Broad and Thematic Remodeling of the Surface Glycoproteome on Isogenic
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Identifying MMP14 and COL12A1 As a Potential Combination of Prognostic Biomarkers in Pancreatic Ductal Adenocarcinoma Using Integrated Bioinformatics Analysis
Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis Jingyi Ding1, Yanxi Liu2 and Yu Lai3 1 Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China 2 University of California, Los Angeles, Los Angeles, CA, United States of America 3 School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China ABSTRACT Background. Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neo- plasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. Methods. The microarray datasets GSE28735, GSE62165, and GSE91035 were down- loaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. Results. A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI Submitted 25 August 2020 network and then identified key modules. From the PPI network, 225 nodes and 803 Accepted 2 November 2020 edges were selected. The most significant module, which comprised 11 DEGs, was Published 23 November 2020 identified using the Molecular Complex Detection plugin. -
Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only. -
Propranolol-Mediated Attenuation of MMP-9 Excretion in Infants with Hemangiomas
Supplementary Online Content Thaivalappil S, Bauman N, Saieg A, Movius E, Brown KJ, Preciado D. Propranolol-mediated attenuation of MMP-9 excretion in infants with hemangiomas. JAMA Otolaryngol Head Neck Surg. doi:10.1001/jamaoto.2013.4773 eTable. List of All of the Proteins Identified by Proteomics This supplementary material has been provided by the authors to give readers additional information about their work. © 2013 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 eTable. List of All of the Proteins Identified by Proteomics Protein Name Prop 12 mo/4 Pred 12 mo/4 Δ Prop to Pred mo mo Myeloperoxidase OS=Homo sapiens GN=MPO 26.00 143.00 ‐117.00 Lactotransferrin OS=Homo sapiens GN=LTF 114.00 205.50 ‐91.50 Matrix metalloproteinase‐9 OS=Homo sapiens GN=MMP9 5.00 36.00 ‐31.00 Neutrophil elastase OS=Homo sapiens GN=ELANE 24.00 48.00 ‐24.00 Bleomycin hydrolase OS=Homo sapiens GN=BLMH 3.00 25.00 ‐22.00 CAP7_HUMAN Azurocidin OS=Homo sapiens GN=AZU1 PE=1 SV=3 4.00 26.00 ‐22.00 S10A8_HUMAN Protein S100‐A8 OS=Homo sapiens GN=S100A8 PE=1 14.67 30.50 ‐15.83 SV=1 IL1F9_HUMAN Interleukin‐1 family member 9 OS=Homo sapiens 1.00 15.00 ‐14.00 GN=IL1F9 PE=1 SV=1 MUC5B_HUMAN Mucin‐5B OS=Homo sapiens GN=MUC5B PE=1 SV=3 2.00 14.00 ‐12.00 MUC4_HUMAN Mucin‐4 OS=Homo sapiens GN=MUC4 PE=1 SV=3 1.00 12.00 ‐11.00 HRG_HUMAN Histidine‐rich glycoprotein OS=Homo sapiens GN=HRG 1.00 12.00 ‐11.00 PE=1 SV=1 TKT_HUMAN Transketolase OS=Homo sapiens GN=TKT PE=1 SV=3 17.00 28.00 ‐11.00 CATG_HUMAN Cathepsin G OS=Homo -
CHL1 and Nrcam Are Primarily Expressed in Low Grade Pediatric
Open Med. 2019; 14: 920-927 Research Article Robin Wachowiak, Steffi Mayer, Anne Suttkus, Illya Martynov, Martin Lacher, Nathaniel Melling, Jakob R. Izbicki, Michael Tachezy CHL1 and NrCAM are primarily expressed in low grade pediatric neuroblastoma https://doi.org/10.1515/med-2019-0109 Keywords: CHL1; NrCAM; Neuroblastoma; Immunohisto- received November 7, 2018; accepted October 19, 2019 chemistry; Tumor markers; Neuropathology Abstract: Background. Neural cell adhesion molecules like close homolog of L1 protein (CHL1) and neuronal glia related cell adhesion molecule (NrCAM) play an impor- tant role in development and regeneration of the central 1 Introduction nervous system. However, they are also associated with Neuroblastoma is an embryonic malignancy deriving cancerogenesis and progression in adult malignancies, from neural crest cells that undergo rapid differentia- thus gain increasing importance in cancer research. We tion during fetal development. As the transition from therefore studied the expression of CHL1 and NrCAM normal to malignant tissue can occur in multiple steps, according to the course of disease in children with neu- its phenotype is highly heterogeneous [1]. Although pro- roblastoma. gress has been made in the treatment of neuroblastoma, Methods. CHL1 and NrCAM expression levels were histo- the outcome of children at high risk remains poor with a logically assessed by tissue microarrays from surgically long-term survival as low as 50 % [2]. Different parameters resected neuroblastoma specimens of 56 children. Expres- such as age, stage and chromosomal aberrations have an sion of both markers was correlated to demographics as impact on prognosis. Still, there is an ongoing need for well as clinical data including metastatic dissemination tumor markers, which allow a better determination of the and survival. -
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. -
Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights Into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle
animals Article Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle Masoumeh Naserkheil 1 , Abolfazl Bahrami 1 , Deukhwan Lee 2,* and Hossein Mehrban 3 1 Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; [email protected] (M.N.); [email protected] (A.B.) 2 Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si, Gyeonggi-do 17579, Korea 3 Department of Animal Science, Shahrekord University, Shahrekord 88186-34141, Iran; [email protected] * Correspondence: [email protected]; Tel.: +82-31-670-5091 Received: 25 August 2020; Accepted: 6 October 2020; Published: 9 October 2020 Simple Summary: Hanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest. -
Yeast Genome Gazetteer P35-65
gazetteer Metabolism 35 tRNA modification mitochondrial transport amino-acid metabolism other tRNA-transcription activities vesicular transport (Golgi network, etc.) nitrogen and sulphur metabolism mRNA synthesis peroxisomal transport nucleotide metabolism mRNA processing (splicing) vacuolar transport phosphate metabolism mRNA processing (5’-end, 3’-end processing extracellular transport carbohydrate metabolism and mRNA degradation) cellular import lipid, fatty-acid and sterol metabolism other mRNA-transcription activities other intracellular-transport activities biosynthesis of vitamins, cofactors and RNA transport prosthetic groups other transcription activities Cellular organization and biogenesis 54 ionic homeostasis organization and biogenesis of cell wall and Protein synthesis 48 plasma membrane Energy 40 ribosomal proteins organization and biogenesis of glycolysis translation (initiation,elongation and cytoskeleton gluconeogenesis termination) organization and biogenesis of endoplasmic pentose-phosphate pathway translational control reticulum and Golgi tricarboxylic-acid pathway tRNA synthetases organization and biogenesis of chromosome respiration other protein-synthesis activities structure fermentation mitochondrial organization and biogenesis metabolism of energy reserves (glycogen Protein destination 49 peroxisomal organization and biogenesis and trehalose) protein folding and stabilization endosomal organization and biogenesis other energy-generation activities protein targeting, sorting and translocation vacuolar and lysosomal -
Congenital Disorders of Glycosylation from a Neurological Perspective
brain sciences Review Congenital Disorders of Glycosylation from a Neurological Perspective Justyna Paprocka 1,* , Aleksandra Jezela-Stanek 2 , Anna Tylki-Szyma´nska 3 and Stephanie Grunewald 4 1 Department of Pediatric Neurology, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland 2 Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland; [email protected] 3 Department of Pediatrics, Nutrition and Metabolic Diseases, The Children’s Memorial Health Institute, W 04-730 Warsaw, Poland; [email protected] 4 NIHR Biomedical Research Center (BRC), Metabolic Unit, Great Ormond Street Hospital and Institute of Child Health, University College London, London SE1 9RT, UK; [email protected] * Correspondence: [email protected]; Tel.: +48-606-415-888 Abstract: Most plasma proteins, cell membrane proteins and other proteins are glycoproteins with sugar chains attached to the polypeptide-glycans. Glycosylation is the main element of the post- translational transformation of most human proteins. Since glycosylation processes are necessary for many different biological processes, patients present a diverse spectrum of phenotypes and severity of symptoms. The most frequently observed neurological symptoms in congenital disorders of glycosylation (CDG) are: epilepsy, intellectual disability, myopathies, neuropathies and stroke-like episodes. Epilepsy is seen in many CDG subtypes and particularly present in the case of mutations -
Supplemental Material Placed on This Supplemental Material Which Has Been Supplied by the Author(S) J Med Genet
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Med Genet Supplement Supplementary Table S1: GENE MEAN GENE NAME OMIM SYMBOL COVERAGE CAKUT CAKUT ADTKD ADTKD aHUS/TMA aHUS/TMA TUBULOPATHIES TUBULOPATHIES Glomerulopathies Glomerulopathies Polycystic kidneys / Ciliopathies Ciliopathies / kidneys Polycystic METABOLIC DISORDERS AND OTHERS OTHERS AND DISORDERS METABOLIC x x ACE angiotensin-I converting enzyme 106180 139 x ACTN4 actinin-4 604638 119 x ADAMTS13 von Willebrand cleaving protease 604134 154 x ADCY10 adenylate cyclase 10 605205 81 x x AGT angiotensinogen 106150 157 x x AGTR1 angiotensin II receptor, type 1 106165 131 x AGXT alanine-glyoxylate aminotransferase 604285 173 x AHI1 Abelson helper integration site 1 608894 100 x ALG13 asparagine-linked glycosylation 13 300776 232 x x ALG9 alpha-1,2-mannosyltransferase 606941 165 centrosome and basal body associated x ALMS1 606844 132 protein 1 x x APOA1 apolipoprotein A-1 107680 55 x APOE lipoprotein glomerulopathy 107741 77 x APOL1 apolipoprotein L-1 603743 98 x x APRT adenine phosphoribosyltransferase 102600 165 x ARHGAP24 Rho GTPase-Activation protein 24 610586 215 x ARL13B ADP-ribosylation factor-like 13B 608922 195 x x ARL6 ADP-ribosylation factor-like 6 608845 215 ATPase, H+ transporting, lysosomal V0, x ATP6V0A4 605239 90 subunit a4 ATPase, H+ transporting, lysosomal x x ATP6V1B1 192132 163 56/58, V1, subunit B1 x ATXN10 ataxin -
Hexosaminidase a in Tay–Sachs Disease
Journal of Genetics (2020)99:42 Ó Indian Academy of Sciences https://doi.org/10.1007/s12041-020-01208-8 (0123456789().,-volV)(0123456789().,-volV) RESEARCH ARTICLE In silico analysis of the effects of disease-associated mutations of b-hexosaminidase A in Tay–Sachs disease MOHAMMAD IHSAN FAZAL1, RAFAL KACPRZYK2 and DAVID J. TIMSON3* 1Brighton and Sussex Medical School, University of Sussex, Falmer, Brighton BN1 9PX, UK 2School of Biological Sciences, Queen’s University Belfast, Medical Biology Centre, 97 Lisburn Road, Belfast BT9 7BL, UK 3School of Pharmacy and Biomolecular Sciences, University of Brighton, Huxley Building, Lewes Road, Brighton BN2 4GJ, UK *For correspondence. E-mail: [email protected]. Received 6 September 2019; revised 25 February 2020; accepted 24 April 2020 Abstract. Tay–Sachs disease (TSD), a deficiency of b-hexosaminidase A (Hex A), is a rare but debilitating hereditary metabolic disorder. Symptoms include extensive neurodegeneration and often result in death in infancy. We report an in silico study of 42 Hex A variants associated with the disease. Variants were separated into three groups according to the age of onset: infantile (n=28), juvenile (n=9) and adult (n=5). Protein stability, aggregation potential and the degree of conservation of residues were predicted using a range of in silico tools. We explored the relationship between these properties and the age of onset of TSD. There was no significant relationship between protein stability and disease severity or between protein aggregation and disease severity. Infantile TSD had a significantly higher mean con- servation score than nondisease associated variants. This was not seen in either juvenile or adult TSD. -
Association of Single Nucleotide Polymorphisms in the ST3GAL4 Gene with VWF Antigen and Factor VIII Activity
RESEARCH ARTICLE Association of Single Nucleotide Polymorphisms in the ST3GAL4 Gene with VWF Antigen and Factor VIII Activity Jaewoo Song1,2, Cheng Xue3, John S. Preisser4, Drake W. Cramer1, Katie L. Houck1, Guo Liu5, Aaron R. Folsom6, David Couper4, Fuli Yu3,7*, Jing-fei Dong1,8* 1 BloodWorks Northwest Research Institute, Seattle, WA, United States of America, 2 Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, Korea, 3 Human Genome Sequencing Center, Molecular and Human Genetics Department, Baylor College of Medicine, Houston, TX, 77030, United States of America, 4 Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United a11111 States of America, 5 Department of Otolaryngology-Head and Neck Surgery, West China Hospital of Sichuan University, Chengdu, China, 6 Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America, 7 Institute of Neurology, Tianjin Medical University General Hospital, Tianjin, 300052, China, 8 Division of Hematology, Department of Medicine, University of Washington School of Medicine, Seattle, WA, United States of America * [email protected] (JFD); [email protected] (FY) OPEN ACCESS Citation: Song J, Xue C, Preisser JS, Cramer DW, Abstract Houck KL, Liu G, et al. (2016) Association of Single Nucleotide Polymorphisms in the ST3GAL4 Gene VWF is extensively glycosylated with biantennary core fucosylated glycans. Most N-linked with VWF Antigen and Factor VIII Activity. PLoS ONE and O-linked glycans on VWF are sialylated. FVIII is also glycosylated, with a glycan struc- 11(9): e0160757. doi:10.1371/journal.pone.0160757 ture similar to that of VWF. -
Rare Variants in the DNA Repair Pathway and the Risk of Colorectal Cancer Marco Matejcic1, Hiba A
Author Manuscript Published OnlineFirst on February 24, 2021; DOI: 10.1158/1055-9965.EPI-20-1457 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Rare variants in the DNA repair pathway and the risk of colorectal cancer Marco Matejcic1, Hiba A. Shaban1, Melanie W. Quintana2, Fredrick R. Schumacher3,4, Christopher K. Edlund5, Leah Naghi6, Rish K. Pai7, Robert W. Haile8, A. Joan Levine8, Daniel D. Buchanan9,10,11, Mark A. Jenkins12, Jane C. Figueiredo13, Gad Rennert14, Stephen B. Gruber15, Li Li16, Graham Casey17, David V. Conti18†, Stephanie L. Schmit1,19† † These authors contributed equally to this work. Affiliations 1 Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA 2 Berry Consultants, Austin, TX, 78746, USA 3 Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA 4 Seidman Cancer Center, University Hospitals, Cleveland, OH 44106, USA 5 Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA 6 Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, NY 10467, USA 7 Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ 85259, USA 8 Department of Medicine, Research Center for Health Equity, Cedars-Sinai Samuel Oschin Comprehensive Cancer Center, Los Angeles, CA 90048, USA 9 Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010, Australia 10 Victorian Comprehensive Cancer Centre, University of Melbourne, Centre for Cancer Research, Parkville, Victoria 3010, Australia 1 Downloaded from cebp.aacrjournals.org on September 29, 2021.