Mutations in FLVCR1 Cause Posterior Column Ataxia and Retinitis Pigmentosa
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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. -
Cell Development and Peripheral Survival Heme Exporter FLVCR Is
Heme Exporter FLVCR Is Required for T Cell Development and Peripheral Survival Mary Philip, Scott A. Funkhouser, Edison Y. Chiu, Susan R. Phelps, Jeffrey J. Delrow, James Cox, Pamela J. Fink and This information is current as Janis L. Abkowitz of September 28, 2021. J Immunol 2015; 194:1677-1685; Prepublished online 12 January 2015; doi: 10.4049/jimmunol.1402172 http://www.jimmunol.org/content/194/4/1677 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2015/01/09/jimmunol.140217 Material 2.DCSupplemental http://www.jimmunol.org/ References This article cites 44 articles, 11 of which you can access for free at: http://www.jimmunol.org/content/194/4/1677.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 28, 2021 • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2015 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Heme Exporter FLVCR Is Required for T Cell Development and Peripheral Survival Mary Philip,*,† Scott A. -
IL21R Expressing CD14+CD16+ Monocytes Expand in Multiple
Plasma Cell Disorders SUPPLEMENTARY APPENDIX IL21R expressing CD14 +CD16 + monocytes expand in multiple myeloma patients leading to increased osteoclasts Marina Bolzoni, 1 Domenica Ronchetti, 2,3 Paola Storti, 1,4 Gaetano Donofrio, 5 Valentina Marchica, 1,4 Federica Costa, 1 Luca Agnelli, 2,3 Denise Toscani, 1 Rosanna Vescovini, 1 Katia Todoerti, 6 Sabrina Bonomini, 7 Gabriella Sammarelli, 1,7 Andrea Vecchi, 8 Daniela Guasco, 1 Fabrizio Accardi, 1,7 Benedetta Dalla Palma, 1,7 Barbara Gamberi, 9 Carlo Ferrari, 8 Antonino Neri, 2,3 Franco Aversa 1,4,7 and Nicola Giuliani 1,4,7 1Myeloma Unit, Dept. of Medicine and Surgery, University of Parma; 2Dept. of Oncology and Hemato-Oncology, University of Milan; 3Hematology Unit, “Fondazione IRCCS Ca’ Granda”, Ospedale Maggiore Policlinico, Milan; 4CoreLab, University Hospital of Parma; 5Dept. of Medical-Veterinary Science, University of Parma; 6Laboratory of Pre-clinical and Translational Research, IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture; 7Hematology and BMT Center, University Hospital of Parma; 8Infectious Disease Unit, University Hospital of Parma and 9“Dip. Oncologico e Tecnologie Avanzate”, IRCCS Arcispedale Santa Maria Nuova, Reggio Emilia, Italy ©2017 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2016.153841 Received: August 5, 2016. Accepted: December 23, 2016. Pre-published: January 5, 2017. Correspondence: [email protected] SUPPLEMENTAL METHODS Immunophenotype of BM CD14+ in patients with monoclonal gammopathies. Briefly, 100 μl of total BM aspirate was incubated in the dark with anti-human HLA-DR-PE (clone L243; BD), anti-human CD14-PerCP-Cy 5.5, anti-human CD16-PE-Cy7 (clone B73.1; BD) and anti-human CD45-APC-H 7 (clone 2D1; BD) for 20 min. -
Analysis of the Indacaterol-Regulated Transcriptome in Human Airway
Supplemental material to this article can be found at: http://jpet.aspetjournals.org/content/suppl/2018/04/13/jpet.118.249292.DC1 1521-0103/366/1/220–236$35.00 https://doi.org/10.1124/jpet.118.249292 THE JOURNAL OF PHARMACOLOGY AND EXPERIMENTAL THERAPEUTICS J Pharmacol Exp Ther 366:220–236, July 2018 Copyright ª 2018 by The American Society for Pharmacology and Experimental Therapeutics Analysis of the Indacaterol-Regulated Transcriptome in Human Airway Epithelial Cells Implicates Gene Expression Changes in the s Adverse and Therapeutic Effects of b2-Adrenoceptor Agonists Dong Yan, Omar Hamed, Taruna Joshi,1 Mahmoud M. Mostafa, Kyla C. Jamieson, Radhika Joshi, Robert Newton, and Mark A. Giembycz Departments of Physiology and Pharmacology (D.Y., O.H., T.J., K.C.J., R.J., M.A.G.) and Cell Biology and Anatomy (M.M.M., R.N.), Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada Received March 22, 2018; accepted April 11, 2018 Downloaded from ABSTRACT The contribution of gene expression changes to the adverse and activity, and positive regulation of neutrophil chemotaxis. The therapeutic effects of b2-adrenoceptor agonists in asthma was general enriched GO term extracellular space was also associ- investigated using human airway epithelial cells as a therapeu- ated with indacaterol-induced genes, and many of those, in- tically relevant target. Operational model-fitting established that cluding CRISPLD2, DMBT1, GAS1, and SOCS3, have putative jpet.aspetjournals.org the long-acting b2-adrenoceptor agonists (LABA) indacaterol, anti-inflammatory, antibacterial, and/or antiviral activity. Numer- salmeterol, formoterol, and picumeterol were full agonists on ous indacaterol-regulated genes were also induced or repressed BEAS-2B cells transfected with a cAMP-response element in BEAS-2B cells and human primary bronchial epithelial cells by reporter but differed in efficacy (indacaterol $ formoterol . -
Systematic Analysis of Palatal Transcriptome to Identify Cleft Palate Genes Within Tgfβ3-Knockout Mice Alleles: RNA-Seq Analysis of Tgfβ3 Mice Ozturk Et Al
Systematic analysis of palatal transcriptome to identify cleft palate genes within TGFβ3-knockout mice alleles: RNA-Seq analysis of TGFβ3 Mice Ozturk et al. Ozturk et al. BMC Genomics 2013, 14:113 http://www.biomedcentral.com/1471-2164/14/113 Ozturk et al. BMC Genomics 2013, 14:113 http://www.biomedcentral.com/1471-2164/14/113 RESEARCH ARTICLE Open Access Systematic analysis of palatal transcriptome to identify cleft palate genes within TGFβ3-knockout mice alleles: RNA-Seq analysis of TGFβ3 Mice Ferhat Ozturk1,4, You Li2, Xiujuan Zhu1, Chittibabu Guda2,3 and Ali Nawshad1* Abstract Background: In humans, cleft palate (CP) accounts for one of the largest number of birth defects with a complex genetic and environmental etiology. TGFβ3 has been established as an important regulator of palatal fusion in mice and it has been shown that TGFβ3-null mice exhibit CP without any other major deformities. However, the genes that regulate cellular decisions and molecular mechanisms maintained by the TGFβ3 pathway throughout palatogenesis are predominantly unexplored. Our objective in this study was to analyze global transcriptome changes within the palate during different gestational ages within TGFβ3 knockout mice to identify TGFβ3-associated genes previously unknown to be associated with the development of cleft palate. We used deep sequencing technology, RNA-Seq, to analyze the transcriptome of TGFβ3 knockout mice at crucial stages of palatogenesis, including palatal growth (E14.5), adhesion (E15.5), and fusion (E16.5). Results: The overall transcriptome analysis of TGFβ3 wildtype mice (C57BL/6) reveals that almost 6000 genes were upregulated during the transition from E14.5 to E15.5 and more than 2000 were downregulated from E15.5 to E16.5. -
Single Cell Derived Clonal Analysis of Human Glioblastoma Links
SUPPLEMENTARY INFORMATION: Single cell derived clonal analysis of human glioblastoma links functional and genomic heterogeneity ! Mona Meyer*, Jüri Reimand*, Xiaoyang Lan, Renee Head, Xueming Zhu, Michelle Kushida, Jane Bayani, Jessica C. Pressey, Anath Lionel, Ian D. Clarke, Michael Cusimano, Jeremy Squire, Stephen Scherer, Mark Bernstein, Melanie A. Woodin, Gary D. Bader**, and Peter B. Dirks**! ! * These authors contributed equally to this work.! ** Correspondence: [email protected] or [email protected]! ! Supplementary information - Meyer, Reimand et al. Supplementary methods" 4" Patient samples and fluorescence activated cell sorting (FACS)! 4! Differentiation! 4! Immunocytochemistry and EdU Imaging! 4! Proliferation! 5! Western blotting ! 5! Temozolomide treatment! 5! NCI drug library screen! 6! Orthotopic injections! 6! Immunohistochemistry on tumor sections! 6! Promoter methylation of MGMT! 6! Fluorescence in situ Hybridization (FISH)! 7! SNP6 microarray analysis and genome segmentation! 7! Calling copy number alterations! 8! Mapping altered genome segments to genes! 8! Recurrently altered genes with clonal variability! 9! Global analyses of copy number alterations! 9! Phylogenetic analysis of copy number alterations! 10! Microarray analysis! 10! Gene expression differences of TMZ resistant and sensitive clones of GBM-482! 10! Reverse transcription-PCR analyses! 11! Tumor subtype analysis of TMZ-sensitive and resistant clones! 11! Pathway analysis of gene expression in the TMZ-sensitive clone of GBM-482! 11! Supplementary figures and tables" 13" "2 Supplementary information - Meyer, Reimand et al. Table S1: Individual clones from all patient tumors are tumorigenic. ! 14! Fig. S1: clonal tumorigenicity.! 15! Fig. S2: clonal heterogeneity of EGFR and PTEN expression.! 20! Fig. S3: clonal heterogeneity of proliferation.! 21! Fig. -
INVESTIGATION INTO SUBGROUP C Felv INTERACTION with ITS HOST RECEPTOR FLVCR1 and the ROLE of FLVCR1 in DIAMOND BLACKFAN ANEMIA
INVESTIGATION INTO SUBGROUP C FeLV INTERACTION WITH ITS HOST RECEPTOR FLVCR1 AND THE ROLE OF FLVCR1 IN DIAMOND BLACKFAN ANEMIA By Michelle Antoinette Rey A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Molecular Genetics University of Toronto © Copyright by Michelle Antoinette Rey 2009 Investigation in subgroup C FeLV interaction with its host receptor FLVCR1 And the role of FLVCR1 in Diamond Blackfan Anemia For the degree of Doctor of Philosophy, 2009 By Michelle Antoinette Rey Graduate Department of Molecular Genetics University of Toronto ABSTRACT Retroviral infection requires an initial interaction between the host cell and the virion. This interaction is predominantly mediated by an envelope (env) protein exposed on the external face of the virion. For gammaretroviruses, such as feline leukemia virus (FeLV), the receptor-binding domain (RBD) is located in the N terminus of env. The RBD forms a distinct domain that is sufficient for binding to the host receptor, but is inefficient in the absence of the corresponding C terminal env, Cdomain, sequence in viral infection studies. I developed a series of hybrid constructs between subgroup C, A and T FeLVs that use distinct receptors for infection to determine the role of Cdom in FeLV binding and infection. Using this approach, I have shown that the C domain (Cdom) of FeLV-C env forms a second receptor-binding domain, distinct from its RBD, which is critical for efficient binding and infection of FeLV-C to host cells expressing FLVCR1. I propose that this mechanism of interaction is conserved for all gammaretroviruses. -
1589467268 490 16.Pdf
Veterinary Microbiology 229 (2019) 147–152 Contents lists available at ScienceDirect Veterinary Microbiology journal homepage: www.elsevier.com/locate/vetmic Expression of the feline leukemia virus subgroup C receptors in normal and neoplastic urothelium of the urinary bladder of cattle associated with bovine T papillomavirus infection Valeria Russoa, Franco Ropertob, Marian Taulescuc, Francesca De Falcoa, Chiara Urraroa, ⁎ Federica Corradod, John S. Mundaye, Cornel Catoic, Sante Ropertoa, a Dipartimento di Medicina Veterinaria e Produzioni Animali, Università di Napoli Federico II, Napoli, Italy b Dipartimento di Biologia, Università di Napoli Federico II, Napoli, Italy c University of Agricultural Sciences and Veterinary Medicine, Faculty of Veterinary Medicine, Pathology Department, Cluj-Napoca, Romania d Istituto Zooprofilattico Sperimentale del Mezzogiorno, Portici, Napoli, Italy e Pathobiology, School of Veterinary Sciences, Massey University, Palmerston North, New Zealand ARTICLE INFO ABSTRACT Keywords: The feline leukemia virus subgroup C receptors (FLVCRs) were originally cloned as virus receptors, but are now Bovine papillomavirus type 2 (BPV2) believed to function also as heme transporters and are expressed in a broad range of tissues in a wide range of BPV13 mammalian species. Expression of FLVCR1 and FLVCR2 was investigated in 19 bovine papillomavirus-associated Deltapapillomavirus urinary bladder cancers and in 15 non-neoplastic samples of bladder from cattle. E5 oncoprotein of bovine Feline leukemia virus subgroup C receptror1 Deltapapillomaviruses (δPVs) was detected in 17 of the 19 bladder cancers. Flvcr1 and Flvcr2 were amplified (FLVCR1) and sequenced both in neoplastic and non-neoplastic samples showing a 100% identity with bovine Flvcr1 and FLVCR2 Urothelial cancer Flvcr2 mRNA sequences present in GenBank database (accession numbers: NM_001206019.1 and NM_001192143.1, respectively). -
Supplementary Table 1 Double Treatment Vs Single Treatment
Supplementary table 1 Double treatment vs single treatment Probe ID Symbol Gene name P value Fold change TC0500007292.hg.1 NIM1K NIM1 serine/threonine protein kinase 1.05E-04 5.02 HTA2-neg-47424007_st NA NA 3.44E-03 4.11 HTA2-pos-3475282_st NA NA 3.30E-03 3.24 TC0X00007013.hg.1 MPC1L mitochondrial pyruvate carrier 1-like 5.22E-03 3.21 TC0200010447.hg.1 CASP8 caspase 8, apoptosis-related cysteine peptidase 3.54E-03 2.46 TC0400008390.hg.1 LRIT3 leucine-rich repeat, immunoglobulin-like and transmembrane domains 3 1.86E-03 2.41 TC1700011905.hg.1 DNAH17 dynein, axonemal, heavy chain 17 1.81E-04 2.40 TC0600012064.hg.1 GCM1 glial cells missing homolog 1 (Drosophila) 2.81E-03 2.39 TC0100015789.hg.1 POGZ Transcript Identified by AceView, Entrez Gene ID(s) 23126 3.64E-04 2.38 TC1300010039.hg.1 NEK5 NIMA-related kinase 5 3.39E-03 2.36 TC0900008222.hg.1 STX17 syntaxin 17 1.08E-03 2.29 TC1700012355.hg.1 KRBA2 KRAB-A domain containing 2 5.98E-03 2.28 HTA2-neg-47424044_st NA NA 5.94E-03 2.24 HTA2-neg-47424360_st NA NA 2.12E-03 2.22 TC0800010802.hg.1 C8orf89 chromosome 8 open reading frame 89 6.51E-04 2.20 TC1500010745.hg.1 POLR2M polymerase (RNA) II (DNA directed) polypeptide M 5.19E-03 2.20 TC1500007409.hg.1 GCNT3 glucosaminyl (N-acetyl) transferase 3, mucin type 6.48E-03 2.17 TC2200007132.hg.1 RFPL3 ret finger protein-like 3 5.91E-05 2.17 HTA2-neg-47424024_st NA NA 2.45E-03 2.16 TC0200010474.hg.1 KIAA2012 KIAA2012 5.20E-03 2.16 TC1100007216.hg.1 PRRG4 proline rich Gla (G-carboxyglutamic acid) 4 (transmembrane) 7.43E-03 2.15 TC0400012977.hg.1 SH3D19 -
Identification of Hub Genes Associated with Hepatocellular Carcinoma Prognosis by Bioinformatics Analysis
Journal of Cancer Therapy, 2021, 12, 186-207 https://www.scirp.org/journal/jct ISSN Online: 2151-1942 ISSN Print: 2151-1934 Identification of Hub Genes Associated with Hepatocellular Carcinoma Prognosis by Bioinformatics Analysis Xi Zhang*, Xiaojun Luo*, Wenbin Liu, Ai Shen# Department of Hepatobiliary and Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, China How to cite this paper: Zhang, X., Luo, Abstract X.J., Liu, W.B. and Shen, A. (2021) Identi- fication of Hub Genes Associated with He- Objective: This study aimed to identify hub genes that are associated with patocellular Carcinoma Prognosis by Bio- hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. Me- informatics Analysis. Journal of Cancer The- thods: Data were collected from the Gene Expression Omnibus (GEO) and rapy, 12, 186-207. https://doi.org/10.4236/jct.2021.124019 The Cancer Genome Atlas (TCGA) liver HCC datasets. The robust rank ag- gregation algorithm was used in integrating the data on differentially expressed Received: March 23, 2021 genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for Accepted: April 26, 2021 gene ontology and pathway enrichment analysis. R software version 3.5.1, Cy- Published: April 29, 2021 toscape, and Kaplan-Meier plotter were used to identify hub genes. Results: Copyright © 2021 by author(s) and Six GEO datasets and the TCGA liver HCC dataset were included in this Scientific Research Publishing Inc. analysis. A total of 151 upregulated and 245 downregulated DEGs were iden- This work is licensed under the Creative tified. The upregulated DEGs most significantly enriched in the functional Commons Attribution International License (CC BY 4.0). -
Epigenetics of Aging and Alzheimer's Disease
Review Epigenetics of Aging and Alzheimer’s Disease: Implications for Pharmacogenomics and Drug Response Ramón Cacabelos 1,2,* and Clara Torrellas 1,2 Received: 30 September 2015; Accepted: 8 December 2015; Published: 21 December 2015 Academic Editor: Sabrina Angelini 1 EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, 15165-Bergondo, Corunna, Spain; [email protected] 2 Chair of Genomic Medicine, Camilo José Cela University, 28692-Madrid, Spain * Correspondence: [email protected]; Tel.: +34-981-780505 Abstract: Epigenetic variability (DNA methylation/demethylation, histone modifications, microRNA regulation) is common in physiological and pathological conditions. Epigenetic alterations are present in different tissues along the aging process and in neurodegenerative disorders, such as Alzheimer’s disease (AD). Epigenetics affect life span and longevity. AD-related genes exhibit epigenetic changes, indicating that epigenetics might exert a pathogenic role in dementia. Epigenetic modifications are reversible and can potentially be targeted by pharmacological intervention. Epigenetic drugs may be useful for the treatment of major problems of health (e.g., cancer, cardiovascular disorders, brain disorders). The efficacy and safety of these and other medications depend upon the efficiency of the pharmacogenetic process in which different clusters of genes (pathogenic, mechanistic, metabolic, transporter, pleiotropic) are involved. Most of these genes are also under the influence of the epigenetic machinery. The information available on the pharmacoepigenomics of most drugs is very limited; however, growing evidence indicates that epigenetic changes are determinant in the pathogenesis of many medical conditions and in drug response and drug resistance. Consequently, pharmacoepigenetic studies should be incorporated in drug development and personalized treatments. -
Gnomad Lof Supplement
1 gnomAD supplement gnomAD supplement 1 Data processing 4 Alignment and read processing 4 Variant Calling 4 Coverage information 5 Data processing 5 Sample QC 7 Hard filters 7 Supplementary Table 1 | Sample counts before and after hard and release filters 8 Supplementary Table 2 | Counts by data type and hard filter 9 Platform imputation for exomes 9 Supplementary Table 3 | Exome platform assignments 10 Supplementary Table 4 | Confusion matrix for exome samples with Known platform labels 11 Relatedness filters 11 Supplementary Table 5 | Pair counts by degree of relatedness 12 Supplementary Table 6 | Sample counts by relatedness status 13 Population and subpopulation inference 13 Supplementary Figure 1 | Continental ancestry principal components. 14 Supplementary Table 7 | Population and subpopulation counts 16 Population- and platform-specific filters 16 Supplementary Table 8 | Summary of outliers per population and platform grouping 17 Finalizing samples in the gnomAD v2.1 release 18 Supplementary Table 9 | Sample counts by filtering stage 18 Supplementary Table 10 | Sample counts for genomes and exomes in gnomAD subsets 19 Variant QC 20 Hard filters 20 Random Forest model 20 Features 21 Supplementary Table 11 | Features used in final random forest model 21 Training 22 Supplementary Table 12 | Random forest training examples 22 Evaluation and threshold selection 22 Final variant counts 24 Supplementary Table 13 | Variant counts by filtering status 25 Comparison of whole-exome and whole-genome coverage in coding regions 25 Variant annotation 30 Frequency and context annotation 30 2 Functional annotation 31 Supplementary Table 14 | Variants observed by category in 125,748 exomes 32 Supplementary Figure 5 | Percent observed by methylation.