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Identification of the Binding Partners for Hspb2 and Cryab Reveals
Brigham Young University BYU ScholarsArchive Theses and Dissertations 2013-12-12 Identification of the Binding arP tners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non- Redundant Roles for Small Heat Shock Proteins Kelsey Murphey Langston Brigham Young University - Provo Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Microbiology Commons BYU ScholarsArchive Citation Langston, Kelsey Murphey, "Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non-Redundant Roles for Small Heat Shock Proteins" (2013). Theses and Dissertations. 3822. https://scholarsarchive.byu.edu/etd/3822 This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactions and Non-Redundant Roles for Small Heat Shock Proteins Kelsey Langston A thesis submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Master of Science Julianne H. Grose, Chair William R. McCleary Brian Poole Department of Microbiology and Molecular Biology Brigham Young University December 2013 Copyright © 2013 Kelsey Langston All Rights Reserved ABSTRACT Identification of the Binding Partners for HspB2 and CryAB Reveals Myofibril and Mitochondrial Protein Interactors and Non-Redundant Roles for Small Heat Shock Proteins Kelsey Langston Department of Microbiology and Molecular Biology, BYU Master of Science Small Heat Shock Proteins (sHSP) are molecular chaperones that play protective roles in cell survival and have been shown to possess chaperone activity. -
Molecular Mechanisms Underlying Noncoding Risk Variations in Psychiatric Genetic Studies
OPEN Molecular Psychiatry (2017) 22, 497–511 www.nature.com/mp REVIEW Molecular mechanisms underlying noncoding risk variations in psychiatric genetic studies X Xiao1,2, H Chang1,2 and M Li1 Recent large-scale genetic approaches such as genome-wide association studies have allowed the identification of common genetic variations that contribute to risk architectures of psychiatric disorders. However, most of these susceptibility variants are located in noncoding genomic regions that usually span multiple genes. As a result, pinpointing the precise variant(s) and biological mechanisms accounting for the risk remains challenging. By reviewing recent progresses in genetics, functional genomics and neurobiology of psychiatric disorders, as well as gene expression analyses of brain tissues, here we propose a roadmap to characterize the roles of noncoding risk loci in the pathogenesis of psychiatric illnesses (that is, identifying the underlying molecular mechanisms explaining the genetic risk conferred by those genomic loci, and recognizing putative functional causative variants). This roadmap involves integration of transcriptomic data, epidemiological and bioinformatic methods, as well as in vitro and in vivo experimental approaches. These tools will promote the translation of genetic discoveries to physiological mechanisms, and ultimately guide the development of preventive, therapeutic and prognostic measures for psychiatric disorders. Molecular Psychiatry (2017) 22, 497–511; doi:10.1038/mp.2016.241; published online 3 January 2017 RECENT GENETIC ANALYSES OF NEUROPSYCHIATRIC neurodevelopment and brain function. For example, GRM3, DISORDERS GRIN2A, SRR and GRIA1 were known to involve in the neuro- Schizophrenia, bipolar disorder, major depressive disorder and transmission mediated by glutamate signaling and synaptic autism are highly prevalent complex neuropsychiatric diseases plasticity. -
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. -
High Definition Analyses of Single Cohort, Whole Genome Sequencing Data Provides a Direct Route
medRxiv preprint doi: https://doi.org/10.1101/2021.08.28.21262560; this version posted September 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. High definition analyses of single cohort, whole genome sequencing data provides a direct route to defining sub-phenotypes and personalising medicine Joyce KE1,2, Onabanjo E3, Brownlow S3, Nur F3, Olupona KO3, Fakayode K3, Sroya M4, Thomas G4, Ferguson T3, Redhead J3, Millar CM3,5, Cooper N3,5, Layton DM3,5, Boardman-Pretty F6, Caulfield MJ6,7, Genomics England Research Consortium6, Shovlin CL2,3,8* 1Imperial College School of Medicine, Imperial College, London UK; 2Genomics England Respiratory Clinical Interpretation Partnership (GeCIP); 3West London Genomic Medicine Centre, Imperial College Healthcare NHS Trust, London UK; 4Department of Surgery and Cancer, Imperial College London, UK; 5Centre for Haematology, Department of Immunology and Inflammation, Imperial College London UK; 6Genomics England, UK; 7 William Harvey Research Institute, Queen Mary University of London, London UK; 8National Heart and Lung Institute, Imperial College London UK. Word Count 4778 Abstract 150 Figures – 5 Data Supplement File- 1 *Corresponding Author: Claire L. Shovlin PhD FRCP, National Heart and Lung Institute, Imperial Centre for Translational and Experimental Medicine, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK. Email [email protected] NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. -
Computational and Experimental Approaches for Evaluating the Genetic Basis of Mitochondrial Disorders
Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Lieber, Daniel Solomon. 2013. Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders. Doctoral dissertation, Harvard University. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:11158264 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders A dissertation presented by Daniel Solomon Lieber to The Committee on Higher Degrees in Systems Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Systems Biology Harvard University Cambridge, Massachusetts April 2013 © 2013 - Daniel Solomon Lieber All rights reserved. Dissertation Adviser: Professor Vamsi K. Mootha Daniel Solomon Lieber Computational and Experimental Approaches For Evaluating the Genetic Basis of Mitochondrial Disorders Abstract Mitochondria are responsible for some of the cell’s most fundamental biological pathways and metabolic processes, including aerobic ATP production by the mitochondrial respiratory chain. In humans, mitochondrial dysfunction can lead to severe disorders of energy metabolism, which are collectively referred to as mitochondrial disorders and affect approximately 1:5,000 individuals. These disorders are clinically heterogeneous and can affect multiple organ systems, often within a single individual. Symptoms can include myopathy, exercise intolerance, hearing loss, blindness, stroke, seizures, diabetes, and GI dysmotility. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Table S1. Identified Proteins with Exclusive Expression in Cerebellum of Rats of Control, 10Mg F/L and 50Mg F/L Groups
Table S1. Identified proteins with exclusive expression in cerebellum of rats of control, 10mg F/L and 50mg F/L groups. Accession PLGS Protein Name Group IDa Score Q3TXS7 26S proteasome non-ATPase regulatory subunit 1 435 Control Q9CQX8 28S ribosomal protein S36_ mitochondrial 197 Control P52760 2-iminobutanoate/2-iminopropanoate deaminase 315 Control Q60597 2-oxoglutarate dehydrogenase_ mitochondrial 67 Control P24815 3 beta-hydroxysteroid dehydrogenase/Delta 5-->4-isomerase type 1 84 Control Q99L13 3-hydroxyisobutyrate dehydrogenase_ mitochondrial 114 Control P61922 4-aminobutyrate aminotransferase_ mitochondrial 470 Control P10852 4F2 cell-surface antigen heavy chain 220 Control Q8K010 5-oxoprolinase 197 Control P47955 60S acidic ribosomal protein P1 190 Control P70266 6-phosphofructo-2-kinase/fructose-2_6-bisphosphatase 1 113 Control Q8QZT1 Acetyl-CoA acetyltransferase_ mitochondrial 402 Control Q9R0Y5 Adenylate kinase isoenzyme 1 623 Control Q80TS3 Adhesion G protein-coupled receptor L3 59 Control B7ZCC9 Adhesion G-protein coupled receptor G4 139 Control Q6P5E6 ADP-ribosylation factor-binding protein GGA2 45 Control E9Q394 A-kinase anchor protein 13 60 Control Q80Y20 Alkylated DNA repair protein alkB homolog 8 111 Control P07758 Alpha-1-antitrypsin 1-1 78 Control P22599 Alpha-1-antitrypsin 1-2 78 Control Q00896 Alpha-1-antitrypsin 1-3 78 Control Q00897 Alpha-1-antitrypsin 1-4 78 Control P57780 Alpha-actinin-4 58 Control Q9QYC0 Alpha-adducin 270 Control Q9DB05 Alpha-soluble NSF attachment protein 156 Control Q6PAM1 Alpha-taxilin 161 -
Genetic Colocalization Atlas Points to Common Regulatory Sites and Genes for Hematopoietic Traits and Hematopoietic Contributions to Disease Phenotypes Christopher S
Thom and Voight BMC Medical Genomics (2020) 13:89 https://doi.org/10.1186/s12920-020-00742-9 RESEARCH ARTICLE Open Access Genetic colocalization atlas points to common regulatory sites and genes for hematopoietic traits and hematopoietic contributions to disease phenotypes Christopher S. Thom1,2,3,4 and Benjamin F. Voight2,3,4* Abstract Background: Genetic associations link hematopoietic traits and disease end-points, but most causal variants and genes underlying these relationships are unknown. Here, we used genetic colocalization to nominate loci and genes related to shared genetic signal for hematopoietic, cardiovascular, autoimmune, neuropsychiatric, and cancer phenotypes. Methods: Our aim was to identify colocalization sites for human traits among established genome-wide significant loci. Using genome-wide association study (GWAS) summary statistics, we determined loci where multiple traits colocalized at a false discovery rate < 5%. We then identified quantitative trait loci among colocalization sites to highlight related genes. In addition, we used Mendelian randomization analysis to further investigate certain trait relationships genome-wide. Results: Our findings recapitulated developmental hematopoietic lineage relationships, identified loci that linked traits with causal genetic relationships, and revealed novel trait associations. Out of 2706 loci with genome-wide significant signal for at least 1 blood trait, we identified 1779 unique sites (66%) with shared genetic signal for 2+ hematologic traits. We could assign some sites to specific developmental cell types during hematopoiesis based on affected traits, including those likely to impact hematopoietic progenitor cells and/or megakaryocyte-erythroid progenitor cells. Through an expanded analysis of 70 human traits, we defined 2+ colocalizing traits at 2123 loci from an analysis of 9852 sites (22%) containing genome-wide significant signal for at least 1 GWAS trait. -
+3 Oxidation State) Methyltransferase (AS3MT
Environmental and Molecular Mutagenesis 58:411^422 (2017) Research Article Associations between Arsenic (13OxidationState) Methyltransferase (AS3MT) and N-6 Adenine-specific DNA Methyltransferase1 (N6AMT1) Polymorphisms, Arsenic Metabolism, and Cancer Risk in a Chilean Population Rosemarie de la Rosa,1 Craig Steinmaus,1 Nicholas K. Akers,1 Lucia Conde,1 Catterina Ferreccio,2 David Kalman,3 Kevin R. Zhang,1 Christine F.Skibola,1 Allan H. Smith,1 Luoping Zhang,1 and Martyn T.Smith1* 1Superfund Research Program, Divisions of Environmental Health Sciences and Epidemiology, School of Public Health, University of California, Berkeley, California 2Departamento de Salud Publica, Facultad de Medicina, Pontificia Universidad Catolica de Chile, Advanced Center for Chronic Diseases, ACCDiS, Santiago, Chile 3Department of Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, DC Inter-individual differences in arsenic metabolism have gene. We found several AS3MT polymorphisms asso- been linked to arsenic-related disease risks. Arsenic ciated with both urinary arsenic metabolite profiles (13) methyltransferase (AS3MT) is the primary and cancer risk. For example, compared to wildtypes, enzyme involved in arsenic metabolism, and we previ- individuals carrying minor alleles in AS3MT ously demonstrated in vitro that N-6 adenine-specific rs3740393 had lower %MMA (mean differ- DNA methyltransferase 1 (N6AMT1) also methylates ence 521.9%, 95% CI: 23.3, 20.4), higher the toxic inorganic arsenic (iAs) metabolite, monome- %DMA (mean difference 5 4.0%, 95% CI: 1.5, 6.5), thylarsonous acid (MMA), to the less toxic dimethylar- and lower odds ratios for bladder (OR 5 0.3; 95% sonic acid (DMA). Here, we evaluated whether CI: 0.1–0.6) and lung cancer (OR 5 0.6; 95% CI: AS3MT and N6AMT1 gene polymorphisms alter 0.2–1.1). -
Arsenic-Associated Diabetes: Mechanisms and the Role of Arsenic Metabolism
ARSENIC-ASSOCIATED DIABETES: MECHANISMS AND THE ROLE OF ARSENIC METABOLISM Madelyn C. Huang A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum of Toxicology within the School of Medicine. Chapel Hill 2018 Approved by: Miroslav Stýblo Rebecca Fry Ilona Jaspers Eric Klett Zuzana Drobná © 2018 Madelyn C. Huang ALL RIGHTS RESERVED ii ABSTRACT Madelyn C. Huang: Arsenic-associated diabetes: mechanisms and the role of arsenic metabolism (Under the direction of Miroslav Stýblo) Inorganic arsenic (iAs) is a ubiquitous drinking water and food contaminant. Chronic exposure to iAs has been linked to diabetes mellitus in many populations. Experimental studies support these associations and suggest mechanisms, but the precise pathogenesis of iAs- associated diabetes is unclear. Efficient metabolism of iAs through methylation, catalyzed by arsenic (+3) methyltransferase (AS3MT), influences the risk of iAs-associated disease. Additionally, higher intake of methyl donor nutrients such as folate and methylcobalamin (B12) has been linked to increased efficiency of iAs metabolism and lowered disease risk. However, while metabolism of iAs facilitates its excretion from the body, iAs metabolites are more toxic than iAs itself. The role of iAs metabolism in iAs toxicity is understudied, especially in cardiometabolic disease. Understanding how iAs metabolism affects iAs-associated diabetes will help design appropriate intervention strategies and improve risk assessment. This project investigated how iAs metabolism influences iAs-associated diabetes. First, we compared metabolite profiles of C57BL6 (WT) with As3mt-KO (KO) mice, which cannot methylate iAs, with and without exposure to iAs. -
Supp Table 6.Pdf
Supplementary Table 6. Processes associated to the 2037 SCL candidate target genes ID Symbol Entrez Gene Name Process NM_178114 AMIGO2 adhesion molecule with Ig-like domain 2 adhesion NM_033474 ARVCF armadillo repeat gene deletes in velocardiofacial syndrome adhesion NM_027060 BTBD9 BTB (POZ) domain containing 9 adhesion NM_001039149 CD226 CD226 molecule adhesion NM_010581 CD47 CD47 molecule adhesion NM_023370 CDH23 cadherin-like 23 adhesion NM_207298 CERCAM cerebral endothelial cell adhesion molecule adhesion NM_021719 CLDN15 claudin 15 adhesion NM_009902 CLDN3 claudin 3 adhesion NM_008779 CNTN3 contactin 3 (plasmacytoma associated) adhesion NM_015734 COL5A1 collagen, type V, alpha 1 adhesion NM_007803 CTTN cortactin adhesion NM_009142 CX3CL1 chemokine (C-X3-C motif) ligand 1 adhesion NM_031174 DSCAM Down syndrome cell adhesion molecule adhesion NM_145158 EMILIN2 elastin microfibril interfacer 2 adhesion NM_001081286 FAT1 FAT tumor suppressor homolog 1 (Drosophila) adhesion NM_001080814 FAT3 FAT tumor suppressor homolog 3 (Drosophila) adhesion NM_153795 FERMT3 fermitin family homolog 3 (Drosophila) adhesion NM_010494 ICAM2 intercellular adhesion molecule 2 adhesion NM_023892 ICAM4 (includes EG:3386) intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)adhesion NM_001001979 MEGF10 multiple EGF-like-domains 10 adhesion NM_172522 MEGF11 multiple EGF-like-domains 11 adhesion NM_010739 MUC13 mucin 13, cell surface associated adhesion NM_013610 NINJ1 ninjurin 1 adhesion NM_016718 NINJ2 ninjurin 2 adhesion NM_172932 NLGN3 neuroligin -
Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in