(V)-Atpase Interactome: Identification of Proteins Involved in Trafficking
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Genome-Wide Analysis of 5-Hmc in the Peripheral Blood of Systemic Lupus Erythematosus Patients Using an Hmedip-Chip
INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE 35: 1467-1479, 2015 Genome-wide analysis of 5-hmC in the peripheral blood of systemic lupus erythematosus patients using an hMeDIP-chip WEIGUO SUI1*, QIUPEI TAN1*, MING YANG1, QIANG YAN1, HUA LIN1, MINGLIN OU1, WEN XUE1, JIEJING CHEN1, TONGXIANG ZOU1, HUANYUN JING1, LI GUO1, CUIHUI CAO1, YUFENG SUN1, ZHENZHEN CUI1 and YONG DAI2 1Guangxi Key Laboratory of Metabolic Diseases Research, Central Laboratory of Guilin 181st Hospital, Guilin, Guangxi 541002; 2Clinical Medical Research Center, the Second Clinical Medical College of Jinan University (Shenzhen People's Hospital), Shenzhen, Guangdong 518020, P.R. China Received July 9, 2014; Accepted February 27, 2015 DOI: 10.3892/ijmm.2015.2149 Abstract. Systemic lupus erythematosus (SLE) is a chronic, Introduction potentially fatal systemic autoimmune disease characterized by the production of autoantibodies against a wide range Systemic lupus erythematosus (SLE) is a typical systemic auto- of self-antigens. To investigate the role of the 5-hmC DNA immune disease, involving diffuse connective tissues (1) and modification with regard to the onset of SLE, we compared is characterized by immune inflammation. SLE has a complex the levels 5-hmC between SLE patients and normal controls. pathogenesis (2), involving genetic, immunologic and envi- Whole blood was obtained from patients, and genomic DNA ronmental factors. Thus, it may result in damage to multiple was extracted. Using the hMeDIP-chip analysis and valida- tissues and organs, especially the kidneys (3). SLE arises from tion by quantitative RT-PCR (RT-qPCR), we identified the a combination of heritable and environmental influences. differentially hydroxymethylated regions that are associated Epigenetics, the study of changes in gene expression with SLE. -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony
Supplementary Data Th2 and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in UBIOPRED Chih-Hsi Scott Kuo1.2, Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony Rowe3, Iaonnis Pandis2, Ana Sousa4, Julie Corfield5, Ratko Djukanovic6, Rene 7 7 8 2 1† Lutter , Peter J. Sterk , Charles Auffray , Yike Guo , Ian M. Adcock & Kian Fan 1†* # Chung on behalf of the U-BIOPRED consortium project team 1Airways Disease, National Heart & Lung Institute, Imperial College London, & Biomedical Research Unit, Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom; 2Department of Computing & Data Science Institute, Imperial College London, United Kingdom; 3Janssen Research and Development, High Wycombe, Buckinghamshire, United Kingdom; 4Respiratory Therapeutic Unit, GSK, Stockley Park, United Kingdom; 5AstraZeneca R&D Molndal, Sweden and Areteva R&D, Nottingham, United Kingdom; 6Faculty of Medicine, Southampton University, Southampton, United Kingdom; 7Faculty of Medicine, University of Amsterdam, Amsterdam, Netherlands; 8European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, France. †Contributed equally #Consortium project team members are listed under Supplementary 1 Materials *To whom correspondence should be addressed: [email protected] 2 List of the U-BIOPRED Consortium project team members Uruj Hoda & Christos Rossios, Airways Disease, National Heart & Lung Institute, Imperial College London, UK & Biomedical Research Unit, Biomedical Research Unit, Royal -
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. -
ATP6V1B1 Gene Atpase H+ Transporting V1 Subunit B1
ATP6V1B1 gene ATPase H+ transporting V1 subunit B1 Normal Function The ATP6V1B1 gene provides instructions for making a part (subunit) of a large protein complex known as vacuolar H+-ATPase (V-ATPase). V-ATPases are a group of similar complexes that act as pumps to move positively charged hydrogen atoms (protons) across membranes. Because acids are substances that can "donate" protons to other molecules, this movement of protons helps regulate the relative acidity (pH) of cells and their surrounding environment. Tight control of pH is necessary for most biological reactions to proceed properly. The V-ATPase that includes the subunit produced from the ATP6V1B1 gene is found in the inner ear and in nephrons, which are the functional structures within the kidneys. The kidneys filter waste products from the blood and remove them in urine. They also reabsorb needed nutrients and release them back into the blood. Each nephron consists of two parts: a renal corpuscle (also known as a glomerulus) that filters the blood, and a renal tubule that reabsorbs substances that are needed and eliminates unneeded substances in urine. The V-ATPase is involved in regulating the amount of acid that is removed from the blood into the urine, and also in maintaining the proper pH of the fluid in the inner ear (endolymph). Health Conditions Related to Genetic Changes Renal tubular acidosis with deafness More than 25 ATP6V1B1 gene mutations have been identified in people with renal tubular acidosis with deafness, a disorder involving excess acid in the blood (metabolic acidosis), bone weakness, and hearing loss caused by changes in the inner ear ( sensorineural hearing loss). -
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). -
Atrazine and Cell Death Symbol Synonym(S)
Supplementary Table S1: Atrazine and Cell Death Symbol Synonym(s) Entrez Gene Name Location Family AR AIS, Andr, androgen receptor androgen receptor Nucleus ligand- dependent nuclear receptor atrazine 1,3,5-triazine-2,4-diamine Other chemical toxicant beta-estradiol (8R,9S,13S,14S,17S)-13-methyl- Other chemical - 6,7,8,9,11,12,14,15,16,17- endogenous decahydrocyclopenta[a]phenanthrene- mammalian 3,17-diol CGB (includes beta HCG5, CGB3, CGB5, CGB7, chorionic gonadotropin, beta Extracellular other others) CGB8, chorionic gonadotropin polypeptide Space CLEC11A AW457320, C-type lectin domain C-type lectin domain family 11, Extracellular growth factor family 11, member A, STEM CELL member A Space GROWTH FACTOR CYP11A1 CHOLESTEROL SIDE-CHAIN cytochrome P450, family 11, Cytoplasm enzyme CLEAVAGE ENZYME subfamily A, polypeptide 1 CYP19A1 Ar, ArKO, ARO, ARO1, Aromatase cytochrome P450, family 19, Cytoplasm enzyme subfamily A, polypeptide 1 ESR1 AA420328, Alpha estrogen receptor,(α) estrogen receptor 1 Nucleus ligand- dependent nuclear receptor estrogen C18 steroids, oestrogen Other chemical drug estrogen receptor ER, ESR, ESR1/2, esr1/esr2 Nucleus group estrone (8R,9S,13S,14S)-3-hydroxy-13-methyl- Other chemical - 7,8,9,11,12,14,15,16-octahydro-6H- endogenous cyclopenta[a]phenanthren-17-one mammalian G6PD BOS 25472, G28A, G6PD1, G6PDX, glucose-6-phosphate Cytoplasm enzyme Glucose-6-P Dehydrogenase dehydrogenase GATA4 ASD2, GATA binding protein 4, GATA binding protein 4 Nucleus transcription TACHD, TOF, VSD1 regulator GHRHR growth hormone releasing -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Inherited Renal Tubulopathies—Challenges and Controversies
G C A T T A C G G C A T genes Review Inherited Renal Tubulopathies—Challenges and Controversies Daniela Iancu 1,* and Emma Ashton 2 1 UCL-Centre for Nephrology, Royal Free Campus, University College London, Rowland Hill Street, London NW3 2PF, UK 2 Rare & Inherited Disease Laboratory, London North Genomic Laboratory Hub, Great Ormond Street Hospital for Children National Health Service Foundation Trust, Levels 4-6 Barclay House 37, Queen Square, London WC1N 3BH, UK; [email protected] * Correspondence: [email protected]; Tel.: +44-2381204172; Fax: +44-020-74726476 Received: 11 February 2020; Accepted: 29 February 2020; Published: 5 March 2020 Abstract: Electrolyte homeostasis is maintained by the kidney through a complex transport function mostly performed by specialized proteins distributed along the renal tubules. Pathogenic variants in the genes encoding these proteins impair this function and have consequences on the whole organism. Establishing a genetic diagnosis in patients with renal tubular dysfunction is a challenging task given the genetic and phenotypic heterogeneity, functional characteristics of the genes involved and the number of yet unknown causes. Part of these difficulties can be overcome by gathering large patient cohorts and applying high-throughput sequencing techniques combined with experimental work to prove functional impact. This approach has led to the identification of a number of genes but also generated controversies about proper interpretation of variants. In this article, we will highlight these challenges and controversies. Keywords: inherited tubulopathies; next generation sequencing; genetic heterogeneity; variant classification. 1. Introduction Mutations in genes that encode transporter proteins in the renal tubule alter kidney capacity to maintain homeostasis and cause diseases recognized under the generic name of inherited tubulopathies. -
Supplementary Table 1. Pain and PTSS Associated Genes (N = 604
Supplementary Table 1. Pain and PTSS associated genes (n = 604) compiled from three established pain gene databases (PainNetworks,[61] Algynomics,[52] and PainGenes[42]) and one PTSS gene database (PTSDgene[88]). These genes were used in in silico analyses aimed at identifying miRNA that are predicted to preferentially target this list genes vs. a random set of genes (of the same length). ABCC4 ACE2 ACHE ACPP ACSL1 ADAM11 ADAMTS5 ADCY5 ADCYAP1 ADCYAP1R1 ADM ADORA2A ADORA2B ADRA1A ADRA1B ADRA1D ADRA2A ADRA2C ADRB1 ADRB2 ADRB3 ADRBK1 ADRBK2 AGTR2 ALOX12 ANO1 ANO3 APOE APP AQP1 AQP4 ARL5B ARRB1 ARRB2 ASIC1 ASIC2 ATF1 ATF3 ATF6B ATP1A1 ATP1B3 ATP2B1 ATP6V1A ATP6V1B2 ATP6V1G2 AVPR1A AVPR2 BACE1 BAMBI BDKRB2 BDNF BHLHE22 BTG2 CA8 CACNA1A CACNA1B CACNA1C CACNA1E CACNA1G CACNA1H CACNA2D1 CACNA2D2 CACNA2D3 CACNB3 CACNG2 CALB1 CALCRL CALM2 CAMK2A CAMK2B CAMK4 CAT CCK CCKAR CCKBR CCL2 CCL3 CCL4 CCR1 CCR7 CD274 CD38 CD4 CD40 CDH11 CDK5 CDK5R1 CDKN1A CHRM1 CHRM2 CHRM3 CHRM5 CHRNA5 CHRNA7 CHRNB2 CHRNB4 CHUK CLCN6 CLOCK CNGA3 CNR1 COL11A2 COL9A1 COMT COQ10A CPN1 CPS1 CREB1 CRH CRHBP CRHR1 CRHR2 CRIP2 CRYAA CSF2 CSF2RB CSK CSMD1 CSNK1A1 CSNK1E CTSB CTSS CX3CL1 CXCL5 CXCR3 CXCR4 CYBB CYP19A1 CYP2D6 CYP3A4 DAB1 DAO DBH DBI DICER1 DISC1 DLG2 DLG4 DPCR1 DPP4 DRD1 DRD2 DRD3 DRD4 DRGX DTNBP1 DUSP6 ECE2 EDN1 EDNRA EDNRB EFNB1 EFNB2 EGF EGFR EGR1 EGR3 ENPP2 EPB41L2 EPHB1 EPHB2 EPHB3 EPHB4 EPHB6 EPHX2 ERBB2 ERBB4 EREG ESR1 ESR2 ETV1 EZR F2R F2RL1 F2RL2 FAAH FAM19A4 FGF2 FKBP5 FLOT1 FMR1 FOS FOSB FOSL2 FOXN1 FRMPD4 FSTL1 FYN GABARAPL1 GABBR1 GABBR2 GABRA2 GABRA4 -
Prognostic and Immunological Value of ATP6AP1 in Breast Cancer: Implications for SARS-Cov-2
www.aging-us.com AGING 2021, Vol. 13, No. 13 Research Paper Prognostic and immunological value of ATP6AP1 in breast cancer: implications for SARS-CoV-2 Jintian Wang1, Yunjiang Liu1, Shuo Zhang1 1Department of Breast Surgery, The Fourth Hospital of Hebei Medical University, Hebei, Shijiazhuang 050011, China Correspondence to: Yunjiang Liu; email: [email protected], https://orcid.org/0000-0001-7202-2004 Keywords: ATP6AP1, breast cancer, prognosis, immune infiltration, bioinformatics, SARS-CoV-2, COVID-19 Received: November 28, 2020 Accepted: May 11, 2021 Published: July 6, 2021 Copyright: © 2021 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Abnormal ATPase H+ Transporting Accessory Protein 1 (ATP6AP1) expression may promote carcinogenesis. We investigated the association of ATP6AP1 with breast cancer (BC) and COVID-19. The Oncomine, Gene Expression Profiling Interactive Analysis, Human Protein Atlas and Kaplan-Meier plotter databases were used to evaluate the expression and prognostic value of ATP6AP1 in BC. ATP6AP1 was upregulated in BC tissues, and higher ATP6AP1 expression was associated with poorer outcomes. Data from the Tumor Immune Estimation Resource, Tumor-Immune System Interaction Database and Kaplan-Meier plotter indicated that ATP6AP1 expression correlated with immune infiltration, and that its prognostic effects in BC depended on tumor-infiltrating immune cell subtype levels. Multiple databases were used to evaluate the association of ATP6AP1 with clinicopathological factors, assess the mutation and methylation of ATP6AP1, and analyze gene co-expression and enrichment. -
Identification of Eight Exonic Variants in the SLC4A1, ATP6V1B1 And
Identification of eight exonic variants in the SLC4A1, ATP6V1B1 and ATP6V0A4 gene that alter RNA splicing by minigene assay Ruixiao Zhang1, Zeqing Chen2, Qijing Song3, Sai Wang1, Zhiying Liu1, Xiangzhong Zhao4, Xiaomeng Shi1, Wencong Guo4, Yanhua Lang1, Irene Bottillo5, and Leping Shao1 1Qingdao Municipal Hospital Group 2Fudan University 3People's Hospital of Jimo District 4The Affiliated Hospital of Qingdao University 5IRCCS-Casa Sollievo della Sofferenza Hospital February 7, 2021 Abstract Primary distal renal tubular acidosis (dRTA) is a rare tubular disease associated with variants in SLC4A1, ATP6V0A4, ATP6V1B1, FOXI1 or WDR72 genes. Currently, there is growing evidence that all types of exonic variants can alter splicing regulatory elements, affecting the pre-mRNA splicing process. This study was to determine the consequences of variants asso- ciated with dRTA on pre-mRNA splicing combined with predictive bioinformatics tools and minigene assay. As a result, among the 15 candidate variants, 8 variants distributed in SLC4A1 (c.1765C>T, p.Arg589Cys), ATP6V1B1( c.368G>T, p.Gly123Val; c.370C>T, p.Arg124Trp; c.484G>T, p.Glu162* and c.1102G>A, p.Glu368Lys) and ATP6V0A4 genes (c.322C>T, p.Gln108*; c.1571C>T, p.Pro524Leu and c.1572G>A, p.Pro524Pro) were identified to result in whole or part of exon skipping by either disruption of ESEs and generation of ESSs, or interference with the recognition of the classic splicing site, or both. To our knowledge, this is the first study on pre-mRNA splicing of exonic variants in the dRTA-related genes. These results highlight the importance of assessing the effects of exonic variants at the mRNA level and suggest that minigene analysis is an effective tool for evaluating the effects of splicing on variants in vitro Introduction Precursor messenger RNAs (Pre-mRNAs) composed of exons and introns are transcribed from the protein- coding genes of most eukaryotic cells[1].