Studying Gene Function Analysis in 3D Tumor Microtissue Models
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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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
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 -
CD Markers Are Routinely Used for the Immunophenotyping of Cells
ptglab.com 1 CD MARKER ANTIBODIES www.ptglab.com Introduction The cluster of differentiation (abbreviated as CD) is a protocol used for the identification and investigation of cell surface molecules. So-called CD markers are routinely used for the immunophenotyping of cells. Despite this use, they are not limited to roles in the immune system and perform a variety of roles in cell differentiation, adhesion, migration, blood clotting, gamete fertilization, amino acid transport and apoptosis, among many others. As such, Proteintech’s mini catalog featuring its antibodies targeting CD markers is applicable to a wide range of research disciplines. PRODUCT FOCUS PECAM1 Platelet endothelial cell adhesion of blood vessels – making up a large portion molecule-1 (PECAM1), also known as cluster of its intracellular junctions. PECAM-1 is also CD Number of differentiation 31 (CD31), is a member of present on the surface of hematopoietic the immunoglobulin gene superfamily of cell cells and immune cells including platelets, CD31 adhesion molecules. It is highly expressed monocytes, neutrophils, natural killer cells, on the surface of the endothelium – the thin megakaryocytes and some types of T-cell. Catalog Number layer of endothelial cells lining the interior 11256-1-AP Type Rabbit Polyclonal Applications ELISA, FC, IF, IHC, IP, WB 16 Publications Immunohistochemical of paraffin-embedded Figure 1: Immunofluorescence staining human hepatocirrhosis using PECAM1, CD31 of PECAM1 (11256-1-AP), Alexa 488 goat antibody (11265-1-AP) at a dilution of 1:50 anti-rabbit (green), and smooth muscle KD/KO Validated (40x objective). alpha-actin (red), courtesy of Nicola Smart. PECAM1: Customer Testimonial Nicola Smart, a cardiovascular researcher “As you can see [the immunostaining] is and a group leader at the University of extremely clean and specific [and] displays Oxford, has said of the PECAM1 antibody strong intercellular junction expression, (11265-1-AP) that it “worked beautifully as expected for a cell adhesion molecule.” on every occasion I’ve tried it.” Proteintech thanks Dr. -
Table SI. Primer List of Genes Used for Reverse Transcription‑Quantitative PCR Validation
Table SI. Primer list of genes used for reverse transcription‑quantitative PCR validation. Genes Forward (5'‑3') Reverse (5'‑3') Length COL1A1 AGTGGTTTGGATGGTGCCAA GCACCATCATTTCCACGAGC 170 COL6A1 CCCCTCCCCACTCATCACTA CGAATCAGGTTGGTCGGGAA 65 COL2A1 GGTCCTGCAGGTGAACCC CTCTGTCTCCTTGCTTGCCA 181 DCT CTACGAAACCAGGATGACCGT ACCATCATTGGTTTGCCTTTCA 192 PDE4D ATTGCCCACGATAGCTGCTC GCAGATGTGCCATTGTCCAC 181 RP11‑428C19.4 ACGCTAGAAACAGTGGTGCG AATCCCCGGAAAGATCCAGC 179 GPC‑AS2 TCTCAACTCCCCTCCTTCGAG TTACATTTCCCGGCCCATCTC 151 XLOC_110310 AGTGGTAGGGCAAGTCCTCT CGTGGTGGGATTCAAAGGGA 187 COL1A1, collagen type I alpha 1; COL6A1, collagen type VI, alpha 1; COL2A1, collagen type II alpha 1; DCT, dopachrome tautomerase; PDE4D, phosphodiesterase 4D cAMP‑specific. Table SII. The differentially expressed mRNAs in the ParoAF_Control group. Gene ID logFC P‑Value Symbol Description ENSG00000165480 ‑6.4838 8.32E‑12 SKA3 Spindle and kinetochore associated complex subunit 3 ENSG00000165424 ‑6.43924 0.002056 ZCCHC24 Zinc finger, CCHC domain containing 24 ENSG00000182836 ‑6.20215 0.000817 PLCXD3 Phosphatidylinositol‑specific phospholipase C, X domain containing 3 ENSG00000174358 ‑5.79775 0.029093 SLC6A19 Solute carrier family 6 (neutral amino acid transporter), member 19 ENSG00000168916 ‑5.761 0.004046 ZNF608 Zinc finger protein 608 ENSG00000134343 ‑5.56371 0.01356 ANO3 Anoctamin 3 ENSG00000110400 ‑5.48194 0.004123 PVRL1 Poliovirus receptor‑related 1 (herpesvirus entry mediator C) ENSG00000124882 ‑5.45849 0.022164 EREG Epiregulin ENSG00000113448 ‑5.41752 0.000577 PDE4D Phosphodiesterase -
Construction of an Mirna‑Gene Regulatory Network in Colorectal Cancer Through Integrated Analysis of Mrna and Mirna Microarrays
MOLECULAR MEDICINE REPORTS 18: 5109-5116, 2018 Construction of an miRNA‑gene regulatory network in colorectal cancer through integrated analysis of mRNA and miRNA microarrays JUN HU, XIN YUE, JIANZHONG LIU and DALU KONG Department of Colorectal Cancer Surgery, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, P.R. China Received December 19, 2017; Accepted August 8, 2018 DOI: 10.3892/mmr.2018.9505 Abstract. The aim of the present study was to identify poten- Introduction tial biomarkers associated with colorectal cancer (CRC). The GSE32323 and GSE53592 mRNA and microRNA (miRNA) Colorectal cancer (CRC) is a common malignancy that ranks expression profiles were selected from the Gene Expression as the second leading cause of cancer-associated mortality in Omnibus database. The differentially expressed genes (DEGs) men and women in the USA (1). Despite improvements in CRC and differentially expressed miRNAs (DEMs) in CRC tissue therapy, CRC remains a major public health problem, and it is samples compared with surrounding control tissue samples estimated that there are 1,000,000 individuals suffering from (DEGs-CC), and DEGs in cells treated with 5-aza-2'-de- CRC worldwide, with the mortality rate is as high as ~50% in oxycitidine compared with untreated cells (DEGs-TC) certain developed countries (2). The tumor stage is the most were identified with the Limma package. The Database for important prognostic indicator for CRC. However, the tumors Annotation, Visualization and Integrated Discovery was used are often diagnosed at an intermediate or late stage, and the to conduct the functional and pathways enrichment analysis. -
Association of Imputed Prostate Cancer Transcriptome with Disease Risk Reveals Novel Mechanisms
Corrected: Author Correction ARTICLE https://doi.org/10.1038/s41467-019-10808-7 OPEN Association of imputed prostate cancer transcriptome with disease risk reveals novel mechanisms Nima C. Emami1,2, Linda Kachuri2, Travis J. Meyers2, Rajdeep Das3,4, Joshua D. Hoffman2, Thomas J. Hoffmann 2,5, Donglei Hu 5,6,7, Jun Shan8, Felix Y. Feng3,4,7, Elad Ziv5,6,7, Stephen K. Van Den Eeden 3,8 & John S. Witte1,2,3,5,7 1234567890():,; Here we train cis-regulatory models of prostate tissue gene expression and impute expression transcriptome-wide for 233,955 European ancestry men (14,616 prostate cancer (PrCa) cases, 219,339 controls) from two large cohorts. Among 12,014 genes evaluated in the UK Biobank, we identify 38 associated with PrCa, many replicating in the Kaiser Permanente RPGEH. We report the association of elevated TMPRSS2 expression with increased PrCa risk (independent of a previously-reported risk variant) and with increased tumoral expression of the TMPRSS2:ERG fusion-oncogene in The Cancer Genome Atlas, suggesting a novel germline-somatic interaction mechanism. Three novel genes, HOXA4, KLK1, and TIMM23, additionally replicate in the RPGEH cohort. Furthermore, 4 genes, MSMB, NCOA4, PCAT1, and PPP1R14A, are associated with PrCa in a trans-ethnic meta-analysis (N = 9117). Many genes exhibit evidence for allele-specific transcriptional activation by PrCa master-regulators (including androgen receptor) in Position Weight Matrix, Chip-Seq, and Hi-C experimental data, suggesting common regulatory mechanisms for the associated genes. 1 Program in Biological and Medical Informatics, University of California San Francisco, San Francisco, CA 94158, USA. -
Disease-Induced Modulation of Drug Transporters at the Blood–Brain Barrier Level
International Journal of Molecular Sciences Review Disease-Induced Modulation of Drug Transporters at the Blood–Brain Barrier Level Sweilem B. Al Rihani 1 , Lucy I. Darakjian 1, Malavika Deodhar 1 , Pamela Dow 1 , Jacques Turgeon 1,2 and Veronique Michaud 1,2,* 1 Tabula Rasa HealthCare, Precision Pharmacotherapy Research and Development Institute, Orlando, FL 32827, USA; [email protected] (S.B.A.R.); [email protected] (L.I.D.); [email protected] (M.D.); [email protected] (P.D.); [email protected] (J.T.) 2 Faculty of Pharmacy, Université de Montréal, Montreal, QC H3C 3J7, Canada * Correspondence: [email protected]; Tel.: +1-856-938-8697 Abstract: The blood–brain barrier (BBB) is a highly selective and restrictive semipermeable network of cells and blood vessel constituents. All components of the neurovascular unit give to the BBB its crucial and protective function, i.e., to regulate homeostasis in the central nervous system (CNS) by removing substances from the endothelial compartment and supplying the brain with nutrients and other endogenous compounds. Many transporters have been identified that play a role in maintaining BBB integrity and homeostasis. As such, the restrictive nature of the BBB provides an obstacle for drug delivery to the CNS. Nevertheless, according to their physicochemical or pharmacological properties, drugs may reach the CNS by passive diffusion or be subjected to putative influx and/or efflux through BBB membrane transporters, allowing or limiting their distribution to the CNS. Drug transporters functionally expressed on various compartments of the BBB involve numerous proteins from either the ATP-binding cassette (ABC) or the solute carrier (SLC) superfamilies. -
Identification of GLUT12/SLC2A12 As a Urate Transporter That
Identification of GLUT12/SLC2A12 as a urate BRIEF REPORT transporter that regulates the blood urate level in hyperuricemia model mice Yu Toyodaa,1, Tappei Takadaa,1,2, Hiroshi Miyataa,1, Hirotaka Matsuob, Hidetoshi Kassaic, Kazuki Nakaoc, Masahiro Nakatochid, Yusuke Kawamurab, Seiko Shimizub, Nariyoshi Shinomiyab, Kimiyoshi Ichidae, Makoto Hosoyamadaf, Atsu Aibac, and Hiroshi Suzukia aDepartment of Pharmacy, The University of Tokyo Hospital, Bunkyo-ku, 113-8655 Tokyo, Japan; bDepartment of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, 359-8513 Saitama, Japan; cLaboratory of Animal Resources, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, 113-0033 Tokyo, Japan; dDivision of Public Health Informatics, Department of Integrative Health Science, Nagoya University Graduate School of Medicine, 461-8673 Nagoya, Japan; eDepartment of Pathophysiology, Tokyo University of Pharmacy and Life Sciences, Hachioji, 192-0392 Tokyo, Japan; and fDepartment of Human Physiology and Pathology, Faculty of Pharma-Sciences, Teikyo University, Itabashi-ku, 173-8605 Tokyo, Japan Edited by Francisco Bezanilla, The University of Chicago, Chicago, IL, and approved June 29, 2020 (received for review April 14, 2020) Recent genome-wide association studies have revealed some ge- affected the urate transport activity of GLUT12 (Fig. 1D); netic loci associated with serum uric acid levels and susceptibility GLUT12 is more active at lower pH (Fig. 1E). As GLUT12- to gout/hyperuricemia which contain potential candidates of mediated urate uptake increased linearly with time over physiologically important urate transporters. One of these novel 10 min (Fig. 1F), uptake at 5 min was evaluated in subsequent loci is located upstream of SGK1 and SLC2A12, suggesting that kinetic analysis. -
Binding of Pregnancy-Specific Glycoprotein 17 to CD9 On
Binding of pregnancy-specific glycoprotein 17 to CD9 on macrophages induces secretion of IL-10, IL-6, PGE2,  and TGF- 1 Cam T. Ha,* Roseann Waterhouse,* Jennifer Wessells,† Julie A. Wu,* and Gabriela S. Dveksler*,1 *Department of Pathology, Uniformed Services University of the Health Sciences, Bethesda, Maryland; and †Laboratory of Protein Dynamics and Signaling, National Cancer Institute-Frederick, Maryland Abstract: Pregnancy-specific glycoproteins (PSGs) inally isolated from the circulation of pregnant women [1]. are a family of secreted proteins produced by the PSGs are detected in maternal blood as early as 7 days post- placenta, which are believed to have a critical role implantation. The serum levels of these proteins reach up to in pregnancy success. Treatment of monocytes 200–400 g/ml at term, far exceeding the concentration of with three members of the human PSGs induces human chorionic gonadotropin and ␣ fetoprotein [2]. Treatment interleukin (IL)-10, IL-6, and transforming growth of monkeys and mice with anti-PSG antibodies resulted in   factor- 1 (TGF- 1) secretion. To determine whether spontaneous abortion [3, 4], and abnormally low levels of PSGs human and murine PSGs have similar functions are associated with several serious complications of pregnancy, and use the same receptor, we treated wild-type including fetal hypoxia, fetal growth retardation, pre-eclamp- and CD9-deficient macrophages with murine sia, and spontaneous abortion [5–8]. PSG homologues have PSG17N and human PSG1 and -11. Our data show been identified in mice, rats, and primates [9–11]. that murine PSG17N induced secretion of IL-10, We have previously shown that treatment of human mono-  IL-6, prostaglandin E2, and TGF- 1 and that CD9 cytes with recombinant human (rh)PSG1, PSG6, and PSG11 expression is required for the observed induction induced the production of anti-inflammatory cytokines [12]. -
Roles for the Uptake 2 Transporter OCT3 in Regulation Of
Marquette University e-Publications@Marquette Biomedical Sciences Faculty Research and Publications Biomedical Sciences, Department of 2-2019 Roles for the Uptake2 Transporter OCT3 in Regulation of Dopaminergic Neurotransmission and Behavior Paul J. Gasser Marquette University, [email protected] Follow this and additional works at: https://epublications.marquette.edu/biomedsci_fac Part of the Neurosciences Commons Recommended Citation Gasser, Paul J., "Roles for the Uptake2 Transporter OCT3 in Regulation of Dopaminergic Neurotransmission and Behavior" (2019). Biomedical Sciences Faculty Research and Publications. 191. https://epublications.marquette.edu/biomedsci_fac/191 Marquette University e-Publications@Marquette Biomedical Sciences Faculty Research and Publications/College of Health Sciences This paper is NOT THE PUBLISHED VERSION; but the author’s final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation below. Neurochemistry International, Vol. 123, (February 2019): 46-49. DOI. This article is © Elsevier and permission has been granted for this version to appear in e-Publications@Marquette. Elsevier does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Elsevier. Roles for the Uptake2 Transporter OCT3 in Regulation of Dopaminergic Neurotransmission and Behavior Paul J. Gasser Department of Biomedical Sciences, Marquette University, Milwaukee, WI Abstract Transporter-mediated uptake determines the