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Recombinant Human Carbonic Anhydrase XIII Protein Catalog Number: ATGP3059
Recombinant human Carbonic Anhydrase XIII protein Catalog Number: ATGP3059 PRODUCT INPORMATION Expression system E.coli Domain 1-262aa UniProt No. Q8N1Q1 NCBI Accession No. NP_940986.1 Alternative Names Carbonic anhydrase 13, CAXIII PRODUCT SPECIFICATION Molecular Weight 31.8 kDa (285aa) confirmed by MALDI-TOF Concentration 0.5mg/ml (determined by Bradford assay) Formulation Liquid in. Phosphate-Buffered Saline (pH 7.4) containing 10% glycerol, 1mM DTT Purity > 90% by SDS-PAGE Biological Activity Specific activity is > 2,500pmol/min/ug, and is defined as the amount of enzyme that hydrolyze 1.0pmole of 4- nitrophenyl acetate to 4-nitrophenol per minute at pH 7.5 at 37C. Tag His-Tag Application SDS-PAGE, Enzyme Activity Storage Condition Can be stored at +2C to +8C for 1 week. For long term storage, aliquot and store at -20C to -80C. Avoid repeated freezing and thawing cycles. BACKGROUND Description CA13 also known as carbonic anhydrase 13 belongs to the alpha-carbonic anhydrase family. The carbonic anhydrase from a family of enzymes that catalyze the rapid interconversion of carbon dioxide and water to bicarbonate and protons, a reversible reaction that occurs relatively slowly in the absence of catalyst. The active 1 Recombinant human Carbonic Anhydrase XIII protein Catalog Number: ATGP3059 site of most carbonic anhydrases contains a zinc ion; they are claasified as metalloenzymes. There are at least five distinct CA families (alpha, beta, gamma, delta, and epsilon). These families have no significant amino acid sequence similarity and in most cases are thought to be an example of convergent evolution. The alpha-CAs are found in humans. -
Implications in Parkinson's Disease
Journal of Clinical Medicine Review Lysosomal Ceramide Metabolism Disorders: Implications in Parkinson’s Disease Silvia Paciotti 1,2 , Elisabetta Albi 3 , Lucilla Parnetti 1 and Tommaso Beccari 3,* 1 Laboratory of Clinical Neurochemistry, Department of Medicine, University of Perugia, Sant’Andrea delle Fratte, 06132 Perugia, Italy; [email protected] (S.P.); [email protected] (L.P.) 2 Section of Physiology and Biochemistry, Department of Experimental Medicine, University of Perugia, Sant’Andrea delle Fratte, 06132 Perugia, Italy 3 Department of Pharmaceutical Sciences, University of Perugia, Via Fabretti, 06123 Perugia, Italy; [email protected] * Correspondence: [email protected] Received: 29 January 2020; Accepted: 20 February 2020; Published: 21 February 2020 Abstract: Ceramides are a family of bioactive lipids belonging to the class of sphingolipids. Sphingolipidoses are a group of inherited genetic diseases characterized by the unmetabolized sphingolipids and the consequent reduction of ceramide pool in lysosomes. Sphingolipidoses include several disorders as Sandhoff disease, Fabry disease, Gaucher disease, metachromatic leukodystrophy, Krabbe disease, Niemann Pick disease, Farber disease, and GM2 gangliosidosis. In sphingolipidosis, lysosomal lipid storage occurs in both the central nervous system and visceral tissues, and central nervous system pathology is a common hallmark for all of them. Parkinson’s disease, the most common neurodegenerative movement disorder, is characterized by the accumulation and aggregation of misfolded α-synuclein that seem associated to some lysosomal disorders, in particular Gaucher disease. This review provides evidence into the role of ceramide metabolism in the pathophysiology of lysosomes, highlighting the more recent findings on its involvement in Parkinson’s disease. Keywords: ceramide metabolism; Parkinson’s disease; α-synuclein; GBA; GLA; HEX A-B; GALC; ASAH1; SMPD1; ARSA * Correspondence [email protected] 1. -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
Supplementary Table S1. Upregulated Genes Differentially
Supplementary Table S1. Upregulated genes differentially expressed in athletes (p < 0.05 and 1.3-fold change) Gene Symbol p Value Fold Change 221051_s_at NMRK2 0.01 2.38 236518_at CCDC183 0.00 2.05 218804_at ANO1 0.00 2.05 234675_x_at 0.01 2.02 207076_s_at ASS1 0.00 1.85 209135_at ASPH 0.02 1.81 228434_at BTNL9 0.03 1.81 229985_at BTNL9 0.01 1.79 215795_at MYH7B 0.01 1.78 217979_at TSPAN13 0.01 1.77 230992_at BTNL9 0.01 1.75 226884_at LRRN1 0.03 1.74 220039_s_at CDKAL1 0.01 1.73 236520_at 0.02 1.72 219895_at TMEM255A 0.04 1.72 201030_x_at LDHB 0.00 1.69 233824_at 0.00 1.69 232257_s_at 0.05 1.67 236359_at SCN4B 0.04 1.64 242868_at 0.00 1.63 1557286_at 0.01 1.63 202780_at OXCT1 0.01 1.63 1556542_a_at 0.04 1.63 209992_at PFKFB2 0.04 1.63 205247_at NOTCH4 0.01 1.62 1554182_at TRIM73///TRIM74 0.00 1.61 232892_at MIR1-1HG 0.02 1.61 204726_at CDH13 0.01 1.6 1561167_at 0.01 1.6 1565821_at 0.01 1.6 210169_at SEC14L5 0.01 1.6 236963_at 0.02 1.6 1552880_at SEC16B 0.02 1.6 235228_at CCDC85A 0.02 1.6 1568623_a_at SLC35E4 0.00 1.59 204844_at ENPEP 0.00 1.59 1552256_a_at SCARB1 0.02 1.59 1557283_a_at ZNF519 0.02 1.59 1557293_at LINC00969 0.03 1.59 231644_at 0.01 1.58 228115_at GAREM1 0.01 1.58 223687_s_at LY6K 0.02 1.58 231779_at IRAK2 0.03 1.58 243332_at LOC105379610 0.04 1.58 232118_at 0.01 1.57 203423_at RBP1 0.02 1.57 AMY1A///AMY1B///AMY1C///AMY2A///AMY2B// 208498_s_at 0.03 1.57 /AMYP1 237154_at LOC101930114 0.00 1.56 1559691_at 0.01 1.56 243481_at RHOJ 0.03 1.56 238834_at MYLK3 0.01 1.55 213438_at NFASC 0.02 1.55 242290_at TACC1 0.04 1.55 ANKRD20A1///ANKRD20A12P///ANKRD20A2/// -
A Genome-Wide Association Study of Body Mass Index
International Journal of Epidemiology, 2015, 700–712 doi: 10.1093/ije/dyv077 Advance Access Publication Date: 7 May 2015 Original article Genetic Epidemiology A genome-wide association study of body mass index across early life and childhood Nicole M Warrington,1,2* Laura D Howe,3,4* Lavinia Paternoster,3,4 Marika Kaakinen,5,6,7 Sauli Herrala,6 Ville Huikari,6 Yan Yan Wu,8 Downloaded from John P Kemp,2,3 Nicholas J Timpson,3,4 Beate St Pourcain,3,4 George Davey Smith,3,4 Kate Tilling,3,4 Marjo-Riitta Jarvelin,5,6,9,10,11 Craig E Pennell,1 David M Evans,2,3,4 Debbie A Lawlor,3,4 Laurent Briollais8,† and Lyle J Palmer12,† http://ije.oxfordjournals.org/ 1School of Women’s and Infants’ Health, University of Western Australia, Perth, WA, Australia, 2University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD, Australia, 3MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK, 4School of Social and Community Medicine, University of Bristol, Bristol, UK, 5Biocenter Oulu, and 6Institute of Health Sciences, University of Oulu, Oulu, Finland, 7Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK, 8Lunenfeld-Tanenbaum Research Institute, Mount 9 Sinai Hospital, Toronto, ON, Canada, Department of Children and Young People and Families, National at MPI Psycholinguistics on September 22, 2015 Institute for Health and Welfare, Oulu, Finland, 10Department of Epidemiology and Biostatistics, MRC- HPA Centre for Environment and Health, School of Public Health, Imperial College London, London, UK, 11Unit of Primary Care, Oulu University Hospital, Oulu, Finland and 12The Joanna Briggs Institute, The Robinson Research Institute, and School of Translational Health Science, University of Adelaide, Adelaide, SA, Australia *Corresponding authors. -
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. -
1 Metabolic Dysfunction Is Restricted to the Sciatic Nerve in Experimental
Page 1 of 255 Diabetes Metabolic dysfunction is restricted to the sciatic nerve in experimental diabetic neuropathy Oliver J. Freeman1,2, Richard D. Unwin2,3, Andrew W. Dowsey2,3, Paul Begley2,3, Sumia Ali1, Katherine A. Hollywood2,3, Nitin Rustogi2,3, Rasmus S. Petersen1, Warwick B. Dunn2,3†, Garth J.S. Cooper2,3,4,5* & Natalie J. Gardiner1* 1 Faculty of Life Sciences, University of Manchester, UK 2 Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK 3 Centre for Endocrinology and Diabetes, Institute of Human Development, Faculty of Medical and Human Sciences, University of Manchester, UK 4 School of Biological Sciences, University of Auckland, New Zealand 5 Department of Pharmacology, Medical Sciences Division, University of Oxford, UK † Present address: School of Biosciences, University of Birmingham, UK *Joint corresponding authors: Natalie J. Gardiner and Garth J.S. Cooper Email: [email protected]; [email protected] Address: University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, United Kingdom Telephone: +44 161 275 5768; +44 161 701 0240 Word count: 4,490 Number of tables: 1, Number of figures: 6 Running title: Metabolic dysfunction in diabetic neuropathy 1 Diabetes Publish Ahead of Print, published online October 15, 2015 Diabetes Page 2 of 255 Abstract High glucose levels in the peripheral nervous system (PNS) have been implicated in the pathogenesis of diabetic neuropathy (DN). However our understanding of the molecular mechanisms which cause the marked distal pathology is incomplete. Here we performed a comprehensive, system-wide analysis of the PNS of a rodent model of DN. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Differential Proteomic Analysis of the Pancreas of Diabetic Db/Db Mice Reveals the Proteins Involved in the Development of Complications of Diabetes Mellitus
Int. J. Mol. Sci. 2014, 15, 9579-9593; doi:10.3390/ijms15069579 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article Differential Proteomic Analysis of the Pancreas of Diabetic db/db Mice Reveals the Proteins Involved in the Development of Complications of Diabetes Mellitus Victoriano Pérez-Vázquez 1,*, Juan M. Guzmán-Flores 1, Daniela Mares-Álvarez 1, Magdalena Hernández-Ortiz 2, Maciste H. Macías-Cervantes 1, Joel Ramírez-Emiliano 1 and Sergio Encarnación-Guevara 2 1 Depto. de Ciencias Médicas, División de Ciencias de la Salud, Campus León, Universidad de Guanajuato, León, Guanajuato 37320, Mexico; E-Mails: [email protected] (J.M.G.-F.); [email protected] (D.M.-A.); [email protected] (M.H.M.-C.); [email protected] (J.R.-E.) 2 Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, Mexico; E-Mails: [email protected] (M.H.-O.); [email protected] (S.E.-G.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +52-477-7143-812; Fax: +52-477-7167-623. Received: 4 April 2014; in revised form: 14 May 2014 / Accepted: 19 May 2014 / Published: 30 May 2014 Abstract: Type 2 diabetes mellitus is characterized by hyperglycemia and insulin-resistance. Diabetes results from pancreatic inability to secrete the insulin needed to overcome this resistance. We analyzed the protein profile from the pancreas of ten-week old diabetic db/db and wild type mice through proteomics. Pancreatic proteins were separated in two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and significant changes in db/db mice respect to wild type mice were observed in 27 proteins. -
The Role of High Density Lipoprotein Compositional and Functional Heterogeneity in Metabolic Disease
The role of high density lipoprotein compositional and functional heterogeneity in metabolic disease By Scott M. Gordon B.S. State University of New York College at Brockport October, 2012 A Dissertation Presented to the Faculty of The University of Cincinnati College of Medicine in partial fulfillment of the requirements for the Degree of Doctor of Philosophy from the Pathobiology and Molecular Medicine graduate program W. Sean Davidson Ph.D. (Chair) David Askew Ph.D. Professor and Thesis Chair Professor Department of Pathology Department of Pathology University of Cincinnati University of Cincinnati Francis McCormack M.D. Gangani Silva Ph.D. Professor Assistant Professor Department of Pathology Department of Pathology University of Cincinnati University of Cincinnati Jason Lu Ph.D. Assistant Professor Division of Bioinformatics Cincinnati Children’s Hospital i Abstract High density lipoproteins (HDL) are complexes of phospholipid, cholesterol and protein that circulate in the blood. Epidemiological studies have demonstrated a strong inverse correlation between plasma levels of HDL associated cholesterol (HDL-C) and the incidence of cardiovascular disease (CVD). Clinically, HDL-C is often measured and used in combination with low density lipoprotein cholesterol (LDL-C) to assess overall cardiovascular health. HDL have been shown to possess a wide variety of functional attributes which likely contribute to this protection including anti-inflammatory and anti- oxidative properties and the ability to remove excess cholesterol from peripheral tissues and deliver it to the liver for excretion, a process known as reverse cholesterol transport. This functional diversity might be explained by the complexity of HDL composition. Recent studies have taken advantage of advances in mass spectrometry technologies to characterize the proteome of total HDL finding that over 50 different proteins can associate with these particles. -
ONLINE SUPPLEMENTARY TABLE Table 2. Differentially Expressed
ONLINE SUPPLEMENTARY TABLE Table 2. Differentially Expressed Probe Sets in Livers of GK Rats. A. Immune/Inflammatory (67 probe sets, 63 genes) Age Strain Probe ID Gene Name Symbol Accession Gene Function 5 WKY 1398390_at small inducible cytokine B13 precursor Cxcl13 AA892854 chemokine activity; lymph node development 5 WKY 1389581_at interleukin 33 Il33 BF390510 cytokine activity 5 WKY *1373970_at interleukin 33 Il33 AI716248 cytokine activity 5 WKY 1369171_at macrophage stimulating 1 (hepatocyte growth factor-like) Mst1; E2F2 NM_024352 serine-throenine kinase; tumor suppression 5 WKY 1388071_x_at major histocompatability antigen Mhc M24024 antigen processing and presentation 5 WKY 1385465_at sialic acid binding Ig-like lectin 5 Siglec5 BG379188 sialic acid-recognizing receptor 5 WKY 1393108_at major histocompatability antigen Mhc BM387813 antigen processing and presentation 5 WKY 1388202_at major histocompatability antigen Mhc BI395698 antigen processing and presentation 5 WKY 1371171_at major histocompatability antigen Mhc M10094 antigen processing and presentation 5 WKY 1370382_at major histocompatability antigen Mhc BI279526 antigen processing and presentation 5 WKY 1371033_at major histocompatability antigen Mhc AI715202 antigen processing and presentation 5 WKY 1383991_at leucine rich repeat containing 8 family, member E Lrrc8e BE096426 proliferation and activation of lymphocytes and monocytes. 5 WKY 1383046_at complement component factor H Cfh; Fh AA957258 regulation of complement cascade 4 WKY 1369522_a_at CD244 natural killer -
Research Article Characterization of the Equine Skeletal Muscle
McGivney et al. BMC Genomics 2010, 11:398 http://www.biomedcentral.com/1471-2164/11/398 RESEARCH ARTICLE Open Access CharacterizationResearch article of the equine skeletal muscle transcriptome identifies novel functional responses to exercise training Beatrice A McGivney1, Paul A McGettigan1, John A Browne1, Alexander CO Evans1,3, Rita G Fonseca2, Brendan J Loftus3, Amanda Lohan3, David E MacHugh1,3, Barbara A Murphy1, Lisa M Katz2 and Emmeline W Hill*1 Abstract Background: Digital gene expression profiling was used to characterize the assembly of genes expressed in equine skeletal muscle and to identify the subset of genes that were differentially expressed following a ten-month period of exercise training. The study cohort comprised seven Thoroughbred racehorses from a single training yard. Skeletal muscle biopsies were collected at rest from the gluteus medius at two time points: T1 - untrained, (9 ± 0.5 months old) and T2 - trained (20 ± 0.7 months old). Results: The most abundant mRNA transcripts in the muscle transcriptome were those involved in muscle contraction, aerobic respiration and mitochondrial function. A previously unreported over-representation of genes related to RNA processing, the stress response and proteolysis was observed. Following training 92 tags were differentially expressed of which 74 were annotated. Sixteen genes showed increased expression, including the mitochondrial genes ACADVL, MRPS21 and SLC25A29 encoded by the nuclear genome. Among the 58 genes with decreased expression, MSTN, a negative regulator of muscle growth, had the greatest decrease. Functional analysis of all expressed genes using FatiScan revealed an asymmetric distribution of 482 Gene Ontology (GO) groups and 18 KEGG pathways.