200, Upstate Biotechnology/Millipore), rabbit anti-trimethyl- K4-histone H3 (1:200, Upstate Biotechnology/Millipore) antibodies as primary antibodies. DNA was stained with 4',6-diamidino-2-phenylindole (1:1000, Sigma). Fluorophore- conjugated secondary antibodies were used to detect the primary antibodies. Immunofluorescence studies for human SMARCAL1 were performed with an anti- SMARCAL1 antibody as previously described 157,223.  3.2.4 High-dimensional (11-Color) flow cytometry Mrinmoy Sanyal at Stanford University performed the flow cytometry analysis. Lymphoid tissues (spleen and thymus) from Smarcal1-/- and Smarcal1+/+ mice were harvested in RPMI-1640 medium containing 10% FCS. The tissues were pressed through 70 µm nylon cell strainer, suspended in 10 mL of PBS, and incubated with red cell lysis buffer (150 mM NH4CI) for 5 minutes. After washing with 10 volumes of PBS, single cell suspensions were stained with fluorochrome-conjugated anti-mouse B220, TCRaβ, CD4,  56 CD8, CD44 and CD25 antibodies (BD Biosciences) (Table 3-1). “Fluorescence-minus-one” controls were included to determine the level of nonspecific staining and autofluorescence associated with subsets of cells in each fluorescence channel. Propidium iodide was added to all samples before data collection to identify dead cells. High-dimensional flow cytometry data were collected on a LSRII FACS instrument (BD Biosciences). FLOWJO (TreeStar, San Carlos, CA) software was used for fluorescence compensation and analysis.  Table 3-1- Antibodies used for flowcytometry. Antigen Species specificity Clone Conjugation TCRβ  Mouse H57-597 APC CD4 Mouse GK1.5      APCCy7 CD8 Mouse 53-6.7 Pacific Blue CD44 Mouse IM7 PerCPCy5.5 CD25 Mouse PC61 PE B220 Mouse      RA3-6B2 Alexa 700  3.2.5 Cell cultures Human dermal fibroblasts from SIOD patients and unaffected individuals, and fibroblasts from Smarcal1+/+ and Smarcal1del/del embryos were cultured in DMEM (Gibco/Invitrogen) containing 15% fetal bovine serum (Gibco/Invitrogen) and 1% antibiotic/antimycotic (Gibco/Invitrogen) at 37°C and 5% CO2. The SIOD patient fibroblasts had the following mutations in the SMARCAL1 protein or gene: SD8: p.[L397fsX40]+[?], SD31: homozygous deletion of the first 5 exons of SMARCAL1 (NT_005403.17:g.[67482574_67497178del]+[67482574_67497178del]), SD60: p.[E848X]+[E848X], SD120: p.[R764Q]+[E848X], and SD123: p.[R17X]+[R17X].   57 3.2.6 Heat stress Drosophila: Kyoung Sang Cho, Joanna Lubieniecka, and Marie Morimoto performed the heat stress experiments in Drosophila. For assessing survival at 30ºC, 200 flies of each genotype (yw and Marcal1del/del) and sex were maintained in a temperature and humidity controlled incubator for 10 days. Surviving flies were tallied on day 10. To assess reproductive capacity, three groups of 100 yw and Marcal1del/del embryos (300 total for each genotype) were reared at different temperatures: group 1 was reared at 25ºC; group 2 was reared at 25ºC for the first 5 days and then shifted to 30ºC (25/30); and group 3 was reared at 30ºC. Flies surviving to adulthood were counted. For assessing gene or protein expression, ten 1-3-day-old female flies from each genotype (yw and Marcal1del/del) enclosed in a plastic vial with a cotton plug were heat stressed at 37°C in a water bath for 15, 30, 45, and 60 minutes with or without recovery for 1 hour at room temperature. Mouse: Three groups of 4- 5 Smarcal1+/+ and 4-12 Smarcal1del/del female mice at 3-4 months of age were heat stressed at 39.5°C for 1, 3 and 10 hours as previously described 224. Human dermal fibroblasts: SIOD (SD31, SD120, and SD123) and control fibroblasts were heat stressed for 1 hour at 43°C followed by 1 hour of recovery at 37°C as previously described 225.  3.2.7 α-amanitin treatment Mice: Beginning at age 30 days, Smarcal1del/del and Smarcal1+/+ mice were injected intra-peritoneal daily with either PBS (n=9 Smarcal1del/del and n=7 Smarcal1+/+) or 0.1 mg/kg α-amanitin (Sigma) (n=9 Smarcal1del/del and n=7 Smarcal1+/+) diluted in PBS for twelve weeks. At the end of treatment radiography was performed using the Faxitron X-ray cabinet. Human dermal fibroblasts: SIOD (SD31, SD120, and SD123) and control dermal fibroblasts  58 were cultured in 96-well plates (3 x 103 cells/well). After 24 hours the media was supplemented with 0 or 1 µg/ml α-amanitin and the cells were analyzed 48 hours later for cell proliferation and viability using the MTT assay as previously described 226. To measure proliferation with Click-iT EdU assay (Invitrogen), fibroblasts were cultured in the Lab-Tek 8 chambered coverglass system (7.5 x 103 cells/well). After 24 hours the media was supplemented with 0 or 1 µg/ml α-amanitin and after another 24 hours 10 µM EdU was added to each well. EdU detection and analysis was performed after 24 hours using Alexa Fluor 555 and Zeiss Axiovert 200 microscope according to manufacturer’s protocol. Murine embryonic fibroblasts: Sensitivity of Smarcal1del/del and Smarcal1+/+ fibroblasts to α- amanitin (0 and 1 µg/ml) was performed as described above for human fibroblasts.  3.2.8 RNA extraction RNA was extracted from the indicated tissue using the RNeasy Mini Kit (Qiagen). Elimination of genomic DNA was performed using either on-column DNase (Qiagen) or DNase I (Ambion) following RNA extraction at 37ºC for 1 hour. cDNA synthesis was performed using SuperScript III First Strand Kit (Invitrogen) or qScript cDNA SuperMix (Quanta Biosciences).  3.2.9 RT-PCR Human and mouse: RT-PCR was performed as described 13,157 using the primers listed in Table 3-2. Quantitative real-time RT-PCR was performed using the ABI 7500 Fast system and PerfeCTa Sybr Fastmx Lrx 1250 (Quanta Biosciences) or human or mouse heat  59 shock proteins and protein folding array (SABiosciences) following the manufacturer’s instructions. Drosophila: For cloning, the Marcal1 cDNA was amplified by PCR using Platinum Taq DNA Polymerase (Invitrogen) with the primers listed in Table 3-2 (94ºC for 5 minutes, 35 cycles of 94°C for 15 seconds, 55°C for 30 seconds and 68°C for 1 minute). PCR amplification of other cDNAs was done using HotStarTaq DNA Polymerase (Qiagen) with the primers listed in Table 3-2 (94ºC for 15 minutes, 30 cycles of 94°C for 1 minute, 57°C for 1 minute and 72°C for 1 minute). Quantitative PCR was performed using the ABI 7500 Fast system and PerfeCTa Sybr Fastmx Lrx 1250 (Quanta Biosciences).                 60 Table 3-2- Oligonucleotide primers. Primer Sequence Marcal1 cloning Coding-F ATGTCCACTTGCAGTTCATCCGAGATAGC HA-coding-F ATGTACCCATACGACGTCCCAGACTACGCTATGTC CACTTGCAGTTCATCCGAGATAGC Coding-R CTAAATATCTAATTCCAAAAAAGCCTCATC  Marcal1 mutagenesis K275R 5′ primer GAAATGGGCCTGGGCAGAACCTATCAGGCCTTG 3′ primer CAAGGCCTGATAGGTTCTGCCCAGGCCCATTTC  Marcal1 deletion identification 5′-primer TCAATTGATGTCGCAGCATGTCCACTT 3′-primer-1 CCAGATACGGGTTTGGCCATCGTAGCACTT 3′-primer-2 GGTGATTAGCACCTTGGCCTCACCCACATA 3′-primer-3 AGGACGACAGTCTCACTGCGGATGGAAGAA 3′-primer-4 TTCCTGAATTGCAGGCTTTTAGGGAGAGCA 3′-primer-5 GCCGCGATATCCGACTCAGCCTTGTTTACT 3′-primer-6 GCCAGGTCACATGAAACCGGCTATCTGAAA  Drosophila heat shock RT-PCR primers Hsp83 5′ primer GGGTTTCTACTCCGCCTACC 3′ primer CAGTCGTTGGTCAGGGATTT Hsp68 5′ primer GACAACGGCAAACCAAAGAT 3′ primer GCGTCAATCTCCAAAGAAGC Hsp67Ba 5′ primer ATCGCCATCATCCGTACAAT 3′ primer CTGCGCATCCTTATCCTTCT Hsp67Bb 5′ primer GAAGGAAGAGCTCCAGCAGA 3′ primer ATTCATTCCAGGAGCCTTTG Hsp67Bc 5′ primer CCACGATATGTTCCCGAATC 3′ primer GAACTCCATCCTCCGACAAA Hsp60 5′ primer GAGGTTATCGAGGGCATGAA 3′ primer TACTCGGAGGTGGTGTCCTC Hsp60B 5′ primer CAAAGTGGGTCCAAGAGGAA 3′ primer CGCATTTCCAAAAGTCCTGT Hsp27 5′ primer GAGGATGACTTCGGTTTTGG 3′ primer ACTTGGCCTGTTCCTTGCT Hsp26 5′ primer GATGGTGCCCTTCTATGAGC 3′ primer CCTTGGGATTCTCCTTCACA Hsp23 5′ primer GTGTCGAAAATCGGAAAGGA 3′ primer  CCTTGGGATTCTCCTTCACA  61 Primer Sequence  Hsp22  5′ primer TGCGTTCCTTACCGATGTTT 3′ primer ACCTTGTCCGCCTCGTATC Hsf 5′ primer CCGCTGGCGGTAATATTCTA 3′ primer CCCAAATTTTTGTTGCTGGT DnaJ-1 (Droj1) 5′ primer CCACATTTGCCCAGTTCTTT 3′ primer TTGTCGCGAATGATGAAGAC Gapdh2 5′ primer ATCGTCGAGGGTCTGATGAC 3′ primer TCAGCTTCACGAACTTGTCG  Drosophila N-Dam-Marcal1 RT-PCR primers 5′ primer GGCACACGTAAAAAGGTGGA 3′ primer GGACCGCTCCTGTTGATCT  Mouse RNA polymerase II large subunit RT-PCR primers    Polr2a 5′ primer GGAGGGTGATGAGGATCTGA 3′ primer GCCCAACACAAAACACTCCT   Polr2b 5′ primer GTATTGAACCGCCTGACGTT 3′ primer TCCAAAATTGGAGAGGGTTG  Human SMARCAL1 RT-PCR primers 5′ primer CGCCTGACAGCTGGCCTGATC 3′ primer TTGGCAACCTTTAGGACCGGCATAG  Human SMARCAL1 RT-PCR primers 5′ primer CGCCTGACAGCTGGCCTGATC 3′ primer TTGGCAACCTTTAGGACCGGCATAG  Human GAPDH RT-PCR primers 5′ primer CTTTTGCGTCGCCAGCCGAG        3′ primer GGTGACCAGGCGCCCAATACG  Human RNA polymerase II large subunit RT-PCR primers   POLR2A         5′ primer CGCTTAAGCCTTCCAACAAG       3′ primer GAGGACGACCTTGCTGTCTC   POLR2B       5′ primer AAGAACCGCCACGATATTTG       3′ primer TTGACCGCAACATAATTGGA  3.2.10 Urinary protein and creatinine measurement Darren Bridgewater from McMaster University performed this experiment. Spot urine was collected from Smarcal1del/del and Smarcal1+/+ mice injected with alpha-amanitin or PBS.  62 Urinary creatinine measurement was performed using creatinine assay kit from Cayman Chemical Company (Ann Arbor) following the manufacturer’s instructions. Urinary albumin measurement was performed using the bromocresol green method. Ten dilutions of albumin standards from 0 to 8 g/dL were prepared. Two microliters of standards and samples were added to a 96-well plate in duplicates and then 200 µL of albumin reagent (Sigma) was added and mixed. After incubation at room temperature for 1 minute, the absorbance was measured at 630 nm and the concentration was calculated. Finally, the albumin/creatinine ratio was calculated for each sample.  3.2.11 Histopathology Smarcal1del/del and Smarcal1+/+ mice tissues were fixed in 4% PFA in PBS, paraffin embedded, and cut into 5 µm sections according to standard protocols 177. After H&E staining, tissues were analyzed using a Zeiss Axiovert 200 microscope. For the distal femoral growth plate analysis, we counted all chondrocytes within the proliferative and hypertrophic zones and within 250 µm of the vertical midline.  3.2.12 Apoptotic analysis Acridine orange staining for detection of apoptotic cells in 20-23 hour Drosophila embryos was performed as previously described 182. For human fibroblasts and MEFs, apoptotic cells were detected by TUNEL assay using ApopTag Peroxidase In Situ Apoptosis Detection Kit (S7100, Chemicon).   63 3.2.13 RpII knock-down Transfection of fibroblasts was carried out according to the Amaxa Biosystems Human Dermal Fibroblast Nucleofector Kit or the Amaxa Biosystems MEF Nucleofector Kit optimized protocol. For human dermal fibroblasts, we electroporated (Program U-023) 5.0 µg of a plasmid expressing a GFP-tagged shRNA targeting POLR2A (insert sequence: AACGAGTTGGAGCGGGAATTT) or POLR2B (insert sequence: TCAGGTTCATGTTTGCAATCT) (SABiosciences) or 5.0 µg of a plasmid expressing a GFP-tagged non-silencing shRNA (insert sequence: GGAATCTCATTCGATGCATAC) (SABiosciences). For MEFs, we electroporated (Program A-023) 2.5 µg of siRNA duplex 1 (Alexa-488-CAGAATCTGGCTACACTTAAA, Qiagen) or siRNA duplex 3 (Alexa-488- CTCAATGATGCTCGAGACAAA, Qiagen) targeting Polr2a or non-silencing siRNA duplex (Alexa-488-ACGUGACACGUUCGGAGAA, Qiagen). Thirty-six hours later, we selected transfected cells by fluorescent activated cell sorting and plated them at a concentration of 1x103-104 cells/100 µL/well of 96-well Wallac cell culture plates. By quantitative real-time RT-PCR at 48 and 96 hours post-transfection, this method reduced expression of POLR2A by 50-80%, of POLR2B by 45-50%, and of Polr2a by 55-65% (siRNA duplex 1) or 70% (siRNA duplex 3). Cell viability and proliferation rate were assessed at 48, 72 and 96 hours post-transfection using the Cell Proliferation Reagent WST-1 (Roche) according to the manufacturer’s protocol and the relative proliferation rates were calculated for either the 24-hour (human fibroblasts) or 48-hour (mouse fibroblasts) intervals. The rate of cell growth in each group was compared for statistically significant differences using the two-tailed Student’s t-test.   64 3.2.14 RNA sequencing and data analysis Chad Shaw at Baylor College of Medicine performed the data analysis for the RNA sequencing. For RNA sequencing, three samples of liver RNA were extracted from each group of 3-4-month-old Smarcal1del/del and Smarcal1+/+ female mice at 20°C and after 1 hour at 39.5°C. The RNA samples for each group were pooled and RNA sequencing libraries were constructed and sequenced using the whole transcriptome shotgun sequencing procedure, as previously described 227-229. The sequencing was performed using two lanes per genotype of an Illumina Genome Analyzer II following the manufacturer’s instructions. Image analysis and base calling were done by the GA pipeline v1.0 (Illumina, Hayward, CA) using phasing and matrix values calculated from a control phiX174 library run on each flow cell. Raw quality scores were calibrated by alignment to the reference mouse genome (NCBI build 37, mm9) using ELAND (Illumina, Hayward, CA). Short read sequences obtained from the Illumina Genome Analyzer for each sample were mapped to the reference mouse genome (NBCI build 37, mm9) plus a database of known exon junctions 230 using MAQ in paired end mode 231. The exon junction sequences consisted of N-1 nucleotides of the donor exon ‘spliced’ to N-1 nucleotides of the acceptor exon for every known pair of exons in the mouse transcriptome, where N is the read length. This was designed to rescue reads that would not map to the mouse reference genome due to their spanning a splice site. The maximum insert size (-a parameter) to MAQ was 500. Mouse sequence summary count data were log2 scaled. All genes with a count per kilobase summary greater than 4 (log2 >2) in either temperature condition in Smarcal1+/+ samples were identified as well enough represented to be considered in our analysis. The mean log2 scaled expression summary (across both Smarcal1+/+ and Smarcal1del/del) was determined for each temperature state. The deviation of  65 each sample (computed by subtracting the observed value from the mean) for the base temperature condition was subsequently determined and plotted in the heatmap. For both heatmaps, the sort order was computed as follows: the difference between Smarcal1+/+ and Smarcal1del/del in each treatment condition was divided by the log2 expression value in the Smarcal1+/+ in the baseline temperature condition. The resulting scores for each treatment condition were averaged and then the genes were sorted according to this score for difference in the high temperature state. The density plots represent the distribution of expression difference values for each temperature state between the Smarcal1+/+ and Smarcal1del/del. The same genes plotted in the heatmap are represented in the line plot (probability density estimate plot).  3.2.15 Microarray gene expression analysis in human cells Chad Shaw at Baylor College of Medicine performed the data analysis for the microarray gene expression in human cells. For analysis of gene expression in primary cultured human dermal fibroblasts, 5.0 µg of total RNA from two biologically independent replicates was extracted from two SIOD (SD8 and SD60) and three from a control skin fibroblast cell lines, labeled and hybridized to Affymetrix Human Genome U133 Plus 2.0 Arrays. CEL file data were normalized using the GCRMA package from the Bioconductor suite 232. Present-absent p values were calculated in R using the Affymetrix test statistic 233. Subsequent to deriving expression measures, an ANOVA model was fit to each genotype (Y=Genotype + Error), where Y is the normalized expression measure for a gene on a particular microarray. The model was fit using the LIMMA R package 234. Coefficients for the linear model were estimated by least squares method. Linear contrast scores were used to  66 compare gene lists between the genotypes. Determination of up- and down-regulation of expression is made by computing a T-statistic for SIOD and control samples, and then adjusting the variance estimate using the empirical Bayes method as implemented in the LIMMA R Package. The p-values obtained are then converted to Q-values using the LBE R package 235,236 to control the false discovery rate (FDR) at 0.05. A pair of lists was determined as well as linear contrast values (log2 fold changes) comparing the SIOD patients and the control. A Q-value cutoff of 0.05 was used to determine a pair of lists for the probe sets either overexpressed or underexpressed.  3.2.16 Microarray gene expression analysis in flies Chad Shaw at Baylor College of Medicine performed the data analysis for the microarray gene expression in flies. For analysis of gene expression in flies, 5.0 µg of total RNA from three biologically independent replicates was extracted from 1) yw, 2) Marcal1del/del, 3) RpII2154/FM7, and 4) RpII2154/FM7;Marcal1del/del ovaries at 20°C and from yw and Marcal1del/del at 25°C, labeled and hybridized to Affymetrix Drosophila Genome 2.0 arrays. Affymetrix CEL file data were processed using the robust multi-array average (RMA) method as implemented in Bioconductor. The yw expression data from both the base and high temperature states were used to determine a set of "expressed" transcripts using the maximum RMA value for each probe set across all individual yw samples. Probe sets were identified as being expressed when the maximum RMA value was greater than or equal to six–a value determined by observation of the distribution of expression values in the yw samples. This analysis identified 6,437 probe sets, which were subsequently analyzed and visualized in the other genotypes. Linear contrast of the RMA values for each treatment was  67 compared by first computing the mean of RMA values with each treatment group and then comparing different pairs of groups of interest. The heatmap was generated by sorting the differenced values and plotting each probe set. The density plots are kernel probability density estimates of the linear contrast scores for the ensemble of 6,437 probe sets, determined to be expressed in yw. FlyBase IDs provided by the Affymetrix website were used to query the FlyBase information.  3.2.17 Gene Ontology (GO) analysis GO analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID ) v6.7 237,238. Differentially expressed genes (q-value <0.05) between yw vs. Marcal1del/del, yw vs. RpII2154, and yw vs. RpII2154;Marcal1del/del ovaries at 20°C, yw vs. Marcal1del/del ovaries at 25°C, differentially expressed genes (fold-change > 2) between Smarcal1+/+ vs. Smarcal1del/del mouse livers at 20°C, Smarcal1+/+ vs. Smarcal1del/del mouse livers at 39.5°C, and differentially expressed genes (q-value <0.05 and fold-change > 2) between control vs. SIOD fibroblasts, were analyzed for their enrichment in biological process GO biological process annotation terms at all levels.  3.2.18 DNA adenine methyltransferase analysis of Marcal1 binding Marie Morimoto made the Dam-Marcal1 constructs and Bas Tolhuis at the Netherlands Cancer Institute performed the DNA adenine methyltransferase analysis of Marcal1 binding. Full-length Marcal1 was cloned into pNDamMyc such that Dam was fused in frame with the amino terminus of Marcal1. This construct was transfected into Kc167 cells as previously described 239. For microarray analyses, we used a 385K tiling array  68 (NimbleGen Systems/Roche, Madison WI), which was designed by the group of Bas van Steensel (NKI – Amsterdam, The Netherlands). This array contained 60-mer oligomers roughly every 300 base pairs across the entire nonrepetitive part of the Drosophila genome (release 4.3). We made two independent biological replicate experiments that were differentially labeled and in parallel hybridized to the microarrays. The individual replicates were normalized, using Lowess Normalization methods. For each replicate, we defined Marcal1 binding sites as described 239. First, we removed random noise by calculating a running median with a window size of 15 consecutive probes (i.e. ~4.5 kbp). We defined a Marcal1 binding site as: at least two consecutive probes along the DNA template that have a log2-transformed binding ratio (N-Dam-Marcal1/Dam) higher than an empirically determined threshold. This threshold was determined on randomly shuffled data, giving an estimate of the FDR. This FDR was defined as the number of binding sites in the random data / the number of sites in observed data. We chose a threshold that equals the standard deviation of log2-transformed binding ratios across all probes on the microarray, giving FDRs of ~1% and 0% for replicates 1 and 2, respectively, indicating that our definition of a binding site is highly stringent. Next, we further increased confidence in our data by selecting only binding sites that were identified in both replicates. Finally, we identified target genes using the start and end coordinates of Marcal1 binding sites and FlyBase R4.3 gene annotation (www.flybase.org). As the minimal requirements for a target gene, we chose at least one nucleotide overlap with at least one Marcal1 binding site. We used this list of target genes to divide previously published data into target and non-target genes. These data sets include chromatin accessibility 240, gene expression 241, DNA replication timing241, histone acetylation 242, histone methylation 239, and a non-specific antibody control (rabbit anti-IgG)  69 239. Data derived from Greil et al. 240 and Schubeler et al. 241,242 compared 638 targets to 4718 non-targets, and data from Tolhius et al. 239 compared 1026 targets to 8964 non-targets. Density distribution plots, statistical tests, and alignment plots of Marcal1 binding around transcription start and termination sites of 1822 target genes were made with R (www.r- project.org).  3.3 Results 3.3.1 Deficiency of Marcal1 is insufficient for disease manifestations in Drosophila To investigate whether deficiency of SMARCAL1 orthologues is sufficient to cause disease, we developed a Drosophila melanogaster model. Similar to human SMARCAL1, the only fly orthologue (Marcal1), also encodes a DNA-specific ATPase (NP_608883.1, Marcal1) (Figure 3-1A and B); it shares 32% amino acid identity and 44% amino acid similarity with SMARCAL1 155. Like human SMARCAL1 and mouse Smarcal1 157,158,162,223, Marcal1 is a nuclear protein and is highly expressed in the early embryo and developing tissues and gonads (Figure 3-2). By mobilizing P-element KG9850 and screening for deletions of the Marcal1 gene, we generated a loss-of-function Marcal1 mutant that lacked 679-bp extending from the middle of the first exon into the second intron (NM_135039.1:c. 673_1258delinsATGATGAAATAACATCATTATATCGATTAACACAG, p.G225MfsX3, Marcal1del, Figure 3-3A). Marcal1del/del flies did not express Marcal1 mRNA and protein (Figure 3-3B-C), and surprisingly, like the unaffected individuals with biallelic SMARCAL1  70 mutations 159,171, the mutant flies exhibited no morphological differences from wild type flies and had a normal life span at 20ºC.            71  Figure 3-1- DNA specificity and chromatin binding of Marcal1, SMARCAL1 and Smarcal1. (A) Radiograph of a thin layer chromatography plate showing that Marcal1 has DNA- dependent ATPase activity. Purified hemagglutinin (HA)-tagged Marcal1 was immunoprecipitated from transgenic flies expressing HA-tagged Marcal1 under induction (I) of the tubulin-GAL4 driver. Control immunoprecipitates were prepared in parallel from transgenic flies carrying the UAS-HA-Marcal1 transgene but not expressing it (U, uninduced). Both sets of immunoprecipitates were assayed for ability to hydrolyze ATP to AMP and pyrophosphate (Pi) in the presence (+) or absence (-) of DNA, mRNA, rRNA, tRNA, or total RNA. Calf intestinal phosphatase (CIP) was used as positive control, and the samples containing only DNA or buffer without immunoprecipitated enzyme were used as negative controls. (B and C) Plots showing the DNA-dependent ATPase activity of SMARCAL1 and Smarcal1 as measured by Kinase-Glo Luminescent Kinase Assay. For these assays, His-tagged SMARCAL1 and Smarcal1 were purified from HEK293 cells using a nickel column following induction of expression with tetracycline. Nickel column elution fractions from uninduced cells were used as negative controls. Error bars represent one standard deviation. (D) Photographs showing immunofluorescence localization of HA-tagged Marcal1 on polytene chromosomes from tub-GAL4, UAS-HA-Marcal1 flies. Note that Marcal1 (green) binds the interband regions and co-localizes with trimethyl-K4-histone H3 (H3K4me3; red). (E) Photographs showing immunofluorescence localization of human SMARCAL1 on polytene chromosomes from MS1096-GAL4, UAS-SMARCAL1 flies. Note that SMARCAL1 (green) also binds the interband regions and co-localizes with H3K4me3 (red). The data for panels A, D and E was provided by Kyoung Sang Cho and data for panel B was provided by Leah Elizondo.   72  Figure 3-2- Temporospatial expression of Marcal1 in Drosophila cells and tissues. (A) Photograph of S2 cells expressing GFP-tagged Marcal1. (B) Photograph of an ovarian nurse cell dissected from a transgenic fly expressing GFP-tagged Marcal1. (C) Photograph of immunofluorescently detected endogenous Marcal1 expression in the wing imaginal disc. The upper panel was treated with anti-Marcal1 serum and the lower panel with preimmune serum. (D) Northern blot analysis of Marcal1 expression during Drosophila development. The data for this figure was provided by Kyoung Sang Cho.    Figure 3-3- Generation of a fly model for the study of Marcal1 function. (A) Diagram showing that imprecise excision of P element KG9850, which is inserted within exon 1 of Marcal1, was used to generate a fly line deficient for Marcal1 (Marcal1del/del). The mutation generated by this imprecise excision was c.673_1258delinsATGATGAAATAACATCA, p.225Mfs228X. (B) Northern blot analysis showing the absence of Marcal1 mRNA in Marcal1del/del flies. 3 µg of mRNA were used for Northern blot analysis. (C) Immunoblot analysis showing the absence of Marcal1 protein in Marcal1del/del flies. One yw, one Marcal1del/del, and one T>GFP-Marcal1 (tubulin-GAL4, UAS-HA-GFP-Marcal11/TM6B) male fly (about 10 µg of protein of extract) were used. The rabbit polyclonal anti-Marcal1 antibody was used to probe the blot. The data for this figure was provided by Kyoung Sang Cho.   73 3.3.2 Deficiency of murine Smarcal1 is insufficient to cause disease To address whether the absence of disease in Marcal1-deficient flies was attributable to the absence of tissues such as bones, mammalian kidneys, and T cells, we analyzed mice deficient for Smarcal1 181. Smarcal1 is the only SMARCAL1 orthologue in mice and encodes a DNA-specific ATPase (NP_061287.2, Smarcal1) (Figure 3-1C) that has 72% amino acid identity and 78% amino acid similarity to the human SMARCAL1 155. The Smarcal1del/del mice did not show developmental, growth or physical abnormalities or other signs of disease through 24 months (Figure 3-4A-L). Analysis of lymphatic tissues showed that Smarcal1del/del mice had a reduction in their B-cell count but no T-cell deficiency (Figure 3- 4M-P). Therefore, like the incomplete penetrance reported for the two human families 159,171, deficiency of the respective SMARCAL1 orthologue in fruit flies and mice was insufficient to cause overt disease.  74  Figure 3-4- Phenotypic comparison of Smarcal1+/+ and Smarcal1del/del mice. (A) Graphs comparing the body weights and lengths of newborn (P0) Smarcal1+/+(n=20), Smarcal1+/del (n=55), and Smarcal1del/del (n=20) littermates. The error bars represent one standard deviation. None of the differences in these graphs are statistically significant. (B) Genotypes of the offspring of Smarcal1+/del x Smarcal1+/del parents. Note that the genotypes of the offspring have a Mendelian ratio both at birth and at weaning. (C) Graphs comparing the body weights and lengths for each sex of 6 month-old Smarcal1+/+ (9 males and 21 females) and Smarcal1del/del (31 males and 24 females) littermates. The error bars represent one standard deviation. None of the differences in these graphs are statistically significant. (D-G) Photographs of representative H&E staining of the proximal tibia of 6 month-old Smarcal1+/+ (D and E) and Smarcal1del/del (F and G) mice. Panels E and G are higher magnifications of the boxed areas on panels D and F, respectively. Note similar structure and cellularity of the growth plates between Smarcal1+/+ and Smarcal1del/del mice. Bar = 100µm. (H-K) Photographs of representative H&E staining of the kidney cortex from 6 month-old Smarcal1+/+ (H and I) and Smarcal1del/del (J and K) mice. Panels I and K are higher magnifications of the glomeruli boxed on panels H and J, respectively. Note similar glomeruli and tubular structures between Smarcal1+/+ and Smarcal1del/del mice. Bar = 100 µm. (L) Graphs comparing the urinary excretion of protein over 24-hours by 2 and 12 month-old Smarcal1+/+ (n=4) and Smarcal1del/del (n=4) mice. The protein excretion was normalized to the urinary creatinine. The error bars represent one standard deviation. (M) Plots show the  75 representative FACS analysis of the thymus from Smarcal1+/+ (n=3) and Smarcal1del/del (n=3) mice for different lymphocyte subsets. Thymocytes were initially analyzed for T-cell developmental stages based on CD4 and CD8 staining. Double negative (CD4-CD8-) cells were further resolved based on CD44 and CD25 expression into DN1 (CD44+CD25-), DN2 (CD44+CD25+), DN3 (CD44-CD25+) and DN4 (CD44-CD25-) fractions. (N) Quantification of lymphocyte subsets analyzed in panel M. The error bars represent one standard deviation. (O) Plots show the representative FACS analysis of the spleen from Smarcal1+/+ (n=3) and Smarcal1del/del (n=3) mice for different lymphocyte subsets. Splenic B and T cells were initially resolved based on expression of B220 (CD45R) and TCRαβ. Subsequently, T cells (TCRab+) were further resolved into CD4+ and CD8+ T-cell subsets. Note that there is a significant reduction of B cells (B220+) in Smarcal1del/del spleen. (P) Quantification of lymphocyte subsets analyzed in panel O. The error bars represent one standard deviation. The data for panel L was provided by Darren Bridgewater and data for panels M-P was provided by Mrinmoy Sanyal.  3.3.3 SMARCAL1 and Marcal1 bind open chromatin To better understand the function of proteins encoded by SMARCAL1 orthologues in the nucleus, we analyzed the association of SMARCAL1 and Marcal1 with chromatin. We used immunofluorescence to localize HA-tagged SMARCAL1 and Marcal1 on polytene chromosomes from third instar fruit fly larvae. Both SMARCAL1 and Marcal1 colocalized with trimethyl-K4 histone H3 and acetylated histone H4, which are markers of transcriptionally active chromatin (Figure 3-1D-E and Figure 3-5). To confirm and to define better this binding, we tagged Marcal1 on the amino terminus with DNA adenine methyltransferase (Dam), which methylates adenine at GATC sequences 243; we judged the fusion protein to be functionally active since in vivo expression of Dam-Marcal1 induced extra wing veins similar to untagged Marcal1 or SMARCAL1 157 (Figure 3-6A-B and  Figure 3-7). Following transient expression of Dam-Marcal1 in Drosophila Kc167 cells, we found increased adenine methylation in regions enriched for trimethyl-K4 histone H3 and for acetylated histone H3 and H4 (Figure 3-6C-E). The regions of increased adenine methylation also corresponded with accessible, transcriptionally active and early replicating chromatin  76 (Figure 3-6F-H and J). In contrast, adenine methylation was decreased in regions enriched for trimethyl-K27 histone H3 (Figure 3-6I), a mark of inactive chromatin 239. The enrichment for adenine methylation spanned transcribed regions and was preferentially enriched in promoter regions compared to the rest of the gene (Figure 3-6K-L). To determine if this preference for promoter regions might correlate with promoter proximal pausing or stalling of RNA polymerase II (RpII) 244, we compared our adenine methylation data to published RpII distribution in S2 cells 232 or Toll10b embryos 245. This showed that adenine methylation was enriched in genes bound by RpII regardless of whether the RpII was stalled or active and was underrepresented in genes without RpII binding. In contrast, transcriptionally inactive Polycomb (Pc) targets 239 were underrepresented among actively transcribed RpII genes (Table 3-3). Thus, Marcal1 likely has a broader role in transcription than modulating promoter proximal pausing or stalling of RpII.  Figure 3-5- Photographs showing immunofluorescent co-localization of Marcal1 (green) with panacetyl-histone H4 (AcH4; red) on Drosophila polytene chromosomes. GFP-Marcal1 was expressed in transgenic flies (tubulin-GAL4, UAS-HA-GFP-Marcal1). The data for this figure was provided by Kyoung Sang Cho.     77  Figure 3-6- Expression of Marcal1 tagged with DNA adenine methyltransferase (Dam) on its amino terminus methylates adenine in genomic regions of Kc167 cells with hallmarks of active transcription. (A) Photograph of an agarose gel showing that by RT-PCR tubulin-GAL4 specifically induces expression of Dam-Marcal1 in transgenic UAS-Dam-Marcal1;tub>GAL4 flies. (B) Expression of Dam-Marcal1 in UAS-Dam-Marcal1;tub>GAL4 flies induces extra wing veins similar to those observed in UAS-Marcal1;tub>GAL4 flies. (C-J) Density distribution plots showing relative frequencies (y-axes) of adenine methylation (Dam-Marcal1 target regions, black) and absence of adenine methylation (non-target regions, grey) for the indicated log2- transformed features: (C) trimethylation levels of lysine 4 of histone H3 (H3K4me3, a mark  78 of actively transcribed chromatin), (D) acetylation levels of histone H3 (a mark of actively transcribed chromatin), (E) acetylation levels of histone H4 (a mark of actively transcribed chromatin), (F) chromatin accessibility (open and closed chromatin), (G) mRNA expression levels, (H) DNA replication timing, (I) trimethylation levels of lysine 27 of histone H3 (H3K27me3, a mark of non-transcribed genes) and (J) a non-specific antibody control (rabbit anti-IgG). The log2-transformed features were obtained from previously published genome- wide data 239-242. All p-values were calculated using the Mann-Whitney U test. (K and L) Alignment plots showing the log2-transformed median adenine methylation level (y-axes) relative to transcription start sites (K) and termination sites (L) of all target genes. The grey shading indicates the transcribed region. Transcription start and termination sites are at position 0 (dashed vertical lines). Note that Dam-Marcal1 preferentially targeted transcriptional start and termination sites. The data for panels A and B was provided by Marie Morimoto and data for panels C-L was provided by Bas Tolhuis.      Figure 3-7- Photographs of Drosophila wings showing the extra wing veins induced by expression of Marcal1 and SMARCAL1. Marcal1 (A) and SMARCAL1 (B) expression were driven in transgenic flies by the GAL4- UAS system. No extra wing veins are observed with the UAS-Marcal1 or UAS-SMARCAL1 constructs alone or with the GAL4 drivers (tubulin>GAL4 or MS1096>GAL4) alone. Also, note the absence of ectopic wing veins when the expressed protein product has no ATPase activity (Marcal1K275R and SMARCAL1R586W) 157,246. The data for this figure was provided by Marie Morimoto.    79 Table 3-3- Distribution of RpII for Marcal1 and Pc target genes. Data  RpII absent  RpII stalled  RpII active  RpII undetermined Source Profile   Obser ved (%) Expe cted (%) χ2   Obse rved (%) Expe cted (%) χ2   Obse rved (%) Expe cted (%) χ2   Obse rved (%) Expe cted (%) χ2 Toll10b Marcal1   15 39 5E-69   21 11 6E-25   33 27 0.2   32 23 7E-09   Pc   45 39 1   27 11 2E-45   11 27 1E-24   17 23 0.1  S2 Marcal1   28 55 2E-82   15 5 8E-41   42 29 3E-22   - - -   Pc   64 55 7E-04   12 5 5E-11   13 29 7E-25   - - - - Expected is based on genome-wide distribution of RpII - P values from the χ2 test were corrected for multiple testing, using Bonferroni correction  3.3.4 Marcal1 and SMARCAL1 genetically interact with transcriptional components To obtain genetic evidence that Marcal1 and SMARCAL1 are involved in transcription, we conducted a screen in Drosophila melanogaster for enhancers and suppressors of the wing vein phenotype induced by expressing Marcal1 or SMARCAL1 under the control of the UAS promoter and the tubulin-GAL4 or MS1096-GAL4 drivers, respectively (Appendix 3) 157. We observed epistatic interactions with mutations of transcriptional regulators including chromatin proteins, mediator complex members, RpII complex components, and transcription initiation, elongation, and termination factors (Appendix 3). Generally, but not always, loss-of-function mutations of transcriptional enhancers suppressed the wing vein phenotype, whereas loss-of-function mutations of transcriptional repressors enhanced the wing vein phenotype.   80 3.3.5 SMARCAL1 deficiency alters gene expression in SIOD skin fibroblasts To clarify whether the SMARCAL1 chromatin binding and its genetic interactions with transcriptional components were indicative of function, we checked if deficiency of SMARCAL1 altered gene expression. Using the Affymetrix Human Genome U133 Plus 2.0 Array, RNA derived from dermal fibroblasts of two SIOD patients (SD8 and SD60) showed significantly (q-value<0.05) altered expression of 5644 genes (log2 median fold change of - 0.255) (Figure 3-8 and http://www.ncbi.nlm.nih.gov/gds GEO accession number GSE35551). Of these, 632 had >2 fold higher expression and 766 genes had >2 fold lower expression. The gene ontology (GO) biological process annotations enriched among these differentially expressed genes included cellular and molecular metabolic processes, programmed cell death, cell cycle, signalling pathways and stress response (Appendices 4 and 5). Identification of the stress response was particularly intriguing since several patients with SMARCAL1 deficiency have developed severe migraine-like headaches, transient weakness, and transient paraplegia during hot weather (C.F.B., unpublished data). To test if SMARCAL1 modulated expression of heat shock genes, we heat stressed skin fibroblasts from a control individual and three SIOD patients (SD31, SD120, and SD123) for 1 hour at 43°C. Using quantitative reverse-transcriptase PCR (qRT-PCR), we found that many heat stress response genes were significantly over- or under-expressed in the SMARCAL1- deficient fibroblasts (Figure 3-9 and Appendix 6).   81  Figure 3-8- SMARCAL1 deficiency alters gene expression. (A) Heat map of the log2 fold differences in RNA levels (q-value < 0.05) between control and SIOD patient (SD8 and SD60) skin fibroblasts. The RNA levels were measured using Affymetrix Human Genome U133 Plus 2.0 arrays and are the average of three biological replicates. (B) Density plot showing the distribution of the log2 fold differences in RNA levels (q-value < 0.05) between control and SIOD fibroblasts. The data for this figure was provided by Chad Shaw.      82  Figure 3-9- SMARCAL1 deficiency causes abnormal expression of heat stress genes. Volcano plots of qRT-PCR comparison of stress gene expression in SIOD dermal fibroblasts (SD31, SD120, and SD123) to that in control unaffected dermal fibroblasts after one hour incubation at 43ºC followed by one hour incubation at 37°C. Each plot is based on three biological replicates.  83 3.3.6 Marcal1 contributes to heat tolerance and modulates heat stress gene expression in Drosophila Given the abnormal response to heat stress by SIOD patients and SMARCAL1- deficient skin fibroblasts, we hypothesized that heat stress modifies the penetrance of Marcal1 deficiency. To test this, we reared Marcal1del/del and yellow white (yw) control flies at 20ºC, 25ºC and 30ºC. Although no differences were noted at 20ºC, Marcal1del/del embryos and flies were significantly less viable at 25ºC (<75% viability) and at 30ºC (<20% viability) than yw control flies (Figure 3-10A-B). Also, compared to yw flies, Marcal1del/del flies had abnormal expression of heat shock genes and proteins both at baseline and after 15 minutes at 37ºC (Figure 3-11). The Marcal1del/del flies also laid smaller eggs than yw controls at 25ºC but not at 20ºC (Figure 3-10C-D). At 20ºC the mean (±SD) egg volume was 0.0116±0.0018 mm3 for yw vs. 0.0115±0.0015 for Marcal1del/del (n=100, p=0.56). In contrast, at 25ºC the mean (±SD) egg volume was 0.0117±0.0013 mm3 for yw vs. 0.0094±0.0015 for Marcal1del/del (n=100, p=7.45e-21). To see if this change in egg size correlated with altered gene expression, we compared RNA extracted from ovaries of yw and Marcal1del/del flies housed at 20ºC to those housed at 25ºC using Affymetrix Drosophila Genome 2.0 Arrays. We found significant differences (q-value<0.05) in the expression of 123 genes at 20ºC and of 148 genes at 25ºC; 81 genes were common in both groups. Furthermore, comparison of the gene expression differences between yw and Marcal1del/del ovaries at 20ºC and 25ºC showed a shift in the log2 median fold change from 0.01 at 20ºC to -0.03 at 25ºC (Figure 3-10E-F). For both 20ºC and 25ºC, the GO biological process annotations enriched among genes differentially expressed  84 between yw and Marcal1del/del ovaries included metabolic processes and response to stress (http://www.ncbi.nlm.nih.gov/gds GEO accession number GSE35552, and Tables 3-4 and 3- 5).    85 Figure 3-10- Marcal1 and Smarcal1 deficiency increase susceptibility to heat stress. (A) Graph of the percent of yw control and Marcal1del/del flies surviving after 10 days at 30ºC. Error bars represent one standard deviation. (B) Graph of the percent of yw control and Marcal1del/del embryos surviving through eclosion when raised at 25ºC, when raised at 25ºC for the first 5 days and then switched to 30ºC, or when raised at 30ºC. Error bars represent one standard deviation. (C and D) Distribution plots of the dimensions of yw and Marcal1del/del eggs laid at 20ºC (C) or at 25ºC (D). (E) Heat maps of the log2 fold differences in all expressed mRNAs between yw and Marcal1del/del ovaries at 20ºC and at 25ºC. The RNA levels were measured using Affymetrix Drosophila Genome 2.0 arrays and are the average of three biological replicates. (F) Density plots showing the distribution of the log2 fold differences in gene expression between yw and Marcal1del/del ovaries at 20ºC and 25ºC. (G) Survival curve for Smarcal1+/+ (n=5) and Smarcal1del/del (n=12) mice maintained for 10 hours at 39.5ºC. (H) Heat map of the log2 fold differences for all expressed RNAs between Smarcal1+/+ and Smarcal1del/del livers at 20ºC and 39.5ºC. The RNA levels were derived from transcriptome sequencing and are the average of three biological replicates. (I) Density plots showing the distribution of the log2 fold differences in RNA levels between Smarcal1+/+ and Smarcal1del/del livers at 20ºC and 39.5ºC. The data for panels A and B was provided by Kyoung Sang Cho, data for panels C and D was provided by Marie Morimoto, and data for panels E, F, H and I was provided by Chad Shaw.         86  Figure 3-11- Effect of Marcal1 deficiency on expression of heat stress genes and proteins in 1-3 day old Drosophila females. (A) Graphs showing qRT-PCR measurement of stress gene mRNA levels in Marcal1del/del flies relative to yw control flies. The duration of heat shock at 37ºC and recovery at 20ºC is shown above the graphs. The values in each graph represent the mean of three independent experiments assessed in triplicate. The data was normalized to Gapdh2 mRNA levels. The error bars represent one standard deviation. (B) Photographs of immunoblots showing the expression of stress gene proteins in Marcal1del/del flies and yw control flies. The duration of heat shock at 37ºC and recovery at 20ºC is shown above. β-tubulin was used as a loading control. Abbreviations: del, Marcal1del/del flies; RQ, relative quotient. The data for panel A was provided by Joanna Lubieniecka, and data for panel B was provided by Marie Morimoto.     87 Table 3-3- Enrichment in biological process GO-terms (all levels) of differentially expressed genes (q<0.05) between Marcal1del/del and yw ovaries at 20oC. Term Count p-value Fold Enrichment GO:0008152~metabolic process 50 0.077378231 1.168941048 GO:0016043~cellular component organization 24 0.04586477 1.453506787 GO:0006950~response to stress 9 0.073882931 2.00765625 GO:0009056~catabolic process 9 0.084881356 1.946818182 GO:0051716~cellular response to stimulus 7 0.037973165 2.776018519 GO:0006259~DNA metabolic process 6 0.052919286 2.907013575 GO:0033554~cellular response to stress 5 0.079115927 3.041903409 GO:0006281~DNA repair 4 0.067768971 4.199019608 GO:0006974~response to DNA damage stimulus 4 0.089674196 3.724347826   Table 3-4- Enrichment in biological process GO-terms (all levels) of differentially expressed genes (q<0.05) between Marcal1del/del and yw ovaries at 25oC. Term Count p-value Fold Enrichment GO:0008152~metabolic process 58 0.096794378 1.141870834 GO:0006950~response to stress 11 0.03703737 2.066359649 GO:0033036~macromolecule localization 11 0.053968425 1.933435929 GO:0008104~protein localization 10 0.027665154 2.300214823 GO:0009888~tissue development 10 0.054081143 2.030820294 GO:0002009~morphogenesis of an epithelium 8 0.011638694 3.205988304 GO:0060429~epithelium development 8 0.014511411 3.06956327 GO:0048729~tissue morphogenesis 8 0.018591517 2.92043469 GO:0002164~larval development 4 0.038431652 5.304024768 GO:0032787~monocarboxylic acid metabolic process 4 0.084146882 3.836954087 GO:0002168~instar larval development 3 0.097172331 5.635526316    88 3.3.7 Smarcal1 contributes to heat tolerance and modulates heat stress gene expression in mice To test if heat stress modified penetrance in the Smarcal1del/del mice, we housed the mice at 39.5ºC. All Smarcal1del/del mice died within 6.5 hours, whereas only 20% of wild- type (Smarcal1+/+) mice died in 10 hours (Figure 3-10G). We used Illumina next generation sequencing to characterize the transcriptomes of livers from Smarcal1del/del and Smarcal1+/+ mice at an ambient temperature of 20ºC or following 1 hour at 39.5ºC (Figure 3-10H-I). At 20ºC, 365 genes had >2 fold higher and 940 genes had >2 fold lower gene expression in Smarcal1del/del livers compared to Smarcal1+/+ livers, and the affected genes mapped to multiple GO biological process annotations including cellular and molecular metabolic processes and gene expression (http://www.ncbi.nlm.nih.gov/gds GEO accession number GSE35553, and Appendices 3-8). Following 1 hour at 39.5ºC, 107 genes had >2 fold higher and 309 genes had >2 fold lower gene expression in Smarcal1del/del livers compared to Smarcal1+/+ livers, and the median of gene expression differences between Smarcal1del/del liver and Smarcal1+/+ liver shifted from a log2 median fold change of -0.06 at 20ºC to -0.24 at 39.5ºC (Figure 3-10H-I). The genes with altered expression mapped to multiple GO biological process annotations including cellular and molecular metabolic processes, gene expression, stress response and immune response (Appendices 9-10). To confirm the altered stress response identified in the liver transcriptome data, we used qRT-PCR to analyze heat shock gene expression in RNA isolated from the livers and brains of the 3-hour heat stressed Smarcal1del/del and Smarcal1+/+ mice. This confirmed dysregulated expression of heat shock genes in both tissues (Table 3-6).   89 Table 3-5- qRT-PCR measurement of mRNA levels of stress genes in the brain and liver of mice following 3 hours of heat shock at 39.5ºC. Gene Brain Liver Gene Brain Liver Fold change* p-value Fold change* p-value Fold change* p-value Fold change* p-value Dnajc21 0.5539 0.103303 0.6619 0.01114 Dnajc5 1.1447 0.283714 0.5081 0.014792 Atf6 1.6647 0.005168 0.6862 0.226613 Dnajc5b 0.7204 0.388559 0.8587 0.44971 Bag1 0.8096 0.073824 1.056 0.825353 Dnajc5g 0.5655 0.379182 0.8058 0.445654 Bag2 0.6678 0.098269 0.3263 0.008426 Dnajc6 0.967 0.607897 0.1212 0.025154 Bag3 1.2282 0.69129 12.4831 0.001895 Dnajc7 1.294 0.026786 0.7721 0.184159 Bag4 0.7621 0.177136 0.4633 0.010735 Dnajc8 0.4721 0.033914 0.5724 0.41244 Bag5 0.9877 0.933817 0.3471 0.002917 Dnajc9 0.6716 0.097667 0.3377 0.145668 Cabc1 0.9734 0.799302 1.9504 0.053862 Hsf1 0.6871 0.09983 0.4489 0.002501 Ccs 1.0806 0.855899 1.8411 0.070995 Hsf2 0.924 0.514831 1.481 0.214062 Cct2 1.3147 0.151575 0.8364 0.516403 Hsf4 0.701 0.260822 0.5157 0.077468 Cct3 0.9056 0.328878 0.8311 0.102503 Hsph1 1.2425 0.213459 2.6855 0.000429 Cct4 1.344 0.133211 1.0124 0.868098 Hsp90aa1 0.9517 0.654922 1.2593 0.732777 Cct5 1.1644 0.031368 1.1865 0.126963 Hsp90ab1 1.1284 0.031751 1.0249 0.986762 Cct6a 1.1063 0.367545 0.8571 0.116482 Hsp90b1 0.8877 0.325235 1.0267 0.755551 Cct6b 0.5358 0.394047 0.5841 0.325782 Hspa14 0.827 0.376506 0.5518 0.043781 Cct7 1.005 0.843311 0.723 0.049662 Hspa1a 19.8949 0.000918 504.2773 0.000067 Cryaa 0.7794 0.396386 0.8414 0.448701 Hspa1b 18.4604 0.000675 232.0417 0.000392 Cryab 0.6529 0.13472 0.9309 0.514309 Hspa1l 0.7101 0.109201 1.7557 0.032631 Dnaja1 1.4577 0.001304 1.6457 0.104811 Hspa2 1.9834 0.03778 1.2014 0.718687 Dnaja2 0.8556 0.220376 0.4867 0.052802 Hspa4 1.2709 0.057047 0.4114 0.028217 Dnaja3 0.7889 0.053368 0.4787 0.130644 Hspa4l 0.4652 0.063573 0.8071 0.385973 Dnaja4 1.8024 0.005696 1.3622 0.394316 Hspa5 1.0546 0.813525 1.4061 0.086402 Dnajb1 2.7974 0.003117 9.8775 0.000192 Hspa8 1.5838 0.00096 2.3528 0.000481 Dnajb2 0.6692 0.023008 0.8844 0.465361 Hspa9 0.832 0.654396 0.7577 0.261593 Dnajb11 0.2557 0.339799 0.1911 0.127867 Hspb1 2.3665 0.003963 3.8697 0.001453 Dnajb12 0.9954 0.900606 0.7651 0.162726 Hspb2 1.1072 0.70863 0.4993 0.330753 Dnajb13 0.7095 0.428443 0.9409 0.473346 Hspb3 1.2617 0.722348 0.4965 0.373041 Dnajb14 0.7177 0.071358 0.4677 0.028752 Hspb6 0.7388 0.265384 0.5696 0.130521 Dnajb5 3.126 0.006149 0.2583 0.061209 Hspb7 0.7674 0.212276 0.6188 0.364641 Dnajb6 0.9888 0.850383 0.6414 0.007803 Hspb8 0.546 0.168098 1.9018 0.04364 Dnajb7 0.6096 0.42118 2.3162 0.067284 Hspd1 1.285 0.004456 1.7756 0.029237 Dnajb8 0.5007 0.377314 0.8587 0.44971 Hspe1 1.1327 0.213775 1.4252 0.249831 Dnajb9 0.6582 0.00112 1.3041 0.458707 Pfdn1 0.9881 0.870701 1.2651 0.259957 Dnajc1 1.4859 0.005896 0.7579 0.235999 Pfdn2 0.7737 0.063529 0.9846 0.870173 Dnajc10 0.9356 0.361546 0.3696 0.002008 Serpinh1 1.4643 0.182529 0.63 0.493928 Dnajc11 0.9057 0.477053 0.7642 0.245459 Sil1 1.2234 0.342094 1.7865 0.015409 Dnajc12 0.4137 0.021857 0.8988 0.572277 Tcp1 1.3309 0.018088 1.0342 0.721104 Dnajc13 0.8298 0.177084 0.422 0.004247 Tor1a 1.2889 0.019206 0.9866 0.968326 Dnajc14 1.0748 0.374884 0.5152 0.001132 Gusb$ 0.5093 0.006591 0.8001 0.270133 Dnajc15 0.7344 0.144963 1.4294 0.246661 Hprt1$ 0.955 0.343731 1.0434 0.661078 Dnajc16 0.7201 0.053174 0.5555 0.090918 Hsp90ab1$ 1.1395 0.020618 1.0932 0.688567 Dnajc17 1.1979 0.177347 0.8909 0.519996 Gapdh$ 1.1904 0.007638 1.417 0.083667 Dnajc18 0.8688 0.336257 0.4949 0.0002 Actb$ 1.5157 0.004774 0.7732 0.352965 Dnajc19 0.8057 0.117401 1.0616 0.917233 Dnajc3 0.711 0.103528 0.8585 0.574645 Dnajc4 0.6764 0.184894 1.273 0.016873 Dnajc5 1.1447 0.283714 0.5081 0.014792 Key: * Fold change of Smarcal1del/del relative to Smarcal1+/+; $ control genes  90 3.3.8 RpII inhibition decreases proliferation in SMARCAL1-deficient human fibroblasts Having found that SMARCAL1, Marcal1 and Smarcal1 modulate gene expression during the heat stress response and are needed for heat tolerance, we hypothesized that other environmental and genetic factors altering transcription also induce penetrance when SMARCAL1 orthologues are deficient. To test this, we incubated skin fibroblasts from a control individual and three SIOD patients (SD31, SD120, and SD123) for 48 hours with 1 µg/ml of α-amanitin, a toxin that preferentially inhibits RpII 247,248. At this dose, SIOD fibroblasts proliferated significantly slower than control fibroblasts as measured by MTT assay and EdU incorporation by the Click-iT EdU assay (Figure 3-12A); there was no difference in apoptosis or necrosis as judged by TUNEL assay and trypan blue staining. Also, knockdown of the largest subunits of RpII using shRNAs against POLR2A or POLR2B in SIOD fibroblasts resulted in significantly decreased proliferation compared to a non-targeting shRNA or to knockdown of RpII components in control fibroblasts (Figure 3-13A-B). This interaction between RpII and SMARCAL1 is unlikely to result from protein-protein interactions since SMARCAL1 did not co-precipitate with RpII (data not shown).  91   92 Figure 3-12- Inhibition of RpII function causes penetrance of SMARCAL1, Marcal1 and Smarcal1 deficiency. (A) Graph showing the proliferation of α-amanitin treated control and SMARCAL1del/del skin fibroblasts relative to untreated cells. The fibroblast cultures were treated with α-amanitin (1 µg/mL) for 48 hours and proliferation was measured by the MTT and Click-iT EdU assays. (B) Graph showing the hatching rate at 20ºC for Marcal1del/del, RpII2153 or 4 or 8 or K1/FM7 and RpII2153 or 4 or 8 or K1/FM7;Marcal1del/del embryos relative to the hatching rate of yw embryos. FM7 is a X chromosome balancer. (C) Distribution plot of egg dimensions showing that RpII2154/FM7;Marcal1del/del flies lay smaller eggs than yw, Marcal1del/del and RpII2154/FM7 flies. (D) Heat map comparing the log2 fold differences in all expressed mRNAs among yw, Marcal1del/del, RpII2154/FM7, and RpII2154/FM7; Marcal1del/del ovaries at 20ºC. The RNA levels were measured using Affymetrix Drosophila Genome 2.0 arrays and are the average of five biological replicates. (E) Density plots showing distribution of the log2 fold differences for transcripts depicted in panel D. (F) Graph showing the proliferation of α-amanitin treated Smarcal1+/+ and Smarcal1del/del MEFs relative to untreated MEFs. The MEFs were treated with α-amanitin (1 µg/mL) for 48 hours and proliferation was measured by the MTT and Click-iT EdU assays. Error bars in panels A, B, and F represent one standard deviation. The data for panels B and C was provided by Marie Morimoto, and data for panel D and E was provided by Chad Shaw.      93  Figure 3-13- Effect of knocking down RpII components in SMARCAL1 and  Smarcal1 deficient fibroblasts. (A) The ratio of WST-1 assay measurement of SMARCAL1del/del (SD31) and unaffected control dermal fibroblasts 72 and 96 hours following electroporation with non-targeting or POLR2A- and POLR2B-targeting shRNAs. The values in each graph represent the mean of 3 independent experiments and were normalized to the non-targeting values. (B) As measured by qRT-PCR, POLR2A and POLR2B mRNA levels were reduced approximately by 50-80% and 45-50%, respectively. The data was normalized to GAPDH mRNA levels and to the non- targeting control. (C) The ratio of WST-1 assay measurement of Smarcal1del/del and Smarcal1+/+ MEFs 48 and 96 hours following electroporation with non-targeting or Polr2a- targeting siRNAs (Polr2a-1 and Polr2a-3). The values in each graph represent the mean of 3 independent experiments and were normalized to the non-targeting values. (D) As measured by qRT-PCR, Polr2a mRNA levels were reduced approximately by 55-65% for siRNA duplex 1 and 70% for siRNA duplex 3. The data was normalized to Gapdh mRNA levels and to the non-targeting control. The error bars throughout represent one standard deviation.    94 3.3.9 RpII mutations decrease the viability and increase gene expression changes in Marcal1del/del flies Given the sensitivity of SMARCAL1-deficient cells to RpII inhibition and knockdown, we tested for analogous epistatic interactions in Drosophila. We introduced a single mutant allele of the largest subunit of RpII (RpII2153, RpII2154, RpII2158 or RpII215K1) into the Marcal1del/del background (RpII2153/FM7;Marcal1del/del, RpII2154/FM7;Marcal1del/del, RpII2158/FM7;Marcal1del/del or RpII215K1/FM7;Marcal1del/del). Comparing the hatching of these embryos to Marcal1del/del and RpII heterozygous mutant embryos showed that these had a significantly reduced hatching rate at 20ºC (Figure 3-12B). This reduced hatching was not associated with increased apoptosis as measured by acridine orange staining or from altered protein-protein interactions between RpII215 and Marcal1 since Marcal1 did not co- precipitate with RpII215 (data not shown). Besides reduced hatching, the introduction of a single mutant allele of RpII215 into the Marcal1del/del background reduced the size of eggs relative to those of Marcal1del/del and RpII215 heterozygous mutants (Figure 3-12C). The mean (±SD) egg volume was 0.0116±0.0018 mm3 for yw vs. 0.0115±0.0015 for Marcal1del/del (n=100, p=0.56), 0.0104±0.0015 for RpII2154/FM7 (n=100, p=9.28e-7), and 0.0094±0.0016 for RpII2154/FM7;Marcal1del/del flies (n=100, p=1.03e-13). To test if this reduced egg size was associated with changes in gene expression, we used the Affymetrix Drosophila Genome 2.0 Array to compare gene expression in the ovaries of Marcal1del/del, heterozygous RpII2154 (RpII2154/FM7), RpII2154/FM7;Marcal1del/del and yw flies. The introduction of the RpII2154 allele increased both the number of differentially expressed genes and the magnitude of the overall  95 expression differences. 744 genes were differentially (q-value<0.05) expressed between RpII2154/FM7;Marcal1del/del and yw ovaries compared to 123 genes between Marcal1del/del and yw ovaries. The log2 median fold change of expression differences for Marcal1del/del vs. yw ovaries was 0.01, whereas for RpII2154/FM7;Marcal1del/del vs. yw ovaries, it was 0.07. The 744 differentially expressed genes mapped to multiple GO biological process annotations including cellular and metabolic processes, cell cycle and response to stress (Figure 3-12D-E, http://www.ncbi.nlm.nih.gov/gds GEO accession number GSE35552, and Appendix 11). Reciprocally the introduction of the biallelic deletion of Marcal1 into the background of the RpII2154 mutation also increased the number of differentially expressed genes and the magnitude of the overall expression differences. Comparison of gene expression in RpII2154/FM7 and yw ovaries identified 378 differentially (q-value<0.05) expressed genes and this increased to 744 for the comparison of RpII2154/FM7;Marcal1del/del and yw ovaries. Also, the log2 median fold change of expression differences was 0.01 for RpII2154/FM7 vs. yw and 0.07 for RpII2154/FM7;Marcal1del/del vs. yw ovaries. The 378 differentially expressed genes mapped to many GO biological process annotations including cellular and metabolic processes and gene expression (Figure 3-12D-E, http://www.ncbi.nlm.nih.gov/gds GEO accession number GSE35552, and Appendix 12).  3.3.10 RpII inhibition decreases proliferation in Smarcal1-deficient mouse embryonic fibroblasts Having observed the interactions of SMARCAL1 and Marcal1 with RpII, we asked if inhibition of RpII function had a similar effect in Smarcal1del/del MEFs. When treated with 1  96 µg/ml of α-amanitin, Smarcal1del/del MEFs proliferated significantly more slowly than Smarcal1+/+ fibroblasts as measured by the MTT assay and EdU incorporation by the Click- iT EdU assay (Figure 3-12F). Also, they exhibited no difference in apoptosis or necrosis as judged by TUNEL assay and trypan blue staining (data not shown). Furthermore, knockdown of the largest subunit of RpII using siRNAs against Polr2a in Smarcal1del/del MEFs resulted in a significantly decreased proliferation rate compared to knockdown of Polr2a in Smarcal1+/+ MEFs or to treatment of Smarcal1del/del MEFs with a non-targeting siRNA (Figure 3-13C-D).  3.3.11 RpII inhibition modifies the penetrance of Smarcal1 deficiency and partially recapitulates SIOD in Smarcal1-deficient mice Based on the preceding findings, we hypothesized that variations in gene expression are critical for expression of SIOD disease features. To test this in vivo, we injected Smarcal1del/del and Smarcal1+/+ mice with either carrier (phosphate buffered saline) or α- amanitin (0.1 mg/kg/day) for 12 weeks. Although they did not develop T-cell deficiency (Figure 3-14), the Smarcal1del/del mice injected with α-amanitin developed features of SIOD that were not observed in α-amanitin-treated Smarcal1+/+ mice or in PBS-treated Smarcal1del/del and Smarcal1+/+ mice (Figure 3-15). First, they had length and weight growth restriction (Figure 3-15A-C), and consistent with the growth restriction in SIOD, the mice had a disproportionately short spine (Figure 3-15D-E). Second, as reported for SIOD growth plates 154,156, the distal femur growth plates were hypocellular, and chondrocytes in the proliferation and hypertrophic zones formed less organized columns (Figure 3-15F-N).  97 Finally, reminiscent of the early renal disease in SIOD, the treated Smarcal1del/del mice developed albuminuria (Figure 3-15O).        98  Figure 3-14- Treatment of Smarcal1del/del mice with α-amanitin does not recapitulate T-cell deficiency in SIOD. (A) Plots show the representative FACS analysis of the thymus from Smarcal1+/+ (n=3) and Smarcal1del/del (n=5) mice treated with α-amanitin for different developmental T-cell subsets. Thymocytes were initially analyzed for T-cell developmental stages based on CD4 and CD8 staining. Double negative (CD4-CD8-) cells were further resolved based on CD44 and CD25 expression into DN1 (CD44+CD25-), DN2 (CD44+CD25+), DN3 (CD44-CD25+) and DN4 (CD44-CD25-) fractions. (B) Quantification of T-cell subsets analyzed in panel A. (C) Plots show the representative FACS analysis of the spleen from Smarcal1+/+ (n=3) and Smarcal1del/del (n=5) mice treated with α-amanitin for different lymphocyte subsets. Splenic B and T cells were initially resolved based on expression of B220 (CD45R) and TCRαβ. Subsequently, T cells (TCRab+) were further resolved into CD4+ and CD8+ T-cell subsets. (D) Quantification of T-cell subsets analyzed in panel C. Note that compared to untreated mice (Figure 19), treatment with α-amanitin did not significantly alter the representation of the lymphocyte subsets. The error bars represent one standard deviation. The data for this figure was provided by Mrinmoy Sanyal.  99   Figure 3-15- Treatment of Smarcal1del/del mice with α-amanitin partially recapitulates SIOD. (A) Radiographs of representative male mice after 12 weeks of daily intra-peritoneal (ip) injections with PBS or 0.1 mg/kg α-amanitin. (B) Growth curve showing that Smarcal1+/+ (n=7) and Smarcal1del/del (n=9) mice gain weight equally when given daily ip-injections of PBS. (C) Growth curve showing that Smarcal1del/del (n=9) mice gain less weight than Smarcal1+/+ (n=7) mice when given daily ip-injections of 0.1 mg/kg α-amanitin. (D and E) Graphs showing the ratio of lumbar spine (L1-L6) length to femur length (D) or humerus length (E) for α-amanitin- and PBS-treated mice. Note that the α-amanitin treatment disproportionately shortened the lumbar spine of the Smarcal1del/del mice. (F) Plot of fold change in chondrocyte number in the proliferative (PZ) and hypertrophic (HZ) zones in the distal femoral growth plate of α-amanitin-treated mice, Smarcal1del/del (n=7) and Smarcal1+/+  100 (n=7), relative to PBS-treated mice, Smarcal1del/del (n=7) and Smarcal1+/+ (n=6). (G-N) Photographs of representative H&E staining of the distal femoral growth plate of Smarcal1+/+ and Smarcal1del/del male mice treated with PBS or α-amanitin for 12 weeks. Panels K-N are higher magnifications of the boxed areas on panels G-J, respectively. Note the hypocellular growth plate and poorly organized columns of chondrocytes in the growth plate of the α-amanitin treated Smarcal1del/del mouse. Bar = 100 µm. (O) Graph showing urine albumin excretion by Smarcal1del/del mice relative to Smarcal1+/+ mice following PBS or α-amanitin treatment. Bars in panels B-F and O represent standard errors. The data for panel O was provided by Darren Bridgewater.   3.4 Discussion We have shown that the proteins encoded by SMARCAL1 orthologues localize to transcriptionally active chromatin, modulate gene expression and have epistatic interactions with transcription factors. We also found that similar to the lack of penetrance for biallelic SMARCAL1 mutations in humans, deficiency of the orthologues in fruit flies and mice is insufficient to cause disease in these organisms and that penetrance is associated with environmental or genetic insults that further modify gene expression. As an annealing helicase, SMARCAL1 resolves single to double-stranded DNA transitions 130. Such transitions occur during DNA replication, repair, recombination, and transcription, and recent studies have shown that SMARCAL1 participates in the DNA stress response both at stalled replication forks and double-strand DNA breaks repaired by recombination or end joining 139,141-144. However, since clinical and molecular analyses of SIOD patients and SMARCAL1-deficient cells and tissues did not detect defects of DNA repair, replication and recombination 161,249,250, we reasoned that, like many DNA repair enzymes 211-213,215,218,251, SMARCAL1 contributes to transcription and that its deficiency results in gene expression changes contributing to the pathophysiology of SIOD. SMARCAL1 could modulate RpII transcription through maintenance of the topology  101 and helicity of duplex DNA. In prokaryotes, changes in DNA helicity cause gene expression to be enhanced, repressed or unchanged 16, and the equilibrium between duplex DNA and strand opening modulates transcription factor binding and production of full-length RNA 18,19. Similarly, in eukaryotes, in vitro and in vivo studies of MYC expression show that transcription-induced supercoiling melts MYC Far Upstream Element (FUSE) to enable binding by structure-sensitive regulatory proteins such as FUSE Binding Protein (FBP) and FBP-Interacting Repressor (FIR) 17,252. Binding of FBP and FIR to FUSE modify the rate of MYC promoter firing 17,252. Since negative and positive supercoiling are generated upstream and downstream of the transcription bubbles 253,254, respectively, SMARCAL1, Marcal1 or Smarcal1 might contribute to the maintenance of DNA topology within and adjacent to transcribed regions. Deficiency of these orthologues would then alter gene expression as a consequence of the changes in DNA helicity or topology. In summary, SMARCAL1 deficiency is insufficient to cause SIOD, but the addition of environmental and genetic insults affecting transcription do cause penetrance and partial recapitulation of SIOD in model organisms. At both the molecular and genetic levels, SMARCAL1 plays a role in modulating gene expression. From these observations, we hypothesize that the annealing helicase function of SMARCAL1, Marcal1, or Smarcal1 maintains DNA topology to buffer variability in gene expression and thereby mitigates penetrance of pathological traits arising from environmental and genetic insults.    102 4. Discussion and concluding remarks In the genomic era, ability to predict a phenotype from a genotype is a privilege that might lead to efficient diagnosis, prognosis, and perhaps therapy. However, expecting a simple genotype-phenotype correlation even in Mendelian disorders is frequently more hope than reality since environmental factors, genetic modifiers and developmental processes often prevent simple correlation between genotype and phenotype. The incomplete penetrance and variable expressivity observed with specific genotypes in Mendelian disorders contradicts the traditional assumption of the qualitative nature of these disorders and suggests that some Mendelian disorders arise from defects in biological pathways that are sensitive to quantitative modulation. In this dissertation, I advanced understanding of the pathophysiology of Schimke immuno-osseous dysplasia. Using clinical, pathological, phenotypic, biochemical, and molecular analyses, I showed that defective DNA repair is not a major contributor to the pathophysiology of SIOD, and that SIOD is a quantitative trait associated with environmental and genetic disturbances of gene expression. In this context, we speculate that SMARCAL1 modulates chromatin structure and thereby higher order regulation of gene expression. 4.1 Improved molecular understanding of SIOD The aim of chapters 2 and 3 of this dissertation was to improve the molecular understanding of SIOD.   103 4.1.1 SMARCAL1 is a multifunctional protein facilitating the cross-talk among DNA repair, replication, recombination and transcription Cellular functions such as DNA repair, replication, recombination and transcription share chromatin as a common substrate. Since these DNA metabolic functions frequently share components and sometimes occur simultaneously on the same DNA fragment, a constant cross-talk among these processes is required for their co-existence and also for the maintenance of genomic structure and integrity (Figure 4-1). Focusing on transcription, examples of such close interactions are illustrated by the following. Several different types of DNA damage constitute obstacles for RpII progression and significantly affect transcription 255. In return, transcription causes substantial changes in DNA supercoiling, sensitizes ss- DNA to genotoxic agents, and increases the rate of mutation (transcription-associated mutagenesis) and DNA recombination (transcription-associated recombination, TAR) 256-259. TAM and TAR in highly transcribed regions can be partially ascribed to the head on collisions between RpII and DNA polymerase machinery 259. Consequent to these clashes, replication forks either collapse causing chromosomal deletions or DNA repair by homologous recombination or replication restart after displacement of RpII 257,260-262. The cross-talk between transcription and other DNA metabolic functions is also intertwined by proteins functioning in several processes. For example, DNA repair components such as ERCC2 211, ERCC3 215, ERCC6 212, GTF2H5 216, and CDK9 217,218 contribute to both initiation and elongation of RpII. It has recently been shown that RPA, a component of DNA damage response and replication, is also associated with RpII during elongation 263. Also, in Arabidopsis mutations in RPA2 leads to impaired transcriptional gene silencing of retrotransposons and of transgene repeat loci 213. Interestingly, Faucher et al. have shown that  104 trimethylated H3K4, which is known as a marker of transcriptionally active genes, is also involved in DNA damage response and replication 214. Moreover, early replication sites are associated with actively transcribed regions in higher eukaryotes, and in mouse embryonic stem cells, it has been shown that most of the replication initiation sites are associated with transcriptional units 242,264-267. SMARCAL1 is an annealing helicase that resolves single to double-stranded DNA transitions 130. Such transitions occur during DNA replication, repair, recombination, and transcription. Consistent with a role for SMARCAL1 in DNA replication, recombination and repair, recent studies have implicated SMARCAL1 in the DNA stress response both at stalled replication forks and double-strand DNA breaks, cell cycle progression, and remodeling of Holliday junctions 139-144. Considering the constant cross-talk among DNA metabolic functions and that DNA repair components frequently modulate transcription, we proposed that SMARCAL1 could facilitate the cross-talk between DNA repair, replication, recombination and transcription (Figure 4-1). To determine which of these functions contribute most to the pathophysiology of SIOD and its phenotypic variability, we evaluated the role of SMARCAL1 in the processes of DNA repair, replication, recombination and transcription.   105  Figure 4-1- Schematic model showing cross-talk between basic DNA metabolic functions and maintenance of genomic structure. Evidence in favour (black) or against (red) SMARCAL1 role in each process is summarized.  4.1.2 SIOD does not arise from defective DNA replication, repair or recombination In vitro analyses suggest that SMARCAL1 facilitates cell cycle progression from the G2 to the M phase 139,141-144. However, despite these findings, the proliferation rate of SMARCAL1-deficient cultured cells is not detectably perturbed relative to control cells 139,141,250. Furthermore, our analysis of the mouse testis revealed that Smarcal1 is highly expressed in quiescent or slowly proliferating Leydig and Sertoli cells and is nearly undetectable in the highly proliferative germ cell lineage (unpublished data). Based on these  106 observations, we conclude that SIOD does not arise from defective DNA replication. Also, our evaluations regarding the efficiency of several DNA repair pathways in SMARCAL1-deficient cells show that SMARCAL1 is not essential for the DNA repair processes of nucleotide excision repair, homologous recombination, and non-homologous end joining. However, our in vivo analyses show that similar to SMARCAL1-deficient cells 139, Smarcal1-deficient mice are hypersensitive to several genotoxic agents. These observations might suggest that SMARCAL1 deficiency causes SIOD and sensitivity to genotoxic agents by compromising a process of DNA repair not assessed by our methods. Processes of DNA repair not assessed by our assays include base excision repair (BER) and mismatch repair (MMR). Although defects in BER and MMR pathways also increase sensitivity to genotoxic agents 177,268, the absence of neurodegenerative symptoms, chromosomal rearrangements, and cancers other than NHL among SIOD patients 161,269 suggest that SMARCAL1 is not a major contributor to BER and MMR and that dysfunction of these pathways is not a major contributor to the pathobiology of SIOD. These data also suggest that SMARCAL1 deficiency may cause SIOD by affecting another basic DNA metabolic function such as transcription.  4.1.3 SIOD is associated with transcriptional alterations beyond a threshold We concluded that defects in DNA replication, repair and recombination are not major contributors to the pathophysiology of SIOD and cannot explain the incomplete penetrance observed in these patients. The other DNA metabolic function that SMARCAL1 participates in is transcription. The hypothesis that SIOD features arise from impaired transcription parallels observations from other DNA repair disorders including xeroderma  107 pigmentosum, trichothiodystrophy and Cockayne syndrome 186,270. Like SIOD, these disorders have phenotypic variability and poor genotype-phenotype correlation and arise in part from direct effects on transcription 212,251. Many features of SIOD such as the T-cell immunodeficiency, hypothyroidism, anemia, skeletal dysplasia, renal disease and heat sensitivity can be explained by impaired gene expression. Specifically, the proliferation of T cells, responsiveness to thyroxine, responsiveness to erythropoietin, chondrocyte proliferation and differentiation, maintenance of podocytes and heat shock response are all dependent on a robust transcriptional responses 271-282. Furthermore, since transcription rates alter sensitivity to DNA damaging agents 283,284, and CPT-11, etoposide, and HU also have deleterious effects on gene expression 285-289, the hypersensitivity of Smarcal1-deficient mice to these agents may, in part, be attributable to impaired gene expression. Consistent with the role of SMARCAL1 in transcriptional regulation, we observed that SMARCAL1 orthologues localize to transcriptionally active chromatin and have epistatic interactions with transcription factors. We also found modest but diffuse alterations in the expression of many genes in SMARCAL1-deficient organisms. However, deficiency of SMARCAL1 orthologues was not sufficient to cause disease in model organisms, and the addition of either environmental or genetic modifiers of gene expression was necessary for the penetrance of disease. Since gene expression is a quantitative trait and many genes are dysregulated in SIOD, there are two nonexclusive models by which alterations in gene expression could lead to SIOD. In the first model, SIOD is defined as a polygenic trait, and its features arise from cumulative effects of multiple dysregulated genes each with small effect size. The observed modest changes in expression of many genes in SMARCAL1-deficient organisms support this  108 idea (Figure 4-2). In the second model, SIOD is defined as an oligogenic trait and its features arise when the expression of a few key regulatory genes pass a certain threshold. In this model, SMARCAL1 orthologues buffer random fluctuations in gene expression and thereby modulate phenotypic changes induced by genetic or non-genetic insults. In contrast, the absence of a SMARCAL1 orthologue creates a chromatin environment permissive for such insults, pushing gene expression beyond a threshold of disease. In this model, although many genes are dysregulated, the expression of only a limited number of them passes a tolerance threshold (Figure 4-3A). Consistent with this model, other members of the Boerkoel Lab have recently linked the arteriosclerosis of SIOD to decreased ELN expression 6 and the renal glomerulosclerosis to increased NOTCH1 expression (unpublished data) (Figure 4-3B). Also, through collaboration with Mrinmoy Sanyal at Stanford University, we have linked the T-cell deficiency in SIOD to decreased IL7RA expression (unpublished data) (Figure 4-3B).     109  Figure 4-2- Polygenic model for SIOD depicting the contribution of many dysregulated genes each with small effect size to the penetrance of SIOD. Left panel: SMARCAL1 orthologues buffer random fluctuations in gene expression by modulating DNA helicity within the promoter and across transcribed regions. Middle panel: Deficiency of the SMARCAL1 orthologues impairs maintenance of DNA structure within the transcriptionally active regions, and thereby alters gene expression. The number of dysregulated genes is still not sufficient for the penetrance of the disease. Right panel: However, when transcription is further compromised by environmental or genetic factors the disease features will arise from the contribution of many mildly dysregulated genes.     110   Figure 4-3- Oligogenic model for SIOD depicting the contribution of a few dysregulated genes to the penetrance of SIOD. (A) Left panel: SMARCAL1 orthologues buffer random fluctuations in gene expression by modulating DNA helicity within the promoter and across transcribed regions. Middle panel: Deficiency of the SMARCAL1 orthologues impairs maintenance of DNA structure within the transcriptionally active regions, and thereby alters gene expression. These alterations in gene expression are within a threshold of tolerance and compensated for such that few or no  111 phenotypic features are apparent in humans and model organisms. Right panel: However, when transcription is further compromised by environmental or genetic factors that cause gene expression of a few key regulatory genes to pass a threshold, then the organism is unable to compensate and manifests a phenotype. (B) Examples of dysregulated NOTCH1, ELN, and IL7RA that contribute respectively to the focal segmental glomerulosclerosis, arteriosclerosis, and T-cell deficiency in SIOD patients.  4.1.4 Possible mechanisms by which SMARCAL1 modulates gene expression There are at least three nonexclusive models by which SMARCAL1 deficiency can alter gene expression. First, unrepaired DNA damage impedes RpII progression and impairs transcription 255. Second, like ERCC6, SMARCAL1 could be part of the transcriptional complex and thus its deficiency directly affects RpII transcription 212. Third, the DNA structure maintained by SMARCAL1 modulates gene expression. Evidence against the first are the findings that i) SMARCAL1-deficient fibroblasts and Smarcal1-deficient MEFs do not have increased DNA breaks compared to control fibroblasts as measured by comet assay, ii) SMARCAL1-deficient fibroblasts do not have hypersensitivity following exposure to ultraviolet light or illuidin S, iii) SIOD patients do not have defective homologous recombination, iv) SIOD patients do not have defects in non-homologous end joining, and v) the proliferation rate of SMARCAL1-deficient cells is not detectably perturbed relative to control cells even though the transition from G2 to M phase is slightly delayed 139,141,250. Evidence against the second proposed mechanism includes i) failure of SMARCAL1 homologues to co-purify or co-immunoprecipitate with RpII and studied transcription complexes 290-292 and ii) failure of RpII to co-purify or co-immunoprecipitate with SMARCAL1 or Marcal1. Evidence supporting the third mechanism that SMARCAL1 influences gene expression through maintenance of DNA structure include i) SMARCAL1- deficient cells accumulate more RPA indicative of increased levels of ss-DNA in these cells  112 139; ii) SMARCAL1-deficient cells show increased S1-nuclease sensitivity when pulsed with potassium permanganate (unpublished data), and iii) ectopic expression of Ubx, which is induced by heterozygous mutations of Polycomb group genes in Drosophila, is suppressed by Marcal1-deficiency and is associated with insensitivity of the Ubx promoter to micrococcal nuclease digestion (unpublished data). Therefore, since alterations in DNA structure and topology are associated with transcriptional alterations in both prokaryotes and eukaryotes 16-19,252, we hypothesize that the SMARCAL1 orthologues influence gene expression through maintenance of DNA structure (Figure 4-1). This model could also explain why, in contrast to transcription factors that bind conserved promoter elements, SMARCAL1, Marcal1 and Smarcal1 deficiency do not consistently affect expression of homologous genes. Homologous genes in humans, flies and mice frequently reside in different genomic neighborhoods or chromatin regions 293; therefore, the need for the SMARCAL1, Marcal1 and Smarcal1 to maintain duplex DNA around homologous genes might vary among species and thereby have differing effects on expression of homologous genes and on recapitulation of disease features in model organisms. The changes in DNA structure could directly or indirectly affect transcription. Our DamID experiment shows Marcal1 enrichment mostly at promoters compared to the rest of the gene bodies that could be suggestive of the direct effect of Marcal1 on those promoters. Alternatively, SMARCAL1 orthologues could modulate the DNA structure of the genes encoding transcription activators or repressors and thus indirectly affect transcription of other genes. Indeed, Morimoto et al. showed that ELN downregulation in aorta is associated with alterations in the expression of ELN transcription factors 168. Also, like several other SNF2  113 members including Lsh, ATRX, BRM, BRG1, and NoRC complex, chromatin remodeling activity of SMARCAL1 could modulate genome-wide methylation and affect gene expression 294-298. Consistent with this, preliminary data from our collaborator has recently identified genome-wide methylation changes in SMARCAL1-deficient lymphoblastoid cell lines (unpublished data). Whether this alteration in methylation pattern correlates with the transcriptional changes in SIOD needs further study.  4.2 Overall significance In summary, my work on elucidating the phenotype and molecular basis of SIOD has led to a better understanding of this disease and uncovered unstudied areas of biology. Firstly, it appears that SMARCAL1 orthologues modulate random fluctuations in gene expression and buffer genetic or non-genetic insults, whereas the absence of the SMARCAL1 orthologues creates a chromatin environment permissive for such insults pushing gene expression beyond a threshold of disease. Secondly, understanding that SMARCAL1 has a role in modulating gene expression is allowing us to identify likely causes of the pathophysiology for each affected organ in SIOD, and this insight will hopefully identify potential therapeutic approaches for SIOD patients.  4.3 Future directions Further studies are required to clarify the mechanisms by which SMARCAL1 orthologues modulate DNA structure and also how changes in DNA structure in SMARCAL1-deficient organisms lead to transcriptional alterations. Also, defining a  114 connection between SMARCAL1 and DNA methylation will likely uncover another layer of SMARCAL1 function in regulating gene expression. Since SMARCAL1-deficient organisms are hypersensitive to heat stress and transient neurological symptoms of SIOD patients are precipitated by stress, further studies are also required to delineate the role of SMARCAL1 in stress responses.      115 Bibliography 1. Badano, J.L. & Katsanis, N. Beyond Mendel: an evolving view of human genetic disease transmission. Nat Rev Genet 3, 779-89 (2002). 2. Dipple, K.M. & McCabe, E.R. Phenotypes of patients with "simple" Mendelian disorders are complex traits: thresholds, modifiers, and systems dynamics. Am J Hum Genet 66, 1729-35 (2000). 3. Scriver, C.R. & Waters, P.J. Monogenic traits are not simple: lessons from phenylketonuria. Trends Genet 15, 267-72 (1999). 4. Davis, E.E. et al. TTC21B contributes both causal and modifying alleles across the ciliopathy spectrum. Nat Genet 43, 189-96 (2011). 5. Zlotogora, J. Penetrance and expressivity in the molecular age. Genet Med 5, 347-52 (2003). 6. Kurnit, D.M., Layton, W.M. & Matthysse, S. Genetics, chance, and morphogenesis. Am J Hum Genet 41, 979-95 (1987). 7. Strobeck, M.W. et al. Compensation of BRG-1 function by Brm: insight into the role of the core SWI-SNF subunits in retinoblastoma tumor suppressor signaling. J Biol Chem 277, 4782-9 (2002). 8. Raj, A., Rifkin, S.A., Andersen, E. & van Oudenaarden, A. Variability in gene expression underlies incomplete penetrance. Nature 463, 913-8 (2010). 9. Riazuddin, S. et al. Dominant modifier DFNM1 suppresses recessive deafness DFNB26. Nat Genet 26, 431-4 (2000). 10. Katsanis, N. et al. Triallelic inheritance in Bardet-Biedl syndrome, a Mendelian recessive disorder. Science 293, 2256-9 (2001). 11. Kiesewetter, S. et al. A mutation in CFTR produces different phenotypes depending on chromosomal background. Nat Genet 5, 274-8 (1993). 12. Sachot, S. et al. Low penetrant hemochromatosis phenotype in eight families: no evidence of modifiers in the MHC region. Blood Cells Mol Dis 27, 518-29 (2001). 13. Clewing, J.M. et al. Schimke immunoosseous dysplasia: suggestions of genetic diversity. Hum Mutat 28, 273-83 (2007). 14. Jiang, Y.H., Bressler, J. & Beaudet, A.L. Epigenetics and human disease. Annu Rev Genomics Hum Genet 5, 479-510 (2004). 15. Felsenfeld, G. & Groudine, M. Controlling the double helix. Nature 421, 448- 53 (2003). 16. Hatfield, G.W. & Benham, C.J. DNA topology-mediated control of global gene expression in Escherichia coli. Annu Rev Genet 36, 175-203 (2002). 17. Kouzine, F., Sanford, S., Elisha-Feil, Z. & Levens, D. The functional response of upstream DNA to dynamic supercoiling in vivo. Nat Struct Mol Biol 15, 146-54 (2008).  116 18. Lim, H.M., Lewis, D.E., Lee, H.J., Liu, M. & Adhya, S. Effect of varying the supercoiling of DNA on transcription and its regulation. Biochemistry 42, 10718-25 (2003). 19. Pruss, G.J. & Drlica, K. DNA supercoiling and prokaryotic transcription. Cell 56, 521-3 (1989). 20. Ethier, S.D., Miura, H. & Dostie, J. Discovering genome regulation with 3C and 3C-related technologies. Biochim Biophys Acta 1819, 401-10 (2012). 21. Roberts, C.W. & Orkin, S.H. The SWI/SNF complex--chromatin and cancer. Nat Rev Cancer 4, 133-42 (2004). 22. Bannister, A.J. & Kouzarides, T. Regulation of chromatin by histone modifications. Cell Res 21, 381-95 (2011). 23. Flaus, A., Martin, D.M., Barton, G.J. & Owen-Hughes, T. Identification of multiple distinct Snf2 subfamilies with conserved structural motifs. Nucleic Acids Res 34, 2887-905 (2006). 24. Jones, P.A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet 13, 484-92 (2012). 25. Parada, L. & Misteli, T. Chromosome positioning in the interphase nucleus. Trends Cell Biol 12, 425-32 (2002). 26. Rabl, C. Über Zelltheilung. Morphol Jarbuch 10, 214-330 (1885). 27. Boveri, T. Die Blastomerenkerne von Ascaris megalocephala und die Theorie der Chromosomenindividualität. Arch Zellforsch 3, 181–268 (1909). 28. Lichter, P., Cremer, T., Borden, J., Manuelidis, L. & Ward, D.C. Delineation of individual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum Genet 80, 224-34 (1988). 29. Pinkel, D. et al. Fluorescence in situ hybridization with human chromosome- specific libraries: detection of trisomy 21 and translocations of chromosome 4. Proc Natl Acad Sci U S A 85, 9138-42 (1988). 30. Cremer, T. & Cremer, C. Chromosome territories, nuclear architecture and gene regulation in mammalian cells. Nat Rev Genet 2, 292-301 (2001). 31. Bischoff, A. et al. Differences of size and shape of active and inactive X- chromosome domains in human amniotic fluid cell nuclei. Microsc Res Tech 25, 68-77 (1993). 32. Cremer, T., Lichter, P., Borden, J., Ward, D.C. & Manuelidis, L. Detection of chromosome aberrations in metaphase and interphase tumor cells by in situ hybridization using chromosome-specific library probes. Hum Genet 80, 235- 46 (1988). 33. Visser, A.E., Jaunin, F., Fakan, S. & Aten, J.A. High resolution analysis of interphase chromosome domains. J Cell Sci 113 ( Pt 14), 2585-93 (2000). 34. Olivares-Chauvet, P., Fennessy, D., Jackson, D.A. & Maya-Mendoza, A. Innate structure of DNA foci restricts the mixing of DNA from different chromosome territories. PLoS One 6, e27527 (2011).  117 35. Markaki, Y. et al. The potential of 3D-FISH and super-resolution structured illumination microscopy for studies of 3D nuclear architecture: 3D structured illumination microscopy of defined chromosomal structures visualized by 3D (immuno)-FISH opens new perspectives for studies of nuclear architecture. Bioessays 34, 412-26 (2012). 36. Dehghani, H., Dellaire, G. & Bazett-Jones, D.P. Organization of chromatin in the interphase mammalian cell. Micron 36, 95-108 (2005). 37. Bridger, J.M., Herrmann, H., Munkel, C. & Lichter, P. Identification of an interchromosomal compartment by polymerization of nuclear-targeted vimentin. J Cell Sci 111 ( Pt 9), 1241-53 (1998). 38. Reichenzeller, M., Burzlaff, A., Lichter, P. & Herrmann, H. In vivo observation of a nuclear channel-like system: evidence for a distinct interchromosomal domain compartment in interphase cells. J Struct Biol 129, 175-85 (2000). 39. Cremer, T. et al. Role of chromosome territories in the functional compartmentalization of the cell nucleus. Cold Spring Harb Symp Quant Biol 58, 777-92 (1993). 40. Zirbel, R.M., Mathieu, U.R., Kurz, A., Cremer, T. & Lichter, P. Evidence for a nuclear compartment of transcription and splicing located at chromosome domain boundaries. Chromosome Res 1, 93-106 (1993). 41. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289-93 (2009). 42. Duan, Z. et al. A three-dimensional model of the yeast genome. Nature 465, 363-7 (2010). 43. Cremer, M. et al. Inheritance of gene density-related higher order chromatin arrangements in normal and tumor cell nuclei. J Cell Biol 162, 809-20 (2003). 44. Murmann, A.E. et al. Local gene density predicts the spatial position of genetic loci in the interphase nucleus. Exp Cell Res 311, 14-26 (2005). 45. Federico, C. et al. Gene-rich and gene-poor chromosomal regions have different locations in the interphase nuclei of cold-blooded vertebrates. Chromosoma 115, 123-8 (2006). 46. Goetze, S. et al. The three-dimensional structure of human interphase chromosomes is related to the transcriptome map. Mol Cell Biol 27, 4475-87 (2007). 47. Grasser, F. et al. Replication-timing-correlated spatial chromatin arrangements in cancer and in primate interphase nuclei. J Cell Sci 121, 1876-86 (2008). 48. Hepperger, C., Mannes, A., Merz, J., Peters, J. & Dietzel, S. Three- dimensional positioning of genes in mouse cell nuclei. Chromosoma 117, 535- 51 (2008). 49. Bridger, J.M., Boyle, S., Kill, I.R. & Bickmore, W.A. Re-modelling of nuclear architecture in quiescent and senescent human fibroblasts. Curr Biol 10, 149- 52 (2000).  118 50. Croft, J.A. et al. Differences in the localization and morphology of chromosomes in the human nucleus. J Cell Biol 145, 1119-31 (1999). 51. Neusser, M., Schubel, V., Koch, A., Cremer, T. & Muller, S. Evolutionarily conserved, cell type and species-specific higher order chromatin arrangements in interphase nuclei of primates. Chromosoma 116, 307-20 (2007). 52. Koehler, D. et al. Changes of higher order chromatin arrangements during major genome activation in bovine preimplantation embryos. Exp Cell Res 315, 2053-63 (2009). 53. Habermann, F.A. et al. Arrangements of macro- and microchromosomes in chicken cells. Chromosome Res 9, 569-84 (2001). 54. Kozubek, S. et al. 3D Structure of the human genome: order in randomness. Chromosoma 111, 321-31 (2002). 55. Tanabe, H. et al. Evolutionary conservation of chromosome territory arrangements in cell nuclei from higher primates. Proc Natl Acad Sci U S A 99, 4424-9 (2002). 56. Mahy, N.L., Perry, P.E. & Bickmore, W.A. Gene density and transcription influence the localization of chromatin outside of chromosome territories detectable by FISH. J Cell Biol 159, 753-63 (2002). 57. Mahy, N.L., Perry, P.E., Gilchrist, S., Baldock, R.A. & Bickmore, W.A. Spatial organization of active and inactive genes and noncoding DNA within chromosome territories. J Cell Biol 157, 579-89 (2002). 58. Clemson, C.M., Hall, L.L., Byron, M., McNeil, J. & Lawrence, J.B. The X chromosome is organized into a gene-rich outer rim and an internal core containing silenced nongenic sequences. Proc Natl Acad Sci U S A 103, 7688- 93 (2006). 59. Dietzel, S. et al. The 3D positioning of ANT2 and ANT3 genes within female X chromosome territories correlates with gene activity. Exp Cell Res 252, 363- 75 (1999). 60. Kurz, A. et al. Active and inactive genes localize preferentially in the periphery of chromosome territories. J Cell Biol 135, 1195-205 (1996). 61. Scheuermann, M.O. et al. Topology of genes and nontranscribed sequences in human interphase nuclei. Exp Cell Res 301, 266-79 (2004). 62. Volpi, E.V. et al. Large-scale chromatin organization of the major histocompatibility complex and other regions of human chromosome 6 and its response to interferon in interphase nuclei. J Cell Sci 113 ( Pt 9), 1565-76 (2000). 63. Williams, R.R., Broad, S., Sheer, D. & Ragoussis, J. Subchromosomal positioning of the epidermal differentiation complex (EDC) in keratinocyte and lymphoblast interphase nuclei. Exp Cell Res 272, 163-75 (2002). 64. Ferrai, C. et al. Poised transcription factories prime silent uPA gene prior to activation. PLoS Biol 8, e1000270 (2010).  119 65. Chambeyron, S. & Bickmore, W.A. Chromatin decondensation and nuclear reorganization of the HoxB locus upon induction of transcription. Genes Dev 18, 1119-30 (2004). 66. Morey, C., Da Silva, N.R., Perry, P. & Bickmore, W.A. Nuclear reorganisation and chromatin decondensation are conserved, but distinct, mechanisms linked to Hox gene activation. Development 134, 909-19 (2007). 67. Chambeyron, S., Da Silva, N.R., Lawson, K.A. & Bickmore, W.A. Nuclear re- organisation of the Hoxb complex during mouse embryonic development. Development 132, 2215-23 (2005). 68. Goldman, R.D., Gruenbaum, Y., Moir, R.D., Shumaker, D.K. & Spann, T.P. Nuclear lamins: building blocks of nuclear architecture. Genes Dev 16, 533-47 (2002). 69. Gant, T.M., Harris, C.A. & Wilson, K.L. Roles of LAP2 proteins in nuclear assembly and DNA replication: truncated LAP2beta proteins alter lamina assembly, envelope formation, nuclear size, and DNA replication efficiency in Xenopus laevis extracts. J Cell Biol 144, 1083-96 (1999). 70. Gruenbaum, Y. et al. The nuclear lamina and its functions in the nucleus. Int Rev Cytol 226, 1-62 (2003). 71. Jenkins, H., Whitfield, W.G., Goldberg, M.W., Allen, T.D. & Hutchison, C.J. Evidence for the direct involvement of lamins in the assembly of a replication competent nucleus. Acta Biochim Pol 42, 133-43 (1995). 72. Martins, S., Eikvar, S., Furukawa, K. & Collas, P. HA95 and LAP2 beta mediate a novel chromatin-nuclear envelope interaction implicated in initiation of DNA replication. J Cell Biol 160, 177-88 (2003). 73. Jagatheesan, G. et al. Colocalization of intranuclear lamin foci with RNA splicing factors. J Cell Sci 112 ( Pt 24), 4651-61 (1999). 74. Shumaker, D.K. et al. Mutant nuclear lamin A leads to progressive alterations of epigenetic control in premature aging. Proc Natl Acad Sci U S A 103, 8703- 8 (2006). 75. Haraguchi, T. et al. Emerin binding to Btf, a death-promoting transcriptional repressor, is disrupted by a missense mutation that causes Emery-Dreifuss muscular dystrophy. Eur J Biochem 271, 1035-45 (2004). 76. Nili, E. et al. Nuclear membrane protein LAP2beta mediates transcriptional repression alone and together with its binding partner GCL (germ-cell-less). J Cell Sci 114, 3297-307 (2001). 77. Spann, T.P., Goldman, A.E., Wang, C., Huang, S. & Goldman, R.D. Alteration of nuclear lamin organization inhibits RNA polymerase II-dependent transcription. J Cell Biol 156, 603-8 (2002). 78. Wilkinson, F.L. et al. Emerin interacts in vitro with the splicing-associated factor, YT521-B. Eur J Biochem 270, 2459-66 (2003). 79. Warren, D.T. & Shanahan, C.M. Defective DNA-damage repair induced by nuclear lamina dysfunction is a key mediator of smooth muscle cell aging. Biochem Soc Trans 39, 1780-5 (2011).  120 80. Shimi, T. et al. The A- and B-type nuclear lamin networks: microdomains involved in chromatin organization and transcription. Genes Dev 22, 3409-21 (2008). 81. Puckelwartz, M.J., Depreux, F.F. & McNally, E.M. Gene expression, chromosome position and lamin A/C mutations. Nucleus 2, 162-7 (2011). 82. Clements, L., Manilal, S., Love, D.R. & Morris, G.E. Direct interaction between emerin and lamin A. Biochem Biophys Res Commun 267, 709-14 (2000). 83. Holaska, J.M., Kowalski, A.K. & Wilson, K.L. Emerin caps the pointed end of actin filaments: evidence for an actin cortical network at the nuclear inner membrane. PLoS Biol 2, E231 (2004). 84. Holaska, J.M., Lee, K.K., Kowalski, A.K. & Wilson, K.L. Transcriptional repressor germ cell-less (GCL) and barrier to autointegration factor (BAF) compete for binding to emerin in vitro. J Biol Chem 278, 6969-75 (2003). 85. Lee, K.K. et al. Distinct functional domains in emerin bind lamin A and DNA- bridging protein BAF. J Cell Sci 114, 4567-73 (2001). 86. Pickersgill, H. et al. Characterization of the Drosophila melanogaster genome at the nuclear lamina. Nat Genet 38, 1005-14 (2006). 87. Guelen, L. et al. Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions. Nature 453, 948-51 (2008). 88. Peric-Hupkes, D. et al. Molecular maps of the reorganization of genome- nuclear lamina interactions during differentiation. Mol Cell 38, 603-13 (2010). 89. Capelson, M. & Corces, V.G. Boundary elements and nuclear organization. Biol Cell 96, 617-29 (2004). 90. Felsenfeld, G. et al. Chromatin boundaries and chromatin domains. Cold Spring Harb Symp Quant Biol 69, 245-50 (2004). 91. Wei, G.H., Liu de, P. & Liang, C.C. Chromatin domain boundaries: insulators and beyond. Cell Res 15, 292-300 (2005). 92. Byrd, K. & Corces, V.G. Visualization of chromatin domains created by the gypsy insulator of Drosophila. J Cell Biol 162, 565-74 (2003). 93. Melnikova, L. et al. Interaction between the GAGA factor and Mod(mdg4) proteins promotes insulator bypass in Drosophila. Proc Natl Acad Sci U S A 101, 14806-11 (2004). 94. Nabirochkin, S., Ossokina, M. & Heidmann, T. A nuclear matrix/scaffold attachment region co-localizes with the gypsy retrotransposon insulator sequence. J Biol Chem 273, 2473-9. (1998). 95. Pai, C.Y., Lei, E.P., Ghosh, D. & Corces, V.G. The centrosomal protein CP190 is a component of the gypsy chromatin insulator. Mol Cell 16, 737-48 (2004). 96. Capelson, M. & Corces, V.G. The ubiquitin ligase dTopors directs the nuclear organization of a chromatin insulator. Mol Cell 20, 105-16 (2005). 97. Gaszner, M. & Felsenfeld, G. Insulators: exploiting transcriptional and epigenetic mechanisms. Nat Rev Genet 7, 703-13 (2006).  121 98. West, A.G. & Fraser, P. Remote control of gene transcription. Hum Mol Genet 14 Spec No 1, R101-11 (2005). 99. Recillas-Targa, F. et al. Position-effect protection and enhancer blocking by the chicken beta-globin insulator are separable activities. Proc Natl Acad Sci U S A 99, 6883-8 (2002). 100. Dunn, K.L., Zhao, H. & Davie, J.R. The insulator binding protein CTCF associates with the nuclear matrix. Exp Cell Res 288, 218-23 (2003). 101. Yusufzai, T.M. & Felsenfeld, G. The 5'-HS4 chicken beta-globin insulator is a CTCF-dependent nuclear matrix-associated element. Proc Natl Acad Sci U S A 101, 8620-4 (2004). 102. Yusufzai, T.M., Tagami, H., Nakatani, Y. & Felsenfeld, G. CTCF tethers an insulator to subnuclear sites, suggesting shared insulator mechanisms across species. Mol Cell 13, 291-8 (2004). 103. Ottaviani, A. et al. Identification of a perinuclear positioning element in human subtelomeres that requires A-type lamins and CTCF. EMBO J 28, 2428-36 (2009). 104. Ling, J.Q. et al. CTCF mediates interchromosomal colocalization between Igf2/H19 and Wsb1/Nf1. Science 312, 269-72 (2006). 105. Zhao, Z. et al. Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nat Genet 38, 1341-7 (2006). 106. Xu, N., Donohoe, M.E., Silva, S.S. & Lee, J.T. Evidence that homologous X- chromosome pairing requires transcription and Ctcf protein. Nat Genet 39, 1390-6 (2007). 107. Donohoe, M.E., Silva, S.S., Pinter, S.F., Xu, N. & Lee, J.T. The pluripotency factor Oct4 interacts with Ctcf and also controls X-chromosome pairing and counting. Nature 460, 128-32 (2009). 108. Yang, J. & Corces, V.G. Chromatin insulators: a role in nuclear organization and gene expression. Adv Cancer Res 110, 43-76 (2011). 109. Fiorentino, F.P. & Giordano, A. The tumor suppressor role of CTCF. J Cell Physiol 227, 479-92 (2012). 110. Michaelis, C., Ciosk, R. & Nasmyth, K. Cohesins: chromosomal proteins that prevent premature separation of sister chromatids. Cell 91, 35-45 (1997). 111. Dorsett, D. et al. Effects of sister chromatid cohesion proteins on cut gene expression during wing development in Drosophila. Development 132, 4743- 53 (2005). 112. Rollins, R.A., Korom, M., Aulner, N., Martens, A. & Dorsett, D. Drosophila nipped-B protein supports sister chromatid cohesion and opposes the stromalin/Scc3 cohesion factor to facilitate long-range activation of the cut gene. Mol Cell Biol 24, 3100-11 (2004). 113. Rollins, R.A., Morcillo, P. & Dorsett, D. Nipped-B, a Drosophila homologue of chromosomal adherins, participates in activation by remote enhancers in the cut and Ultrabithorax genes. Genetics 152, 577-93 (1999).  122 114. Misulovin, Z. et al. Association of cohesin and Nipped-B with transcriptionally active regions of the Drosophila melanogaster genome. Chromosoma 117, 89- 102 (2008). 115. Parelho, V. et al. Cohesins functionally associate with CTCF on mammalian chromosome arms. Cell 132, 422-33 (2008). 116. Rubio, E.D. et al. CTCF physically links cohesin to chromatin. Proc Natl Acad Sci U S A 105, 8309-14 (2008). 117. Wendt, K.S. et al. Cohesin mediates transcriptional insulation by CCCTC- binding factor. Nature 451, 796-801 (2008). 118. Kagey, M.H. et al. Mediator and cohesin connect gene expression and chromatin architecture. Nature 467, 430-5 (2010). 119. Liu, J. et al. Transcriptional dysregulation in NIPBL and cohesin mutant human cells. PLoS Biol 7, e1000119 (2009). 120. Nitzsche, A. et al. RAD21 cooperates with pluripotency transcription factors in the maintenance of embryonic stem cell identity. PLoS One 6, e19470 (2011). 121. Stedman, W. et al. Cohesins localize with CTCF at the KSHV latency control region and at cellular c-myc and H19/Igf2 insulators. EMBO J 27, 654-66 (2008). 122. Nativio, R. et al. Cohesin is required for higher-order chromatin conformation at the imprinted IGF2-H19 locus. PLoS Genet 5, e1000739 (2009). 123. Hou, C., Dale, R. & Dean, A. Cell type specificity of chromatin organization mediated by CTCF and cohesin. Proc Natl Acad Sci U S A 107, 3651-6 (2010). 124. Hadjur, S. et al. Cohesins form chromosomal cis-interactions at the developmentally regulated IFNG locus. Nature 460, 410-3 (2009). 125. Roberts, R.J. & Cheng, X. Base flipping. Annu Rev Biochem 67, 181-98 (1998). 126. Lock, L.F., Takagi, N. & Martin, G.R. Methylation of the Hprt gene on the inactive X occurs after chromosome inactivation. Cell 48, 39-46 (1987). 127. Sims, R.J., 3rd et al. Human but not yeast CHD1 binds directly and selectively to histone H3 methylated at lysine 4 via its tandem chromodomains. J Biol Chem 280, 41789-92 (2005). 128. Huang, Y., Fang, J., Bedford, M.T., Zhang, Y. & Xu, R.M. Recognition of histone H3 lysine-4 methylation by the double tudor domain of JMJD2A. Science 312, 748-51 (2006). 129. Lusser, A. & Kadonaga, J.T. Chromatin remodeling by ATP-dependent molecular machines. Bioessays 25, 1192-200 (2003). 130. Yusufzai, T. & Kadonaga, J.T. HARP is an ATP-driven annealing helicase. Science 322, 748-50 (2008). 131. Yusufzai, T. & Kadonaga, J.T. Annealing helicase 2 (AH2), a DNA-rewinding motor with an HNH motif. Proc Natl Acad Sci U S A 107, 20970-3 (2010). 132. Koonin, E.V. A common set of conserved motifs in a vast variety of putative nucleic acid-dependent ATPases including MCM proteins involved in the initiation of eukaryotic DNA replication. Nucleic Acids Res 21, 2541-7. (1993).  123 133. Yu, S., Owen-Hughes, T., Friedberg, E.C., Waters, R. & Reed, S.H. The yeast Rad7/Rad16/Abf1 complex generates superhelical torsion in DNA that is required for nucleotide excision repair. DNA Repair (Amst) 3, 277-87 (2004). 134. Havas, K. et al. Generation of superhelical torsion by ATP-dependent chromatin remodeling activities. Cell 103, 1133-42 (2000). 135. Ristic, D., Wyman, C., Paulusma, C. & Kanaar, R. The architecture of the human Rad54-DNA complex provides evidence for protein translocation along DNA. Proc Natl Acad Sci U S A 98, 8454-60 (2001). 136. Zofall, M., Persinger, J., Kassabov, S.R. & Bartholomew, B. Chromatin remodeling by ISW2 and SWI/SNF requires DNA translocation inside the nucleosome. Nat Struct Mol Biol 13, 339-46 (2006). 137. Sprouse, R.O., Brenowitz, M. & Auble, D.T. Snf2/Swi2-related ATPase Mot1 drives displacement of TATA-binding protein by gripping DNA. EMBO J 25, 1492-504 (2006). 138. Clapier, C.R. & Cairns, B.R. The biology of chromatin remodeling complexes. Annu Rev Biochem 78, 273-304 (2009). 139. Bansbach, C.E., Betous, R., Lovejoy, C.A., Glick, G.G. & Cortez, D. The annealing helicase SMARCAL1 maintains genome integrity at stalled replication forks. Genes Dev 23, 2405-14 (2009). 140. Betous, R. et al. SMARCAL1 catalyzes fork regression and Holliday junction migration to maintain genome stability during DNA replication. Genes Dev 26, 151-62 (2012). 141. Ciccia, A. et al. The SIOD disorder protein SMARCAL1 is an RPA-interacting protein involved in replication fork restart. Genes Dev 23, 2415-25 (2009). 142. Postow, L., Woo, E.M., Chait, B.T. & Funabiki, H. Identification of SMARCAL1 as a component of the DNA damage response. J Biol Chem 284, 35951-61 (2009). 143. Yuan, J., Ghosal, G. & Chen, J. The annealing helicase HARP protects stalled replication forks. Genes Dev 23, 2394-9 (2009). 144. Yusufzai, T., Kong, X., Yokomori, K. & Kadonaga, J.T. The annealing helicase HARP is recruited to DNA repair sites via an interaction with RPA. Genes Dev 23, 2400-4 (2009). 145. Euskirchen, G.M. et al. Diverse roles and interactions of the SWI/SNF chromatin remodeling complex revealed using global approaches. PLoS Genet 7, e1002008 (2011). 146. Coleman, M.A., Eisen, J.A. & Mohrenweiser, H.W. Cloning and characterization of HARP/SMARCAL1: a prokaryotic HepA-related SNF2 helicase protein from human and mouse. Genomics 65, 274-82. (2000). 147. Muthuswami, R., Truman, P.A., Mesner, L.D. & Hockensmith, J.W. A eukaryotic SWI2/SNF2 domain, an exquisite detector of double-stranded to single-stranded DNA transition elements. J Biol Chem 275, 7648-55 (2000). 148. Ghosal, G., Yuan, J. & Chen, J. The HARP domain dictates the annealing helicase activity of HARP/SMARCAL1. EMBO Rep 12, 574-80 (2011).  124 149. Ciccia, A. et al. Polyubiquitinated PCNA recruits the ZRANB3 translocase to maintain genomic integrity after replication stress. Mol Cell 47, 396-409 (2012). 150. Bansbach, C.E., Boerkoel, C.F. & Cortez, D. SMARCAL1 and replication stress: An explanation for SIOD? Nucleus 1, 245 - 248 (2010). 151. Huang, C. et al. Deficiency of smarcal1 causes cell cycle arrest and developmental abnormalities in zebrafish. Dev Biol 339, 89-100 (2010). 152. Schimke, R.N., Horton, W.A. & King, C.R. Chondroitin-6-sulphaturia, defective cellular immunity, and nephrotic syndrome. Lancet 2, 1088-9 (1971). 153. Ehrich, J.H. et al. Association of spondylo-epiphyseal dysplasia with nephrotic syndrome. Pediatr Nephrol 4, 117-21 (1990). 154. Spranger, J. et al. Schimke immuno-osseous dysplasia: a newly recognized multisystem disease. J Pediatr 119, 64-72 (1991). 155. Boerkoel, C.F. et al. Mutant chromatin remodeling protein SMARCAL1 causes Schimke immuno-osseous dysplasia. Nat Genet 30, 215-20 (2002). 156. Clewing, J.M. et al. Schimke immuno-osseous dysplasia: a clinicopathological correlation. J Med Genet 44, 122-30 (2007). 157. Elizondo, L.I. et al. Schimke immuno-osseous dysplasia: SMARCAL1 loss-of- function and phenotypic correlation. J Med Genet 46, 49-59 (2009). 158. Elizondo, L.I. et al. Schimke immuno-osseous dysplasia: a cell autonomous disorder? Am J Med Genet A 140, 340-8 (2006). 159. Bokenkamp, A. et al. R561C missense mutation in the SMARCAL1 gene associated with mild Schimke immuno-osseous dysplasia. Pediatr Nephrol 20, 1724-8 (2005). 160. da Fonseca, M.A. Dental findings in the Schimke immuno-osseous dysplasia. Am J Med Genet 93, 158-60. (2000). 161. Boerkoel, C.F. et al. Manifestations and treatment of Schimke immuno- osseous dysplasia: 14 new cases and a review of the literature. Eur J Pediatr 159, 1-7 (2000). 162. Kilic, S.S. et al. Association of migraine-like headaches with Schimke immuno-osseous dysplasia. Am J Med Genet A 135, 206-10 (2005). 163. Lücke, T. et al. Schimke versus non-Schimke chronic kidney disease: an anthropometric approach. Pediatrics 118, e400-7 (2006). 164. Hunter, K.B. et al. Schimke immunoosseous dysplasia: defining skeletal features. Eur J Pediatr 169, 801-11 (2010). 165. Zieg, J. et al. Rituximab resistant evans syndrome and autoimmunity in Schimke immuno-osseous dysplasia. Pediatr Rheumatol Online J 9, 27 (2011). 166. Kaitila, I., Savilahti, E. & Ormala, T. Autoimmune enteropathy in Schimke immunoosseous dysplasia. Am J Med Genet 77, 427-30. (1998). 167. Lücke, T. et al. Generalized atherosclerosis sparing the transplanted kidney in Schimke disease. Pediatr Nephrol 19, 672-5 (2004).  125 168. Morimoto, M. et al. Reduced elastogenesis: a clue to the arteriosclerosis and emphysematous changes in Schimke immuno-osseous dysplasia? Orphanet J Rare Dis 7, 70 (2012). 169. Urban, Z. et al. Connection between elastin haploinsufficiency and increased cell proliferation in patients with supravalvular aortic stenosis and Williams- Beuren syndrome. Am J Hum Genet 71, 30-44 (2002). 170. Lou, S., Lamfers, P., McGuire, N. & Boerkoel, C.F. Longevity in Schimke immuno-osseous dysplasia. J Med Genet 39, 922-5 (2002). 171. Dekel, B. et al. Schimke immuno-osseous dysplasia: expression of SMARCAL1 in blood and kidney provides novel insight into disease phenotype. Pediatr Res 63, 398-403 (2008). 172. Lücke, T. et al. Schimke-immuno-osseous dysplasia: new mutation with weak genotype-phenotype correlation in siblings. Am J Med Genet A 135, 202-5 (2005). 173. Lama, G. et al. Spondyloepiphyseal dysplasia tarda and nephrotic syndrome in three siblings. Pediatr Nephrol 9, 19-23 (1995). 174. Lucke, T. et al. Improved outcome with immunosuppressive monotherapy after renal transplantation in Schimke-immuno-osseous dysplasia. Pediatr Transplant 13, 482-9 (2009). 175. Petty, E.M. et al. Successful bone marrow transplantation in a patient with Schimke immuno- osseous dysplasia. J Pediatr 137, 882-6. (2000). 176. Baradaran-Heravi, A. et al. Bone marrow transplantation in Schimke immuno- osseous dysplasia. Am J Med Genet A (2013-submitted). 177. Hirano, R. et al. Spinocerebellar ataxia with axonal neuropathy: consequence of a Tdp1 recessive neomorphic mutation? EMBO J 26, 4732-43 (2007). 178. Pan-Hammarstrom, Q. et al. Impact of DNA ligase IV on nonhomologous end joining pathways during class switch recombination in human cells. J Exp Med 201, 189-94 (2005). 179. Pan, Q., Rabbani, H., Mills, F.C., Severinson, E. & Hammarstrom, L. Allotype-associated variation in the human gamma3 switch region as a basis for differences in IgG3 production. J Immunol 158, 5849-59 (1997). 180. Pan, Q. et al. Regulation of switching and production of IgA in human B cells in donors with duplicated alpha1 genes. Eur J Immunol 31, 3622-30 (2001). 181. Elizondo, L.I. Baylor College of Medicine (2008). 182. Arama, E. & Steller, H. Detection of apoptosis by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling and acridine orange in Drosophila embryos and adult male gonads. Nat Protoc 1, 1725-31 (2006). 183. Jaspers, N.G. et al. Anti-tumour compounds illudin S and Irofulven induce DNA lesions ignored by global repair and exclusively processed by transcription- and replication-coupled repair pathways. DNA Repair (Amst) 1, 1027-38 (2002). 184. Taha, D. et al. Fatal lymphoproliferative disorder in a child with Schimke immuno-osseous dysplasia. Am J Med Genet A 131, 194-9 (2004).  126 185. Basiratnia, M., Baradaran-Heravi, A., Yavarian, M., Gerami zadeh, B. & Karimi, M. Non-Hodgkin Lymphoma in A Child with Schimke immuno osseous dysplasia. IJMS 36, 222-225 (2011). 186. Friedberg, E.C. et al. DNA Repair and Mutagenesis, (ASM Press, Washington, DC, 2006). 187. Scriver, C.R. et al. (eds.). The metabolic and molecular bases of inherited disease, (McGraw-Hill, New York, 2001). 188. Kubota, M. et al. IgG4 deficiency with Rothmund-Thomson syndrome: a case report. Eur J Pediatr 152, 406-8 (1993). 189. Ciccia, A. & Elledge, S.J. The DNA damage response: making it safe to play with knives. Mol Cell 40, 179-204 (2010). 190. Chaganti, R.S., Schonberg, S. & German, J. A manyfold increase in sister chromatid exchanges in Bloom's syndrome lymphocytes. Proc Natl Acad Sci U S A 71, 4508-12 (1974). 191. Schatz, D.G. & Ji, Y. Recombination centres and the orchestration of V(D)J recombination. Nat Rev Immunol 11, 251-63 (2011). 192. Gellert, M. V(D)J recombination: RAG proteins, repair factors, and regulation. Annu Rev Biochem 71, 101-32 (2002). 193. Tylki-Szymanska, A. et al. Schimke immuno-osseous dysplasia: two cases. Pediatr Radiol 33, 216-8 (2003). 194. Alexander, D.D. et al. The non-Hodgkin lymphomas: a review of the epidemiologic literature. Int J Cancer 120 Suppl 12, 1-39 (2007). 195. Hill, D.A. et al. Risk of non-Hodgkin lymphoma (NHL) in relation to germline variation in DNA repair and related genes. Blood 108, 3161-7 (2006). 196. Grulich, A.E. & Vajdic, C.M. The epidemiology of non-Hodgkin lymphoma. Pathology 37, 409-19 (2005). 197. Howlader, N. et al. SEER Cancer Statistics Review, 1975-2008, National Cancer Institute. (Bethesda, MD, http://seer.cancer.gov/csr/1975_2008/, based on November 2010 SEER data submission, posted to the SEER web site, 2011). 198. Goto, M., Miller, R.W., Ishikawa, Y. & Sugano, H. Excess of rare cancers in Werner syndrome (adult progeria). Cancer Epidemiol Biomarkers Prev 5, 239- 46 (1996). 199. Lindor, N.M. et al. Rothmund-Thomson syndrome due to RECQ4 helicase mutations: report and clinical and molecular comparisons with Bloom syndrome and Werner syndrome. Am J Med Genet 90, 223-8 (2000). 200. Siitonen, H.A. et al. Molecular defect of RAPADILINO syndrome expands the phenotype spectrum of RECQL diseases. Hum Mol Genet 12, 2837-44 (2003). 201. Romaschoff, D.D. Über die Variabilität in der Manifestierung eines erblichen Merkmales (Abdomen abnormalis) bei Drosophila funebris F. J Psychol Neurol 31, 323-5 (1925). 202. Timoféeff-Ressovsky, N.W. Über den Einfluss des Genotypus auf das phänotypen Auftreten eines einzelnes Gens. J Psychol Neurol 31, 305-10 (1925).  127 203. Vogt, O. Psychiatrisch wichtige Tatsachen der zoologisch-botanischen Systematik. Zeitschrift für die gesamte. Neurol Psychiatr (Bucur) 101(1926). 204. Khoury, M.J., Beaty, T.H. & Cohen, B.H. Fundamentals of Genetic Epidemiology, (Oxford University Press, New York, 1993). 205. Strachan, T. & Read, A.P. Human Molecular Genetics, 2nd edition, (Wiley- Liss, New York, 1999). 206. Nachury, M.V. et al. A core complex of BBS proteins cooperates with the GTPase Rab8 to promote ciliary membrane biogenesis. Cell 129, 1201-13 (2007). 207. Seo, S. et al. BBS6, BBS10, and BBS12 form a complex with CCT/TRiC family chaperonins and mediate BBSome assembly. Proc Natl Acad Sci U S A 107, 1488-93 (2010). 208. Chu, C.S., Trapnell, B.C., Curristin, S., Cutting, G.R. & Crystal, R.G. Genetic basis of variable exon 9 skipping in cystic fibrosis transmembrane conductance regulator mRNA. Nat Genet 3, 151-6 (1993). 209. Thauvin-Robinet, C. et al. The very low penetrance of cystic fibrosis for the R117H mutation: a reappraisal for genetic counselling and newborn screening. J Med Genet 46, 752-8 (2009). 210. Weiss, G. Genetic mechanisms and modifying factors in hereditary hemochromatosis. Nat Rev Gastroenterol Hepatol 7, 50-8 (2010). 211. Lehmann, A.R. The xeroderma pigmentosum group D (XPD) gene: one gene, two functions, three diseases. Genes Dev 15, 15-23 (2001). 212. Proietti-De-Santis, L., Drane, P. & Egly, J.M. Cockayne syndrome B protein regulates the transcriptional program after UV irradiation. EMBO J 25, 1915- 23 (2006). 213. Elmayan, T., Proux, F. & Vaucheret, H. Arabidopsis RPA2: a genetic link among transcriptional gene silencing, DNA repair, and DNA replication. Curr Biol 15, 1919-25 (2005). 214. Faucher, D. & Wellinger, R.J. Methylated H3K4, a transcription-associated histone modification, is involved in the DNA damage response pathway. PLoS Genet 6(2010). 215. Guzder, S.N., Sung, P., Bailly, V., Prakash, L. & Prakash, S. RAD25 is a DNA helicase required for DNA repair and RNA polymerase II transcription. Nature 369, 578-81 (1994). 216. Coin, F. et al. p8/TTD-A as a repair-specific TFIIH subunit. Mol Cell 21, 215- 26 (2006). 217. Liu, H. et al. 55K isoform of CDK9 associates with Ku70 and is involved in DNA repair. Biochem Biophys Res Commun 397, 245-50 (2010). 218. Yu, D.S. & Cortez, D. A role for cdk9-cyclin k in maintaining genome integrity. Cell Cycle 10, 28-32 (2011). 219. Robertson, H.M. et al. A stable genomic source of P element transposase in Drosophila melanogaster. Genetics 118, 461-70 (1988).  128 220. Zhao, K. et al. Rapid and phosphoinositol-dependent binding of the SWI/SNF- like BAF complex to chromatin after T lymphocyte receptor signaling. Cell 95, 625-36 (1998). 221. Zink, D. & Paro, R. Drosophila Polycomb-group regulated chromatin inhibits the accessibility of a trans-activator to its target DNA. Embo J 14, 5660-71 (1995). 222. Ashburner, M. Drosophila: A Laboratory Handbook and Manual, (Cold Spring Harbor Laboratory Press, Plainview, 1989). 223. Deguchi, K. et al. Neurologic phenotype of Schimke immuno-osseous dysplasia and neurodevelopmental expression of SMARCAL1. J Neuropathol Exp Neurol 67, 565-77 (2008). 224. Leon, L.R., DuBose, D.A. & Mason, C.W. Heat stress induces a biphasic thermoregulatory response in mice. Am J Physiol Regul Integr Comp Physiol 288, R197-204 (2005). 225. Miyakoda, M., Suzuki, K., Kodama, S. & Watanabe, M. Activation of ATM and phosphorylation of p53 by heat shock. Oncogene 21, 1090-6 (2002). 226. Mosmann, T. Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods 65, 55- 63 (1983). 227. Shah, S.P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809-13 (2009). 228. Shah, S.P. et al. Mutation of FOXL2 in granulosa-cell tumors of the ovary. N Engl J Med 360, 2719-29 (2009). 229. Morin, R.D. et al. Somatic mutations altering EZH2 (Tyr641) in follicular and diffuse large B-cell lymphomas of germinal-center origin. Nat Genet 42, 181-5 (2010). 230. Morin, R. et al. Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45, 81-94 (2008). 231. Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18, 1851-8 (2008). 232. Muse, G.W. et al. RNA polymerase is poised for activation across the genome. Nat Genet 39, 1507-11 (2007). 233. Fritsch, C., Brown, J.L., Kassis, J.A. & Muller, J. The DNA-binding polycomb group protein pleiohomeotic mediates silencing of a Drosophila homeotic gene. Development 126, 3905-13 (1999). 234. Elfring, L.K. et al. Genetic analysis of brahma: the Drosophila homolog of the yeast chromatin remodeling factor SWI2/SNF2. Genetics 148, 251-65 (1998). 235. Dalmasso, C., Broet, P. & Moreau, T. A simple procedure for estimating the false discovery rate. Bioinformatics 21, 660-8 (2005). 236. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100, 9440-5 (2003).  129 237. Dennis, G., Jr. et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 4, P3 (2003). 238. Huang da, W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4, 44-57 (2009). 239. Tolhuis, B. et al. Genome-wide profiling of PRC1 and PRC2 Polycomb chromatin binding in Drosophila melanogaster. Nat Genet 38, 694-9 (2006). 240. Greil, F. et al. Distinct HP1 and Su(var)3-9 complexes bind to sets of developmentally coexpressed genes depending on chromosomal location. Genes Dev 17, 2825-38 (2003). 241. Schubeler, D. et al. The histone modification pattern of active genes revealed through genome-wide chromatin analysis of a higher eukaryote. Genes Dev 18, 1263-71 (2004). 242. Schubeler, D. et al. Genome-wide DNA replication profile for Drosophila melanogaster: a link between transcription and replication timing. Nat Genet 32, 438-42 (2002). 243. van Steensel, B., Delrow, J. & Henikoff, S. Chromatin profiling using targeted DNA adenine methyltransferase. Nat Genet 27, 304-8 (2001). 244. Gilchrist, D.A. et al. NELF-mediated stalling of Pol II can enhance gene expression by blocking promoter-proximal nucleosome assembly. Genes Dev 22, 1921-33 (2008). 245. Zeitlinger, J. et al. RNA polymerase stalling at developmental control genes in the Drosophila melanogaster embryo. Nat Genet 39, 1512-6 (2007). 246. Deuring, R. et al. The ISWI chromatin-remodeling protein is required for gene expression and the maintenance of higher order chromatin structure in vivo. Mol Cell 5, 355-65. (2000). 247. Lindell, T.J., Weinberg, F., Morris, P.W., Roeder, R.G. & Rutter, W.J. Specific inhibition of nuclear RNA polymerase II by alpha-amanitin. Science 170, 447- 9 (1970). 248. Ljungman, M., Zhang, F., Chen, F., Rainbow, A.J. & McKay, B.C. Inhibition of RNA polymerase II as a trigger for the p53 response. Oncogene 18, 583-92 (1999). 249. Baradaran-Heravi, A. et al. SMARCAL1 deficiency predisposes to non- Hodgkin lymphoma and hypersensitivity to genotoxic agents in vivo. Am J Med Genet A 158A, 2204-13 (2012). 250. Morimoto, M. et al. Dental abnormalities in Schimke immuno-osseous dysplasia. J Dent Res 91, 29S-37S (2012). 251. Merino, C., Reynaud, E., Vazquez, M. & Zurita, M. DNA repair and transcriptional effects of mutations in TFIIH in Drosophila development. Mol Biol Cell 13, 3246-56 (2002). 252. Liu, J. et al. The FUSE/FBP/FIR/TFIIH system is a molecular machine programming a pulse of c-myc expression. EMBO J 25, 2119-30 (2006).  130 253. Rahmouni, A.R. Z-DNA as a probe for localized supercoiling in vivo. Mol Microbiol 6, 569-72 (1992). 254. Wang, Z. & Droge, P. Differential control of transcription-induced and overall DNA supercoiling by eukaryotic topoisomerases in vitro. EMBO J 15, 581-9 (1996). 255. Tornaletti, S. Transcription arrest at DNA damage sites. Mutat Res 577, 131-45 (2005). 256. Aguilera, A. The connection between transcription and genomic instability. EMBO J 21, 195-201 (2002). 257. Gottipati, P. & Helleday, T. Transcription-associated recombination in eukaryotes: link between transcription, replication and recombination. Mutagenesis 24, 203-10 (2009). 258. Svejstrup, J.Q. The interface between transcription and mechanisms maintaining genome integrity. Trends Biochem Sci 35, 333-8 (2010). 259. Kim, N. & Jinks-Robertson, S. Transcription as a source of genome instability. Nat Rev Genet 13, 204-14 (2012). 260. Kim, N., Abdulovic, A.L., Gealy, R., Lippert, M.J. & Jinks-Robertson, S. Transcription-associated mutagenesis in yeast is directly proportional to the level of gene expression and influenced by the direction of DNA replication. DNA Repair (Amst) 6, 1285-96 (2007). 261. Pomerantz, R.T. & O'Donnell, M. Direct restart of a replication fork stalled by a head-on RNA polymerase. Science 327, 590-2 (2010). 262. Vilette, D., Ehrlich, S.D. & Michel, B. Transcription-induced deletions in plasmid vectors: M13 DNA replication as a source of instability. Mol Gen Genet 252, 398-403 (1996). 263. Sikorski, T.W. et al. Sub1 and RPA associate with RNA polymerase II at different stages of transcription. Mol Cell 44, 397-409 (2011). 264. Sequeira-Mendes, J. et al. Transcription initiation activity sets replication origin efficiency in mammalian cells. PLoS Genet 5, e1000446 (2009). 265. White, E.J. et al. DNA replication-timing analysis of human chromosome 22 at high resolution and different developmental states. Proc Natl Acad Sci U S A 101, 17771-6 (2004). 266. Woodfine, K. et al. Replication timing of the human genome. Hum Mol Genet 13, 191-202 (2004). 267. Koryakov, D.E. et al. Induced transcription results in local changes in chromatin structure, replication timing, and DNA polytenization in a site of intercalary heterochromatin. Chromosoma 121, 573-83 (2012). 268. Jacob, S., Aguado, M., Fallik, D. & Praz, F. The role of the DNA mismatch repair system in the cytotoxicity of the topoisomerase inhibitors camptothecin and etoposide to human colorectal cancer cells. Cancer Res 61, 6555-62 (2001). 269. Baradaran-Heravi, A. et al. Clinical and genetic distinction of Schimke immuno-osseous dysplasia and cartilage-hair hypoplasia. Am J Med Genet A 146A, 2013-7 (2008).  131 270. Ito, S. et al. XPG stabilizes TFIIH, allowing transactivation of nuclear receptors: implications for Cockayne syndrome in XP-G/CS patients. Mol Cell 26, 231-43 (2007). 271. Nel, A.E. T-cell activation through the antigen receptor. Part 1: signaling components, signaling pathways, and signal integration at the T-cell antigen receptor synapse. J Allergy Clin Immunol 109, 758-70 (2002). 272. Nel, A.E. & Slaughter, N. T-cell activation through the antigen receptor. Part 2: role of signaling cascades in T-cell differentiation, anergy, immune senescence, and development of immunotherapy. J Allergy Clin Immunol 109, 901-15 (2002). 273. Song, Y., Yao, X. & Ying, H. Thyroid hormone action in metabolic regulation. Protein Cell 2, 358-68 (2011). 274. Cheng, S.Y., Leonard, J.L. & Davis, P.J. Molecular aspects of thyroid hormone actions. Endocr Rev 31, 139-70 (2010). 275. Chiba, T., Ikawa, Y. & Todokoro, K. GATA-1 transactivates erythropoietin receptor gene, and erythropoietin receptor-mediated signals enhance GATA-1 gene expression. Nucleic Acids Res 19, 3843-8 (1991). 276. Noguchi, C.T., Wang, L., Rogers, H.M., Teng, R. & Jia, Y. Survival and proliferative roles of erythropoietin beyond the erythroid lineage. Expert Rev Mol Med 10, e36 (2008). 277. St-Jacques, B., Hammerschmidt, M. & McMahon, A.P. Indian hedgehog signaling regulates proliferation and differentiation of chondrocytes and is essential for bone formation. Genes Dev 13, 2072-86 (1999). 278. Vortkamp, A. et al. Regulation of rate of cartilage differentiation by Indian hedgehog and PTH-related protein. Science 273, 613-22 (1996). 279. Lasagni, L. et al. Notch activation differentially regulates renal progenitors proliferation and differentiation toward the podocyte lineage in glomerular disorders. Stem Cells 28, 1674-85 (2010). 280. Castells-Roca, L. et al. Heat shock response in yeast involves changes in both transcription rates and mRNA stabilities. PLoS One 6, e17272 (2011). 281. Richter, K., Haslbeck, M. & Buchner, J. The heat shock response: life on the verge of death. Mol Cell 40, 253-66 (2010). 282. de Nadal, E., Ammerer, G. & Posas, F. Controlling gene expression in response to stress. Nat Rev Genet 12, 833-45 (2011). 283. Bartlett, J.D., Scicchitano, D.A. & Robison, S.H. Two expressed human genes sustain slightly more DNA damage after alkylating agent treatment than an inactive gene. Mutat Res 255, 247-56 (1991). 284. Warters, R.L., Lyons, B.W., Chiu, S.M. & Oleinick, N.L. Induction of DNA strand breaks in transcriptionally active DNA sequences of mouse cells by low doses of ionizing radiation. Mutat Res 180, 21-9 (1987). 285. Capranico, G. et al. The effects of camptothecin on RNA polymerase II transcription: roles of DNA topoisomerase I. Biochimie 89, 482-9 (2007).  132 286. Bowen, J.M., Gibson, R.J., Cummins, A.G., Tyskin, A. & Keefe, D.M. Irinotecan changes gene expression in the small intestine of the rat with breast cancer. Cancer Chemother Pharmacol 59, 337-48 (2007). 287. Mondal, N. et al. Elongation by RNA polymerase II on chromatin templates requires topoisomerase activity. Nucleic Acids Res 31, 5016-24 (2003). 288. Mondal, N. & Parvin, J.D. DNA topoisomerase IIalpha is required for RNA polymerase II transcription on chromatin templates. Nature 413, 435-8 (2001). 289. Cui, P. et al. Hydroxyurea-induced global transcriptional suppression in mouse ES cells. Carcinogenesis 31, 1661-8 (2010). 290. Aygun, O., Svejstrup, J. & Liu, Y. A RECQ5-RNA polymerase II association identified by targeted proteomic analysis of human chromatin. Proc Natl Acad Sci U S A 105, 8580-4 (2008). 291. Jeronimo, C. et al. RPAP1, a novel human RNA polymerase II-associated protein affinity purified with recombinant wild-type and mutated polymerase subunits. Mol Cell Biol 24, 7043-58 (2004). 292. Robert, F., Blanchette, M., Maes, O., Chabot, B. & Coulombe, B. A human RNA polymerase II-containing complex associated with factors necessary for spliceosome assembly. J Biol Chem 277, 9302-6 (2002). 293. Brown, J.M. et al. Coregulated human globin genes are frequently in spatial proximity when active. J Cell Biol 172, 177-87 (2006). 294. Tao, Y. et al. Lsh, chromatin remodeling family member, modulates genome- wide cytosine methylation patterns at nonrepeat sequences. Proc Natl Acad Sci U S A 108, 5626-31 (2011). 295. Dennis, K., Fan, T., Geiman, T., Yan, Q. & Muegge, K. Lsh, a member of the SNF2 family, is required for genome-wide methylation. Genes Dev 15, 2940-4 (2001). 296. Santoro, R., Li, J. & Grummt, I. The nucleolar remodeling complex NoRC mediates heterochromatin formation and silencing of ribosomal gene transcription. Nat Genet 32, 393-6 (2002). 297. Gibbons, R.J. et al. Mutations in ATRX, encoding a SWI/SNF-like protein, cause diverse changes in the pattern of DNA methylation. Nat Genet 24, 368- 71 (2000). 298. Banine, F. et al. SWI/SNF chromatin-remodeling factors induce changes in DNA methylation to promote transcriptional activation. Cancer Res 65, 3542-7 (2005).     133 Appendices                     134 Appendix 1- Figure showing microhomology usage at Sµ-Sα1 junctions in the immunoglobulins of SIOD patients.  The Sµ and Sα1 reference sequences are aligned above and below the switch junctional sequences from 7 SIOD patients (SD106, SD107, SD111, SD114, SD115, SD120, and SD123) and 4 controls (SD106.1, SD107.1, SD114.1, and SD121.1). Microhomology (solid- line boxes) at each switch junction is the longest region of identity with both the Sµ and Sα1 reference sequences, whereas imperfect microhomology (dashed-line boxes) at each switch junction is the longest region of near identity (1 mismatch on either side of the breakpoint). Red nucleotides indicate insertion of nucleotides at the switch region breakpoints.    135     136 Appendix 2- Figure showing microhomology usage at Sµ-Sγ1 junctions in the immunoglobulins of SIOD patients.  The Sµ and Sγ1 reference sequences are aligned above and below the switch junctional sequences from 7 SIOD patients (SD106, SD107, SD111, SD114, SD115, SD120, and SD121) and 4 controls (SD106.1, SD107.1, SD114.1, and SD121.1). Microhomology (solid- line boxes) at each switch junction is the longest region of identity with both the Sµ and Sγ1 reference sequences, whereas imperfect microhomology (dashed-line boxes) at each switch junction is the longest region of near identity (1 mismatch on either side of the breakpoint). Red nucleotides indicate insertion of nucleotides at the switch region breakpoints.   137       138 Appendix 3- Table showing suppressors and enhancers of ectopic wing veins induced by the overexpression of Drosophila Marcal1 and human SMARCAL1.  Gene Allele Drosophila Marcal1 Human SMARCAL1 Gene Allele Drosophila Marcal1 Human SMARCAL1 Nuclear Matrix and Chromatin Structure Interactors BEAF-32 e00756 E E Nipped-B 02047 S S  KG069 04 S 0 ord 1 S S Lam 91 E E san KG04816 0 0  98 E E SMC1 c00402 E E  sz18 E E su(Hw) 2 S S LamC G0015 8 S S  8 S S  k11904 0 0  e04061 E E mei-S332 1 S 0 Trl s2325 S E mod(mdg4) u1 S S  KG08276 S S  KG085 15 S S Chromatin Remodeling or Chromatin Structural Factors Acf1 1 0 0 Pcaf f02830 S 0  2 E E  f05456 0 E  EY0862 9 E E  C137T S S mod 07570 S S  E333st 0 E Nap1 KG039 59 E E  Q186st S 0  EY0859 6 E E  DeltaT280 -F285 S S  K01 E E Sir2 17 0 E  K02 0 E  2A-7-11 E E nej P S S Su(var)20 5 5 E E  Q7 S S Su(var)3- 1 3 S S nhk-1 Z3- 0437 E 0 Su(var)3- 4 1 S E Nipped-A NC116 S S  2 S S  NC186 S S trr 1 S S pr-set7 1 0 0  3 S S  EY0466 8 E E  4 S S      EY06632 E E Mediator Complex Cdk8 A162 S 0 MED18 e03853 S S MED6 EY0508 7 E E MED20 C6R20 0 S MED12 (Kto) 1 E E  f00955 S E MED13 (skuld) 2 E E MED23 KG00948 E 0  rK760 E E MED24 BG01670 E E  L7062 0 S MED28 e03165 E E  EY0736 9 E 0 MED29 (intersex) 1 0 S MED15 f04180 0 E  KG04582 0 0 MED17 s2956 E E     139 Gene Allele Drosophila Marcal1 Human SMARCAL1 Gene Allele Drosophila Marcal1 Human SMARCAL1 RNA Polymerase II Complex cul-4 KG029 00 E E RpII215 3 0 0  EP2518 S 0  4 S S RpII33 1 0 0  8 E S  k05605 0 S  12 E E RpII140 wimp E E  102 E E  S S S  G0040 0 S      K1 S S      ts S E      Ubl E S      EY06155 E E RNA Polymerase II Initiation Factors pb (srb) 1 S E Taf2 (TFIID) c03353 S 0  10 S S Taf4 (TFIID) 1 E S  23 S S  e02502 S E  27 S 0 Taf5 (TFIID) EY01764b E 0 Trf2 PL28#1 S S Taf6 (TFIID) 1 0 E TfIIA-L e01040 0 E  f06930 0 E  d08487 S E Taf10 (TFIID) KG07031 0 0 TfIIA-S E32 S E Taf10b (TFIID) KG01574 E E  E73 S 0 Taf12L (TFIID) KG00946 E S  EY0232 3 E S TfIIEα e01382 S 0 TfIIB DG143 11 S E TfIIEβ e00364 S E caz (TFIID) EP1564 E E TfIIFα d01485 S E e(y)1 (TFIID) BG009 48 S S TfIIFβ j3C1 E S mia (TFIID) B560 S S Cdk7 (TFIIH) T170A E, blistered E  EY0788 3 E E  D136R E E Taf1 (TFIID) 1 S S CycH (TFIIH) KG02273 E E  EP421 E E hay (TFIIH, Xpb, Ercc3)            f00028 0 E  140 Gene Allele Drosophila Marcal1 Human SMARCAL1 Gene Allele Drosophila Marcal1 Human SMARCAL1 RNA Polymerase II Elongation Factors Cdk9 f05537 E E mus209 (PCNA) 02448 S S CycT c04764 S S  k00704 0 S  c06571 E E  EY09082 E E dre4 (FACT, Spt16) EY0160 4 E S Spt4 k05316 S S Elongin-B EP3132 S 0 Spt5 SIE-27 S S  EY0402 2 E E  W049 S, small 0 Elongin-C e01107 S 0 Spt6 G0063 S S Mi-2 DG125 05 S 0 Su(Tpl) (ELL) 10 S S  EY0813 8 E E  c00783 0 0  EY0869 6 E E  c03115 S 0      S-192 S E     TfIIS 2 S 0 RNA Polymerase II Transcription Termination Factors Ids 1 S E Pcf11 k08015 S E  A190 0 E Slbp EP1045 S 0  e00889 S E  Hor-1 E E Regulators of DNA Replication Gene Transcription cdc2 B47 S E Dp 49Fk-1 0 S  c03495 E S  a1 S S  E1-23 E E  KG00660 S S  E1-24 E E  EY09085 E E  GT- 000294 E E E2f 07172 S S CycA 03946 E 0  i2 0 S  c05304 E S  KG03332 S S  C8LR1 S E  EY14408 E 0  H170 S S grh 06850 E E  EY1174 6 E S  IM E E Dref KG092 94 0 S  s2140 E S  NP471 9 0 0 Regulators of Chromatin Condensation and Segregation barr k14014 S S mei-9 f01366 S S  L305 S S mei-P22 A054 E E Cap-G (condensin) BG018 73 E E  P22 E E  EP2346 E E mei-W68 1 S S gluon 88-37 S 0  k05603 S S  k08819 S S pros 17 S S  Key: S = suppressor, 0 = Neither a suppressor nor an enhancer, E = enhancer.     141 Appendix 4- Table showing enrichment in biological process GO-terms (all levels) of upregulated expressed genes (q<0.05 and fold change >2) in SIOD compared to control human skin fibroblasts.  Term Count p-value Fold Enrichment GO:0009987~cellular process 401 3.81E-06 1.114107687 GO:0008152~metabolic process 292 0.002569399 1.118294973 GO:0044237~cellular metabolic process 265 2.59E-04 1.169510453 GO:0044238~primary metabolic process 265 0.005201134 1.121027209 GO:0043170~macromolecule metabolic process 229 9.89E-04 1.174529652 GO:0044260~cellular macromolecule metabolic process 216 2.30E-04 1.213241719 GO:0060255~regulation of macromolecule metabolic process 128 0.046000907 1.150244646 GO:0019538~protein metabolic process 123 0.001809831 1.281015565 GO:0016043~cellular component organization 115 4.26E-04 1.348248723 GO:0044267~cellular protein metabolic process 113 9.19E-05 1.405245307 GO:0048518~positive regulation of biological process 99 1.77E-04 1.426140875 GO:0048522~positive regulation of cellular process 90 3.81E-04 1.427053403 GO:0048519~negative regulation of biological process 82 0.005801357 1.325318531 GO:0048523~negative regulation of cellular process 75 0.008995627 1.323176524 GO:0006950~response to stress 72 0.033087325 1.251403031 GO:0006996~organelle organization 71 1.58E-04 1.561056908 GO:0007242~intracellular signaling cascade 57 0.022358971 1.329076037 GO:0043687~post-translational protein modification 54 0.023643914 1.337953114 GO:0008219~cell death 51 1.21E-06 2.077331933 GO:0016265~death 51 1.49E-06 2.062985718 GO:0033036~macromolecule localization 49 0.032408923 1.333670117 GO:0051641~cellular localization 47 0.006627596 1.483250465 GO:0016070~RNA metabolic process 45 0.019158633 1.404993409 GO:0042981~regulation of apoptosis 44 0.002250946 1.602733222 GO:0043067~regulation of programmed cell death 44 0.002678309 1.586942747 GO:0010941~regulation of cell death 44 0.002905771 1.58110124 GO:0031325~positive regulation of cellular metabolic process 43 0.016797479 1.431035458  142 Term Count p-value Fold Enrichment GO:0008104~protein localization 43 0.017376476 1.42779048 GO:0009893~positive regulation of metabolic process 43 0.032409111 1.367330297 GO:0007049~cell cycle 42 0.003465433 1.585083629 GO:0051649~establishment of localization in cell 42 0.016008514 1.443691193 GO:0012501~programmed cell death 41 5.87E-05 1.965202274 GO:0006915~apoptosis 40 9.04E-05 1.945934024 GO:0009057~macromolecule catabolic process 39 0.017024787 1.462440429 GO:0044265~cellular macromolecule catabolic process 38 0.009034006 1.535006439 GO:0015031~protein transport 38 0.01909319 1.460472005 GO:0045184~establishment of protein localization 38 0.021206917 1.447177722 GO:0042127~regulation of cell proliferation 37 0.043581024 1.37686577 GO:0046907~intracellular transport 36 0.005863484 1.604729154 GO:0051246~regulation of protein metabolic process 35 5.15E-04 1.877327375 GO:0010605~negative regulation of macromolecule metabolic process 35 0.042952226 1.396486031 GO:0006396~RNA processing 34 0.001076341 1.820355466 GO:0051603~proteolysis involved in cellular protein catabolic process 34 0.00454528 1.6595574 GO:0044257~cellular protein catabolic process 34 0.005102412 1.651300895 GO:0030163~protein catabolic process 34 0.007935734 1.600859228 GO:0019941~modification-dependent protein catabolic process 32 0.007731843 1.632686108 GO:0043632~modification-dependent macromolecule catabolic process 32 0.007731843 1.632686108 GO:0022402~cell cycle process 30 0.018796618 1.555025153 GO:0043065~positive regulation of apoptosis 29 8.12E-04 1.975123034 GO:0043068~positive regulation of programmed cell death 29 9.10E-04 1.961438579 GO:0010942~positive regulation of cell death 29 9.82E-04 1.95242047 GO:0032268~regulation of cellular protein metabolic process 29 0.003414836 1.79177828 GO:0033554~cellular response to stress 29 0.030924616 1.500535167 GO:0016071~mRNA metabolic process 26 9.13E-04 2.057956712 GO:0006397~mRNA processing 25 2.75E-04 2.280865035 GO:0000278~mitotic cell cycle 25 0.00198551 1.978804531  143 Term Count p-value Fold Enrichment GO:0051128~regulation of cellular component organization 25 0.02463692 1.598597547 GO:0022403~cell cycle phase 24 0.014968538 1.697756931 GO:0008283~cell proliferation 24 0.025690403 1.612090297 GO:0008380~RNA splicing 23 2.98E-04 2.371778388 GO:0043066~negative regulation of apoptosis 23 0.004995943 1.902782662 GO:0043069~negative regulation of programmed cell death 23 0.005812809 1.87628151 GO:0060548~negative regulation of cell death 23 0.006090459 1.871069617 GO:0007243~protein kinase cascade 21 0.028709615 1.662195806 GO:0006917~induction of apoptosis 19 0.026168464 1.738874481 GO:0012502~induction of programmed cell death 19 0.027214012 1.733457427 GO:0000279~M phase 19 0.033125271 1.691306486 GO:0044419~interspecies interaction between organisms 18 0.01720956 1.862733311 GO:0031399~regulation of protein modification process 18 0.024629861 1.786961108 GO:0010608~posttranscriptional regulation of gene expression 17 0.002389789 2.359560284 GO:0002520~immune system development 17 0.027336969 1.803866739 GO:0051301~cell division 17 0.045796846 1.687685491 GO:0048285~organelle fission 16 0.011981384 2.04620486 GO:0006511~ubiquitin-dependent protein catabolic process 16 0.018921815 1.936284764 GO:0010627~regulation of protein kinase cascade 16 0.02375317 1.881851056 GO:0048534~hemopoietic or lymphoid organ development 16 0.033108102 1.80223428 GO:0080134~regulation of response to stress 16 0.048419471 1.710149317 GO:0007067~mitosis 15 0.018694996 1.996793663 GO:0000280~nuclear division 15 0.018694996 1.996793663 GO:0000087~M phase of mitotic cell cycle 15 0.021450152 1.961136633 GO:0000398~nuclear mRNA splicing, via spliceosome 14 0.002222452 2.679792802 GO:0000377~RNA splicing, via transesterification reactions with bulged adenosine as nucleophile 14 0.002222452 2.679792802 GO:0000375~RNA splicing, via transesterification reactions 14 0.002222452 2.679792802 GO:0010035~response to inorganic substance 14 0.023503298 2.000040482 GO:0033043~regulation of organelle organization 14 0.034963921 1.889439165  144 Term Count p-value Fold Enrichment GO:0010740~positive regulation of protein kinase cascade 13 0.011949331 2.279772405 GO:0051248~negative regulation of protein metabolic process 13 0.026591009 2.03594648 GO:0044087~regulation of cellular component biogenesis 12 0.009309886 2.474899188 GO:0018193~peptidyl-amino acid modification 12 0.022914376 2.169356078 GO:0006979~response to oxidative stress 12 0.024814323 2.142900516 GO:0006457~protein folding 12 0.040034652 1.985512343 GO:0032269~negative regulation of cellular protein metabolic process 12 0.044306334 1.95242047 GO:0051130~positive regulation of cellular component organization 12 0.045797201 1.941633617 GO:0009101~glycoprotein biosynthetic process 11 0.044035898 2.038920111 GO:0070647~protein modification by small protein conjugation or removal 11 0.047264982 2.01343361 GO:0043523~regulation of neuron apoptosis 10 0.003520201 3.254034117 GO:0010038~response to metal ion 10 0.032311256 2.270256361 GO:0030155~regulation of cell adhesion 10 0.044730063 2.137686646 GO:0007005~mitochondrion organization 10 0.04647877 2.122196163 GO:0051640~organelle localization 9 0.013115858 2.86496482 GO:0045937~positive regulation of phosphate metabolic process 9 0.020771924 2.635767635 GO:0010562~positive regulation of phosphorus metabolic process 9 0.020771924 2.635767635 GO:0043122~regulation of I-kappaB kinase/NF- kappaB cascade 9 0.029700616 2.463334238 GO:0009791~post-embryonic development 8 0.011993542 3.209458307 GO:0007059~chromosome segregation 8 0.020337977 2.892474771 GO:0060191~regulation of lipase activity 8 0.02878751 2.692993752 GO:0043123~positive regulation of I-kappaB kinase/NF-kappaB cascade 8 0.047622255 2.41536553 GO:0042327~positive regulation of phosphorylation 8 0.047622255 2.41536553 GO:0043524~negative regulation of neuron apoptosis 7 0.00745701 4.019689203 GO:0051592~response to calcium ion 7 0.010715017 3.727348171 GO:0051656~establishment of organelle localization 7 0.029862946 2.971074629 GO:0031349~positive regulation of defense response 7 0.037918032 2.808276019  145 Term Count p-value Fold Enrichment GO:0000302~response to reactive oxygen species 7 0.04241817 2.733388658 GO:0031647~regulation of protein stability 6 0.013549339 4.183758151 GO:0006944~membrane fusion 6 0.033795482 3.315430987 GO:0042542~response to hydrogen peroxide 6 0.041454286 3.137818613 GO:0016197~endosome transport 6 0.047097032 3.029617971 GO:0000186~activation of MAPKK activity 5 0.009538241 5.857261411 GO:0002831~regulation of response to biotic stimulus 5 0.016095999 5.049363285 GO:0006984~ER-nuclear signaling pathway 5 0.030268257 4.183758151 GO:0002711~positive regulation of T cell mediated immunity 4 0.013178866 7.809681881 GO:0002709~regulation of T cell mediated immunity 4 0.033143367 5.578344201 GO:0048538~thymus development 4 0.037432742 5.324783101 GO:0006829~zinc ion transport 4 0.046814116 4.881051176 GO:0001916~positive regulation of T cell mediated cytotoxicity 3 0.021717286 12.55127445 GO:0002839~positive regulation of immune response to tumor cell 3 0.02831063 10.98236515 GO:0002833~positive regulation of response to biotic stimulus 3 0.02831063 10.98236515 GO:0002834~regulation of response to tumor cell 3 0.02831063 10.98236515 GO:0002836~positive regulation of response to tumor cell 3 0.02831063 10.98236515 GO:0002837~regulation of immune response to tumor cell 3 0.02831063 10.98236515 GO:0001914~regulation of T cell mediated cytotoxicity 3 0.035590042 9.762102351 GO:0009435~NAD biosynthetic process 3 0.035590042 9.762102351 GO:0019359~nicotinamide nucleotide biosynthetic process 3 0.043501312 8.785892116 GO:0001953~negative regulation of cell-matrix adhesion 3 0.043501312 8.785892116     146 Appendix 5- Table showing enrichment in biological process GO-terms (all levels) of downregulated expressed genes (q<0.05 and fold change >2) in SIOD compared to control human skin fibroblasts.  Term Count p-value Fold Enrichment GO:0009987~cellular process 494 4.46E-09 1.130839368 GO:0008152~metabolic process 353 0.00138666 1.113882546 GO:0044238~primary metabolic process 333 6.81E-05 1.160661785 GO:0044237~cellular metabolic process 325 1.72E-05 1.181769473 GO:0043170~macromolecule metabolic process 288 8.74E-06 1.217060488 GO:0044260~cellular macromolecule metabolic process 271 1.65E-06 1.254163183 GO:0006807~nitrogen compound metabolic process 181 0.014593108 1.156038785 GO:0034641~cellular nitrogen compound metabolic process 179 0.00724506 1.176908638 GO:0009058~biosynthetic process 169 0.021436468 1.151314386 GO:0006139~nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 167 0.008790601 1.182075602 GO:0044249~cellular biosynthetic process 165 0.020140687 1.156721644 GO:0019538~protein metabolic process 155 6.88E-05 1.330062856 GO:0010467~gene expression 153 0.002703185 1.231035986 GO:0009059~macromolecule biosynthetic process 143 0.005928128 1.218424357 GO:0034645~cellular macromolecule biosynthetic process 142 0.00602068 1.218509197 GO:0044267~cellular protein metabolic process 139 6.72E-06 1.424228501 GO:0016043~cellular component organization 132 0.001720248 1.275079551 GO:0048518~positive regulation of biological process 100 0.04314565 1.186911684 GO:0048519~negative regulation of biological process 99 0.002875626 1.318356257 GO:0048522~positive regulation of cellular process 96 0.013345635 1.254180723 GO:0048523~negative regulation of cellular process 92 0.00281572 1.337320564 GO:0006996~organelle organization 88 1.04E-05 1.594168528 GO:0051641~cellular localization 64 6.48E-05 1.664132037 GO:0016070~RNA metabolic process 64 8.77E-05 1.646390757  147 Term Count p-value Fold Enrichment GO:0033036~macromolecule localization 63 0.004707418 1.412810981 GO:0051649~establishment of localization in cell 59 1.19E-04 1.67096826 GO:0006508~proteolysis 59 0.015287291 1.350725766 GO:0007049~cell cycle 56 6.29E-05 1.741334038 GO:0008104~protein localization 56 0.001470351 1.532058065 GO:0044248~cellular catabolic process 55 0.039031249 1.296040331 GO:0046907~intracellular transport 54 2.30E-06 1.983280646 GO:0015031~protein transport 51 8.20E-04 1.61499428 GO:0045184~establishment of protein localization 51 9.81E-04 1.600293421 GO:0022607~cellular component assembly 49 0.03380139 1.332994151 GO:0009892~negative regulation of metabolic process 48 0.006139044 1.484917817 GO:0009057~macromolecule catabolic process 47 0.009906301 1.452120337 GO:0022402~cell cycle process 46 1.93E-05 1.964559413 GO:0044265~cellular macromolecule catabolic process 46 0.004102933 1.531001474 GO:0010605~negative regulation of macromolecule metabolic process 46 0.005301377 1.512228976 GO:0031324~negative regulation of cellular metabolic process 45 0.006204503 1.508119658 GO:0043067~regulation of programmed cell death 45 0.040055776 1.337248958 GO:0010941~regulation of cell death 45 0.04235056 1.332326569 GO:0051603~proteolysis involved in cellular protein catabolic process 41 0.00198611 1.648877493 GO:0044257~cellular protein catabolic process 41 0.002190998 1.640674122 GO:0030163~protein catabolic process 41 0.003785536 1.590557067 GO:0019941~modification-dependent protein catabolic process 40 0.001617958 1.681527145 GO:0043632~modification-dependent macromolecule catabolic process 40 0.001617958 1.681527145 GO:0006396~RNA processing 39 0.001236903 1.720414381 GO:0006412~translation 38 3.71E-08 2.770201668 GO:0051246~regulation of protein metabolic process 38 0.002198151 1.679371341 GO:0006259~DNA metabolic process 36 0.002058718 1.716752812 GO:0016192~vesicle-mediated transport 36 0.014772651 1.508119658 GO:0000278~mitotic cell cycle 34 2.84E-05 2.217343497  148 Term Count p-value Fold Enrichment GO:0022403~cell cycle phase 34 2.45E-04 1.981683802 GO:0009890~negative regulation of biosynthetic process 34 0.034713442 1.431792485 GO:0032268~regulation of cellular protein metabolic process 33 0.004526851 1.679930758 GO:0045934~negative regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 33 0.013118819 1.555248397 GO:0051172~negative regulation of nitrogen compound metabolic process 33 0.01596552 1.534272022 GO:0010558~negative regulation of macromolecule biosynthetic process 33 0.030433277 1.455735246 GO:0031327~negative regulation of cellular biosynthetic process 33 0.041037632 1.419406737 GO:0051128~regulation of cellular component organization 32 0.004946304 1.685932893 GO:0016071~mRNA metabolic process 31 3.49E-04 2.021695542 GO:0034613~cellular protein localization 31 0.001922284 1.820017884 GO:0070727~cellular macromolecule localization 31 0.002104962 1.806829349 GO:0045595~regulation of cell differentiation 31 0.021870139 1.520380794 GO:0010629~negative regulation of gene expression 31 0.029362287 1.484181251 GO:0006397~mRNA processing 30 6.83E-05 2.255132199 GO:0008380~RNA splicing 29 1.91E-05 2.463970146 GO:0007010~cytoskeleton organization 29 0.014571112 1.604971379 GO:0034621~cellular macromolecular complex subunit organization 28 0.001893405 1.892542316 GO:0016481~negative regulation of transcription 28 0.04203877 1.471977357 GO:0006886~intracellular protein transport 27 0.006916062 1.741999177 GO:0032989~cellular component morphogenesis 26 0.02459913 1.580296669 GO:0000279~M phase 25 0.005188806 1.833580131 GO:0034622~cellular macromolecular complex assembly 24 0.006777963 1.821125625 GO:0043069~negative regulation of programmed cell death 24 0.025433379 1.613141918 GO:0060548~negative regulation of cell death 24 0.026055164 1.608660969 GO:0006325~chromatin organization 24 0.042046722 1.532058065  149 Term Count p-value Fold Enrichment GO:0044419~interspecies interaction between organisms 23 0.003413072 1.96108846 GO:0007264~small GTPase mediated signal transduction 23 0.008230071 1.819632899 GO:0006519~cellular amino acid and derivative metabolic process 23 0.035545322 1.576670552 GO:0043066~negative regulation of apoptosis 23 0.037848088 1.567762808 GO:0000902~cell morphogenesis 23 0.039923928 1.558955152 GO:0006511~ubiquitin-dependent protein catabolic process 22 0.001119248 2.193628594 GO:0032535~regulation of cellular component size 22 0.004380449 1.95888605 GO:0001501~skeletal system development 22 0.024485677 1.664132037 GO:0006414~translational elongation 21 4.54E-09 5.017110942 GO:0000398~nuclear mRNA splicing, via spliceosome 21 5.04E-06 3.311949053 GO:0000377~RNA splicing, via transesterification reactions with bulged adenosine as nucleophile 21 5.04E-06 3.311949053 GO:0000375~RNA splicing, via transesterification reactions 21 5.04E-06 3.311949053 GO:0000087~M phase of mitotic cell cycle 20 0.00245136 2.154456654 GO:0044106~cellular amine metabolic process 20 0.049954855 1.582289477 GO:0006605~protein targeting 19 0.003619753 2.132411051 GO:0007067~mitosis 19 0.004602839 2.083947164 GO:0000280~nuclear division 19 0.004602839 2.083947164 GO:0048285~organelle fission 19 0.006936248 2.00204531 GO:0051169~nuclear transport 18 2.93E-04 2.748977605 GO:0030036~actin cytoskeleton organization 18 0.012866985 1.9218516 GO:0030029~actin filament-based process 18 0.022611851 1.80223428 GO:0006913~nucleocytoplasmic transport 17 7.44E-04 2.629541968 GO:0006260~DNA replication 17 0.005577274 2.158992353 GO:0006916~anti-apoptosis 17 0.011730717 1.991303626 GO:0010608~posttranscriptional regulation of gene expression 17 0.014481678 1.944116337 GO:0000904~cell morphogenesis involved in differentiation 17 0.04681793 1.68118257 GO:0051130~positive regulation of cellular component organization 16 0.008244027 2.133031119 GO:0009968~negative regulation of signal transduction 16 0.041107244 1.746962138  150 Term Count p-value Fold Enrichment GO:0006520~cellular amino acid metabolic process 16 0.048692909 1.708312533 GO:0044087~regulation of cellular component biogenesis 14 0.006020286 2.379005658 GO:0032269~negative regulation of cellular protein metabolic process 14 0.036411103 1.87677113 GO:0051248~negative regulation of protein metabolic process 14 0.047054499 1.806517665 GO:0060348~bone development 13 0.004942309 2.55031617 GO:0048193~Golgi vesicle transport 13 0.008105847 2.394571671 GO:0006417~regulation of translation 13 0.011379132 2.289699919 GO:0051329~interphase of mitotic cell cycle 12 0.003513838 2.811252178 GO:0051325~interphase 12 0.004384042 2.731688437 GO:0001503~ossification 12 0.008055309 2.517904125 GO:0006333~chromatin assembly or disassembly 12 0.01621979 2.279991924 GO:0051493~regulation of cytoskeleton organization 12 0.025568552 2.129110106 GO:0043254~regulation of protein complex assembly 11 0.003970442 2.949211776 GO:0031400~negative regulation of protein modification process 11 0.025700206 2.230496301 GO:0042692~muscle cell differentiation 11 0.028433831 2.193628594 GO:0031398~positive regulation of protein ubiquitination 10 0.007776489 2.872608873 GO:0031396~regulation of protein ubiquitination 10 0.022583441 2.412991453 GO:0007498~mesoderm development 9 0.011191389 2.934719335 GO:0051028~mRNA transport 9 0.027340759 2.496198055 GO:0032956~regulation of actin cytoskeleton organization 9 0.030790635 2.440103717 GO:0065004~protein-DNA complex assembly 9 0.034527709 2.386475063 GO:0032970~regulation of actin filament- based process 9 0.036506704 2.360535117 GO:0009894~regulation of catabolic process 9 0.045181887 2.262179487 GO:0051348~negative regulation of transferase activity 9 0.045181887 2.262179487 GO:0050658~RNA transport 9 0.047544783 2.238858049 GO:0051236~establishment of RNA localization 9 0.047544783 2.238858049 GO:0050657~nucleic acid transport 9 0.047544783 2.238858049  151 Term Count p-value Fold Enrichment GO:0051168~nuclear export 8 0.01156751 3.217321937 GO:0009116~nucleoside metabolic process 8 0.01616677 3.016239316 GO:0009896~positive regulation of catabolic process 7 0.01508882 3.447130647 GO:0000082~G1/S transition of mitotic cell cycle 7 0.027544912 3.016239316 GO:0030832~regulation of actin filament length 7 0.045400815 2.681101614 GO:0018209~peptidyl-serine modification 6 0.021467463 3.712294543 GO:0006888~ER to Golgi vesicle-mediated transport 6 0.028689536 3.447130647 GO:0030111~regulation of Wnt receptor signaling pathway 6 0.040465698 3.147380156 GO:0009156~ribonucleoside monophosphate biosynthetic process 5 0.013722009 5.245633593 GO:0009161~ribonucleoside monophosphate metabolic process 5 0.018370462 4.825982906 GO:0045732~positive regulation of protein catabolic process 5 0.023889125 4.468502691 GO:0000245~spliceosome assembly 5 0.041676657 3.770299145 GO:0006406~mRNA export from nucleus 5 0.045931594 3.656047656 GO:0046112~nucleobase biosynthetic process 4 0.018232464 6.894261294 GO:0060070~Wnt receptor signaling pathway through beta-catenin 4 0.03107872 5.677626948 GO:0006611~protein export from nucleus 4 0.036177914 5.362203229 GO:0006541~glutamine metabolic process 4 0.041678094 5.079982006 GO:0060260~regulation of transcription initiation from RNA polymerase II promoter 3 0.031246927 10.34139194 GO:0006563~L-serine metabolic process 3 0.040537411 9.048717949 GO:0008535~respiratory chain complex IV assembly 3 0.040537411 9.048717949     152 Appendix 6- Table showing qRT-PCR measurement of mRNA levels of stress response genes in skin fibroblasts of three SIOD patients (SD31, SD120, and SD123) compared to control fibroblasts after 1 hour incubation at 43°C followed by 1 hour of recovery at 37°C.  Gene SD31 SD120 SD123 Fold Change1 p-value Fold Change1 p-value Fold Change1 p-value ATF6 1.2622 0.135459 1.1527 0.341137 1.1267 0.229573 BAG1 2.8363 0.000352 1.0019 0.966192 3.5748 0.001308 BAG2 2.6069 0.015457 1.3677 0.076477 1.7561 0.005454 BAG3 2.3752 0.005095 1.1335 0.246809 2.1359 0.000965 BAG4 1.7519 0.007574 -1.6417 0.015668 -1.4703 0.04208 BAG5 1.6503 0.000079 -1.3089 0.007806 1.0188 0.713173 CABC1 4.4718 0.00748 -1.1867 0.322074 3.1199 0.002126 CCS 1.6052 0.091673 1.5811 0.033582 1.6779 0.00864 CCT2 1.0555 0.71912 1.2794 0.07323 1.0454 0.731147 CCT3 1.1803 0.371998 1.2696 0.041069 -1.0921 0.802387 CCT4 1.2538 0.291481 1.0296 0.782612 -1.1474 0.155044 CCT5 1.703 0.044326 1.5918 0.021589 2.2955 0.000936 CCT6A -1.2678 0.182014 1.3918 0.019737 -1.226 0.117257 CCT6B 2.4445 0.008641 2.232 0.003443 2.6615 0.000856 CCT7 1.121 0.318941 1.1537 0.159775 1.0567 0.589396 CRYAA 2.0308 0.031656 -1.6865 0.013274 1.1369 0.397599 CRYAB 2.7728 0.000798 1.1662 0.170989 2.3195 0.000731 DNAJA1 1.8902 0.004721 -1.0411 0.59516 1.4992 0.003205 DNAJA2 1.8288 0.015735 -1.0806 0.533833 1.1494 0.133854 DNAJA3 1.5186 0.086909 -1.3755 0.062798 -1.1313 0.21911 DNAJA4 1.6543 0.04226 1.1148 0.410201 4.9672 0.000418 DNAJC21 1.6838 0.004414 -1.3175 0.01605 1.8026 0.00075 DNAJB1 3.0693 0.003061 -1.3566 0.048129 2.3388 0.000324 DNAJB11 1.256 0.391398 1.556 0.019928 2.1741 0.002693 DNAJB12 1.5516 0.062903 -1.1573 0.2057 -1.0132 0.837028 DNAJB13 4.908 0.011193 2.574 0.008645 1.3815 0.192883 DNAJB14 2.4886 0.009531 -1.216 0.160202 1.3437 0.031363 DNAJB2 1.8237 0.053194 1.7778 0.017276 1.8953 0.006963 DNAJB5 1.5158 0.023366 -1.5119 0.00172 1.1922 0.007919 DNAJB6 2.309 0.013448 -1.2422 0.128193 1.5142 0.011459 DNAJB7 -1.8478 0.122297 -2.4916 0.034937 -11.5765 0.00032 DNAJB8 2.0308 0.031656 -1.6865 0.013274 1.1369 0.397599 DNAJB9 1.6876 0.047743 1.1626 0.134903 1.428 0.214447 DNAJC1 1.24 0.240858 1.066 0.507613 1.5821 0.002946 DNAJC10 2.0484 0.013372 1.2707 0.097916 1.373 0.027786 DNAJC11 1.3614 0.148716 -1.3231 0.129836 -1.0958 0.414228 DNAJC12 4.0663 0.00048 4.9156 0.002463 5.1697 0.000236 DNAJC13 2.8202 0.00894 -1.0573 0.651368 1.8742 0.001514 DNAJC14 1.6656 0.037315 -1.3898 0.060453 -1.0039 0.941755 DNAJC15 1.0088 0.875137 -1.2968 0.048745 1.834 0.001982 DNAJC16 2.0805 0.007253 -1.5147 0.00464 -1.1601 0.244467 DNAJC17 -1.1704 0.550877 -1.0757 0.647931 1.0509 0.727862   153 Gene SD31 SD120 SD123 Fold Change1 p-value Fold Change1 p-value Fold Change1 p-value DNAJC18 2.3974 0.000544 1.0137 0.761849 1.2689 0.05454 DNAJC19 2.0104 0.02115 -1.0308 0.732268 1.3744 0.042366 DNAJC3 1.6272 0.0203 -1.0526 0.483296 1.1837 0.045033 DNAJC4 2.1059 0.007588 1.213 0.134052 1.672 0.002261 DNAJC5 1.4992 0.089455 -1.0945 0.556829 1.0841 0.581058 DNAJC5B -1.7485 0.05082 -13.9052 0.001171 -6.931 0.002672 DNAJC5G 2.7951 0.074388 -2.2499 0.04763 2.627 0.08139 DNAJC6 4.3437 0.002866 -4.2096 0.00158 5.0898 0.000102 DNAJC7 1.4678 0.079094 -1.2378 0.105793 1.0816 0.430962 DNAJC8 2.9316 0.082972 1.1456 0.606909 1.2251 0.402511 DNAJC9 -1.2187 0.059206 -1.297 0.002987 -1.6626 0.000321 HSF1 1.7037 0.001014 1.2039 0.012965 1.693 0.000554 HSF2 3.7187 0.000054 1.1685 0.245527 2.6181 0.000216 HSF4 3.0363 0.000718 1.3445 0.035484 -1.1376 0.042188 HSP90AA1 2.938 0.00552 1.0408 0.692262 3.2181 0.000208 HSP90AB1 2.1555 0.002423 1.0505 0.454187 1.8069 0.000318 HSP90B1 1.3814 0.011546 1.1989 0.025172 1.4283 0.000416 HSPA14 1.1383 0.063373 -1.0317 0.782365 1.1064 0.333144 HSPA1A 2.5847 0.046463 -1.2247 0.344951 2.1791 0.007469 HSPA1B 2.9215 0.005535 -1.4627 0.002436 1.7568 0.00079 HSPA1L 5.5563 0.000001 -1.2233 0.036845 3.9498 0.00004 HSPA2 2.8723 0.007686 -1.1206 0.283253 4.2841 0.000186 HSPA4 2.2979 0.002071 1.0027 0.932654 1.6326 0.007511 HSPA4L 3.8847 0.016677 -1.1327 0.339189 1.761 0.002674 HSPA5 3.8825 0.00498 1.6645 0.002534 7.3608 0.00008 HSPA8 1.5769 0.002565 -1.2295 0.00289 1.1998 0.01201 HSPA9 1.197 0.234855 1.0549 0.51817 -1.3069 0.023021 HSPB1 1.4327 0.141766 1.0467 0.748537 1.4403 0.023439 HSPB2 2.071 0.017525 -1.1902 0.111889 1.5279 0.002922 HSPB3 2.8403 0.014347 -1.5145 0.018669 3.3656 0.000012 HSPB6 -1.0588 0.751031 -1.1489 0.165979 -1.1973 0.092469 HSPB7 14.0196 0.000908 -1.3056 0.047857 3.3633 0.000037 HSPB8 -1.2165 0.248509 -1.0672 0.531006 -2.5981 0.00206 HSPD1 3.1641 0.002914 -1.2302 0.118704 1.6812 0.006083 HSPE1 2.0426 0.000039 1.0677 0.301475 1.6065 0.000192 HSPH1 4.2396 0.001114 1.0436 0.598721 4.093 0.000038 PFDN1 2.8813 0.001943 -1.2699 0.384879 1.2868 0.385838 PFDN2 1.2087 0.320503 1.2494 0.075045 1.3943 0.01892 SERPINH1 1.0267 0.771942 1.4885 0.003026 1.0247 0.744577 SIL1 1.2471 0.001528 1.1006 0.053786 1.1353 0.206732 TCP1 1.7705 0.214631 -1.1336 0.604575 1.0429 0.994102 TOR1A 2.0642 0.016952 1.1888 0.229031 1.7942 0.008074  Key: 1 Fold change of SMARCAL1-deficient fibroblast relative to control fibroblasts.    154 Appendix 7- Table showing enrichment in biological process GO-terms (all levels) of upregulated expressed genes (fold change >2) between Smarcal1del/del and Smarcal1+/+ mouse livers at 20°C.  Term Count p-value Fold Enrichment GO:0009987~cellular process 168 0.002641961 1.137409509 GO:0008152~metabolic process 126 0.041472607 1.124285553 GO:0044238~primary metabolic process 119 0.005718276 1.200130932 GO:0044237~cellular metabolic process 110 0.018369419 1.176415284 GO:0043170~macromolecule metabolic process 104 0.001672704 1.274839867 GO:0044260~cellular macromolecule metabolic process 95 0.001393976 1.308129879 GO:0010467~gene expression 77 1.23E-08 1.87599573 GO:0034641~cellular nitrogen compound metabolic process 77 2.61E-05 1.553360716 GO:0006807~nitrogen compound metabolic process 77 7.25E-05 1.509131734 GO:0006139~nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 71 8.23E-05 1.54368359 GO:0009058~biosynthetic process 69 0.001702868 1.405095868 GO:0019222~regulation of metabolic process 68 8.38E-04 1.447802897 GO:0044249~cellular biosynthetic process 68 0.001191447 1.429340981 GO:0034645~cellular macromolecule biosynthetic process 63 4.07E-05 1.645638987 GO:0009059~macromolecule biosynthetic process 63 4.50E-05 1.640167203 GO:0060255~regulation of macromolecule metabolic process 63 0.00102386 1.46809609 GO:0031323~regulation of cellular metabolic process 63 0.002514426 1.416454519 GO:0080090~regulation of primary metabolic process 62 0.001479369 1.453444433 GO:0032502~developmental process 59 0.010833413 1.347315795 GO:0010468~regulation of gene expression 57 0.001966547 1.469909301 GO:0007275~multicellular organismal development 57 0.004506159 1.416267232 GO:0031326~regulation of cellular biosynthetic process 56 0.00455405 1.421877148 GO:0009889~regulation of biosynthetic process 56 0.004919706 1.416708774 GO:0010556~regulation of macromolecule biosynthetic process 55 0.003461829 1.446926568 GO:0019219~regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 54 0.004396992 1.437523754 GO:0051171~regulation of nitrogen compound metabolic process 54 0.005191711 1.426009019  155 Term Count p-value Fold Enrichment GO:0045449~regulation of transcription 53 0.002262042 1.490729513 GO:0006350~transcription 49 1.30E-04 1.732110361 GO:0048856~anatomical structure development 47 0.02301666 1.356692178 GO:0048731~system development 43 0.04103519 1.328794751 GO:0048518~positive regulation of biological process 40 0.001247878 1.678198701 GO:0030154~cell differentiation 38 0.009112483 1.517063816 GO:0048869~cellular developmental process 38 0.016942011 1.454934675 GO:0048513~organ development 37 0.033211865 1.393646639 GO:0048522~positive regulation of cellular process 36 0.001771508 1.708329996 GO:0006950~response to stress 32 0.003969109 1.692939636 GO:0048523~negative regulation of cellular process 32 0.004381093 1.681577625 GO:0048519~negative regulation of biological process 32 0.018742393 1.511644441 GO:0042981~regulation of apoptosis 28 2.13E-07 3.171583115 GO:0043067~regulation of programmed cell death 28 2.77E-07 3.131938326 GO:0010941~regulation of cell death 28 3.05E-07 3.115249489 GO:0007242~intracellular signaling cascade 28 0.00138857 1.916814713 GO:0016070~RNA metabolic process 25 1.43E-04 2.365512331 GO:0043065~positive regulation of apoptosis 19 8.82E-08 4.798937758 GO:0043068~positive regulation of programmed cell death 19 9.95E-08 4.760546256 GO:0010942~positive regulation of cell death 19 1.12E-07 4.722764142 GO:0009790~embryonic development 19 0.020838927 1.77632323 GO:0051716~cellular response to stimulus 18 0.007637899 2.027873736 GO:0009605~response to external stimulus 18 0.026914448 1.761715308 GO:0048468~cell development 17 0.034895793 1.742813471 GO:0006357~regulation of transcription from RNA polymerase II promoter 17 0.037331128 1.728667258 GO:0006396~RNA processing 16 0.004303463 2.293410216 GO:0033554~cellular response to stress 15 0.005324625 2.325696777 GO:0043009~chordate embryonic development 15 0.007488582 2.23178503 GO:0009792~embryonic development ending in birth or egg hatching 15 0.008095696 2.210779995 GO:0031324~negative regulation of cellular metabolic process 15 0.024559617 1.921434556 GO:0009892~negative regulation of metabolic process 15 0.046216925 1.759515913 GO:0031327~negative regulation of cellular biosynthetic process 14 0.019983076 2.039401701  156 Term Count p-value Fold Enrichment GO:0009890~negative regulation of biosynthetic  14 0.021588102 2.020605372 GO:0012501~programmed cell death 14 0.03909713 1.854001546 GO:0006917~induction of apoptosis 13 1.46E-05 4.876071645 GO:0012502~induction of programmed cell death 13 1.46E-05 4.876071645 GO:0010558~negative regulation of macromolecule biosynthetic process 13 0.035038155 1.948095609 GO:0001701~in utero embryonic development 12 0.003666728 2.815225462 GO:0006412~translation 12 0.013128177 2.356317236 GO:0000122~negative regulation of transcription from RNA polymerase II promoter 11 0.003909218 2.982798406 GO:0007264~small GTPase mediated signal transduction 11 0.008344968 2.670645084 GO:0006974~response to DNA damage stimulus 11 0.016570406 2.400788961 GO:0045892~negative regulation of transcription, DNA-dependent 11 0.02551341 2.237098804 GO:0051253~negative regulation of RNA metabolic process 11 0.026733462 2.222665909 GO:0034470~ncRNA processing 10 9.56E-04 3.964478894 GO:0034660~ncRNA metabolic process 10 0.005046953 3.100929036 GO:0001944~vasculature development 10 0.018670222 2.505550661 GO:0001568~blood vessel development 9 0.04153762 2.310446306 GO:0046578~regulation of Ras protein signal transduction 8 0.025696357 2.768564266 GO:0048514~blood vessel morphogenesis 8 0.038985868 2.530859253 GO:0006351~transcription, DNA-dependent 7 0.01325368 3.594027587 GO:0032774~RNA biosynthetic process 7 0.015334994 3.479931473 GO:0009991~response to extracellular stimulus 7 0.01950439 3.296777185 GO:0002460~adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 6 0.010847465 4.474197609 GO:0002250~adaptive immune response 6 0.010847465 4.474197609 GO:0035023~regulation of Rho protein signal transduction 6 0.011930806 4.370146501 GO:0051789~response to protein stimulus 6 0.012499086 4.319914932 GO:0031667~response to nutrient levels 6 0.036506779 3.268109558 GO:0009451~RNA modification 5 0.00433406 7.456996014 GO:0006959~humoral immune response 5 0.010565775 5.799885789 GO:0007050~cell cycle arrest 5 0.012721112 5.494628642 GO:0042770~DNA damage response, signal transduction 5 0.012721112 5.494628642  157 Term Count p-value Fold Enrichment GO:0001666~response to hypoxia 5 0.018785915 4.893653634 GO:0070482~response to oxygen levels 5 0.019775435 4.818366655 GO:0008033~tRNA processing 5 0.02520166 4.474197609 GO:0030330~DNA damage response, signal transduction by p53 class mediator 4 0.003181089 13.18710874 GO:0008630~DNA damage response, signal transduction resulting in induction of apoptosis 4 0.005548627 10.89369853 GO:0007266~Rho protein signal transduction 4 0.008747268 9.279817262 GO:0008629~induction of apoptosis by intracellular signals 4 0.017840719 7.158716174 GO:0002455~humoral immune response mediated by circulating immunoglobulin 4 0.017840719 7.158716174 GO:0031668~cellular response to extracellular stimulus 4 0.034479228 5.567890357 GO:0009266~response to temperature stimulus 4 0.047250824 4.912844433 GO:0042771~DNA damage response, signal transduction by p53 class mediator resulting in induction of apoptosis 3 0.020182964 13.42259283 GO:0002886~regulation of myeloid leukocyte mediated immunity 3 0.023047212 12.5277533 GO:0006400~tRNA modification 3 0.026067652 11.74476872 GO:0045682~regulation of epidermis development 3 0.032554682 10.43979442 GO:0032764~negative regulation of mast cell cytokine production 2 0.031536926 62.63876652 GO:0042092~T-helper 2 type immune response 2 0.031536926 62.63876652 GO:0002701~negative regulation of production of molecular mediator of immune response 2 0.046932062 41.75917768 GO:0002719~negative regulation of cytokine production during immune response 2 0.046932062 41.75917768 GO:0032763~regulation of mast cell cytokine production 2 0.046932062 41.75917768       158 Appendix 8- Table showing enrichment in biological process GO-terms (all levels) of downregulated expressed genes (fold change >2) between Smarcal1del/del and Smarcal1+/+ mouse livers at 20°C.  Term Count p-value Fold Enrichment GO:0009987~cellular process 391 1.17E-05 1.131659221 GO:0008152~metabolic process 343 5.69E-13 1.308372903 GO:0044238~primary metabolic process 293 4.96E-08 1.263224745 GO:0044237~cellular metabolic process 290 1.99E-10 1.325858896 GO:0043170~macromolecule metabolic process 231 1.85E-04 1.210502283 GO:0044260~cellular macromolecule metabolic process 218 6.15E-06 1.283261278 GO:0009058~biosynthetic process 159 4.32E-06 1.384156914 GO:0044249~cellular biosynthetic process 155 4.28E-06 1.392803878 GO:0006807~nitrogen compound metabolic process 152 5.89E-04 1.273536365 GO:0034641~cellular nitrogen compound metabolic process 148 6.43E-04 1.276364287 GO:0006139~nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 141 2.93E-04 1.310540322 GO:0019222~regulation of metabolic process 128 0.034484236 1.165042677 GO:0010467~gene expression 126 6.91E-04 1.312329833 GO:0034645~cellular macromolecule biosynthetic process 122 1.94E-04 1.362338986 GO:0009059~macromolecule biosynthetic process 122 2.29E-04 1.357809181 GO:0060255~regulation of macromolecule metabolic process 121 0.015912722 1.205398479 GO:0080090~regulation of primary metabolic process 119 0.023253486 1.192573187 GO:0019538~protein metabolic process 115 0.034991225 1.177607818 GO:0010468~regulation of gene expression 110 0.019242851 1.212661818 GO:0031326~regulation of cellular biosynthetic process 109 0.03676522 1.183128406 GO:0009889~regulation of biosynthetic process 109 0.039317286 1.178827859 GO:0051171~regulation of nitrogen compound metabolic process 107 0.023060951 1.20793517 GO:0010556~regulation of macromolecule biosynthetic process 107 0.025084653 1.203369266 GO:0006810~transport 106 0.021882276 1.211974571 GO:0019219~regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 106 0.025307516 1.206308731 GO:0051234~establishment of localization 106 0.026288695 1.203750825 GO:0044267~cellular protein metabolic process 105 9.52E-04 1.346583653  159 Term Count p-value Fold Enrichment GO:0045449~regulation of transcription 103 0.013613902 1.238487252 GO:0006350~transcription 89 0.002767204 1.344933534 GO:0006464~protein modification process 60 0.039632935 1.275132275 GO:0009056~catabolic process 54 0.012234347 1.390384615 GO:0033036~macromolecule localization 48 0.012274357 1.424981523 GO:0044248~cellular catabolic process 47 0.006673421 1.484145702 GO:0006629~lipid metabolic process 46 2.25E-04 1.769795658 GO:0009057~macromolecule catabolic process 45 1.04E-04 1.842507645 GO:0030163~protein catabolic process 43 1.08E-05 2.070943245 GO:0044265~cellular macromolecule catabolic process 43 9.07E-05 1.890713373 GO:0051603~proteolysis involved in cellular protein catabolic process 41 2.14E-05 2.055971702 GO:0044257~cellular protein catabolic process 41 2.45E-05 2.044485827 GO:0008104~protein localization 39 0.035654369 1.386896857 GO:0019941~modification-dependent protein catabolic process 38 7.88E-05 2.003062117 GO:0043632~modification-dependent macromolecule catabolic process 38 7.88E-05 2.003062117 GO:0007049~cell cycle 36 0.007789552 1.577741408 GO:0015031~protein transport 36 0.018992568 1.480798771 GO:0045184~establishment of protein localization 36 0.020583667 1.469512195 GO:0044255~cellular lipid metabolic process 28 0.022949004 1.555555556 GO:0022402~cell cycle process 24 0.022339819 1.635284139 GO:0022403~cell cycle phase 21 0.021679455 1.714430894 GO:0000278~mitotic cell cycle 19 0.004626069 2.085154827 GO:0055086~nucleobase, nucleoside and nucleotide metabolic process 19 0.012146806 1.891367204 GO:0051301~cell division 18 0.03441481 1.715302491 GO:0008610~lipid biosynthetic process 18 0.038563972 1.69122807 GO:0009117~nucleotide metabolic process 17 0.023018101 1.843004948 GO:0006753~nucleoside phosphate metabolic process 17 0.023018101 1.843004948 GO:0006511~ubiquitin-dependent protein catabolic process 14 0.002354923 2.658786446 GO:0070647~protein modification by small protein conjugation or removal 13 4.84E-04 3.347222222 GO:0008202~steroid metabolic process 13 0.017438114 2.162180814 GO:0009165~nucleotide biosynthetic process 13 0.02979772 2.00063857  160 Term Count p-value Fold Enrichment GO:0034654~nucleobase, nucleoside, nucleotide and nucleic acid biosynthetic process 13 0.035923876 1.944754811 GO:0034404~nucleobase, nucleoside and nucleotide biosynthetic process 13 0.035923876 1.944754811 GO:0032446~protein modification by small protein conjugation 10 0.002625486 3.389592124 GO:0009152~purine ribonucleotide biosynthetic process 10 0.018414059 2.502596054 GO:0009260~ribonucleotide biosynthetic process 10 0.02281297 2.412412412 GO:0022900~electron transport chain 10 0.024020833 2.390873016 GO:0009150~purine ribonucleotide metabolic process 10 0.033774613 2.250233427 GO:0009259~ribonucleotide metabolic process 10 0.044069951 2.142222222 GO:0016567~protein ubiquitination 9 0.003033492 3.651515152 GO:0009206~purine ribonucleoside triphosphate biosynthetic process 9 0.022608486 2.591397849 GO:0009201~ribonucleoside triphosphate biosynthetic process 9 0.022608486 2.591397849 GO:0009145~purine nucleoside triphosphate biosynthetic process 9 0.023937549 2.563829787 GO:0009142~nucleoside triphosphate biosynthetic process 9 0.025320479 2.536842105 GO:0009205~purine ribonucleoside triphosphate metabolic process 9 0.034801002 2.386138614 GO:0009199~ribonucleoside triphosphate metabolic process 9 0.036586194 2.362745098 GO:0009144~purine nucleoside triphosphate metabolic process 9 0.04434035 2.273584906 GO:0045454~cell redox homeostasis 8 0.008026038 3.455197133 GO:0051236~establishment of RNA localization 8 0.011197479 3.245791246 GO:0050657~nucleic acid transport 8 0.011197479 3.245791246 GO:0050658~RNA transport 8 0.011197479 3.245791246 GO:0006403~RNA localization 8 0.012114748 3.1973466 GO:0015931~nucleobase, nucleoside, nucleotide and nucleic acid transport 8 0.020105616 2.894894895 GO:0006754~ATP biosynthetic process 8 0.033055356 2.612466125 GO:0051028~mRNA transport 7 0.02751857 3.023297491 GO:0046777~protein amino acid autophosphorylation 7 0.035972482 2.84006734 GO:0051325~interphase 6 0.04063239 3.150326797  161 Term Count p-value Fold Enrichment GO:0030518~steroid hormone receptor signaling pathway 4 0.041583328 5.100529101 GO:0060322~head development 4 0.041583328 5.100529101 GO:0030521~androgen receptor signaling pathway 3 0.025712762 11.47619048       162 Appendix 9- Table showing enrichment in biological process GO-terms (all levels) of upregulated expressed genes (fold change >2) between Smarcal1del/del and Smarcal1+/+ mouse livers at 39.5°C.  Term Count p-value Fold Enrichment GO:0008152~metabolic process 23 0.003300414 1.552882241 GO:0044237~cellular metabolic process 20 0.007034519 1.618462239 GO:0044238~primary metabolic process 20 0.014637179 1.526216927 GO:0043170~macromolecule metabolic process 18 0.01055318 1.669549902 GO:0044260~cellular macromolecule metabolic process 17 0.00816963 1.771253755 GO:0009058~biosynthetic process 13 0.013022419 2.0031101 GO:0010467~gene expression 12 0.00933004 2.212213147 GO:0044249~cellular biosynthetic process 12 0.027232697 1.908590604 GO:0034645~cellular macromolecule biosynthetic process 11 0.016557519 2.174159021 GO:0009059~macromolecule biosynthetic process 11 0.016931463 2.166929897 GO:0044267~cellular protein metabolic process 10 0.019537026 2.2699553 GO:0009057~macromolecule catabolic process 6 0.009614252 4.348318043 GO:0006412~translation 5 0.003791732 7.428944619 GO:0044257~cellular protein catabolic process 5 0.022593811 4.413097455 GO:0030163~protein catabolic process 5 0.025292651 4.262290168 GO:0044265~cellular macromolecule catabolic process 5 0.03383289 3.891351943 GO:0001889~liver development 3 0.003443706 33.06744186 GO:0006351~transcription, DNA-dependent 3 0.025489793 11.65491803 GO:0032774~RNA biosynthetic process 3 0.027060159 11.28492063        163 Appendix 10- Table showing enrichment in biological process GO-terms (all levels) of downregulated expressed genes (fold change >2) between Smarcal1del/del and Smarcal1+/+ mouse livers at 39.5°C.  Term Count p-value Fold Enrichment GO:0008152~metabolic process 103 0.016272015 1.172058164 GO:0050896~response to stimulus 45 0.005603062 1.475047604 GO:0002376~immune system process 34 7.00E-10 3.433614114 GO:0048518~positive regulation of biological process 27 0.048865517 1.444617955 GO:0006955~immune response 23 9.55E-08 3.90082063 GO:0007242~intracellular signaling cascade 22 0.004868256 1.92066065 GO:0048583~regulation of response to stimulus 19 1.58E-07 4.599267961 GO:0002682~regulation of immune system process 18 5.61E-07 4.465454672 GO:0002684~positive regulation of immune system process 16 4.33E-08 6.20442893 GO:0050776~regulation of immune response 15 1.14E-07 6.273457262 GO:0002520~immune system development 15 1.95E-05 4.061797753 GO:0007049~cell cycle 14 0.041924611 1.830357307 GO:0048534~hemopoietic or lymphoid organ development 13 2.09E-04 3.695609581 GO:0032879~regulation of localization 13 0.004894491 2.557798749 GO:0042981~regulation of apoptosis 13 0.043815882 1.877877563 GO:0043067~regulation of programmed cell death 13 0.047401685 1.854404093 GO:0010941~regulation of cell death 13 0.048998886 1.844522721 GO:0050778~positive regulation of immune response 12 1.03E-06 7.048413748 GO:0048584~positive regulation of response to stimulus 12 2.12E-05 5.153678869 GO:0006952~defense response 12 0.024600279 2.13969703 GO:0051249~regulation of lymphocyte activation 11 1.26E-05 6.102098939 GO:0002694~regulation of leukocyte activation 11 2.27E-05 5.705858748 GO:0050865~regulation of cell activation 11 2.53E-05 5.632706713 GO:0001775~cell activation 11 0.001033108 3.571960354 GO:0002253~activation of immune response 10 1.18E-06 9.288607264 GO:0051251~positive regulation of lymphocyte activation 10 2.51E-06 8.498087497 GO:0002696~positive regulation of leukocyte activation 10 3.88E-06 8.068891159  164 Term Count p-value Fold Enrichment GO:0050867~positive regulation of cell activation 10 4.58E-06 7.909111136 GO:0045321~leukocyte activation 10 0.00169721 3.647580935 GO:0030097~hemopoiesis 10 0.004225054 3.182550696 GO:0046649~lymphocyte activation 9 0.002706634 3.764074357 GO:0043065~positive regulation of apoptosis 9 0.012474879 2.898944364 GO:0043068~positive regulation of programmed cell death 9 0.013084144 2.875752809 GO:0010942~positive regulation of cell death 9 0.013697532 2.852929374 GO:0008284~positive regulation of cell proliferation 9 0.025881579 2.531472543 GO:0002460~adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 8 8.32E-05 7.607811664 GO:0002250~adaptive immune response 8 8.32E-05 7.607811664 GO:0002697~regulation of immune effector process 8 1.12E-04 7.262002043 GO:0080134~regulation of response to stress 8 0.009212006 3.399234999 GO:0007243~protein kinase cascade 8 0.028380712 2.707865169 GO:0000278~mitotic cell cycle 8 0.033158027 2.619082704 GO:0050851~antigen receptor-mediated signaling pathway 7 4.88E-06 15.53261548 GO:0002429~immune response-activating cell surface receptor signaling pathway 7 1.07E-05 13.63839408 GO:0002768~immune response-regulating cell surface receptor signaling pathway 7 1.63E-05 12.70850358 GO:0002699~positive regulation of immune effector process 7 2.12E-05 12.15595994 GO:0002757~immune response-activating signal transduction 7 2.72E-05 11.64946161 GO:0050671~positive regulation of lymphocyte proliferation 7 3.45E-05 11.18348315 GO:0032946~positive regulation of mononuclear cell proliferation 7 3.45E-05 11.18348315 GO:0070665~positive regulation of leukocyte proliferation 7 4.33E-05 10.75334918 GO:0002764~immune response-regulating signal transduction 7 4.33E-05 10.75334918 GO:0050864~regulation of B cell activation 7 5.98E-05 10.16680286 GO:0002703~regulation of leukocyte mediated immunity 7 2.32E-04 7.988202247  165 Term Count p-value Fold Enrichment GO:0042113~B cell activation 7 4.18E-04 7.168899453 GO:0050670~regulation of lymphocyte proliferation 7 4.79E-04 6.989676966 GO:0032944~regulation of mononuclear cell proliferation 7 4.79E-04 6.989676966 GO:0070663~regulation of leukocyte proliferation 7 5.47E-04 6.81919704 GO:0050863~regulation of T cell activation 7 0.002293823 5.177538494 GO:0002252~immune effector process 7 0.004917707 4.437890137 GO:0002521~leukocyte differentiation 7 0.008689088 3.937846178 GO:0010627~regulation of protein kinase cascade 7 0.013010348 3.607575208 GO:0009967~positive regulation of signal transduction 7 0.020681219 3.251012542 GO:0010647~positive regulation of cell communication 7 0.030931302 2.958593425 GO:0007067~mitosis 7 0.031660108 2.943021881 GO:0000280~nuclear division 7 0.031660108 2.943021881 GO:0000087~M phase of mitotic cell cycle 7 0.03454287 2.882341017 GO:0048285~organelle fission 7 0.036832348 2.838447499 GO:0050853~B cell receptor signaling pathway 6 5.16E-07 34.23515249 GO:0050871~positive regulation of B cell activation 6 8.92E-05 12.95384148 GO:0016064~immunoglobulin mediated immune response 6 0.001107776 7.607811664 GO:0019724~B cell mediated immunity 6 0.001276053 7.373725151 GO:0002706~regulation of lymphocyte mediated immunity 6 0.001276053 7.373725151 GO:0002449~lymphocyte mediated immunity 6 0.002557754 6.306475458 GO:0002443~leukocyte mediated immunity 6 0.005050415 5.385304886 GO:0043408~regulation of MAPKKK cascade 6 0.006077862 5.153678869 GO:0031347~regulation of defense response 6 0.009273909 4.653321697 GO:0030098~lymphocyte differentiation 6 0.01395435 4.204316972 GO:0016053~organic acid biosynthetic process 6 0.031544275 3.399234999 GO:0046394~carboxylic acid biosynthetic process 6 0.031544275 3.399234999 GO:0006260~DNA replication 6 0.041482863 3.153237729 GO:0030888~regulation of B cell proliferation 5 6.36E-04 12.48156601 GO:0002705~positive regulation of leukocyte mediated immunity 5 0.001495156 9.985252809  166 Term Count p-value Fold Enrichment GO:0002708~positive regulation of lymphocyte mediated immunity 5 0.001495156 9.985252809 GO:0030183~B cell differentiation 5 0.002519638 8.68282853 GO:0006959~humoral immune response 5 0.004521448 7.396483562 GO:0002822~regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 5 0.005151322 7.132323435 GO:0002819~regulation of adaptive immune response 5 0.005151322 7.132323435 GO:0050727~regulation of inflammatory response 5 0.005487218 7.007194954 GO:0050870~positive regulation of T cell activation 5 0.009185436 6.051668369 GO:0002526~acute inflammatory response 5 0.018381732 4.930989041 GO:0010740~positive regulation of protein kinase cascade 5 0.024110205 4.538751277 GO:0032101~regulation of response to external stimulus 5 0.039684279 3.877768081 GO:0042330~taxis 5 0.047201078 3.664312957 GO:0006935~chemotaxis 5 0.047201078 3.664312957 GO:0030890~positive regulation of B cell proliferation 4 0.002453711 14.52400409 GO:0002889~regulation of immunoglobulin mediated immune response 4 0.006014582 10.65093633 GO:0002712~regulation of B cell mediated immunity 4 0.006014582 10.65093633 GO:0002824~positive regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 4 0.008549035 9.397884997 GO:0046634~regulation of alpha-beta T cell activation 4 0.008549035 9.397884997 GO:0002821~positive regulation of adaptive immune response 4 0.008549035 9.397884997 GO:0042102~positive regulation of T cell proliferation 4 0.009266223 9.129373997 GO:0002455~humoral immune response mediated by circulating immunoglobulin 4 0.009266223 9.129373997 GO:0046651~lymphocyte proliferation 4 0.015257265 7.607811664 GO:0032943~mononuclear cell proliferation 4 0.016254455 7.430885811 GO:0070661~leukocyte proliferation 4 0.016254455 7.430885811  167 Term Count p-value Fold Enrichment GO:0043410~positive regulation of MAPKKK cascade 4 0.020602171 6.798469998 GO:0050730~regulation of peptidyl-tyrosine phosphorylation 4 0.028205284 6.028831885 GO:0031349~positive regulation of defense response 4 0.033992386 5.605755963 GO:0045619~regulation of lymphocyte differentiation 4 0.03552811 5.509104998 GO:0051052~regulation of DNA metabolic process 4 0.03552811 5.509104998 GO:0042129~regulation of T cell proliferation 4 0.037099138 5.415730337 GO:0043406~positive regulation of MAP kinase activity 4 0.037099138 5.415730337 GO:0007059~chromosome segregation 4 0.04547771 4.992626404 GO:0051605~protein maturation by peptide bond cleavage 4 0.047256716 4.915816768 GO:0048585~negative regulation of response to stimulus 4 0.047256716 4.915816768 GO:0046641~positive regulation of alpha-beta T cell proliferation 3 0.006493239 23.96460674 GO:0002863~positive regulation of inflammatory response to antigenic stimulus 3 0.010982563 18.43431288 GO:0007159~leukocyte adhesion 3 0.012708992 17.11757624 GO:0002714~positive regulation of B cell mediated immunity 3 0.012708992 17.11757624 GO:0002891~positive regulation of immunoglobulin mediated immune response 3 0.012708992 17.11757624 GO:0046640~regulation of alpha-beta T cell proliferation 3 0.012708992 17.11757624 GO:0002886~regulation of myeloid leukocyte mediated immunity 3 0.014545318 15.97640449 GO:0009067~aspartate family amino acid biosynthetic process 3 0.01648857 14.97787921 GO:0045577~regulation of B cell differentiation 3 0.01648857 14.97787921 GO:0002861~regulation of inflammatory response to antigenic stimulus 3 0.020684244 13.31367041 GO:0048535~lymph node development 3 0.022931001 12.61295092 GO:0051053~negative regulation of DNA metabolic process 3 0.025273351 11.98230337 GO:0009066~aspartate family amino acid metabolic process 3 0.030234083 10.89300306 GO:0000018~regulation of DNA recombination 3 0.035545458 9.985252809  168 Term Count p-value Fold Enrichment GO:0050729~positive regulation of inflammatory response 3 0.035545458 9.985252809 GO:0002637~regulation of immunoglobulin production 3 0.0383263 9.585842697 GO:0046635~positive regulation of alpha-beta T cell activation 3 0.041187296 9.217156439 GO:0042098~T cell proliferation 3 0.047140192 8.558788122 GO:0051307~meiotic chromosome separation 2 0.036884021 53.25468165 GO:0051304~chromosome separation 2 0.036884021 53.25468165 GO:0032413~negative regulation of ion transmembrane transporter activity 2 0.048875547 39.94101124 GO:0032410~negative regulation of transporter activity 2 0.048875547 39.94101124     169 Appendix 11- Table showing enrichment in biological process GO-terms (all levels) of differentially expressed genes (q<0.05) between RpII2154/FM7;Marcal1del/del and yw fly ovaries.  Term Count p-value Fold Enrichment GO:0009987~cellular process 350 7.11E-08 1.182962437 GO:0008152~metabolic process 300 2.90E-05 1.168941048 GO:0044238~primary metabolic process 250 8.69E-05 1.195140191 GO:0044237~cellular metabolic process 223 2.26E-05 1.242854726 GO:0043170~macromolecule metabolic process 191 0.027556497 1.121973063 GO:0065007~biological regulation 171 0.012492805 1.158556378 GO:0044260~cellular macromolecule metabolic process 169 1.09E-04 1.281200439 GO:0050789~regulation of biological process 160 0.005729838 1.191211236 GO:0050794~regulation of cellular process 148 0.010568064 1.184387145 GO:0016043~cellular component organization 140 4.61E-06 1.413131599 GO:0051179~localization 118 0.009257764 1.225732441 GO:0006807~nitrogen compound metabolic process 115 8.13E-04 1.320637602 GO:0034641~cellular nitrogen compound metabolic process 111 5.10E-05 1.425098921 GO:0051234~establishment of localization 102 0.00849426 1.256228433 GO:0006139~nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 99 2.04E-04 1.41339 GO:0006810~transport 97 0.016600255 1.234697456 GO:0006996~organelle organization 81 1.39E-04 1.502611746 GO:0043412~biopolymer modification 61 4.57E-04 1.557361707 GO:0006464~protein modification process 58 5.60E-04 1.568270202 GO:0033036~macromolecule localization 53 1.71E-05 1.843721572 GO:0007049~cell cycle 52 0.002722216 1.50646645 GO:0044085~cellular component biogenesis 49 0.006815028 1.459842794 GO:0043687~post-translational protein modification 48 9.74E-04 1.616226415 GO:0051641~cellular localization 47 0.001688624 1.585546629 GO:0016070~RNA metabolic process 47 0.002271936 1.561925822 GO:0065008~regulation of biological quality 47 0.013660778 1.414425239 GO:0009056~catabolic process 46 7.73E-04 1.658400673 GO:0048519~negative regulation of biological process 46 0.02861235 1.359119757 GO:0022402~cell cycle process 45 0.008840369 1.468121572  170 Term Count p-value Fold Enrichment GO:0008104~protein localization 43 3.21E-05 1.957578656 GO:0006793~phosphorus metabolic process 43 0.024241749 1.392687538 GO:0007010~cytoskeleton organization 42 0.002320114 1.61188172 GO:0022607~cellular component assembly 42 0.025399639 1.393169145 GO:0022403~cell cycle phase 41 0.011826147 1.47515961 GO:0048523~negative regulation of cellular process 41 0.03795045 1.359998451 GO:0051649~establishment of localization in cell 40 0.002594879 1.626044039 GO:0048699~generation of neurons 40 0.010288425 1.49650594 GO:0000279~M phase 40 0.010691232 1.493375174 GO:0006950~response to stress 40 0.011361063 1.487152778 GO:0022008~neurogenesis 40 0.020110822 1.430527722 GO:0007017~microtubule-based process 38 0.002135805 1.670299672 GO:0000902~cell morphogenesis 36 0.022882898 1.453506787 GO:0044248~cellular catabolic process 35 0.002475963 1.70191871 GO:0000278~mitotic cell cycle 34 0.00307323 1.69485568 GO:0050793~regulation of developmental process 33 6.51E-04 1.881509585 GO:0000226~microtubule cytoskeleton organization 32 5.86E-04 1.916331096 GO:0046907~intracellular transport 32 0.001329529 1.824494143 GO:0016192~vesicle-mediated transport 32 0.030243692 1.460528559 GO:0051716~cellular response to stimulus 31 2.35E-04 2.048966049 GO:0051128~regulation of cellular component organization 31 3.88E-04 1.990002998 GO:0048518~positive regulation of biological process 31 0.01176345 1.589715038 GO:0030030~cell projection organization 31 0.021537837 1.515673516 GO:0051276~chromosome organization 29 0.002171798 1.835209811 GO:0051239~regulation of multicellular organismal process 29 0.002398442 1.822285798 GO:0045184~establishment of protein localization 29 0.002512304 1.815891813 GO:0006629~lipid metabolic process 29 0.003934782 1.760303288 GO:0006396~RNA processing 29 0.008777374 1.658747329 GO:0006259~DNA metabolic process 28 9.92E-05 2.261010558 GO:0009057~macromolecule catabolic process 28 0.003317298 1.803910951 GO:0015031~protein transport 27 0.006769389 1.733228417 GO:0048858~cell projection morphogenesis 27 0.035267791 1.505742188  171 Term Count p-value Fold Enrichment GO:0032990~cell part morphogenesis 27 0.049511237 1.455702417 GO:0048667~cell morphogenesis involved in neuron differentiation 26 0.018881001 1.611082176 GO:0009966~regulation of signal transduction 26 0.023674724 1.578202948 GO:0000904~cell morphogenesis involved in differentiation 26 0.033093738 1.531325633 GO:0048522~positive regulation of cellular process 26 0.042167891 1.49193462 GO:0010646~regulation of cell communication 26 0.042167891 1.49193462 GO:0043933~macromolecular complex subunit organization 25 0.025116097 1.587707592 GO:0048812~neuron projection morphogenesis 25 0.030173439 1.559950466 GO:0031175~neuron projection development 25 0.030993553 1.554515099 GO:0045595~regulation of cell differentiation 24 2.49E-04 2.315135135 GO:0007051~spindle organization 24 0.003623308 1.903555556 GO:0010324~membrane invagination 23 0.031610381 1.590907623 GO:0006897~endocytosis 23 0.031610381 1.590907623 GO:0016071~mRNA metabolic process 22 0.010804123 1.792732116 GO:0044265~cellular macromolecule catabolic process 22 0.014372148 1.744925926 GO:0009605~response to external stimulus 21 0.00621043 1.921858974 GO:0030163~protein catabolic process 21 0.00817114 1.8738125 GO:0033554~cellular response to stress 20 0.004358407 2.027935606 GO:0006397~mRNA processing 20 0.012084936 1.839776632 GO:0006909~phagocytosis 20 0.014066487 1.811759729 GO:0051726~regulation of cell cycle 19 0.005198615 2.042595382 GO:0044255~cellular lipid metabolic process 19 0.013769279 1.852846084 GO:0006911~phagocytosis, engulfment 19 0.016878101 1.813213012 GO:0007052~mitotic spindle organization 19 0.023601968 1.747787801 GO:0007409~axonogenesis 19 0.028265593 1.712478956 GO:0034621~cellular macromolecular complex subunit organization 19 0.035028892 1.670299672 GO:0007059~chromosome segregation 18 2.53E-04 2.745512821 GO:0042592~homeostatic process 18 0.009510974 1.970705521 GO:0044257~cellular protein catabolic process 18 0.027037229 1.755327869 GO:0051603~proteolysis involved in cellular protein catabolic process 18 0.027037229 1.755327869 GO:0032879~regulation of localization 17 1.08E-04 3.064436027 GO:0016311~dephosphorylation 17 3.40E-04 2.783295107  172 Term Count p-value Fold Enrichment GO:0019941~modification-dependent protein catabolic process 17 0.026868496 1.795142998 GO:0043632~modification-dependent macromolecule catabolic process 17 0.028195026 1.784583333 GO:0060284~regulation of cell development 16 0.006283738 2.179643766 GO:0022603~regulation of anatomical structure morphogenesis 16 0.033909195 1.784583333 GO:0007067~mitosis 15 0.033572894 1.833476027 GO:0000087~M phase of mitotic cell cycle 15 0.037073808 1.808699324 GO:0000280~nuclear division 15 0.037073808 1.808699324 GO:0048285~organelle fission 15 0.046957866 1.749591503 GO:0006470~protein amino acid dephosphorylation 14 5.70E-04 3.046849593 GO:0019725~cellular homeostasis 14 0.008806922 2.250825826 GO:0006974~response to DNA damage stimulus 14 0.011710995 2.172536232 GO:0009968~negative regulation of signal transduction 13 0.011212454 2.274468954 GO:0006281~DNA repair 13 0.011212454 2.274468954 GO:0010648~negative regulation of cell communication 13 0.012070809 2.252386731 GO:0033043~regulation of organelle organization 13 0.018324001 2.128402141 GO:0022604~regulation of cell morphogenesis 13 0.03372718 1.949544818 GO:0006403~RNA localization 13 0.04651578 1.855966667 GO:0000398~nuclear mRNA splicing, via spliceosome 13 0.048932949 1.841236772 GO:0000377~RNA splicing, via transesterification reactions with bulged adenosine as nucleophile 13 0.048932949 1.841236772 GO:0006260~DNA replication 12 0.01781505 2.230729167 GO:0007346~regulation of mitotic cell cycle 12 0.020496925 2.185204082 GO:0007264~small GTPase mediated signal transduction 12 0.040774274 1.964678899 GO:0045454~cell redox homeostasis 11 6.03E-04 3.703852201 GO:0060341~regulation of cellular localization 11 0.004245002 2.88682598 GO:0044087~regulation of cellular component biogenesis 11 0.013258339 2.453802083 GO:0019637~organophosphate metabolic process 11 0.043964131 2.023754296 GO:0051049~regulation of transport 10 0.002427666 3.367138365  173 Term Count p-value Fold Enrichment GO:0006261~DNA-dependent DNA replication 10 0.004043948 3.130847953 GO:0046486~glycerolipid metabolic process 10 0.005731113 2.974305556 GO:0006644~phospholipid metabolic process 10 0.044237619 2.124503968 GO:0045132~meiotic chromosome segregation 9 0.01329798 2.817763158 GO:0050657~nucleic acid transport 9 0.02332665 2.549404762 GO:0050658~RNA transport 9 0.02332665 2.549404762 GO:0051236~establishment of RNA localization 9 0.025406222 2.509570312 GO:0050767~regulation of neurogenesis 9 0.032413809 2.397201493 GO:0002164~larval development 9 0.035016101 2.361948529 GO:0015931~nucleobase, nucleoside, nucleotide and nucleic acid transport 9 0.035016101 2.361948529 GO:0051169~nuclear transport 9 0.046827578 2.230729167 GO:0006913~nucleocytoplasmic transport 9 0.046827578 2.230729167 GO:0031023~microtubule organizing center organization 8 0.014725806 3.037588652 GO:0002168~instar larval development 8 0.01643681 2.974305556 GO:0045995~regulation of embryonic development 8 0.029728228 2.64382716 GO:0006650~glycerophospholipid metabolic process 8 0.029728228 2.64382716 GO:0032880~regulation of protein localization 7 0.017794576 3.287390351 GO:0048193~Golgi vesicle transport 7 0.031122715 2.905135659 GO:0051297~centrosome organization 7 0.031122715 2.905135659 GO:0010627~regulation of protein kinase cascade 7 0.034401593 2.839109848 GO:0007127~meiosis I 7 0.037895519 2.776018519 GO:0000070~mitotic sister chromatid segregation 7 0.041607995 2.71567029 GO:0000819~sister chromatid segregation 7 0.045541974 2.657890071 GO:0048585~negative regulation of response to stimulus 6 0.006380093 4.867045455 GO:0051050~positive regulation of transport 6 0.013259737 4.118269231 GO:0022411~cellular component disassembly 6 0.018078793 3.824107143 GO:0010769~regulation of cell morphogenesis involved in differentiation 6 0.023929637 3.569166667 GO:0010975~regulation of neuron projection development 6 0.023929637 3.569166667 GO:0030261~chromosome condensation 6 0.023929637 3.569166667  174 Term Count p-value Fold Enrichment GO:0046822~regulation of nucleocytoplasmic transport 6 0.034779589 3.24469697 GO:0032386~regulation of intracellular transport 6 0.038972833 3.149264706 GO:0006270~DNA replication initiation 5 0.008043526 5.948611111 GO:0010741~negative regulation of protein kinase cascade 5 0.015787154 4.957175926 GO:0050770~regulation of axonogenesis 5 0.015787154 4.957175926 GO:0007426~tracheal outgrowth, open tracheal system 5 0.027058084 4.249007937 GO:0043269~regulation of ion transport 4 0.003060597 11.89722222 GO:0032508~DNA duplex unwinding 4 0.00787683 8.922916667 GO:0032392~DNA geometric change 4 0.00787683 8.922916667 GO:0035099~hemocyte migration 4 0.039881238 5.098809524 GO:0043624~cellular protein complex disassembly 4 0.039881238 5.098809524 GO:0043241~protein complex disassembly 4 0.039881238 5.098809524 GO:0033044~regulation of chromosome organization 4 0.047840666 4.758888889 GO:0034765~regulation of ion transmembrane transport 3 0.017362884 13.384375 GO:0034762~regulation of transmembrane transport 3 0.017362884 13.384375 GO:0032412~regulation of ion transmembrane transporter activity 3 0.017362884 13.384375 GO:0051924~regulation of calcium ion transport 3 0.017362884 13.384375 GO:0032409~regulation of transporter activity 3 0.017362884 13.384375 GO:0022898~regulation of transmembrane transporter activity 3 0.017362884 13.384375 GO:0010959~regulation of metal ion transport 3 0.027873818 10.7075 GO:0032837~distributive segregation 3 0.027873818 10.7075 GO:0006268~DNA unwinding during replication 3 0.040280429 8.922916667 GO:0006415~translational termination 3 0.040280429 8.922916667      175 Appendix 12- Table showing enrichment in biological process GO-terms (all levels) of differentially expressed genes (q<0.05) between RpII2154/FM7 and yw fly ovaries.  Term Count p-value Fold Enrichment GO:0009987~cellular process 181 1.57E-04 1.179297493 GO:0008152~metabolic process 161 2.29E-04 1.209314112 GO:0044238~primary metabolic process 125 0.022834886 1.151942353 GO:0044237~cellular metabolic process 124 4.59E-05 1.332229605 GO:0044260~cellular macromolecule metabolic process 89 0.003132667 1.300655468 GO:0050789~regulation of biological process 84 0.031554616 1.205563179 GO:0016043~cellular component organization 66 0.019002973 1.284222864 GO:0006807~nitrogen compound metabolic process 57 0.042133297 1.261834985 GO:0034641~cellular nitrogen compound metabolic process 55 0.012305381 1.361214643 GO:0010467~gene expression 51 0.019970999 1.346494189 GO:0006139~nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 50 0.014752062 1.376064257 GO:0043412~biopolymer modification 31 0.018527719 1.525679255 GO:0048519~negative regulation of biological process 28 0.015566646 1.594776457 GO:0007389~pattern specification process 27 0.001468989 1.935090361 GO:0033036~macromolecule localization 27 0.003662632 1.810610865 GO:0044085~cellular component biogenesis 27 0.024350343 1.550656717 GO:0003002~regionalization 26 0.00144818 1.970136051 GO:0016070~RNA metabolic process 26 0.012464648 1.665627127 GO:0006796~phosphate metabolic process 26 0.016586938 1.623306292 GO:0006793~phosphorus metabolic process 26 0.016586938 1.623306292 GO:0048523~negative regulation of cellular process 24 0.038173228 1.534644153 GO:0008104~protein localization 22 0.00482586 1.930702401 GO:0007444~imaginal disc development 21 0.01614927 1.753479939 GO:0016310~phosphorylation 21 0.021749883 1.699844082 GO:0006396~RNA processing 20 0.001736517 2.205231181 GO:0035220~wing disc development 18 0.003229869 2.203661622 GO:0048736~appendage development 15 0.012058473 2.097658928 GO:0007560~imaginal disc morphogenesis 15 0.036210253 1.816986255 GO:0048563~post-embryonic organ morphogenesis 15 0.036210253 1.816986255  176 Term Count p-value Fold Enrichment GO:0048569~post-embryonic organ development 15 0.049294478 1.73745487 GO:0002009~morphogenesis of an epithelium 14 0.01361855 2.1405444 GO:0060429~epithelium development 14 0.018897383 2.049457404 GO:0035107~appendage morphogenesis 14 0.023418194 1.990175578 GO:0048737~imaginal disc-derived appendage development 14 0.024129689 1.981985555 GO:0048729~tissue morphogenesis 14 0.027116667 1.949888623 GO:0045595~regulation of cell differentiation 13 0.007439984 2.417410181 GO:0007476~imaginal disc-derived wing morphogenesis 13 0.024419573 2.051471943 GO:0007472~wing disc morphogenesis 13 0.026027967 2.032822198 GO:0035120~post-embryonic appendage morphogenesis 13 0.033956683 1.952929622 GO:0035114~imaginal disc-derived appendage morphogenesis 13 0.044715196 1.871217086 GO:0034621~cellular macromolecular complex subunit organization 12 0.033607907 2.033592498 GO:0007167~enzyme linked receptor protein signaling pathway 11 0.009320195 2.609777039 GO:0009891~positive regulation of biosynthetic process 10 0.033453144 2.248471008 GO:0031328~positive regulation of cellular biosynthetic process 10 0.033453144 2.248471008 GO:0031325~positive regulation of cellular metabolic process 10 0.047008098 2.110528002 GO:0022613~ribonucleoprotein complex biogenesis 9 0.00574828 3.293770828 GO:0016331~morphogenesis of embryonic epithelium 9 0.022953709 2.580120482 GO:0006403~RNA localization 9 0.028449931 2.476915663 GO:0006730~one-carbon metabolic process 8 0.006261493 3.621221729 GO:0009968~negative regulation of signal transduction 8 0.028229202 2.69816521 GO:0010648~negative regulation of cell communication 8 0.029569966 2.671969431 GO:0032259~methylation 7 0.004072624 4.543608396 GO:0015931~nucleobase, nucleoside, nucleotide and nucleic acid transport 7 0.013572868 3.541341838 GO:0043414~biopolymer methylation 6 0.007662472 4.800224152 GO:0017145~stem cell division 6 0.015523668 4.047247815  177 Term Count p-value Fold Enrichment GO:0008356~asymmetric cell division 6 0.02575562 3.558786872 GO:0050657~nucleic acid transport 6 0.035215408 3.276343469 GO:0050658~RNA transport 6 0.035215408 3.276343469 GO:0051236~establishment of RNA localization 6 0.037333192 3.225150602 GO:0042254~ribosome biogenesis 6 0.037333192 3.225150602 GO:0035222~wing disc pattern formation 6 0.04657453 3.035435861 GO:0007173~epidermal growth factor receptor signaling pathway 5 0.019532476 4.778000892 GO:0006364~rRNA processing 5 0.027717191 4.300200803 GO:0016072~rRNA metabolic process 5 0.030037905 4.195317857 GO:0006446~regulation of translational initiation 4 0.014180198 7.644801428 GO:0007179~transforming growth factor beta receptor signaling pathway 4 0.027686318 5.982888074 GO:0033227~dsRNA transport 4 0.034474406 5.504257028 GO:0008213~protein amino acid alkylation 4 0.046111135 4.914515204 GO:0006479~protein amino acid methylation 4 0.046111135 4.914515204 GO:0022411~cellular component disassembly 4 0.046111135 4.914515204 GO:0048096~chromatin-mediated maintenance of transcription 3 0.032228518 10.32048193 GO:0045815~positive regulation of gene expression, epigenetic 3 0.038650506 9.382256298 

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
https://iiif.library.ubc.ca/presentation/dsp.24.1-0073847/manifest

Comment

Related Items