Latrophilin's Social Protein Network
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Supplemental Figure 1. Vimentin
Double mutant specific genes Transcript gene_assignment Gene Symbol RefSeq FDR Fold- FDR Fold- FDR Fold- ID (single vs. Change (double Change (double Change wt) (single vs. wt) (double vs. single) (double vs. wt) vs. wt) vs. single) 10485013 BC085239 // 1110051M20Rik // RIKEN cDNA 1110051M20 gene // 2 E1 // 228356 /// NM 1110051M20Ri BC085239 0.164013 -1.38517 0.0345128 -2.24228 0.154535 -1.61877 k 10358717 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 /// BC 1700025G04Rik NM_197990 0.142593 -1.37878 0.0212926 -3.13385 0.093068 -2.27291 10358713 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 1700025G04Rik NM_197990 0.0655213 -1.71563 0.0222468 -2.32498 0.166843 -1.35517 10481312 NM_027283 // 1700026L06Rik // RIKEN cDNA 1700026L06 gene // 2 A3 // 69987 /// EN 1700026L06Rik NM_027283 0.0503754 -1.46385 0.0140999 -2.19537 0.0825609 -1.49972 10351465 BC150846 // 1700084C01Rik // RIKEN cDNA 1700084C01 gene // 1 H3 // 78465 /// NM_ 1700084C01Rik BC150846 0.107391 -1.5916 0.0385418 -2.05801 0.295457 -1.29305 10569654 AK007416 // 1810010D01Rik // RIKEN cDNA 1810010D01 gene // 7 F5 // 381935 /// XR 1810010D01Rik AK007416 0.145576 1.69432 0.0476957 2.51662 0.288571 1.48533 10508883 NM_001083916 // 1810019J16Rik // RIKEN cDNA 1810019J16 gene // 4 D2.3 // 69073 / 1810019J16Rik NM_001083916 0.0533206 1.57139 0.0145433 2.56417 0.0836674 1.63179 10585282 ENSMUST00000050829 // 2010007H06Rik // RIKEN cDNA 2010007H06 gene // --- // 6984 2010007H06Rik ENSMUST00000050829 0.129914 -1.71998 0.0434862 -2.51672 -
CHL1 and Nrcam Are Primarily Expressed in Low Grade Pediatric
Open Med. 2019; 14: 920-927 Research Article Robin Wachowiak, Steffi Mayer, Anne Suttkus, Illya Martynov, Martin Lacher, Nathaniel Melling, Jakob R. Izbicki, Michael Tachezy CHL1 and NrCAM are primarily expressed in low grade pediatric neuroblastoma https://doi.org/10.1515/med-2019-0109 Keywords: CHL1; NrCAM; Neuroblastoma; Immunohisto- received November 7, 2018; accepted October 19, 2019 chemistry; Tumor markers; Neuropathology Abstract: Background. Neural cell adhesion molecules like close homolog of L1 protein (CHL1) and neuronal glia related cell adhesion molecule (NrCAM) play an impor- tant role in development and regeneration of the central 1 Introduction nervous system. However, they are also associated with Neuroblastoma is an embryonic malignancy deriving cancerogenesis and progression in adult malignancies, from neural crest cells that undergo rapid differentia- thus gain increasing importance in cancer research. We tion during fetal development. As the transition from therefore studied the expression of CHL1 and NrCAM normal to malignant tissue can occur in multiple steps, according to the course of disease in children with neu- its phenotype is highly heterogeneous [1]. Although pro- roblastoma. gress has been made in the treatment of neuroblastoma, Methods. CHL1 and NrCAM expression levels were histo- the outcome of children at high risk remains poor with a logically assessed by tissue microarrays from surgically long-term survival as low as 50 % [2]. Different parameters resected neuroblastoma specimens of 56 children. Expres- such as age, stage and chromosomal aberrations have an sion of both markers was correlated to demographics as impact on prognosis. Still, there is an ongoing need for well as clinical data including metastatic dissemination tumor markers, which allow a better determination of the and survival. -
FLRT Proteins Are Endogenous Latrophilin Ligands and Regulate Excitatory Synapse Development
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Neuron Report FLRT Proteins Are Endogenous Latrophilin Ligands and Regulate Excitatory Synapse Development Matthew L. O’Sullivan,1,5 Joris de Wit,1,5 Jeffrey N. Savas,2 Davide Comoletti,3 Stefanie Otto-Hitt,1,6 John R. Yates III,2 and Anirvan Ghosh1,4,* 1Neurobiology Section, Division of Biology, University of California San Diego, La Jolla, CA 92093, USA 2Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037, USA 3Child Health Institute of New Jersey and Department of Neuroscience and Cell Biology, UMDNJ/Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA 4CNS Discovery, F. Hoffmann-La Roche, 4070 Basel, Switzerland 5These authors contributed equally to this work 6Present address: Department of Natural Sciences, Carroll College, Helena, MT 59625, USA *Correspondence: [email protected] DOI 10.1016/j.neuron.2012.01.018 SUMMARY much effort has been expended investigating the mechanisms of a-latrotoxin action (Su¨ dhof, 2001), nothing is known about Latrophilins (LPHNs) are a small family of G protein- the endogenous function of latrophilins in vertebrates. Further coupled receptors known to mediate the massive evidence for the importance of latrophilins in the proper synaptic exocytosis caused by the black widow functioning of neural circuits comes from recent human spider venom a-latrotoxin, but their endogenous genetics studies that have linked LPHN3 mutations to attention ligands and function remain unclear. Mutations in deficit hyperactivity disorder (ADHD), a common and highly LPHN3 are strongly associated with attention deficit heritable developmental psychiatric disorder (Arcos-Burgos et al., 2010; Domene´ et al., 2011; Jain et al., 2011; Ribase´ s hyperactivity disorder, suggesting a role for latrophi- et al., 2011). -
Edinburgh Research Explorer
Edinburgh Research Explorer International Union of Basic and Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list Citation for published version: Davenport, AP, Alexander, SPH, Sharman, JL, Pawson, AJ, Benson, HE, Monaghan, AE, Liew, WC, Mpamhanga, CP, Bonner, TI, Neubig, RR, Pin, JP, Spedding, M & Harmar, AJ 2013, 'International Union of Basic and Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list: recommendations for new pairings with cognate ligands', Pharmacological reviews, vol. 65, no. 3, pp. 967-86. https://doi.org/10.1124/pr.112.007179 Digital Object Identifier (DOI): 10.1124/pr.112.007179 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Pharmacological reviews Publisher Rights Statement: U.S. Government work not protected by U.S. copyright General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 1521-0081/65/3/967–986$25.00 http://dx.doi.org/10.1124/pr.112.007179 PHARMACOLOGICAL REVIEWS Pharmacol Rev 65:967–986, July 2013 U.S. -
Upregulation of NETO2 Gene in Colorectal Cancer Maria S
Fedorova et al. BMC Genetics 2017, 18(Suppl 1):117 DOI 10.1186/s12863-017-0581-8 RESEARCH Open Access Upregulation of NETO2 gene in colorectal cancer Maria S. Fedorova1†, Anastasiya V. Snezhkina1†, Elena A. Pudova1, Ivan S. Abramov1, Anastasiya V. Lipatova1, Sergey L. Kharitonov1, Asiya F. Sadritdinova1, Kirill M. Nyushko2, Kseniya M. Klimina3, Mikhail M. Belyakov2, Elena N. Slavnova2, Nataliya V. Melnikova1, Maria A. Chernichenko2, Dmitry V. Sidorov2, Marina V. Kiseleva2, Andrey D. Kaprin2, Boris Y. Alekseev2, Alexey A. Dmitriev1 and Anna V. Kudryavtseva1,2* From Belyaev Conference Novosibirsk, Russia. 07-10 August 2017 Abstract Background: Neuropilin and tolloid-like 2 (NETO2) is a single-pass transmembrane protein that has been shown primarily implicated in neuron-specific processes. Upregulation of NETO2 gene was also detected in several cancer types. In colorectal cancer (CRC), it was associated with tumor progression, invasion, and metastasis, and seems to be involved in epithelial-mesenchymal transition (EMT). However, the mechanism of NETO2 action is still poorly understood. Results: We have revealed significant increase in the expression of NETO2 gene and deregulation of eight EMT-related genes in CRC. Four of them were upregulated (TWIST1, SNAIL1, LEF1,andFOXA2); the mRNA levels of other genes (FOXA1, BMP2, BMP5,andSMAD7) were decreased. Expression of NETO2 gene was weakly correlated with that of genes involved in the EMT process. Conclusions: We found considerable NETO2 upregulation, but no significant correlation between the expression of NETO2 and EMT-related genes in CRC. Thus, NETO2 may be involved in CRC progression, but is not directly associated with EMT. Keywords: Colorectal cancer, NETO2, Epithelial-mesenchymal transition, Gene expression, QPCR Background and signaling pathways [3–6]. -
Supplementary Data
Supplemental Data A novel mouse model of X-linked nephrogenic diabetes insipidus: Phenotypic analysis and therapeutic implications Jian Hua Li, Chung-Lin Chou, Bo Li, Oksana Gavrilova, Christoph Eisner, Jürgen Schnermann, Stasia A. Anderson, Chu-Xia Deng, Mark A. Knepper, and Jürgen Wess Supplemental Methods Metabolic cage studies. Animals were maintained in mouse metabolic cages (Hatteras Instruments, Cary, NC) under controlled temperature and light conditions (12 hr light and dark cycles). Mice received a fixed daily ration of 6.5 g of gelled diet per 20 g of body weight per day. The gelled diet was composed of 4 g of Basal Diet 5755 (Test Diet, Richmond, IN), 2.5 ml of deionized water, and 65 mg agar. Preweighted drinking water was provided ad libitum during the course of the study. Mice were acclimated in the metabolic cages for 1-2 days. Urine was collected under mineral oil in preweighted collection vials for successive 24 hr periods. Analysis of GPCR expression in mouse IMCD cells via TaqMan real-time qRT-PCR. Total RNA prepared from mouse IMCD tubule suspensions was reverse transcribed as described under Experimental Procedures. Tissues from ten 10-week old C57BL/6 WT mice were collected and pooled for each individual experiment. cDNA derived from 640 ng of RNA was mixed with an equal volume of TaqMan gene expression 2 x master mix (Applied Biosystems, Foster City, CA). 100 μl-aliquots of this mixture (corresponding to 80 ng of RNA) were added to each of the 8 fill ports of a 384-well plate of a mouse GPCR array panel (Applied Biosystems). -
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. -
1 Metabolic Dysfunction Is Restricted to the Sciatic Nerve in Experimental
Page 1 of 255 Diabetes Metabolic dysfunction is restricted to the sciatic nerve in experimental diabetic neuropathy Oliver J. Freeman1,2, Richard D. Unwin2,3, Andrew W. Dowsey2,3, Paul Begley2,3, Sumia Ali1, Katherine A. Hollywood2,3, Nitin Rustogi2,3, Rasmus S. Petersen1, Warwick B. Dunn2,3†, Garth J.S. Cooper2,3,4,5* & Natalie J. Gardiner1* 1 Faculty of Life Sciences, University of Manchester, UK 2 Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK 3 Centre for Endocrinology and Diabetes, Institute of Human Development, Faculty of Medical and Human Sciences, University of Manchester, UK 4 School of Biological Sciences, University of Auckland, New Zealand 5 Department of Pharmacology, Medical Sciences Division, University of Oxford, UK † Present address: School of Biosciences, University of Birmingham, UK *Joint corresponding authors: Natalie J. Gardiner and Garth J.S. Cooper Email: [email protected]; [email protected] Address: University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, United Kingdom Telephone: +44 161 275 5768; +44 161 701 0240 Word count: 4,490 Number of tables: 1, Number of figures: 6 Running title: Metabolic dysfunction in diabetic neuropathy 1 Diabetes Publish Ahead of Print, published online October 15, 2015 Diabetes Page 2 of 255 Abstract High glucose levels in the peripheral nervous system (PNS) have been implicated in the pathogenesis of diabetic neuropathy (DN). However our understanding of the molecular mechanisms which cause the marked distal pathology is incomplete. Here we performed a comprehensive, system-wide analysis of the PNS of a rodent model of DN. -
Monitoring Nociception by Analyzing Gene Expression Changes in the Central Nervous System of Mice
Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2010 Monitoring nociception by analyzing gene expression changes in the central nervous system of mice Asner, I N Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-46678 Dissertation Originally published at: Asner, I N. Monitoring nociception by analyzing gene expression changes in the central nervous system of mice. 2010, University of Zurich, Vetsuisse Faculty. Monitoring Nociception by Analyzing Gene Expression Changes in the Central Nervous System of Mice Dissertation zur Erlangung der naturwissenschaftlichen Doktorwürde (Dr. sc. nat) vorgelegt der Mathematisch-naturwissenschaftlichen Fakultät der Universität Zürich von Igor Asner von St. Cergue VD Promotionskomitee Prof. Dr. Peter Sonderegger Prof. Dr. Kurt Bürki Prof. Dr. Hanns Ulrich Zeilhofer Dr. Paolo Cinelli (Leitung der Dissertation) Zürich, 2010 Table of contents Table of content Curriculum vitae 6 Publications 9 Summary 11 Zusammenfassung 14 1. Introduction 17 1.1. Pain and nociception 17 1.1.1 Nociceptive neurons and Mechanoceptors 18 1.1.2 Activation of the nociceptive neurons at the periphery 21 1.1.2.1 Response to noxious heat 22 1.1.2.2 Response to noxious cold 23 1.1.2.3 Response to mechanical stress 24 1.1.3 Nociceptive message processing in the Spinal Cord 25 1.1.3.1 The lamina I and the ascending pathways 25 1.1.3.2 The lamina II and the descending pathways 26 1.1.4 Pain processing and integration in the brain 27 1.1.4.1 The Pain Matrix 27 1.1.4.2 Activation of the descending pathways 29 1.1.5 Inflammatory Pain 31 1.2. -
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 -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes.