Genetic Disruption of WASHC4 Drives Endo-Lysosomal Dysfunction
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ATP6V0C Rabbit Pab
Leader in Biomolecular Solutions for Life Science ATP6V0C Rabbit pAb Catalog No.: A16350 Basic Information Background Catalog No. This gene encodes a component of vacuolar ATPase (V-ATPase), a multisubunit enzyme A16350 that mediates acidification of eukaryotic intracellular organelles. V-ATPase dependent organelle acidification is necessary for such intracellular processes as protein sorting, Observed MW zymogen activation, receptor-mediated endocytosis, and synaptic vesicle proton 16kDa gradient generation. V-ATPase is composed of a cytosolic V1 domain and a transmembrane V0 domain. The V1 domain consists of three A and three B subunits, two Calculated MW G subunits plus the C, D, E, F, and H subunits. The V1 domain contains the ATP catalytic 15kDa site. The V0 domain consists of five different subunits: a, c, c', c", and d. This gene encodes the V0 subunit c. Alternative splicing results in transcript variants. Pseudogenes Category have been identified on chromosomes 6 and 17. Primary antibody Applications WB, IF Cross-Reactivity Mouse, Rat Recommended Dilutions Immunogen Information WB 1:500 - 1:2000 Gene ID Swiss Prot 527 P27449 IF 1:50 - 1:100 Immunogen A synthetic peptide corresponding to a sequence within amino acids 1-100 of human ATP6V0C (NP_001185498.1). Synonyms ATP6V0C;ATP6C;ATP6L;ATPL;VATL;VPPC;Vma3 Contact Product Information www.abclonal.com Source Isotype Purification Rabbit IgG Affinity purification Storage Store at -20℃. Avoid freeze / thaw cycles. Buffer: PBS with 0.02% sodium azide,50% glycerol,pH7.3. Validation Data Western blot analysis of extracts of various cell lines, using ATP6V0C Rabbit pAb (A16350) at 1:1000 dilution. Secondary antibody: HRP Goat Anti-Rabbit IgG (H+L) (AS014) at 1:10000 dilution. -
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. -
PROTEOMIC ANALYSIS of HUMAN URINARY EXOSOMES. Patricia
ABSTRACT Title of Document: PROTEOMIC ANALYSIS OF HUMAN URINARY EXOSOMES. Patricia Amalia Gonzales Mancilla, Ph.D., 2009 Directed By: Associate Professor Nam Sun Wang, Department of Chemical and Biomolecular Engineering Exosomes originate as the internal vesicles of multivesicular bodies (MVBs) in cells. These small vesicles (40-100 nm) have been shown to be secreted by most cell types throughout the body. In the kidney, urinary exosomes are released to the urine by fusion of the outer membrane of the MVBs with the apical plasma membrane of renal tubular epithelia. Exosomes contain apical membrane and cytosolic proteins and can be isolated using differential centrifugation. The analysis of urinary exosomes provides a non- invasive means of acquiring information about the physiological or pathophysiological state of renal cells. The overall objective of this research was to develop methods and knowledge infrastructure for urinary proteomics. We proposed to conduct a proteomic analysis of human urinary exosomes. The first objective was to profile the proteome of human urinary exosomes using liquid chromatography-tandem spectrometry (LC- MS/MS) and specialized software for identification of peptide sequences from fragmentation spectra. We unambiguously identified 1132 proteins. In addition, the phosphoproteome of human urinary exosomes was profiled using the neutral loss scanning acquisition mode of LC-MS/MS. The phosphoproteomic profiling identified 19 phosphorylation sites corresponding to 14 phosphoproteins. The second objective was to analyze urinary exosomes samples isolated from patients with genetic mutations. Polyclonal antibodies were generated to recognize epitopes on the gene products of these genetic mutations, NKCC2 and MRP4. The potential usefulness of urinary exosome analysis was demonstrated using the well-defined renal tubulopathy, Bartter syndrome type I and using the single nucleotide polymorphism in the ABCC4 gene. -
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. -
Exploring Prostate Cancer Genome Reveals Simultaneous Losses of PTEN, FAS and PAPSS2 in Patients with PSA Recurrence After Radical Prostatectomy
Int. J. Mol. Sci. 2015, 16, 3856-3869; doi:10.3390/ijms16023856 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article Exploring Prostate Cancer Genome Reveals Simultaneous Losses of PTEN, FAS and PAPSS2 in Patients with PSA Recurrence after Radical Prostatectomy Chinyere Ibeawuchi 1, Hartmut Schmidt 2, Reinhard Voss 3, Ulf Titze 4, Mahmoud Abbas 5, Joerg Neumann 6, Elke Eltze 7, Agnes Marije Hoogland 8, Guido Jenster 9, Burkhard Brandt 10 and Axel Semjonow 1,* 1 Prostate Center, Department of Urology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebaeude 1A, Muenster D-48149, Germany; E-Mail: [email protected] 2 Center for Laboratory Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebaeude 1A, Muenster D-48149, Germany; E-Mail: [email protected] 3 Interdisciplinary Center for Clinical Research, University of Muenster, Albert-Schweitzer-Campus 1, Gebaeude D3, Domagkstrasse 3, Muenster D-48149, Germany; E-Mail: [email protected] 4 Pathology, Lippe Hospital Detmold, Röntgenstrasse 18, Detmold D-32756, Germany; E-Mail: [email protected] 5 Institute of Pathology, Mathias-Spital-Rheine, Frankenburg Street 31, Rheine D-48431, Germany; E-Mail: [email protected] 6 Institute of Pathology, Klinikum Osnabrueck, Am Finkenhuegel 1, Osnabrueck D-49076, Germany; E-Mail: [email protected] 7 Institute of Pathology, Saarbrücken-Rastpfuhl, Rheinstrasse 2, Saarbrücken D-66113, Germany; E-Mail: [email protected] 8 Department -
Primate Specific Retrotransposons, Svas, in the Evolution of Networks That Alter Brain Function
Title: Primate specific retrotransposons, SVAs, in the evolution of networks that alter brain function. Olga Vasieva1*, Sultan Cetiner1, Abigail Savage2, Gerald G. Schumann3, Vivien J Bubb2, John P Quinn2*, 1 Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, U.K 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK 3 Division of Medical Biotechnology, Paul-Ehrlich-Institut, Langen, D-63225 Germany *. Corresponding author Olga Vasieva: Institute of Integrative Biology, Department of Comparative genomics, University of Liverpool, Liverpool, L69 7ZB, [email protected] ; Tel: (+44) 151 795 4456; FAX:(+44) 151 795 4406 John Quinn: Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, The University of Liverpool, Liverpool L69 3BX, UK, [email protected]; Tel: (+44) 151 794 5498. Key words: SVA, trans-mobilisation, behaviour, brain, evolution, psychiatric disorders 1 Abstract The hominid-specific non-LTR retrotransposon termed SINE–VNTR–Alu (SVA) is the youngest of the transposable elements in the human genome. The propagation of the most ancient SVA type A took place about 13.5 Myrs ago, and the youngest SVA types appeared in the human genome after the chimpanzee divergence. Functional enrichment analysis of genes associated with SVA insertions demonstrated their strong link to multiple ontological categories attributed to brain function and the disorders. SVA types that expanded their presence in the human genome at different stages of hominoid life history were also associated with progressively evolving behavioural features that indicated a potential impact of SVA propagation on a cognitive ability of a modern human. -
Centre for Arab Genomic Studies a Division of Sheikh Hamdan Award for Medical Sciences
Centre for Arab Genomic Studies A Division of Sheikh Hamdan Award for Medical Sciences The atalogue for ransmission enetics in rabs C T G A CTGA Database KIAA0196 Gene Alternative Names (Dandy-Walker malformation and cerebellar vermis KIAA0196 hypoplasia), congenital heart deformities (septal defects and aortic stenosis) and craniofacial Record Category dysmorphia (prominent occiput and forehead, low- Gene locus set ears, down-slanting palpebral fissures, depressed nasal bridge and micrognathia). WHO-ICD N/A to gene loci Molecular Genetics The KIAA0196 gene, located on the long arm of Incidence per 100,000 Live Births chromosome 8, spans a length of 67 kb. Its coding N/A to gene loci sequence consists of 31 exons and it encodes a 134 kDa protein product made up of 1159 amino acids. OMIM Number While the gene is ubiquitously expressed in the 610657 human body, it is found to be overexpressed in skeletal muscles. Heterozygous missense mutations Mode of Inheritance in the KIAA0196 gene are associated with Spastic N/A to gene loci Paraplegia 8, the most common being Val626Phe caused by a 1956G-T transversion; while a Gene Map Locus homozygous splice site mutation in the gene has 8q24.13 been linked to Ritscher-Schinzel Syndrome 1. Description Epidemiology in the Arab World The KIAA0196 gene encodes the strumpellin Saudi Arabia protein. The protein, found in the cytosol and Anazi et al. (2016) carried out a study to determine endoplasmic reticulum, forms a part of the WASH the diagnostic yield of genetic analysis tools core complex along with F-actin-capping protein compared to standard clinical evaluations. -
Appendix 2. Significantly Differentially Regulated Genes in Term Compared with Second Trimester Amniotic Fluid Supernatant
Appendix 2. Significantly Differentially Regulated Genes in Term Compared With Second Trimester Amniotic Fluid Supernatant Fold Change in term vs second trimester Amniotic Affymetrix Duplicate Fluid Probe ID probes Symbol Entrez Gene Name 1019.9 217059_at D MUC7 mucin 7, secreted 424.5 211735_x_at D SFTPC surfactant protein C 416.2 206835_at STATH statherin 363.4 214387_x_at D SFTPC surfactant protein C 295.5 205982_x_at D SFTPC surfactant protein C 288.7 1553454_at RPTN repetin solute carrier family 34 (sodium 251.3 204124_at SLC34A2 phosphate), member 2 238.9 206786_at HTN3 histatin 3 161.5 220191_at GKN1 gastrokine 1 152.7 223678_s_at D SFTPA2 surfactant protein A2 130.9 207430_s_at D MSMB microseminoprotein, beta- 99.0 214199_at SFTPD surfactant protein D major histocompatibility complex, class II, 96.5 210982_s_at D HLA-DRA DR alpha 96.5 221133_s_at D CLDN18 claudin 18 94.4 238222_at GKN2 gastrokine 2 93.7 1557961_s_at D LOC100127983 uncharacterized LOC100127983 93.1 229584_at LRRK2 leucine-rich repeat kinase 2 HOXD cluster antisense RNA 1 (non- 88.6 242042_s_at D HOXD-AS1 protein coding) 86.0 205569_at LAMP3 lysosomal-associated membrane protein 3 85.4 232698_at BPIFB2 BPI fold containing family B, member 2 84.4 205979_at SCGB2A1 secretoglobin, family 2A, member 1 84.3 230469_at RTKN2 rhotekin 2 82.2 204130_at HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 81.9 222242_s_at KLK5 kallikrein-related peptidase 5 77.0 237281_at AKAP14 A kinase (PRKA) anchor protein 14 76.7 1553602_at MUCL1 mucin-like 1 76.3 216359_at D MUC7 mucin 7, -
81115468.Pdf
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Biochimica et Biophysica Acta 1792 (2009) 903–914 Contents lists available at ScienceDirect Biochimica et Biophysica Acta journal homepage: www.elsevier.com/locate/bbadis Review Vacuolar H+-ATPase meets glycosylation in patients with cutis laxa Mailys Guillard a,1, Aikaterini Dimopoulou b,1, Björn Fischer b,1, Eva Morava c, Dirk J. Lefeber a, Uwe Kornak b,d,1,⁎, Ron A. Wevers a,1 a Laboratory of Pediatrics and Neurology, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB, Nijmegen, The Netherlands b Institute for Medical Genetics, Charité Universitaetsmedizin Berlin, Campus Virchow Augustenburger Platz 1, 13353 Berlin, Germany c Department of Pediatrics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands d Max Planck Institute for Molecular Genetics, Berlin, Germany article info abstract Article history: Glycosylation of proteins is one of the most important post-translational modifications. Defects in the glycan Received 7 November 2008 biosynthesis result in congenital malformation syndromes, also known as congenital disorders of Received in revised form 22 December 2008 glycosylation (CDG). Based on the iso-electric focusing patterns of plasma transferrin and apolipoprotein Accepted 29 December 2008 C-III a combined defect in N- and O-glycosylation was identified in patients with autosomal recessive cutis Available online 8 January 2009 laxa type II (ARCL II). Disease-causing mutations were identified in the ATP6V0A2 gene, encoding the a2 + Keywords: subunit of the vacuolar H -ATPase (V-ATPase). The V-ATPases are multi-subunit, ATP-dependent proton Glycosylation pumps located in membranes of cells and organels. -
Transcriptome-Wide Profiling of Multiple RNA Modifications Simultaneously at Single-Base Resolution,” by Vahid Khoddami, Archana Yerra, Timothy L
Correction BIOCHEMISTRY, CHEMISTRY Correction for “Transcriptome-wide profiling of multiple RNA modifications simultaneously at single-base resolution,” by Vahid Khoddami, Archana Yerra, Timothy L. Mosbruger, Aaron M. Fleming, Cynthia J. Burrows, and Bradley R. Cairns, which was first published March 14, 2019; 10.1073/pnas.1817334116 (Proc Natl Acad Sci USA 116:6784–6789). The authors note that the following statement should be added to the Acknowledgments: “This work was also supported by Grant R01 GM093099 from the NIH/National Institute of General Medical Sciences (to C.J.B.).” Published under the PNAS license. Published online April 22, 2019. www.pnas.org/cgi/doi/10.1073/pnas.1905628116 9136 | PNAS | April 30, 2019 | vol. 116 | no. 18 www.pnas.org Downloaded by guest on October 2, 2021 Transcriptome-wide profiling of multiple RNA modifications simultaneously at single-base resolution Vahid Khoddamia,b,c,1,2, Archana Yerrab,c,1, Timothy L. Mosbrugerd, Aaron M. Fleminge, Cynthia J. Burrowse,3, and Bradley R. Cairnsb,c,3 aDepartment of Cell Biology, Harvard Medical School, Boston, MA 02115; bHoward Hughes Medical Institute, University of Utah School of Medicine, Salt Lake City, UT 84112; cDepartment of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112; dBioinformatics Shared Resource, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112; and eDepartment of Chemistry, University of Utah, Salt Lake City, UT 84112 Contributed by Cynthia J. Burrows, January 25, 2019 (sent for review October 9, 2018; reviewed by Juan D. Alfonzo, Thomas Carell, and Peter C.