Kidney Vacuolar H -Atpase: Physiology and Regulation
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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. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
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. -
A Network of Bhlhzip Transcription Factors in Melanoma: Interactions of MITF, TFEB and TFE3
A network of bHLHZip transcription factors in melanoma: Interactions of MITF, TFEB and TFE3 Josué A. Ballesteros Álvarez Thesis for the degree of Philosophiae Doctor January 2019 Net bHLHZip umritunarþátta í sortuæxlum: Samstarf milli MITF, TFEB og TFE3 Josué A. Ballesteros Álvarez Ritgerð til doktorsgráðu Leiðbeinandi/leiðbeinendur: Eiríkur Steingrímsson Doktorsnefnd: Margrét H. Ögmundsdóttir Þórarinn Guðjónsson Jórunn E. Eyfjörð Lars Rönnstrand Janúar 2019 Thesis for a doctoral degree at tHe University of Iceland. All rigHts reserved. No Part of tHis Publication may be reProduced in any form witHout tHe Prior permission of the copyright holder. © Josue A. Ballesteros Álvarez. 2019 ISBN 978-9935-9421-4-2 Printing by HáskólaPrent Reykjavik, Iceland 2019 Ágrip StjórnPróteinin MITF , TFEB, TFE3 og TFEC (stundum nefnd MiT-TFE þættirnir) tilheyra bHLHZip fjölskyldu umritunarþátta sem bindast DNA og stjórna tjáningu gena. MITF er mikilvægt fyrir myndun og starfsemi litfruma en ættingjar þess, TFEB og TFE3, stjórna myndun og starfsemi lysósóma og sjálfsáti. Sjálfsát er líffræðilegt ferli sem gegnir mikilvægu hlutverki í starfsemi fruma en getur einnig haft áHrif á myndun og meðHöndlun sjúkdóma. Í verkefni þessu var samstarf MITF, TFE3 og TFEB Próteinanna skoðað í sortuæxlisfrumum og hvaða áhrif þau Hafa á tjáningu hvers annars. Eins og MITF eru TFEB og TFE3 genin tjáð í sortuæxlisfrumum og sortuæxlum; TFEC er ekki tjáð í þessum frumum og var því ekki skoðað í þessu verkefni. Með notkun sérvirkra hindra var sýnt að boðleiðir hafa áhrif á staðsetningu próteinanna þriggja í sortuæxlisfrumum. Umritunarþættir þessir geta bundist skyldum DNA-bindisetum og haft áhrif á tjáningu gena sem eru nauðsynleg fyrir myndun bæði lýsósóma og melanósóma. -
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). -
Molecular Structure and Physiological Function of Chloride Channels
Physiol Rev 82: 503–568, 2002; 10.1152/physrev.00029.2001. Molecular Structure and Physiological Function of Chloride Channels THOMAS J. JENTSCH, VALENTIN STEIN, FRANK WEINREICH, AND ANSELM A. ZDEBIK Zentrum fu¨r Molekulare Neurobiologie Hamburg, Universita¨t Hamburg, Hamburg, Germany I. Introduction 504 II. Cellular Functions of Chloride Channels 506 A. Plasma membrane channels 506 B. Channels of intracellular organelles 507 III. The CLC Chloride Channel Family 508 A. General features of CLC channels 510 B. ClC-0: the Torpedo electric organ ClϪ channel 516 C. ClC-1: a muscle-specific ClϪ channel that stabilizes the membrane voltage 517 D. ClC-2: a broadly expressed channel activated by hyperpolarization, cell swelling, and acidic pH 519 Ϫ E. ClC-K/barttin channels: Cl channels involved in transepithelial transport in the kidney and the Downloaded from inner ear 523 F. ClC-3: an intracellular ClϪ channel that is present in endosomes and synaptic vesicles 525 G. ClC-4: a poorly characterized vesicular channel 527 H. ClC-5: an endosomal channel involved in renal endocytosis 527 I. ClC-6: an intracellular channel of unknown function 531 J. ClC-7: a lysosomal ClϪ channel whose disruption leads to osteopetrosis in mice and humans 531 K. CLC proteins in model organisms 532 on October 3, 2014 IV. Cystic Fibrosis Transmembrane Conductance Regulator: a cAMP-Activated Chloride Channel 533 A. Structure and function of the CFTR ClϪ channel 533 B. Cellular regulation of CFTR activity 534 C. CFTR as a regulator of other ion channels 534 V. Swelling-Activated Chloride Channels 535 A. Biophysical characteristics of swelling-activated ClϪ currents 536 B. -
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. -
Proteomics Analysis of Colon Cancer Progression
Saleem et al. Clin Proteom (2019) 16:44 https://doi.org/10.1186/s12014-019-9264-y Clinical Proteomics RESEARCH Open Access Proteomics analysis of colon cancer progression Saira Saleem1* , Sahrish Tariq1, Ifat Aleem1, Sadr‑ul Shaheed2, Muhammad Tahseen3, Aribah Atiq3, Sadia Hassan4, Muhammad Abu Bakar5, Shahid Khattak6, Aamir Ali Syed6, Asad Hayat Ahmad3, Mudassar Hussain3, Muhammed Aasim Yusuf7 and Chris Sutton2 Abstract Background: The aim of this pilot study was to identify proteins associated with advancement of colon cancer (CC). Methods: A quantitative proteomics approach was used to determine the global changes in the proteome of pri‑ mary colon cancer from patients with non‑cancer normal colon (NC), non‑adenomatous colon polyp (NAP), non‑met‑ astatic tumor (CC NM) and metastatic tumor (CC M) tissues, to identify up‑ and down‑regulated proteins. Total protein was extracted from each biopsy, trypsin‑digested, iTRAQ‑labeled and the resulting peptides separated using strong cation exchange (SCX) and reverse‑phase (RP) chromatography on‑line to electrospray ionization mass spectrometry (ESI‑MS). Results: Database searching of the MS/MS data resulted in the identifcation of 2777 proteins which were clustered into groups associated with disease progression. Proteins which were changed in all disease stages including benign, and hence indicative of the earliest molecular perturbations, were strongly associated with spliceosomal activity, cell cycle division, and stromal and cytoskeleton disruption refecting increased proliferation and expansion into the surrounding healthy tissue. Those proteins changed in cancer stages but not in benign, were linked to infammation/ immune response, loss of cell adhesion, mitochondrial function and autophagy, demonstrating early evidence of cells within the nutrient‑poor solid mass either undergoing cell death or adjusting for survival. -
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 -
Ophthalmic Phenotype of TCIRG1 Gene Mutations in Chinese Infantile Malignant Osteopetrosis
BMJ Open Ophth: first published as 10.1136/bmjophth-2018-000180 on 17 November 2018. Downloaded from Original articles Ophthalmic phenotype of TCIRG1 gene mutations in Chinese infantile malignant osteopetrosis Wenhong Cao,1,2 Wenbin Wei,2 Qian Wu1 To cite: Cao W, Wei W, Wu Q. ABSTRACT Key messages Ophthalmic phenotype of Objective To evaluate the ophthalmic phenotypes TCIRG1 gene mutations associated with T-cell immune regulator 1 (TCIRG1) What is already known about this subject? in Chinese infantile mutations in Chinese patients with infantile malignant malignant osteopetrosis. T-cell immune regulator 1 (TCIRG1) is one of the osteopetrosis (IMO). ► BMJ Open Ophthalmology main genes that are responsible for the majority of Methods and analysis 27 Chinese TCIRG1-related 2018;3:e000180. doi:10.1136/ infantile malignant osteopetrosis (IMO) cases, which osteoporosis infants were enrolled using direct DNA bmjophth-2018-000180 are characterised by neonatal and infantile onset, a sequencing of PCR-amplified exons. 12 cases had systemic sclerosis of bones, vulnerability to fracture, frameshift mutation (the frameshift mutation group, group progressive anaemia, infection, hepatosplenomega- Received 30 May 2018 F), and 15 cases had point mutation (the point mutation ly and cranial nerve dysfunction, including poor gaze Revised 25 September 2018 group, group P). The clinical features of the two groups qualities, optic atrophy and optic canal stenosis. Accepted 26 September 2018 were compared, including age at onset, gaze qualities, To our knowledge, there is no study on the ophthal- optic atrophy, optic canal stenosis and waveforms of Flash ► mic phenotypes of TCIRG1 mutations in patients visual-evoked potential (FVEP). -
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].