Snp Associations with Tuberculosis Susceptibility in a Ugandan

Total Page:16

File Type:pdf, Size:1020Kb

Snp Associations with Tuberculosis Susceptibility in a Ugandan SNP ASSOCIATIONS WITH TUBERCULOSIS SUSCEPTIBILITY IN A UGANDAN HOUSEHOLD CONTACT STUDY by ALLISON REES BAKER Submitted in partial fulfillment of the requirements For the degree of Master of Science Thesis Advisor: Dr. Catherine M. Stein Department of Epidemiology and Biostatistics CASE WESTERN RESERVE UNIVERSITY August, 2010 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of ______________________________________________________ candidate for the ________________________________degree *. (signed)_______________________________________________ (chair of the committee) ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ (date) _______________________ *We also certify that written approval has been obtained for any proprietary material contained therein. Table of Contents Table of Contents...............................................................................................................iii List of Tables ..................................................................................................................... iv Acknowledgements............................................................................................................. v List of Commonly Used Abbreviations ............................................................................. vi Chapter 1: Literature Review.............................................................................................. 8 1.1. Genetics of Susceptibility to Tuberculosis .............................................................. 8 1.1.1. History and Epidemiology of Tuberculosis ...................................................... 8 1.1.2. Candidate Genes ............................................................................................. 10 1.1.3. Genome-wide Linkage Scans ......................................................................... 15 1.2. Methods for Fine Mapping Analysis ..................................................................... 20 1.3. Imputation.............................................................................................................. 23 Chapter 2: Specific Aims.................................................................................................. 27 2.1. Specific Aim 1 ....................................................................................................... 27 2.2. Specific Aim 2 ....................................................................................................... 27 2.3. Specific Aim 3 ....................................................................................................... 28 Chapter 3: Methods........................................................................................................... 29 3.1. Data Description .................................................................................................... 29 3.1.1. Sample............................................................................................................. 29 3.1.2. Descriptive Statistics....................................................................................... 30 3.2. Genotyping............................................................................................................. 31 3.3. Analysis Strategy ................................................................................................... 34 3.3.1. Aim 1: Candidate Gene Analysis.................................................................... 34 3.3.2. Aim 2: Fine Mapping Analysis....................................................................... 37 3.3.3. Aim 3: Imputation........................................................................................... 38 Chapter 4: Results and Discussion.................................................................................... 41 4.1. Results.................................................................................................................... 41 4.1.1. Candidate Gene Analysis................................................................................ 41 4.1.2. Fine-Mapping Analysis................................................................................... 43 4.1.3. Imputation....................................................................................................... 44 4.2. Discussion.............................................................................................................. 53 4.3. Conclusions and Future Directions........................................................................ 56 Bibliography ..................................................................................................................... 60 iii List of Tables Table 1. Descriptive Statistics........................................................................................... 34 Table 2. Candidate Gene SNPs Departing from HWE..................................................... 41 Table 3. Candidate Gene Analysis Results....................................................................... 42 Table 4. Fine Mapping SNPs Departing from HWE ........................................................ 44 Table 5. Haplotype Analysis Results for TLR2................................................................ 46 Table 6. Haplotype Analysis Results for TLR4................................................................ 46 Table 7. Haplotype Analysis Results for TLR6................................................................ 46 Table 8. Haplotype Analysis Results for TIRAP.............................................................. 46 Table 9. Results for Imputed Genotypes on Chromosome 7p.......................................... 48 Table 10. Results for Imputed Genotypes on Chromosome 20q...................................... 52 iv Acknowledgements I would like to acknowledge and thank my thesis advisor, Dr. Catherine Stein, for providing me with direction and leadership throughout my academic program. I am exceedingly grateful for the incredible mentoring, support and guidance received from Drs. Courtney Gray and Emma Larkin. Thank you also to Drs. Robert Igo and Robert Elston, and thanks to Robert Goodloe for his programming assistance and sharing in the student experience. A very special thank you is dedicated to my devoted mother and father, and to my husband, Dave, for without his endearing love and endless support, none of my success would be possible. v List of Commonly Used Abbreviations AIDS Acquired Immune Deficiency Syndrome ASW African Ancestry in Southwest USA CARD11 Caspase recruitment domain family, member 11 cM Centimorgan CTSZ Cathepsin Z GLMM Generalized Linear Mixed Model HIV Human Immunodeficiency Virus IL-1 Interleukin-1 IL-10 Interleukin-10 IL-12 Interleukin-12 IFNG1- γ Interferon Gamma HIV Human Immunodeficiency Virus HMM Hidden Markov Model HWE Hardy Weinberg Equilibrium kb Kilobasepair LD Linkage Disequilibrium LTBI Latent Mycobacterium Tuberculosis Infection LWK Luhya in Webuye, Kenya MAF Minor Allele Frequency Mb Megabasepair MC3R Melanocortin 3 Receptor MKK Maasai in Kinyawa, Kenya Mtb Mycobacterium tuberculosis NOS2A Nitric Oxide Synthase 2A NRAMP1 Natural-Resistance-Associated Macrophage Protein 1 PPD Purified Protein Derivative QC Quality Control QTL Quantitative Trait Locus SLC11A1 Solute Carrier Family 11, Member 13 SNP Single Nucleotide Polymorphism TB Tuberculosis TBSCPB Tuberculosis Susceptibility Variable TDT Transmission Disequilibrium Test TLR-2 Toll-Like Receptor-2 TLR-4 Toll-Like Receptor-4 TNF Tumor Necrosis Factor TNF-α Tumor Necrosis Factor-α TST Tuberculin Skin Test UG Uganda YRI Yoruba in Ibadan, Nigeria vi SNP Associations with Tuberculosis Susceptibility in a Ugandan Household Contact Study Abstract by ALLISON REES BAKER The World Health Organization reports that over 9 million new cases of tuberculosis (TB) are diagnosed each year, killing between 1.6 and 2 million individuals worldwide. TB is an infectious disease caused by the bacterium Mycobacterium tuberculosis (Mtb), and reports indicate that only 10% of individuals infected with Mtb actually advance to disease. Genetic linkage and association analyses have established several chromosome regions involved in TB susceptibility. This study examines the association of TB susceptibility with a selection of biologically relevant markers, a chromosome 7 region identified through a previous genome scan, and association with imputed genotypes. Across chromosomes 7 and 20, 564 Ugandan individuals were genotyped at 1,417 SNPs. None of the candidate genes or fine mapping SNPs were found significantly associated with TB susceptibility (P > 0.10). Five imputed SNPs were significant at the P = 0.01 level. Suggested future work includes GWAS and resequencing analyses. vii Chapter 1: Literature Review 1.1. Genetics of Susceptibility to Tuberculosis 1.1.1. History and Epidemiology of Tuberculosis The World Health Organization (WHO) reports that over 9 million new cases of tuberculosis (TB) are diagnosed each year, killing between 1.6 and 2 million individuals worldwide (World Health Organization 2009). TB is an infectious disease caused by the bacterium Mycobacterium tuberculosis (Mtb), but reports indicate that only 10% of individuals infected with Mtb actually advance to disease (Murray et al. 1990). The pathogenesis of
Recommended publications
  • Association Analyses of Known Genetic Variants with Gene
    ASSOCIATION ANALYSES OF KNOWN GENETIC VARIANTS WITH GENE EXPRESSION IN BRAIN by Viktoriya Strumba A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Bioinformatics) in The University of Michigan 2009 Doctoral Committee: Professor Margit Burmeister, Chair Professor Huda Akil Professor Brian D. Athey Assistant Professor Zhaohui S. Qin Research Statistician Thomas Blackwell To Sam and Valentina Dmitriy and Elizabeth ii ACKNOWLEDGEMENTS I would like to thank my advisor Professor Margit Burmeister, who tirelessly guided me though seemingly impassable corridors of graduate work. Throughout my thesis writing period she provided sound advice, encouragement and inspiration. Leading by example, her enthusiasm and dedication have been instrumental in my path to becoming a better scientist. I also would like to thank my co-advisor Tom Blackwell. His careful prodding always kept me on my toes and looking for answers, which taught me the depth of careful statistical analysis. His diligence and dedication have been irreplaceable in most difficult of projects. I also would like to thank my other committee members: Huda Akil, Brian Athey and Steve Qin as well as David States. You did not make it easy for me, but I thank you for believing and not giving up. Huda’s eloquence in every subject matter she explained have been particularly inspiring, while both Huda’s and Brian’s valuable advice made the completion of this dissertation possible. I would also like to thank all the members of the Burmeister lab, both past and present: Sandra Villafuerte, Kristine Ito, Cindy Schoen, Karen Majczenko, Ellen Schmidt, Randi Burns, Gang Su, Nan Xiang and Ana Progovac.
    [Show full text]
  • Analysis of Trans Esnps Infers Regulatory Network Architecture
    Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation.
    [Show full text]
  • Bayesian Hierarchical Modeling of High-Throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation
    BAYESIAN HIERARCHICAL MODELING OF HIGH-THROUGHPUT GENOMIC DATA WITH APPLICATIONS TO CANCER BIOINFORMATICS AND STEM CELL DIFFERENTIATION by Keegan D. Korthauer A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2015 Date of final oral examination: 05/04/15 The dissertation is approved by the following members of the Final Oral Committee: Christina Kendziorski, Professor, Biostatistics and Medical Informatics Michael A. Newton, Professor, Statistics Sunduz Kele¸s,Professor, Biostatistics and Medical Informatics Sijian Wang, Associate Professor, Biostatistics and Medical Informatics Michael N. Gould, Professor, Oncology © Copyright by Keegan D. Korthauer 2015 All Rights Reserved i in memory of my grandparents Ma and Pa FL Grandma and John ii ACKNOWLEDGMENTS First and foremost, I am deeply grateful to my thesis advisor Christina Kendziorski for her invaluable advice, enthusiastic support, and unending patience throughout my time at UW-Madison. She has provided sound wisdom on everything from methodological principles to the intricacies of academic research. I especially appreciate that she has always encouraged me to eke out my own path and I attribute a great deal of credit to her for the successes I have achieved thus far. I also owe special thanks to my committee member Professor Michael Newton, who guided me through one of my first collaborative research experiences and has continued to provide key advice on my thesis research. I am also indebted to the other members of my thesis committee, Professor Sunduz Kele¸s,Professor Sijian Wang, and Professor Michael Gould, whose valuable comments, questions, and suggestions have greatly improved this dissertation.
    [Show full text]
  • Anti-ARL4A Antibody (ARG41291)
    Product datasheet [email protected] ARG41291 Package: 100 μl anti-ARL4A antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes ARL4A Tested Reactivity Hu, Ms, Rat Tested Application ICC/IF, IHC-P Host Rabbit Clonality Polyclonal Isotype IgG Target Name ARL4A Antigen Species Human Immunogen Recombinant fusion protein corresponding to aa. 121-200 of Human ARL4A (NP_001032241.1). Conjugation Un-conjugated Alternate Names ARL4; ADP-ribosylation factor-like protein 4A Application Instructions Application table Application Dilution ICC/IF 1:50 - 1:200 IHC-P 1:50 - 1:200 Application Note * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 23 kDa Properties Form Liquid Purification Affinity purified. Buffer PBS (pH 7.3), 0.02% Sodium azide and 50% Glycerol. Preservative 0.02% Sodium azide Stabilizer 50% Glycerol Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles. Suggest spin the vial prior to opening. The antibody solution should be gently mixed before use. Note For laboratory research only, not for drug, diagnostic or other use. www.arigobio.com 1/2 Bioinformation Gene Symbol ARL4A Gene Full Name ADP-ribosylation factor-like 4A Background ADP-ribosylation factor-like 4A is a member of the ADP-ribosylation factor family of GTP-binding proteins. ARL4A is similar to ARL4C and ARL4D and each has a nuclear localization signal and an unusually high guaninine nucleotide exchange rate.
    [Show full text]
  • Sorting Nexins in Protein Homeostasis Sara E. Hanley1,And Katrina F
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 6 November 2020 doi:10.20944/preprints202011.0241.v1 Sorting nexins in protein homeostasis Sara E. Hanley1,and Katrina F. Cooper2* 1Department of Molecular Biology, Graduate School of Biomedical Sciences, Rowan University, Stratford, NJ, 08084, USA 1 [email protected] 2 [email protected] * [email protected] Tel: +1 (856)-566-2887 1Department of Molecular Biology, Graduate School of Biomedical Sciences, Rowan University, Stratford, NJ, 08084, USA Abstract: Sorting nexins (SNXs) are a highly conserved membrane-associated protein family that plays a role in regulating protein homeostasis. This family of proteins is unified by their characteristic phox (PX) phosphoinositides binding domain. Along with binding to membranes, this family of SNXs also comprises a diverse array of protein-protein interaction motifs that are required for cellular sorting and protein trafficking. SNXs play a role in maintaining the integrity of the proteome which is essential for regulating multiple fundamental processes such as cell cycle progression, transcription, metabolism, and stress response. To tightly regulate these processes proteins must be expressed and degraded in the correct location and at the correct time. The cell employs several proteolysis mechanisms to ensure that proteins are selectively degraded at the appropriate spatiotemporal conditions. SNXs play a role in ubiquitin-mediated protein homeostasis at multiple levels including cargo localization, recycling, degradation, and function. In this review, we will discuss the role of SNXs in three different protein homeostasis systems: endocytosis lysosomal, the ubiquitin-proteasomal, and the autophagy-lysosomal system. The highly conserved nature of this protein family by beginning with the early research on SNXs and protein trafficking in yeast and lead into their important roles in mammalian systems.
    [Show full text]
  • Identification of Genes Concordantly Expressed with Atoh1 During Inner Ear Development
    Original Article doi: 10.5115/acb.2011.44.1.69 pISSN 2093-3665 eISSN 2093-3673 Identification of genes concordantly expressed with Atoh1 during inner ear development Heejei Yoon, Dong Jin Lee, Myoung Hee Kim, Jinwoong Bok Department of Anatomy, Brain Korea 21 Project for Medical Science, College of Medicine, Yonsei University, Seoul, Korea Abstract: The inner ear is composed of a cochlear duct and five vestibular organs in which mechanosensory hair cells play critical roles in receiving and relaying sound and balance signals to the brain. To identify novel genes associated with hair cell differentiation or function, we analyzed an archived gene expression dataset from embryonic mouse inner ear tissues. Since atonal homolog 1a (Atoh1) is a well known factor required for hair cell differentiation, we searched for genes expressed in a similar pattern with Atoh1 during inner ear development. The list from our analysis includes many genes previously reported to be involved in hair cell differentiation such as Myo6, Tecta, Myo7a, Cdh23, Atp6v1b1, and Gfi1. In addition, we identified many other genes that have not been associated with hair cell differentiation, including Tekt2, Spag6, Smpx, Lmod1, Myh7b, Kif9, Ttyh1, Scn11a and Cnga2. We examined expression patterns of some of the newly identified genes using real-time polymerase chain reaction and in situ hybridization. For example, Smpx and Tekt2, which are regulators for cytoskeletal dynamics, were shown specifically expressed in the hair cells, suggesting a possible role in hair cell differentiation or function. Here, by re- analyzing archived genetic profiling data, we identified a list of novel genes possibly involved in hair cell differentiation.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Download Download
    Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/)
    [Show full text]
  • Diverse Species-Specific Phenotypic Consequences of Loss of Function
    www.nature.com/scientificreports OPEN Diverse species‑specifc phenotypic consequences of loss of function sorting nexin 14 mutations Dale Bryant1, Marian Seda1, Emma Peskett1, Constance Maurer1, Gideon Pomeranz1, Marcus Ghosh2, Thomas A. Hawkins2, James Cleak3, Sanchari Datta4, Hanaa Hariri4, Kaitlyn M. Eckert5,6, Daniyal J. Jafree1, Claire Walsh7, Charalambos Demetriou1, Miho Ishida1, Cristina Alemán‑Charlet1, Letizia Vestito1, Rimante Seselgyte1, Jefrey G. McDonald5,6, Maria Bitner‑Glindzicz1, Myriam Hemberger8, Jason Rihel2, Lydia Teboul3, W. Mike Henne4, Dagan Jenkins1, Gudrun E. Moore1 & Philip Stanier1* Mutations in the SNX14 gene cause spinocerebellar ataxia, autosomal recessive 20 (SCAR20) in both humans and dogs. Studies implicating the phenotypic consequences of SNX14 mutations to be consequences of subcellular disruption to autophagy and lipid metabolism have been limited to in vitro investigation of patient‑derived dermal fbroblasts, laboratory engineered cell lines and developmental analysis of zebrafsh morphants. SNX14 homologues Snz (Drosophila) and Mdm1 (yeast) have also been conducted, demonstrated an important biochemical role during lipid biogenesis. In this study we report the efect of loss of SNX14 in mice, which resulted in embryonic lethality around mid‑gestation due to placental pathology that involves severe disruption to syncytiotrophoblast cell diferentiation. In contrast to other vertebrates, zebrafsh carrying a homozygous, maternal zygotic snx14 genetic loss‑of‑function mutation were both viable and anatomically normal. Whilst no obvious behavioural efects were observed, elevated levels of neutral lipids and phospholipids resemble previously reported efects on lipid homeostasis in other species. The biochemical role of SNX14 therefore appears largely conserved through evolution while the consequences of loss of function varies between species.
    [Show full text]
  • Preclinical Evaluation of Protein Disulfide Isomerase Inhibitors for the Treatment of Glioblastoma by Andrea Shergalis
    Preclinical Evaluation of Protein Disulfide Isomerase Inhibitors for the Treatment of Glioblastoma By Andrea Shergalis A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Medicinal Chemistry) in the University of Michigan 2020 Doctoral Committee: Professor Nouri Neamati, Chair Professor George A. Garcia Professor Peter J. H. Scott Professor Shaomeng Wang Andrea G. Shergalis [email protected] ORCID 0000-0002-1155-1583 © Andrea Shergalis 2020 All Rights Reserved ACKNOWLEDGEMENTS So many people have been involved in bringing this project to life and making this dissertation possible. First, I want to thank my advisor, Prof. Nouri Neamati, for his guidance, encouragement, and patience. Prof. Neamati instilled an enthusiasm in me for science and drug discovery, while allowing me the space to independently explore complex biochemical problems, and I am grateful for his kind and patient mentorship. I also thank my committee members, Profs. George Garcia, Peter Scott, and Shaomeng Wang, for their patience, guidance, and support throughout my graduate career. I am thankful to them for taking time to meet with me and have thoughtful conversations about medicinal chemistry and science in general. From the Neamati lab, I would like to thank so many. First and foremost, I have to thank Shuzo Tamara for being an incredible, kind, and patient teacher and mentor. Shuzo is one of the hardest workers I know. In addition to a strong work ethic, he taught me pretty much everything I know and laid the foundation for the article published as Chapter 3 of this dissertation. The work published in this dissertation really began with the initial identification of PDI as a target by Shili Xu, and I am grateful for his advice and guidance (from afar!).
    [Show full text]
  • Supplemental Information
    Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig.
    [Show full text]
  • A CRISPR-Based Screen for Hedgehog Signaling Provides Insights Into Ciliary Function and Ciliopathies
    ARTICLES https://doi.org/10.1038/s41588-018-0054-7 A CRISPR-based screen for Hedgehog signaling provides insights into ciliary function and ciliopathies David K. Breslow 1,2,7*, Sascha Hoogendoorn 3,7, Adam R. Kopp2, David W. Morgens4, Brandon K. Vu2, Margaret C. Kennedy1, Kyuho Han4, Amy Li4, Gaelen T. Hess4, Michael C. Bassik4, James K. Chen 3,5* and Maxence V. Nachury 2,6* Primary cilia organize Hedgehog signaling and shape embryonic development, and their dysregulation is the unifying cause of ciliopathies. We conducted a functional genomic screen for Hedgehog signaling by engineering antibiotic-based selection of Hedgehog-responsive cells and applying genome-wide CRISPR-mediated gene disruption. The screen can robustly identify factors required for ciliary signaling with few false positives or false negatives. Characterization of hit genes uncovered novel components of several ciliary structures, including a protein complex that contains δ -tubulin and ε -tubulin and is required for centriole maintenance. The screen also provides an unbiased tool for classifying ciliopathies and showed that many congenital heart disorders are caused by loss of ciliary signaling. Collectively, our study enables a systematic analysis of ciliary function and of ciliopathies, and also defines a versatile platform for dissecting signaling pathways through CRISPR-based screening. he primary cilium is a surface-exposed microtubule-based approach. Indeed, most studies to date have searched for genes that compartment that serves as an organizing center for diverse either intrinsically affect cell growth or affect sensitivity to applied Tsignaling pathways1–3. Mutations affecting cilia cause ciliopa- perturbations16–23. thies, a group of developmental disorders including Joubert syn- Here, we engineered a Hh-pathway-sensitive reporter to enable drome, Meckel syndrome (MKS), nephronophthisis (NPHP), and an antibiotic-based selection platform.
    [Show full text]