Microarray Analysis of Colorectal Cancer Stromal Tissue Reveals Upregulation of Two Oncogenic Mirna Clusters
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Cytokine Nomenclature
RayBiotech, Inc. The protein array pioneer company Cytokine Nomenclature Cytokine Name Official Full Name Genbank Related Names Symbol 4-1BB TNFRSF Tumor necrosis factor NP_001552 CD137, ILA, 4-1BB ligand receptor 9 receptor superfamily .2. member 9 6Ckine CCL21 6-Cysteine Chemokine NM_002989 Small-inducible cytokine A21, Beta chemokine exodus-2, Secondary lymphoid-tissue chemokine, SLC, SCYA21 ACE ACE Angiotensin-converting NP_000780 CD143, DCP, DCP1 enzyme .1. NP_690043 .1. ACE-2 ACE2 Angiotensin-converting NP_068576 ACE-related carboxypeptidase, enzyme 2 .1 Angiotensin-converting enzyme homolog ACTH ACTH Adrenocorticotropic NP_000930 POMC, Pro-opiomelanocortin, hormone .1. Corticotropin-lipotropin, NPP, NP_001030 Melanotropin gamma, Gamma- 333.1 MSH, Potential peptide, Corticotropin, Melanotropin alpha, Alpha-MSH, Corticotropin-like intermediary peptide, CLIP, Lipotropin beta, Beta-LPH, Lipotropin gamma, Gamma-LPH, Melanotropin beta, Beta-MSH, Beta-endorphin, Met-enkephalin ACTHR ACTHR Adrenocorticotropic NP_000520 Melanocortin receptor 2, MC2-R hormone receptor .1 Activin A INHBA Activin A NM_002192 Activin beta-A chain, Erythroid differentiation protein, EDF, INHBA Activin B INHBB Activin B NM_002193 Inhibin beta B chain, Activin beta-B chain Activin C INHBC Activin C NM005538 Inhibin, beta C Activin RIA ACVR1 Activin receptor type-1 NM_001105 Activin receptor type I, ACTR-I, Serine/threonine-protein kinase receptor R1, SKR1, Activin receptor-like kinase 2, ALK-2, TGF-B superfamily receptor type I, TSR-I, ACVRLK2 Activin RIB ACVR1B -
Potassium Channels in Epilepsy
Downloaded from http://perspectivesinmedicine.cshlp.org/ on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Potassium Channels in Epilepsy Ru¨diger Ko¨hling and Jakob Wolfart Oscar Langendorff Institute of Physiology, University of Rostock, Rostock 18057, Germany Correspondence: [email protected] This review attempts to give a concise and up-to-date overview on the role of potassium channels in epilepsies. Their role can be defined from a genetic perspective, focusing on variants and de novo mutations identified in genetic studies or animal models with targeted, specific mutations in genes coding for a member of the large potassium channel family. In these genetic studies, a demonstrated functional link to hyperexcitability often remains elusive. However, their role can also be defined from a functional perspective, based on dy- namic, aggravating, or adaptive transcriptional and posttranslational alterations. In these cases, it often remains elusive whether the alteration is causal or merely incidental. With 80 potassium channel types, of which 10% are known to be associated with epilepsies (in humans) or a seizure phenotype (in animals), if genetically mutated, a comprehensive review is a challenging endeavor. This goal may seem all the more ambitious once the data on posttranslational alterations, found both in human tissue from epilepsy patients and in chronic or acute animal models, are included. We therefore summarize the literature, and expand only on key findings, particularly regarding functional alterations found in patient brain tissue and chronic animal models. INTRODUCTION TO POTASSIUM evolutionary appearance of voltage-gated so- CHANNELS dium (Nav)andcalcium (Cav)channels, Kchan- nels are further diversified in relation to their otassium (K) channels are related to epilepsy newer function, namely, keeping neuronal exci- Psyndromes on many different levels, ranging tation within limits (Anderson and Greenberg from direct control of neuronal excitability and 2001; Hille 2001). -
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. -
Transcriptomic Analysis of Native Versus Cultured Human and Mouse Dorsal Root Ganglia Focused on Pharmacological Targets Short
bioRxiv preprint doi: https://doi.org/10.1101/766865; this version posted September 12, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia focused on pharmacological targets Short title: Comparative transcriptomics of acutely dissected versus cultured DRGs Andi Wangzhou1, Lisa A. McIlvried2, Candler Paige1, Paulino Barragan-Iglesias1, Carolyn A. Guzman1, Gregory Dussor1, Pradipta R. Ray1,#, Robert W. Gereau IV2, # and Theodore J. Price1, # 1The University of Texas at Dallas, School of Behavioral and Brain Sciences and Center for Advanced Pain Studies, 800 W Campbell Rd. Richardson, TX, 75080, USA 2Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine # corresponding authors [email protected], [email protected] and [email protected] Funding: NIH grants T32DA007261 (LM); NS065926 and NS102161 (TJP); NS106953 and NS042595 (RWG). The authors declare no conflicts of interest Author Contributions Conceived of the Project: PRR, RWG IV and TJP Performed Experiments: AW, LAM, CP, PB-I Supervised Experiments: GD, RWG IV, TJP Analyzed Data: AW, LAM, CP, CAG, PRR Supervised Bioinformatics Analysis: PRR Drew Figures: AW, PRR Wrote and Edited Manuscript: AW, LAM, CP, GD, PRR, RWG IV, TJP All authors approved the final version of the manuscript. 1 bioRxiv preprint doi: https://doi.org/10.1101/766865; this version posted September 12, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Glucose-Induced Changes in Gene Expression in Human Pancreatic Islets: Causes Or Consequences of Chronic Hyperglycemia
Diabetes Volume 66, December 2017 3013 Glucose-Induced Changes in Gene Expression in Human Pancreatic Islets: Causes or Consequences of Chronic Hyperglycemia Emilia Ottosson-Laakso,1 Ulrika Krus,1 Petter Storm,1 Rashmi B. Prasad,1 Nikolay Oskolkov,1 Emma Ahlqvist,1 João Fadista,2 Ola Hansson,1 Leif Groop,1,3 and Petter Vikman1 Diabetes 2017;66:3013–3028 | https://doi.org/10.2337/db17-0311 Dysregulation of gene expression in islets from patients In patients with type 2 diabetes (T2D), islet function de- with type 2 diabetes (T2D) might be causally involved clines progressively. Although the initial pathogenic trigger in the development of hyperglycemia, or it could develop of impaired b-cell function is still unknown, elevated glu- as a consequence of hyperglycemia (i.e., glucotoxicity). cose levels are known to further aggravate b-cell function, a To separate the genes that could be causally involved condition referred to as glucotoxicity, which can stimulate in pathogenesis from those likely to be secondary to hy- apoptosis and lead to reduced b-cell mass (1–5). Prolonged perglycemia, we exposed islets from human donors to exposure to hyperglycemia also can induce endoplasmic re- ISLET STUDIES normal or high glucose concentrations for 24 h and ana- ticulum (ER) stress and production of reactive oxygen spe- fi lyzed gene expression. We compared these ndings with cies (6), which can further impair islet function and thereby gene expression in islets from donors with normal glucose the ability of islets to secrete the insulin needed to meet the tolerance and hyperglycemia (including T2D). The genes increased demands imposed by insulin resistance and obe- whose expression changed in the same direction after sity (7). -
Secretome Screening Reveals Immunomodulating Functions Of
www.nature.com/scientificreports OPEN Secretome screening reveals immunomodulating functions of IFNα‑7, PAP and GDF‑7 on regulatory T‑cells Mei Ding1*, Rajneesh Malhotra2, Tomas Ottosson2, Magnus Lundqvist3, Aman Mebrahtu3, Johan Brengdahl1, Ulf Gehrmann2, Elisabeth Bäck4, Douglas Ross‑Thriepland5, Ida Isaksson6, Björn Magnusson1, Kris F. Sachsenmeier7, Hanna Tegel3, Sophia Hober3, Mathias Uhlén3, Lorenz M. Mayr5, Rick Davies5, Johan Rockberg3 & Lovisa Holmberg Schiavone1* Regulatory T cells (Tregs) are the key cells regulating peripheral autoreactive T lymphocytes. Tregs exert their function by suppressing efector T cells. Tregs have been shown to play essential roles in the control of a variety of physiological and pathological immune responses. However, Tregs are unstable and can lose the expression of FOXP3 and suppressive functions as a consequence of outer stimuli. Available literature suggests that secreted proteins regulate Treg functional states, such as diferentiation, proliferation and suppressive function. Identifcation of secreted proteins that afect Treg cell function are highly interesting for both therapeutic and diagnostic purposes in either hyperactive or immunosuppressed populations. Here, we report a phenotypic screening of a human secretome library in human Treg cells utilising a high throughput fow cytometry technology. Screening a library of 575 secreted proteins allowed us to identify proteins stabilising or destabilising the Treg phenotype as suggested by changes in expression of Treg marker proteins FOXP3 and/or CTLA4. Four proteins including GDF‑7, IL‑10, PAP and IFNα‑7 were identifed as positive regulators that increased FOXP3 and/or CTLA4 expression. PAP is a phosphatase. A catalytic‑dead version of the protein did not induce an increase in FOXP3 expression. -
Affiliations
Supplementary material Proteome-wide survey of the autoimmune target repertoire in autoimmune polyendocrine syndrome type 1 *Nils Landegren1,2, Donald Sharon3,4, Eva Freyhult2,5,6,, Åsa Hallgren1,2, Daniel Eriksson1,2, Per-Henrik Edqvist7, Sophie Bensing8, Jeanette Wahlberg9, Lawrence M. Nelson10, Jan Gustafsson11, Eystein S Husebye12, Mark S Anderson13, Michael Snyder3, Olle Kämpe1,2 Nils Landegren and Donald Sharon contributed equally to the work Affiliations 1Department of Medicine (Solna), Karolinska University Hospital, Karolinska Institutet, Sweden 2Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Sweden 3Department of Genetics, Stanford University, California, USA 4Department of Molecular, Cellular, and Developmental Biology, Yale University, Connecticut, USA 1 5Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University 6Bioinformatics Infrastructure for Life Sciences 7Department of Immunology, Genetics and Pathology, Uppsala University, Sweden and Science for Life Laboratory 8 Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden 9Department of Endocrinology and Department of Medical and Health Sciences and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden 10Integrative Reproductive Medicine Group, Intramural Research Program on Reproductive and Adult Endocrinology, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. 11Department -
Análise Integrativa De Perfis Transcricionais De Pacientes Com
UNIVERSIDADE DE SÃO PAULO FACULDADE DE MEDICINA DE RIBEIRÃO PRETO PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Ribeirão Preto – 2012 ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Tese apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo para obtenção do título de Doutor em Ciências. Área de Concentração: Genética Orientador: Prof. Dr. Eduardo Antonio Donadi Co-orientador: Prof. Dr. Geraldo A. S. Passos Ribeirão Preto – 2012 AUTORIZO A REPRODUÇÃO E DIVULGAÇÃO TOTAL OU PARCIAL DESTE TRABALHO, POR QUALQUER MEIO CONVENCIONAL OU ELETRÔNICO, PARA FINS DE ESTUDO E PESQUISA, DESDE QUE CITADA A FONTE. FICHA CATALOGRÁFICA Evangelista, Adriane Feijó Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. Ribeirão Preto, 2012 192p. Tese de Doutorado apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo. Área de Concentração: Genética. Orientador: Donadi, Eduardo Antonio Co-orientador: Passos, Geraldo A. 1. Expressão gênica – microarrays 2. Análise bioinformática por module maps 3. Diabetes mellitus tipo 1 4. Diabetes mellitus tipo 2 5. Diabetes mellitus gestacional FOLHA DE APROVAÇÃO ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. -
Proteome-Wide Survey of the Autoimmune Target Repertoire In
www.nature.com/scientificreports OPEN Proteome-wide survey of the autoimmune target repertoire in autoimmune polyendocrine Received: 28 October 2015 Accepted: 23 December 2015 syndrome type 1 Published: 01 February 2016 Nils Landegren1,2,*, Donald Sharon3,4,*, Eva Freyhult2,5,6, Åsa Hallgren1,2, Daniel Eriksson1,2, Per-Henrik Edqvist7, Sophie Bensing8, Jeanette Wahlberg9, Lawrence M. Nelson10, Jan Gustafsson11, Eystein S Husebye12, Mark S Anderson13, Michael Snyder3 & Olle Kämpe1,2 Autoimmune polyendocrine syndrome type 1 (APS1) is a monogenic disorder that features multiple autoimmune disease manifestations. It is caused by mutations in the Autoimmune regulator (AIRE) gene, which promote thymic display of thousands of peripheral tissue antigens in a process critical for establishing central immune tolerance. We here used proteome arrays to perform a comprehensive study of autoimmune targets in APS1. Interrogation of established autoantigens revealed highly reliable detection of autoantibodies, and by exploring the full panel of more than 9000 proteins we further identified MAGEB2 and PDILT as novel major autoantigens in APS1. Our proteome-wide assessment revealed a marked enrichment for tissue-specific immune targets, mirroringAIRE ’s selectiveness for this category of genes. Our findings also suggest that only a very limited portion of the proteome becomes targeted by the immune system in APS1, which contrasts the broad defect of thymic presentation associated with AIRE-deficiency and raises novel questions what other factors are needed for break of tolerance. Autoimmune responses can ultimately be defined at the molecular level by the specific interaction between T- or B-cell receptors and a distinct self-molecule. In tissue-specific autoimmune disorders the immune system typically target molecules that are exclusively expressed in the affected tissue and involve a combined cellular and humoral response with cognate specificities1–3. -
Heatmaps - the Gene Expression Edition
Heatmaps - the gene expression edition Jeff Oliver 20 July, 2020 An application of heatmap visualization to investigate differential gene expression. Learning objectives 1. Manipulate data into a ‘tidy’ format 2. Visualize data in a heatmap 3. Become familiar with ggplot syntax for customizing plots Heatmaps for differential gene expression Heatmaps are a great way of displaying three-dimensional data in only two dimensions. But how can we easily translate tabular data into a format for heatmap plotting? By taking advantage of “data munging” and graphics packages, heatmaps are relatively easy to produce in R. Getting started We are going to start by isolating different types of information by imposing structure in our file managment. That is, we are going to put our input data in one folder and any output such as plots or analytical results in a different folder. We can use the dir.create to create these two folders: dir.create("data") dir.create("output") For this lesson, we will use a subset of data on a study of gene expression in cells infected with the influenza virus (doi: 10.4049/jimmunol.1501557). The study infected human plasmacytoid dendritic cells with the influenza virus, and compared gene expression in those cells to gene expression in uninfected cells. Thegoal was to see how the flu virus affected the function of these immune system cells. The data for this lesson is available at: http://tinyurl.com/flu-expression-data or https://jcoliver.github.io/ learn-r/data/GSE68849-expression.csv. Download this comma separated file and put it in the data folder. -
NIH Public Access Author Manuscript Hum Genet
NIH Public Access Author Manuscript Hum Genet. Author manuscript; available in PMC 2015 May 01. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Hum Genet. 2014 May ; 133(5): 509–521. doi:10.1007/s00439-013-1387-z. A genome-wide association study of prostate cancer in West African men Michael B. Cook1,*, Zhaoming Wang1,2,*, Edward D. Yeboah3,4,*, Yao Tettey3,4, Richard B. Biritwum3,4, Andrew A. Adjei3,4, Evelyn Tay3,4, Ann Truelove5, Shelley Niwa5, Charles C. Chung1, Annand P. Chokkalingam6, Lisa W. Chu7, Meredith Yeager1,2, Amy Hutchinson1,2, Kai Yu1, Kristin A. Rand8, Christopher A. Haiman8, African Ancestry Prostate Cancer GWAS Consortium, Robert N. Hoover1, Ann W. Hsing6,9,#, and Stephen J. Chanock1,# 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Bethesda, MD, USA 2Cancer Genomics Research Laboratory, NCI-DCEG, SAIC-Frederick Inc., Frederick, MD, USA 3Korle Bu Teaching Hospital, PO BOX 77, Accra, Ghana 4University of Ghana Medical School, PO Box 4236, Accra, Ghana 5Westat, 1600 Research Boulevard, Rockville, MD 20850-3129, USA 6School of Public Health, University of California, Berkeley, CA, USA 7Cancer Prevention Institute of California, 2201 Walnut Avenue, Suite 300, Fremont, CA 94538, USA 8Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California. Los Angeles, CA 90033, USA 9Stanford Cancer Institute, Stanford University, 875 Blake Wilbur Drive Stanford, CA 94305 Abstract Background—Age-adjusted mortality rates for prostate cancer are higher for African American men compared with those of European ancestry. Recent data suggest that West African men also have elevated risk for prostate cancer relative to European men. -
Ion Channels 3 1
r r r Cell Signalling Biology Michael J. Berridge Module 3 Ion Channels 3 1 Module 3 Ion Channels Synopsis Ion channels have two main signalling functions: either they can generate second messengers or they can function as effectors by responding to such messengers. Their role in signal generation is mainly centred on the Ca2 + signalling pathway, which has a large number of Ca2+ entry channels and internal Ca2+ release channels, both of which contribute to the generation of Ca2 + signals. Ion channels are also important effectors in that they mediate the action of different intracellular signalling pathways. There are a large number of K+ channels and many of these function in different + aspects of cell signalling. The voltage-dependent K (KV) channels regulate membrane potential and + excitability. The inward rectifier K (Kir) channel family has a number of important groups of channels + + such as the G protein-gated inward rectifier K (GIRK) channels and the ATP-sensitive K (KATP) + + channels. The two-pore domain K (K2P) channels are responsible for the large background K current. Some of the actions of Ca2 + are carried out by Ca2+-sensitive K+ channels and Ca2+-sensitive Cl − channels. The latter are members of a large group of chloride channels and transporters with multiple functions. There is a large family of ATP-binding cassette (ABC) transporters some of which have a signalling role in that they extrude signalling components from the cell. One of the ABC transporters is the cystic − − fibrosis transmembrane conductance regulator (CFTR) that conducts anions (Cl and HCO3 )and contributes to the osmotic gradient for the parallel flow of water in various transporting epithelia.