DOCSLIB.ORG

Development of Non-Overlapping Multiplex ELISA Arrays for the Quantitative Measurement of 400 Human and 200 Mouse Proteins in Parallel

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Development of non-overlapping multiplex ELISA arrays for the quantitative measurement of 400 human and 200 mouse proteins in parallel Yingqing Mao1, Zhiqiang Lv1, Haw-Han Yen1, Yanni Sun1 and Ruo-Pan Huang1,2,3 1RayBiotech, Inc., 3607 Parkway Lane, Suite 100, Norcross, GA 30092, USA; 2RayBiotech Inc., Guangzhou, 510600 China; 3South China Biochip Research Center, Guangzhou, China 510630 Web: www.raybiotech.com Email: [email protected] Phone: 770-729-2992 Abstract Platform & Principal Sensitivity & X-reactivity Highlights Accurate detection of multiple cytokines in complex biological samples is essential to progress in immunological research. However, development of Human receptor array • Proven sandwich ELISA detection high-density multiplex sandwich-based ELISA panels is hampered by 60,000 50,000 Control Array sensitivity test nonspecific cross-reactivity between and among target-specific reagents 40,000 Std7 (Left): One set of 3x • High throughput multiplex array format (i.e., capture antibodies, detection antibodies, and/or antigens); this is 30,000 Std6 Std5 diluted standards were 20,000 particularly challenging for bead-based assays, in which all three are free to Std4 run on human receptor 10,000 • Quadruplicate data for each target per run interact in solution. To overcome this bottleneck, we tested for nonspecific Std3 0 Std2 array. 2 1 1 3 1 1 2 1 1 - - - - - - - - - 3L b1 Fas - Dtk - 1 RI 1 17R 21R 1BB DR6 2 Rg 2 1 R4 1 - B7 GITR - - - - Std1 - uPAR CD14 CD30 MICB MICA RAGE 10 Rb 10 Flt cross-reactivity among reagents used on our quantitative antibody arrays (in TIM 4 ErbB3 IL BCMA HVEM LIMPII - IL IL IL XEDAR IL LYVE CD40L ALCAM ICAM Selectin Selectin Fluor dye (cy3 equivalent) IL VCAM - PDGF Rb Endoglin - TRAIL R3 TRAIL NRG1 • ELISA sensitivity with wider dynamic range PECAM L Trappin E CEACAM which capture antibodies are affixed to a glass slide). Combinations of Lipocalin Biotin-Streptavidin complex reagents exhibiting nonspecific interactions were separated on discrete B Detect antibody • High specificity, accuracy & reproducibility arrays. Using this divide-and-conquer strategy, we developed and validated Cytokine 25,000 10 human and 5 mouse non-overlapping 40-plex arrays for parallel, Capture antibody Array X-reactivity test 20,000 • Less sample, time, and cost quantitative detection of 400 human and 200 mouse proteins respectively. To Glass Slide Support (Right): Individual DAB 15,000 our best knowledge, this is the highest density multiplex ELISA developed was tested in the 10,000 TRAIL R3 NRG-b1 • Suitable for diverse sample types 5,000 Lipocalin-2 to date. Such a system hold a great promise in determining key factors and Configuration of array platform and illustration of sandwich presence of mixed IL-10 Rb 0 GITR mechanisms in immune cell physiology and pathology, in identifying novel ELISA principal. One standard glass slide is spotted with 16 wells of antigens (10 ng/ml Endoglin identical cytokine antibody arrays. The sandwich ELISA detection CD30 • Software supports one step data computation each) with all CAB. 4-1BB drug targets, and in screening for putative biomarkers from patients' principal is depicted below after the consequent reaction of sample, samples. biotinylated detection antibody, and Streptavidin labeled fluor dye. Strategy Array vs. ELISA Accuracy & Precision Sample Test Step 1: ELISA confirmation of cytokine antibody IL-6 Detection Dynamic Range Example of Levey Jennings (pg/ml) LOD Normal Serum (n=31) pairs; System optimization in array platform. in Quantibody and ELISA Quadruplicate data IL-1a 5 6 ± 2 106 Chart for IL-6 in 14 runs Coefficient of Variation < IL-1b 1 1 ± 0 IL-1ra 59 222 ± 60 150.3 105 (n=8124) LOD IL-2 5 27 ± 5 IL-4 4 7 ± 2 Step 2: Print capture antibody(CAB) arrays, and 4 35 17% 10 140.3 UCL 139.53 IL-5 11 82 ± 15 test individual detection antibody (DAB) in the IL-6 7 42 ± 7 3 10 IL-6sR 7 755 ± 25 presence of mixed antigens at 10 ng/ml each. ELISA 30 < Quantibody 130.3 IL-7 11 50 ± 6 102 > 25 LOQ IL-8 1 13 ± 2 IL-10 3 7 ± 1 101 120.3 LOQ 16% IL-11 34 39 ± 11 Step 3: Do inter-array signal normalization, and 0 20 IL-12p40 18 30 ± 4 10 CL 109.87 67% create cross reactivity table (DAB vs. CAB). 110.3 IL-12p70 1 0 ± 0 10-1 15 IL-13 3 16 ± 3 IL-15 17 158 ± 33 Signal Intensity (Quantibody) or (ELISA) or OD (Quantibody) Intensity Signal 100.3 10-2 IL-16 16 69 ± 14 6 Concentration Concentration 6 (pg/ml) 0.1 1 10 100 1000 10000 - 10 IL-17 18 2 ± 0 IL Step 4: Divide cytokines into different subgroups IL-6 Concentration (pg/ml) 90.3 MCP-1 7 180 ± 23 Percentage of Assays of Percentage based upon the serum abundance, functionality 5 MCSF 3 1 ± 0 LCL 80.21 etc. and generate multiple 40-plex arrays. 80.3 Comparison of array and ELISA dynamic range and detection sensitivity. The 0 <5% 5-10% 10-15% 15-20% >20% dynamic range of IL-6 in array and ELISA (Left) and the correlation of IL-6 detection 70.3 Cytokine quantification in normal human sera (n=31). CV Range Step 5:Test individual array sensitivity, cross by both methods in 40 human culture media samples (Right). The same IL-6 pair of Limit of Detection (LOD): 3 standard deviation above negative control. reactivity, and reproducibility. Real sample test. antibodies together with the same antigen resource were used in both systems. Limit of Quantification (LOQ) = 10 standard deviation above negative control. Array Development Multiplex ELISA Arrays List of Target Proteins Conclusions Human targets Mouse targets Individual cross reactivity test Cross Reactivity Database 70000 IL-17 1. Here we developed and validated multiple non- # Cytokine Gene Symbol Uniprot Number HUGO Approved Name # Cytokine Gene Symbol Uniprot Number HUGO Approved Name # Cytokine Gene Symbol Uniprot Number HUGO Approved Name # Cytokine Gene Symbol Uniprot Number HUGO Approved Name # Cytokine In Array Gene Symbol # Cytokine In Array Gene Symbol 60000 1 2B4 CD244 Q9BZW8 CD244 molecule, natural killer cell receptor 2B4 101 Endostatin COL18A1 P39060 collagen, type XVIII, alpha 1 201 IL-1F6 IL36A Q9UHA7 interleukin 36, alpha 301 P-Cadherin CDH3 P22223 cadherin 3, type 1, P-cadherin (placental) 1 4-1BB mCYT6 TNFRSF9 101 IL-21 mCYT5 IL21 5PL Curve Signal Intensity 2 4-1BB TNFRSF9 Q07011 tumor necrosis factor receptor superfamily, member 9 102 Eotaxin-1 CCL11 P51671 chemokine (C-C motif) ligand 11 202 IL-1F7 IL37 Q9NZH6 interleukin 37 302 PD-1 PDCD1 Q15116 programmed cell death 1 2 6Ckine mCYT8 CCL21 102 IL-22 mCYT8 IL22 Array Cross Reactivity Check 3 6Ckine CCL21 O00585 chemokine (C-C motif) ligand 21 103 Eotaxin-2 Ccl24 O00175 chemokine (C-C motif) ligand 24 203 IL-1F8 IL36B Q9NZH7 interleukin 36, beta 303 PD-ECGF TYMP P19971 thymidine phosphorylase 3 ACE mCYT6 ACE 103 IL-23 mCYT4 IL23A CEA 4 A2M A2M P01023 alpha-2-macroglobulin 104 Eotaxin-3 CCL26 Q9Y258 chemokine (C-C motif) ligand 26 204 IL-1F9 IL36G Q9NZH8 interleukin 36, gamma 304 PDGF R alpha PDGFRA P16234 platelet-derived growth factor receptor, alpha polypeptide 4 Activin A mCYT8 INHBA 104 IL-28 mCYT4 IFNL2 50000 5 ACE-2 ACE2 Q9BYF1 angiotensin I converting enzyme 2 105 EpCAM EPCAM P16422 epithelial cell adhesion molecule 205 IL-1R3 IL1RAP Q9NPH3 interleukin 1 receptor accessory protein 305 PDGF Rb PDGFRB P09619 platelet-derived growth factor receptor, beta polypeptide 2,000 5 ADAMTS1 mCYT8 ADAMTS1 105 IL-3 mCYT5 IL3 3500 6 Activin A INHBA P08476 inhibin, beta A 106 Epo R EPOR P19235 erythropoietin receptor 206 IL-1R5 Il18r1 Q13478 interleukin 18 receptor 1 306 PDGF-AA PDGFA P04085 platelet-derived growth factor alpha polypeptide 6 Adiponectin mCYT8 ADIPOQ 106 IL-3 Rb mCYT6 Csf2rb 7 ADAM-9 ADAM9 Q13443 ADAM metallopeptidase domain 9 107 ErbB2 ErbB2 P04626 v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2 207 IL-1ra IL1RN P18510 interleukin 1 receptor antagonist 307 PDGF-AB PDGFB P01127 platelet-derived growth factor beta polypeptide overlapping multiplex ELISA arrays for quantitative 7 ALK-1 mCYT6 SLPI 107 IL-33 mCYT7 IL33 8 Adiponectin ADIPOQ Q15848 adiponectin, C1Q and collagen domain containing 108 ErbB3 ErbB3 P21860 v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 3 208 IL-2 IL2 P60568 interleukin 2 308 PDGF-BB PDGFB P01127 platelet-derived growth factor beta polypeptide 8 ANG-3 mCYT8 ANGPTL1 108 IL-4 mCYT5 IL4 3000 40000 9 Adipsin CFD P00746 complement factor D (adipsin) 109 E-Selectin SELE P16581 selectin E 209 IL-2 Ra IL2RA P01589 interleukin 2 receptor, alpha 309 PECAM-1 PECAM1 P16284 platelet/endothelial cell adhesion molecule 1 9 ANGPTL3 mCYT8 ANGPTL3 109 IL-5 mCYT5 IL5 10 aFGF FGF1 P05230 fibroblast growth factor 1 (acidic) 110 FABP2 FABP2 P12104 fatty acid binding protein 2, intestinal 210 IL-2 Rb IL2RA P01589 interleukin 2 receptor, alpha 310 Pepsinogen I PGA3 P0DJD8 pepsinogen 3, group I (pepsinogen A) 10 AR mCYT4 AREG 110 IL-6 mCYT5 IL6 1,500 11 AFP AFP P02771 alpha-fetoprotein 111 FAP FAP Q12884 fibroblast activation protein, alpha 211 IL-2 Rg IL2RG P31785 interleukin 2 receptor, gamma 311 Pepsinogen II PGC P20142 progastricsin (pepsinogen C) 11 Artemin mCYT8 ARTN 111 IL-7 mCYT5 IL7 2500 12 AgRP AgRP O00253 agouti related protein homolog (mouse) 112 FAS Fas P25445 Fas cell surface death receptor 212 IL-20 IL20 Q9NYY1 interleukin 20 312 Periostin POSTN Q15063 periostin, osteoblast specific factor 12 Axl mCYT4 Axl 112 IL-7 Rα mCYT7 IL7R 30000 13 Albumin ALB P02768 albumin 113 FAS L FASLG P48023 Fas ligand (TNF superfamily, member 6) 213 IL-21 IL21 Q9HBE4 interleukin 21 313 Persephin PSPN O60542 persephin 13 B7-1 mCYT7 CD80 113 IL-9 mCYT6 IL9 14 ALCAM ALCAM Q13740 activated leukocyte cell adhesion molecule 114 Fcr RIIBC FCGR2B P31994 Fc fragment of IgG, low affinity IIb, receptor (CD32) 214 IL-21R IL21R Q9HBE5 interleukin 21 receptor 314 PF4 CXCL14 O95715 chemokine (C-X-C motif) ligand 14 14 BAFF R mCYT7 TNFRSF13C 114 I-TAC mCYT4 CXCL11 measurement of 400 human and 200 mouse proteins.
Recommended publications
  • Gene Expression Polarization

    Gene Expression Polarization

    Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression This information is current as of September 27, 2021. Fernando O. Martinez, Siamon Gordon, Massimo Locati and Alberto Mantovani J Immunol 2006; 177:7303-7311; ; doi: 10.4049/jimmunol.177.10.7303 http://www.jimmunol.org/content/177/10/7303 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2006/11/03/177.10.7303.DC1 Material http://www.jimmunol.org/ References This article cites 61 articles, 22 of which you can access for free at: http://www.jimmunol.org/content/177/10/7303.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 27, 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 © 2006 by The American Association of Immunologists All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression1 Fernando O.
  • Freedom of Information Act Request – Reference Foi/13/116 New Drugs Added to Formulary

    Freedom of Information Act Request – Reference Foi/13/116 New Drugs Added to Formulary

    Freedom of Information Act Request – Reference FoI/13/116 New Drugs Added to Formulary Request details Please will you provide me with numbers of new drugs that your LHB has introduced over the past three years, listing the number of new drugs introduced by year in each of those years? Response 2010 = 41 2011 = 53 2012 = 71 2010 Generic Name 1. adalimumab, etanercept, infliximab, rituximab and abatacept 2. beclometasone and formoterol (Fostair®) 3. bortezomib (Velcade®) in combination with melphalan and prednisone 4. Brinzolamide/timolol (Azarga®) 5. bromocriptine 6. calcium and vitamin D3 (Calceos®) 7. calcium and vitamin D (Adcal D3 Dissolve®) 8. capecitabine 9. Carmellose eye drops (Optive®) 10. Certolizumab pegol 11. Darunavir (Prezista®▼) 12. epoetin alfa (Binocrit®) 13. epoetin theta (Eporatio®) 14. Eslicarbazepine acetate (Zebinix®) 15. Evicel 16. fentanyl buccal tablets (Effentora) 17. fentanyl intranasal spray (Instanyl®) 18. Fesoterodine (Toviaz®) 19. filgrastim (TevaGrastim®) 20. filgrastim (Zarzio®) 21. gefitinib 22. infliximab and adalimumab 23. liraglutide (Victoza®) 24. Loperamide tablets 25. losartan 26. Mepilex® Ag 27. Movicol Paediatric 28. Nebuchamber 29. paclitaxel albumin (Abraxane®) monotherapy 30. pemetrexed 31. Plerixafor (Mozobil®q) 32. pramipexole prolonged release (Mirapexin®) 33. Prontosan® wound irrigation solution and gel 34. quinagolide (Norprolac®) 35. raltegravir (Isentress®) 36. rituximab sildenafil (Revatio®) tablets 2010 Generic Name 37. sodium chloride (7%) Nebusal® 38. somatropin (NutropinAq®) 39. Topotecan 40. Trabectedin 41. Xamiol 2011 Generic Name 1. artemether and lumefantrine (Riamet®) 2. artesunate 3. atazanavir (Reyataz®) co-administered with low dose ritonavir 4. azacitidine 5. aztreonam (Azactam®) 6. bendamustine 7. Bivalirudin 8. Bortezomib 9. Calcium acetate and magnesium carbonate (Osvaren®) 10. darunavir (Prezista®) 11.
  • FABP2 Ala54thr Polymorphism and Post-Training Changes of Body Composition and Biochemical Parameters in Caucasian Women

    FABP2 Ala54thr Polymorphism and Post-Training Changes of Body Composition and Biochemical Parameters in Caucasian Women

    G C A T T A C G G C A T genes Article FABP2 Ala54Thr Polymorphism and Post-Training Changes of Body Composition and Biochemical Parameters in Caucasian Women Agata Leo ´nska-Duniec 1,*, Katarzyna Switała´ 1, Ildus I. Ahmetov 2,3 , Craig Pickering 4 , Myosotis Massidda 5 , Maciej Buryta 6, Andrzej Mastalerz 7 and Ewelina Maculewicz 7 1 Faculty of Physical Education, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland; [email protected] 2 Laboratory of Molecular Genetics, Kazan State Medical University, 420012 Kazan, Russia; [email protected] 3 Department of Physical Education, Plekhanov Russian University of Economics, 117997 Moscow, Russia 4 Institute of Coaching and Performance, School of Sport and Wellbeing, University of Central Lancashire, Preston PR1 2HE, UK; [email protected] 5 Department of Life and Environmental Sciences, University of Cagliari, 09124 Cagliari, Italy; [email protected] 6 Institute of Physical Culture Sciences, University of Szczecin, 70-453 Szczecin, Poland; [email protected] 7 Faculty of Physical Education, Jozef Pilsudski University of Physical Education in Warsaw, 00-968 Warsaw, Poland; [email protected] (A.M.); [email protected] (E.M.) * Correspondence: [email protected] Abstract: The functional FABP2 Ala54Thr polymorphism (rs1799883) is strongly associated with lipid Citation: Leo´nska-Duniec,A.; Switała,´ K.; Ahmetov, I.I.; Pickering, and carbohydrate metabolism, although the function of its potential modifying effect on training- C.; Massidda, M.; Buryta, M.; induced changes in obesity-related parameters is still unknown. The aim of the present study was Mastalerz, A.; Maculewicz, E.
  • Endothelial Cells and the IGF System

    Endothelial Cells and the IGF System

    L A Bach Endothelial cells as IGF targets 54:1 R1–R13 Review Endothelial cells and the IGF system Correspondence 1,2 Leon A Bach should be addressed to L A Bach 1Department of Medicine (Alfred), Monash University, Prahran 3181, Australia Email 2Department of Endocrinology and Diabetes, Alfred Hospital, Commercial Road, Melbourne 3004, Australia [email protected] Abstract Endothelial cells line blood vessels and modulate vascular tone, thrombosis, inflammatory Key Words responses and new vessel formation. They are implicated in many disease processes including " insulin-like growth factor atherosclerosis and cancer. IGFs play a significant role in the physiology of endothelial cells " binding protein by promoting migration, tube formation and production of the vasodilator nitric oxide. " receptor These actions are mediated by the IGF1 and IGF2/mannose 6-phosphate receptors and are " endothelial cell modulated by a family of high-affinity IGF binding proteins. IGFs also increase the number " angiogenesis and function of endothelial progenitor cells, which may contribute to protection from atherosclerosis. IGFs promote angiogenesis, and dysregulation of the IGF system may contribute to this process in cancer and eye diseases including retinopathy of prematurity and diabetic retinopathy. In some situations, IGF deficiency appears to contribute to endothelial dysfunction, whereas IGF may be deleterious in others. These differences may be due to tissue-specific endothelial cell phenotypes or IGFs having distinct roles in different phases of vascular disease. Further studies are therefore required to delineate the Journal of Molecular therapeutic potential of IGF system modulation in pathogenic processes. Endocrinology (2015) 54, R1–R13 Journal of Molecular Endocrinology Introduction Insulin-like growth factor 1 (IGF1) and IGF2 are essential metabolically active and regulate vascular tone, thrombosis, for normal pre- and postnatal growth and development inflammatory responses and new vessel formation.
  • Complementary DNA Microarray Analysis of Chemokines and Their Receptors in Allergic Rhinitis RX Zhang,1 SQ Yu,2 JZ Jiang,3 GJ Liu3

    Complementary DNA Microarray Analysis of Chemokines and Their Receptors in Allergic Rhinitis RX Zhang,1 SQ Yu,2 JZ Jiang,3 GJ Liu3

    RX Zhang, et al ORIGINAL ARTICLE Complementary DNA Microarray Analysis of Chemokines and Their Receptors in Allergic Rhinitis RX Zhang,1 SQ Yu,2 JZ Jiang,3 GJ Liu3 1 Department of Otolaryngology, Huadong Hospital, Fudan University, Shanghai, China 2 Department of Otolaryngology , Jinan General Hospital of PLA, Shandong, China 3 Department of Otolaryngology, Changhai Hospital, Second Military Medical University, Shanghai, China ■ Abstract Objective: To analyze the roles of chemokines and their receptors in the pathogenesis of allergic rhinitis by observing the complementary DNA (cDNA) expression of the chemokines and their receptors in the nasal mucosa of patients with and without allergic rhinitis, using gene chips. Methods: The total RNAs were isolated from the nasal mucosa of 20 allergic rhinitis patients and purifi ed to messenger RNAs, and then reversely transcribed to cDNAs and incorporated with samples of fl uorescence-labeled with Cy5-dUPT (rhinitis patient samples) or Cy3- dUTP (control samples of nonallergic nasal mucosa). Thirty-nine cDNAs of chemokines and their receptors were latticed into expression profi le chips, which were hybridized with probes and then scanned with the computer to study gene expression according to the different fl uorescence intensities. Results: The cDNAs of the following chemokines were upregulated: CCL1, CCL2, CCL5, CCL7, CCL8, CCL11, CCL13, CCL14, CCL17, CCL18, CCL19, CCL24, and CX3CL1 in most of the allergic rhinitis sample chips. CCR2, CCR3, CCR4, CCR5, CCR8 and CX3CR1 were the highly expressed receptor genes. Low expression of CXCL4 was found in these tissues. Conclusion: The T helper cell (TH) immune system is not well regulated in allergic rhinitis.
  • ARTICLES Fibroblast Growth Factors 1, 2, 17, and 19 Are The

    ARTICLES Fibroblast Growth Factors 1, 2, 17, and 19 Are The

    0031-3998/07/6103-0267 PEDIATRIC RESEARCH Vol. 61, No. 3, 2007 Copyright © 2007 International Pediatric Research Foundation, Inc. Printed in U.S.A. ARTICLES Fibroblast Growth Factors 1, 2, 17, and 19 Are the Predominant FGF Ligands Expressed in Human Fetal Growth Plate Cartilage PAVEL KREJCI, DEBORAH KRAKOW, PERTCHOUI B. MEKIKIAN, AND WILLIAM R. WILCOX Medical Genetics Institute [P.K., D.K., P.B.M., W.R.W.], Cedars-Sinai Medical Center, Los Angeles, California 90048; Department of Obstetrics and Gynecology [D.K.] and Department of Pediatrics [W.R.W.], UCLA School of Medicine, Los Angeles, California 90095 ABSTRACT: Fibroblast growth factors (FGF) regulate bone growth, (G380R) or TD (K650E) mutations (4–6). When expressed at but their expression in human cartilage is unclear. Here, we deter- physiologic levels, FGFR3-G380R required, like its wild-type mined the expression of entire FGF family in human fetal growth counterpart, ligand for activation (7). Similarly, in vitro cul- plate cartilage. Using reverse transcriptase PCR, the transcripts for tivated human TD chondrocytes as well as chondrocytes FGF1, 2, 5, 8–14, 16–19, and 21 were found. However, only FGF1, isolated from Fgfr3-K644M mice had an identical time course 2, 17, and 19 were detectable at the protein level. By immunohisto- of Fgfr3 activation compared with wild-type chondrocytes and chemistry, FGF17 and 19 were uniformly expressed within the showed no receptor activation in the absence of ligand (8,9). growth plate. In contrast, FGF1 was found only in proliferating and hypertrophic chondrocytes whereas FGF2 localized predominantly to Despite the importance of the FGF ligand for activation of the resting and proliferating cartilage.
  • ALCAM Regulates Mediolateral Retinotopic Mapping in the Superior Colliculus

    ALCAM Regulates Mediolateral Retinotopic Mapping in the Superior Colliculus

    15630 • The Journal of Neuroscience, December 16, 2009 • 29(50):15630–15641 Development/Plasticity/Repair ALCAM Regulates Mediolateral Retinotopic Mapping in the Superior Colliculus Mona Buhusi,1 Galina P. Demyanenko,1 Karry M. Jannie,2 Jasbir Dalal,1 Eli P. B. Darnell,1 Joshua A. Weiner,2 and Patricia F. Maness1 1Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599, and 2Department of Biology, University of Iowa, Iowa City, Iowa 52242 ALCAM [activated leukocyte cell adhesion molecule (BEN/SC-1/DM-GRASP)] is a transmembrane recognition molecule of the Ig superfamily (IgSF) containing five Ig domains (two V-type, three C2-type). Although broadly expressed in the nervous and immune systems, few of its developmental functions have been elucidated. Because ALCAM has been suggested to interact with the IgSF adhesion molecule L1, a determi- nant of retinocollicular mapping, we hypothesized that ALCAM might direct topographic targeting to the superior colliculus (SC) by serving as a substrate within the SC for L1 on incoming retinal ganglion cell (RGC) axons. ALCAM was expressed in the SC during RGC axon targeting and on RGC axons as they formed the optic nerve; however, it was downregulated distally on RGC axons as they entered the SC. Axon tracing with DiI revealedpronouncedmistargetingofRGCaxonsfromthetemporalretinahalfofALCAMnullmicetoabnormallylateralsitesinthecontralateral SC, in which these axons formed multiple ectopic termination zones. ALCAM null mutant axons were
  • Disruption of Fibroblast Growth Factor Signal

    Disruption of Fibroblast Growth Factor Signal

    Cancer Therapy: Preclinical Disruption of Fibroblast Growth Factor Signal Pathway Inhibits the Growth of Synovial Sarcomas: Potential Application of Signal Inhibitors to MolecularTarget Therapy Ta t s u y a I s hi b e , 1, 2 Tomitaka Nakayama,2 Ta k e s h i O k a m o t o, 1, 2 Tomoki Aoyama,1Koichi Nishijo,1, 2 Kotaro Roberts Shibata,1, 2 Ya s u ko Shim a ,1, 2 Satoshi Nagayama,3 Toyomasa Katagiri,4 Yusuke Nakamura, 4 Takashi Nakamura,2 andJunya Toguchida 1 Abstract Purpose: Synovial sarcoma is a soft tissue sarcoma, the growth regulatory mechanisms of which are unknown.We investigatedthe involvement of fibroblast growth factor (FGF) signals in synovial sarcoma andevaluatedthe therapeutic effect of inhibiting the FGF signal. Experimental Design:The expression of 22 FGF and4 FGF receptor (FGFR) genes in18prima- ry tumors andfive cell lines of synovial sarcoma were analyzedby reverse transcription-PCR. Effects of recombinant FGF2, FGF8, andFGF18 for the activation of mitogen-activatedprotein kinase (MAPK) andthe growth of synovial sarcoma cell lines were analyzed.Growth inhibitory effects of FGFR inhibitors on synovial sarcoma cell lines were investigated in vitro and in vivo. Results: Synovial sarcoma cell lines expressedmultiple FGF genes especially those expressed in neural tissues, among which FGF8 showedgrowth stimulatory effects in all synovial sarcoma cell lines. FGF signals in synovial sarcoma induced the phosphorylation of extracellular signal ^ regulatedkinase (ERK1/2) andp38MAPK but not c-Jun NH 2-terminal kinase. Disruption of the FGF signaling pathway in synovial sarcoma by specific inhibitors of FGFR causedcell cycle ar- rest leading to significant growth inhibition both in vitro and in vivo.Growthinhibitionbythe FGFR inhibitor was associatedwith a down-regulation of phosphorylatedERK1/2 but not p38MAPK, andan ERK kinase inhibitor also showedgrowth inhibitory effects for synovial sar- coma, indicating that the growth stimulatory effect of FGF was transmitted through the ERK1/2.
  • (Epoetin and Darbepoetin) for Treating Cancer Treatment-Induced Anaemia (Including Review of Technology Appraisal No

    (Epoetin and Darbepoetin) for Treating Cancer Treatment-Induced Anaemia (Including Review of Technology Appraisal No

    HEALTH TECHNOLOGY ASSESSMENT VOLUME 20 ISSUE 13 FEBRUARY 2016 ISSN 1366-5278 The effectiveness and cost-effectiveness of erythropoiesis-stimulating agents (epoetin and darbepoetin) for treating cancer treatment-induced anaemia (including review of technology appraisal no. 142): a systematic review and economic model Louise Crathorne, Nicola Huxley, Marcela Haasova, Tristan Snowsill, Tracey Jones-Hughes, Martin Hoyle, Simon Briscoe, Helen Coelho, Linda Long, Antonieta Medina-Lara, Ruben Mujica-Mota, Mark Napier and Chris Hyde DOI 10.3310/hta20130 The effectiveness and cost-effectiveness of erythropoiesis-stimulating agents (epoetin and darbepoetin) for treating cancer treatment-induced anaemia (including review of technology appraisal no. 142): a systematic review and economic model Louise Crathorne,1* Nicola Huxley,1 Marcela Haasova,1 Tristan Snowsill,1 Tracey Jones-Hughes,1 Martin Hoyle,1 Simon Briscoe,1 Helen Coelho,1 Linda Long,1 Antonieta Medina-Lara,2 Ruben Mujica-Mota,1 Mark Napier3 and Chris Hyde1 1Peninsula Technology Assessment Group (PenTAG), University of Exeter Medical School, Exeter, UK 2University of Exeter Medical School, Exeter, UK 3Royal Devon and Exeter Hospital, Exeter, UK *Corresponding author Declared competing interests of authors: none Published February 2016 DOI: 10.3310/hta20130 This report should be referenced as follows: Crathorne L, Huxley N, Haasova M, Snowsill T, Jones-Hughes T, Hoyle M, et al. The effectiveness and cost-effectiveness of erythropoiesis-stimulating agents (epoetin and darbepoetin) for treating cancer treatment-induced anaemia (including review of technology appraisal no. 142): a systematic review and economic model. Health Technol Assess 2016;20(13). Health Technology Assessment is indexed and abstracted in Index Medicus/MEDLINE, Excerpta Medica/EMBASE, Science Citation Index Expanded (SciSearch®) and Current Contents®/ Clinical Medicine.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    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.
  • United States Patent (19) 11 Patent Number: 5,766,866 Center Et Al

    United States Patent (19) 11 Patent Number: 5,766,866 Center Et Al

    USOO5766866A United States Patent (19) 11 Patent Number: 5,766,866 Center et al. 45) Date of Patent: Jun. 16, 1998 54 LYMPHOCYTE CHEMOATTRACTANT Center, et al. The Journal of Immunology. 128:2563-2568 FACTOR AND USES THEREOF (1982). 75 Inventors: David M. Center. Wellesley Hills; Cruikshank, et al. The Journal of Immunology, William W. Cruikshank. Westford; 138:3817-3823 (1987). Hardy Kornfeld, Brighton, all of Mass. Cruikshank, et al., The Journal of Immunology, 73) Assignee: Research Corporation Technologies, 146:2928-2934 (1991). Inc., Tucson, Ariz. Cruikshank, et al., EMBL Database. Accession No. M90301 (21) Appl. No.: 580,680 (1992). 22 Filed: Dec. 29, 1995 Cruikshank. et al. The Journal of Immunology, 128:2569-2574 (1982). Related U.S. Application Data Rand, et al., J. Exp. Med., 173:1521-1528 (1991). 60 Division of Ser. No. 480,156, Jun. 7, 1995, which is a continuation-in-part of Ser. No. 354,961, Dec. 13, 1994, Harlow (1988) Antibodies, a laboratory manual. Cold Spring which is a continuation of Ser. No. 68,949, May 21, 1993, Harbor Laboratory, 285, 287. abandoned. Waldman (1991) Science. vol. 252, 1657-1662. (51) Int. Cl. ....................... G01N 33/53; CO7K 1700; CO7K 16/00: A61K 45/05 Hams et al. (1993) TIBTECH Feb. 1993 vol. 111, 42-44. 52 U.S. Cl. ......................... 424/7.24; 435/7.1; 435/7.92: 435/975; 530/350:530/351; 530/387.1: Center, et al. (Feb. 1995) "The Lymphocyte Chemoattractant 530/388.23: 530/389.2: 424/85.1: 424/130.1 Factor”. J. Lab. Clin. Med. 125(2):167-172.
  • Cellular and Molecular Signatures in the Disease Tissue of Early

    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