DOCSLIB.ORG

Supplementary Table S5. List of All the Peptides That Were Included in the Peptide Biomarker Panels for Detecting Primary UBC

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supplementary Table S5. List of All the Peptides That Were Included in the Peptide Biomarker Panels for Detecting Primary UBC Supplementary Table S5. List of all the peptides that were included in the peptide biomarker panels for detecting primary UBC Sequence information Peptide- SwissProt Amino acid Sequence‡ Protein name ID† Accession ID 1747 DCGDNSDEEN Low-density lipoprotein receptor-related protein 2 LRP2 1907 SAYQEAMDIS 14-3-3 protein sigma, SFN SFN 1944 PpGPpGpPGPpS Collagen alpha-1(I) chain CO1A1 2032 GGpGSDGKpGPpG Collagen alpha-1(III) chain CO3A1 2200 TPEEKSAVTAL Hemoglobin subunit beta HBA 2451 PTALGVRGASRS Bromodomain-containing protein 1 BRD1 2522 KPLKEPGLGQL Mitogen-activated protein kinase 14 MAPK14 2543 LSALEEYTKK Apolipoprotein A-I APOA1 2592 DDGEAGKpGRpG Collagen alpha-1(I) chain CO1A1 2597 TCGDSCDQCCPG Laminin subunit alpha-2 LAMA2 2794 DGPAGApGTPGpQG Collagen alpha-1(I) chain COL1A1 Basement membrane-specific heparan sulfate 2801 GTCSGCNCNGHAS HSPG2 proteoglycan core protein 2830 VDEVGGEALGRL Hemoglobin subunit beta HBB 2858 EAGGGSNSLQNSP FERM domain-containing protein 4A FRM4A 3183 KPWAAQDGPKPG Leucine-rich repeat-containing protein 25 LRC25 3202 EEYTKKLNTQ Apolipoprotein A-I APOA1 3223 PGYTGNGYGPNGC Cubilin CUBN 3430 KVVAGVANALAHK Hemoglobin subunit delta HBD 3651 MYVSGpPGpPGpP Collagen alpha-1(XVII) chain, COL17A1 COL17A1 3758 VHLTPEEKSAVT Hemoglobin subunit beta HBB 4152 4157 VAGVANALAHKYH Hemoglobin subunit beta HBB 4270 AVADTRDQADGSR Polymeric immunoglobulin receptor PIGR 4532 GSYNLPSLPDIDC Transcription intermediary factor 1-alpha, TRIM24 TRIM24 4535 GPPGpPGPpGPPGPPS Collagen alpha-1(I) chain CO1A1 4759 GpPGEGLPGPpGpPGS Collagen alpha-1(XVII) chain, COL17A1 COL17A1 4957 ALEEYTKKLNTQ Apolipoprotein A-I APOA1 5087 VVAGVANALAHKYH Hemoglobin subunit beta HBB Membrane-associated progesterone receptor 5216 DSDDDEPPPLPRL PGRMC1 component 1, PGRMC1 5423 VGpPGPPGpPGPPGPPS Collagen alpha-1(I) chain CO1A1 5438 PRGDqGqDGAAGPpGP Collagen alpha-1(XXIII) chain, COL23A1 COL23A1 5444 TKVPEPGSIKVPDQ Small proline-rich protein 3 SPRR3 5673 KGDpGpAGLpGKDGpP Collagen alpha-1(V) chain COL5A1 5705 DGQPGAKGEpGDAGAKG Collagen alpha-1(I) chain COL1A1 5781 ADSGEGDFLAEGGGVR Fibrinogen alpha chain FIBA 5998 EDGHPGKPGRpGERG Collagen alpha-2(I) chain COL1A2 6355 DpGPpGQSGRDGYPGp Collagen alpha-1(XXIII) chain CONA1 6373 PpGFGRpGDPGPPGPpG Collagen alpha-1(XV) chain COL15A1 6498 GPpGPPGPPGPPGVSGGGY Collagen alpha-2(I) chain COL1A2 6571 6708 GSSVGTGTNLHSESASF Serine/threonine-protein kinase D1 PRKD1 6924 GPpGPpGTSGHPGSpGSpG Collagen alpha-1(III) chain CO3A1 6985 TGDAGpVGPpGPpGPPGPP Collagen alpha-1(I) chain CO1A1 6998 RINHTVILDDPFDD Peptidyl-prolyl cis-trans isomerase-like 4 PPIL4 7390 PpGEAGKpGEQGVpGDLG Collagen alpha-1(I) chain CO1A1 7502 7648 TGLSMDGGGSPKGDVDPF Sodium/potassium-transporting ATPase subunit gamma ATNG 7711 EpGSpGENGApGQMGPRG Collagen alpha-1(I) chain CO1A1 7772 SDLHAHKLRVDPVNF Hemoglobin subunit alpha HBA1 7778 pGqpGIKGDRGFpGEMG Collagen alpha-1(X) chain COL10A1 8185 EEAPSLRPAPPPISGGGY Fibrinogen beta chain FGB 8222 WGKVNVDEVGGEALGRL Hemoglobin subunit beta, HBB HBB 8225 IGTYCGQPVCENGCQNGG Fibrillin-2, FBN2 FBN2 8322 GpAGSpGSNGApGQRGEpGP Collagen alpha-1(III) chain COL3A1 8371 GEpGSpGENGAPGQmGpRG Collagen alpha-1(I) chain COL1A1 8694 DEPPQSPWDRVKDLAT Apolipoprotein A-I APOA1 8739 NpGPPGpSGSpGKDGPpGPAG Collagen alpha-1(III) chain COL3A1 8755 EGSpGRDGSpGAKGDRGET Collagen alpha-1(I) chain CO1A1 8878 pPGPTGPGGDKGDTGPpGPQG Collagen alpha-1(III) chain, COL3A1 COL3A1 8946 HLPAEFTPAVHASLDKF Hemoglobin subunit alpha HBA1 9003 ADLADGVSGGEGKGGSDGGGSH CD99 antigen, CD99 CD99 9061 GApGApGGKGDAGAPGERGppG Collagen alpha-1(III) chain CO3A1 9121 TQPPEEETLSQAPESEE Rho guanine nucleotide exchange factor 25, ARHGEF25 ARHGEF25 9385 AARLEALKENGGARLAEY Apolipoprotein A-I APOA1 9588 DGVWNGMIGEVFYQRAD Glutamate receptor ionotropic, NMDA1 GRIN2D 10081 AAHLPAEFTPAVHASLDKF Hemoglobin subunit alpha HBA 10200 GppGESGREGApGAEGSPGRDG Collagen alpha-1(I) chain COL1A1 Disintegrin and metalloproteinase domain-containing 10706 KPGDGDSFYSDIPPGVSTNSA ADAM22 protein 22 10745 DEPPQSPWDRVKDLATVY Apolipoprotein A-I APOA1 GpTGpIGPpGpAGQPGDKGEGGA 10838 Collagen alpha-1(III) chain CO3A1 P 11343 EAEDLQVGQVELGGGPGAGSLQP Insulin INS 11348 11407 AAAAQGTAPPQDGEQPAESPEPP Diacylglycerol kinase kappa DGKK NGDDGEAGKPGRpGERGPpGPQ 11536 Collagen alpha-1(I) chain CO1A1 G NGDDGEAGKPGRPGERGppGpQ 11672 Collagen alpha-1(I) chain CO1A1 G Thioredoxin-dependent peroxide reductase, 11802 TFVCPTEIVAFSDKANEFHD PRDX3 mitochondrial QNGEpGGKGERGApGEKGEGGp 11905 Collagen alpha-1(III) chain, COL3A1 COL3A1 pG 11977 EHGPPPPPDERDHSHGPPLPQ Histidine-rich glycoprotein HRG1 12210 GDSMDRIEKDRLqGMAPAAGAD Collagen alpha-1(XVII) chain, COL17A1 COL17A1 TGGPpGENGKPGEpGpKGDAGA 12312 Collagen alpha-1(III) chain COL3A1 pGAP GPIGppGVRGSVGEAGpEGPPGE 12707 Collagen alpha-2(V) chain COL5A2 pGP GPpGKNGDDGEAGKpGRpGERG 13322 Collagen alpha-1(I) chain CO1A1 PPGP 1-phosphatidylinositol 4,5-bisphosphate 13763 ELDCWDGKGEDQEPIITHGKAMC PLCB4 phosphodiesterase beta-4, PLCB4 13845 LAPEPLSAPPGSPPPSAAPTSATSN 13932 Homeobox protein Hox-B3 HOXB3 SSN NRGERGSEGSPGHpGQPGPpGPP 14224 Collagen alpha-1(III) chain CO3A1 GApGP DGKTGPpGpAGQDGRPGPPGpP 14251 Collagen alpha-1(I) chain COL1A1 GARGQAG ERGEAGIpGVpGAKGEDGKDGSp 14253 Collagen alpha-1(III) chain CO3A1 GEpGA DGVSGGEGKGGSDGGGSHRKEG 14255 CD99 antigen, CD99 CD99 EEADAPG GGEGKGGSDGGGSHRKEGEEAD 14316 CD99 antigen CD99 APGVIPG 14586 EVEMKPDSSPSEVPEGVSETEGAL 14737 SH3 and multiple ankyrin repeat domains protein 2 SHANK2 QI GPPGESGREGApGAEGSPGRDGS 14943 Collagen alpha-1(I) chain COL1A1 PGAKGDR TGARGLVGEpGpAGSKGESGNKG 15400 Collagen alpha-2(I) chain COL1A2 EpGSAGPQ SPADKTNVKAAWGKVGAHAGEY 16053 Hemoglobin subunit alpha HBA GAEALER DGLQAAFTTAHELGHVFNMPHD A disintegrin and metalloproteinase with 16219 ADAMTS1 DAKQCA thrombospondin motifs 1, ADAMTS1 LTPEEKSAVTALWGKVNVDEVGG 16283 Hemoglobin subunit beta, HBB HBB EALGRL pGPPGSpGPAGPTGKQGDRGEA 16632 Collagen alpha-1(II) chain COL2A1 GAQGPMGpSGpAG GLPGPKGEKGApGDFGpRGDQG 16811 Collagen alpha-1(XXIII) chain COL23A1 QDGAAGpPGPpG VLSPADKTNVKAAWGKVGAHAG 16970 Hemoglobin subunit alpha HBA EYGAEALER 17307 PAGDRGPRGERGPpGppGRDGE 17317 Collagen alpha-2(I) chain COL1A2 DGPTGPPGPpGP VLSPADKTNVKAAWGKVGAHAG 17614 Hemoglobin subunit alpha HBA EYGAEALERm PpGPSGpRGQPGVMGFpGPKGN 17983 Collagen alpha-1(III) chain COL3A1 DGAPGKNGERGGPGG DGTESSEDSFSFTVTDGTHTDFYVF 18106 FRAS1-related extracellular matrix protein 2, FREM2 FREM2 PDTVFE DGQPGPKGDQGEKGERGTPGIG 18941 Collagen alpha-1(VII) chain COL7A1 GFpGPSGNDGSAGpPGpp RERpQNQQpHRAQRSPQQqPSRL 18981 Collagen alpha-2(XI) chain COL11A2 HRpQNQE 19288 SNGNpGPPGPSGSPGKDGPPGpA 19346 Collagen alpha-1(III) chain COL3A1 GNTGApGSpGVSGPKGDAGQPG GAGPGSGSnVSMNQQNPqAPQA 20775 qSLGGMHVNGAPPLMqASMQG Cleavage stimulation factor subunit 2 CSTF2 GVPAPGQMP DGETGAAGppGPAGPAGERGEq 20853 GAPGPSGFQGLPGPPGPPGEGGK Collagen alpha-1(II) chain, COL2A1 COL2A1 pGDQGVPGEAGAPG 21031 21321 21329 IQRTPKIQVYSRHPAENGKSNFLNc YVSGFHPSDIEVDLLKNGERIEKVE 21470 HSDLSFSKDWSFYLLYYTEFTPTEK Beta-2-microglobulin B2M DEYAcRVNHVTLSQPKIVKWDRD M IQRTPKIQVYSRHPAENGKSNFLNc YVSGFHPSDIEVDLLKNGERIEKVE 21471 HSDLSFSKDWSFYLLYYTEFTPTEK Beta-2-microglobulin B2M DEYAcRVNHVTLSQPKIVKWDRD m * Abbreviations: Amp, amplitude; AUC, Area under the ROC curve; CE-MS, capillary electrophoresis mass spectrometry; Da, Dalton; min, minutes; N/A,not available; N, number of patients; STD, standard deviation; UBC, Urothelial bladder † Peptide identification number. ‡ Lower case p, k and m indicate hydroxyproline, hydroxylysine and oxidized methionine. Supplementary Table S5. List of all the peptides that were included in the peptide biomarker panels for detecting primary UBC PRIMARY BIOMARKER PANEL CE-MS characteristics Distribution in Primary UBC cases Distribution in non-UBC controls (n=341) (n=110) CE migration Peptide-ID† Mass [Da] Frequency Mean Amp ± STD Frequency Mean Amp ± STD time [min] 1747 1096.33 35.81 33.53 89.33±184.65 32.73 110.17±308.29 1907 1113.47 27.24 42.77 160.96±261.95 50.91 206.5±379.02 1944 1116.47 36.90 12.14 18.08±82.78 29.09 77.79±227.39 2032 1126.51 25.59 38.73 96.93±174.92 35.45 76.27±140.51 2200 1144.59 26.51 21.68 376.8±1863.93 5.45 79.55±510.76 2451 1170.64 21.36 30.64 3170.92±17424.53 14.55 2267.26±17324.91 2522 1178.71 19.19 14.45 49.74±178.01 11.82 53.09±211.26 2543 1180.62 21.29 17.63 109.63±396.36 10.91 80.42±409.25 2592 1186.53 22.31 74.57 510.08±686.58 81.82 476.61±491.92 2597 1187.34 36.35 13.87 263.26±886.92 12.73 327.48±1196.92 2794 1209.52 39.52 26.88 56.76±201.05 42.73 84.01±131.73 2801 1210.37 36.45 57.23 559.75±953.72 44.55 574.97±1198.88 2830 1213.62 27.33 20.52 334.58±1878.8 10 270.82±2258.01 2858 1216.54 24.59 77.75 1189.96±1308.9 84.55 1242.82±1452.25 3183 1250.64 20.57 45.95 637.11±3324.82 40.91 224.65±1452.35 3202 1252.64 21.63 17.92 226.45±1245.28 13.64 152.23±587.87 3223 1255.48 35.73 26.59 315.32±1133.54 15.45 214.12±1215.58 3430 1276.71 19.96 24.57 51.48±180.33 27.27 51.34±124.02 3651 1299.58 22.37 65.61 287.8±314.73 57.27 241.79±295.12 3758 1309.70 21.89 37.57 4147.69±17313.78 19.09 1627.88±7028.46 4152 1349.41 36.43 70.23 339.46±400.76 73.64 393.01±405.18 4157 1349.70 20.28 22.54 713.65±4427.13 10.91 764.04±4988.64 4270 1360.62 22.65 21.1 22.87±58.62 34.55 44.03±83.75 4532 1392.63 27.65 8.67 6.91±34.24 21.82 14.94±34.22 4535 1392.67 39.08 16.18 40.12±172.68 23.64 264.03±2199.37 4759 1416.64 28.20 14.74 12.34±40.15 10.91 8.09±25.56 4957 1436.73 23.26 17.63 149.88±1016.4 7.27 138.86±844.5 5087 1448.79 20.38 25.72 1199.97±6759 15.45 1069.5±9877.39 5216 1464.66 31.31 45.09 3467.74±23409.26 41.82 1548.86±6065.55 5423 1491.73 39.89 68.5 614.67±867.63 73.64
Load more

Recommended publications
  • Global Analysis Reveals the Complexity of the Human Glomerular Extracellular Matrix
    Global analysis reveals the complexity of the human glomerular extracellular matrix Rachel Lennon,1,2 Adam Byron,1,* Jonathan D. Humphries,1 Michael J. Randles,1,2 Alex Carisey,1 Stephanie Murphy,1,2 David Knight,3 Paul E. Brenchley,2 Roy Zent,4,5 and Martin J. Humphries.1 1Wellcome Trust Centre for Cell-Matrix Research, Faculty of Life Sciences, University of Manchester, Manchester, UK; 2Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK; 3Biological Mass Spectrometry Core Facility, Faculty of Life Sciences, University of Manchester, Manchester, UK; 4Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; and 5Veterans Affairs Hospital, Nashville, TN, USA. *Present address: Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. Running title: Proteome of the glomerular matrix Word count: Abstract: 208, main text 2765 Corresponding author: Dr Rachel Lennon, Wellcome Trust Centre for Cell-Matrix Research, Michael Smith Building, University of Manchester, Manchester M13 9PT, UK. Phone: 0044 (0) 161 2755498. Fax: 0044 (0) 161 2755082. Email: [email protected] Abstract The glomerulus contains unique cellular and extracellular matrix (ECM) components, which are required for intact barrier function. Studies of the cellular components have helped to build understanding of glomerular disease; however, the full composition and regulation of glomerular ECM remains poorly understood. Here, we employed mass spectrometry–based proteomics of enriched ECM extracts for a global analysis of human glomerular ECM in vivo and identified a tissue-specific proteome of 144 structural and regulatory ECM proteins. This catalogue includes all previously identified glomerular components, plus many new and abundant components.
    [Show full text]
  • Propranolol-Mediated Attenuation of MMP-9 Excretion in Infants with Hemangiomas
    Supplementary Online Content Thaivalappil S, Bauman N, Saieg A, Movius E, Brown KJ, Preciado D. Propranolol-mediated attenuation of MMP-9 excretion in infants with hemangiomas. JAMA Otolaryngol Head Neck Surg. doi:10.1001/jamaoto.2013.4773 eTable. List of All of the Proteins Identified by Proteomics This supplementary material has been provided by the authors to give readers additional information about their work. © 2013 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 10/01/2021 eTable. List of All of the Proteins Identified by Proteomics Protein Name Prop 12 mo/4 Pred 12 mo/4 Δ Prop to Pred mo mo Myeloperoxidase OS=Homo sapiens GN=MPO 26.00 143.00 ‐117.00 Lactotransferrin OS=Homo sapiens GN=LTF 114.00 205.50 ‐91.50 Matrix metalloproteinase‐9 OS=Homo sapiens GN=MMP9 5.00 36.00 ‐31.00 Neutrophil elastase OS=Homo sapiens GN=ELANE 24.00 48.00 ‐24.00 Bleomycin hydrolase OS=Homo sapiens GN=BLMH 3.00 25.00 ‐22.00 CAP7_HUMAN Azurocidin OS=Homo sapiens GN=AZU1 PE=1 SV=3 4.00 26.00 ‐22.00 S10A8_HUMAN Protein S100‐A8 OS=Homo sapiens GN=S100A8 PE=1 14.67 30.50 ‐15.83 SV=1 IL1F9_HUMAN Interleukin‐1 family member 9 OS=Homo sapiens 1.00 15.00 ‐14.00 GN=IL1F9 PE=1 SV=1 MUC5B_HUMAN Mucin‐5B OS=Homo sapiens GN=MUC5B PE=1 SV=3 2.00 14.00 ‐12.00 MUC4_HUMAN Mucin‐4 OS=Homo sapiens GN=MUC4 PE=1 SV=3 1.00 12.00 ‐11.00 HRG_HUMAN Histidine‐rich glycoprotein OS=Homo sapiens GN=HRG 1.00 12.00 ‐11.00 PE=1 SV=1 TKT_HUMAN Transketolase OS=Homo sapiens GN=TKT PE=1 SV=3 17.00 28.00 ‐11.00 CATG_HUMAN Cathepsin G OS=Homo
    [Show full text]
  • Environmental Influences on Endothelial Gene Expression
    ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community.
    [Show full text]
  • Original Article FREM2 Is an Independent Predictor of Poor Survival in Clear Cell Renal Cell Carcinoma-Evidence from the Cancer Genome Atlas (TCGA)
    Int J Clin Exp Med 2019;12(12):13741-13748 www.ijcem.com /ISSN:1940-5901/IJCEM0076963 Original Article FREM2 is an independent predictor of poor survival in clear cell renal cell carcinoma-evidence from the cancer genome atlas (TCGA) Weiping Huang, Yongyong Lu, Xixi Huang, Feng Wang, Zhixian Yu Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China Received February 26, 2018; Accepted October 7, 2018; Epub December 15, 2019; Published December 30, 2019 Abstract: Fraser syndrome protein 1 (FRAS1) and FRAS1 related extracellular matrix protein 1 and 2 (FREM1, FREM2) are a novel group of basement membrane proteins. The relationship between the three gene (FRAS1, FREM1, FREM2) and renal clear cell carcinoma is completely unclear. Thus, in this research, we used the mRNA sequencing data derived from TCGA kidney renal clear cell carcinoma cohort to assess the association of FRAS1, FREM1 and FREM2 with different clinical features. FRAS1, FREM1 and FREM2 mRNA levels were downregulated in KIRC (kidney renal clear cell carcinoma) tissues than normal tissues (FRAS1, P < 0.0001; FREM1, P < 0.0001, FREM2, P = 0.0001), respectively. FRAS1, FREM1 and FREM2 were significantly different in histologic grade, patho- logic stage and pathologic T (all P < 0.001). Low FRAS1, FREM1 and FREM2 expression were correlated to worsen overall survival (all P < 0.01), and Low FREM1 and FREM2 expression had worse relapse-free survival (FREM1, P = 0.0113; FREM2, P = 0.0424). Multivariate Cox regression analysis revealed that FREM2 was an independent prog- nostic factor for overall survival. Taken together, FREM2 expression is an independent predictor of poor survival in renal clear cell carcinoma and is positively associated with advanced stage, high histologic grade.
    [Show full text]
  • Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
    Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database.
    [Show full text]
  • Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
    BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in
    [Show full text]
  • Nephronectin As a Matrix Effector in Cancer
    cancers Review Nephronectin as a Matrix Effector in Cancer Synnøve Norvoll Magnussen 1,* , Jimita Toraskar 2,3 , Elin Hadler-Olsen 1,4, Tonje S. Steigedal 2,3 and Gunbjørg Svineng 1 1 Department of Medical Biology, Faculty of Health Sciences, UiT—The Arctic University of Norway, 9037 Tromsø, Norway; [email protected] (E.H.-O.); [email protected] (G.S.) 2 Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway; [email protected] (J.T.); [email protected] (T.S.S.) 3 Cancer Clinic, St. Olavs Hospital HF, 7006 Trondheim, Norway 4 The Public Dental Health Service Competence Center of Northern Norway, 9271 Tromsø, Norway * Correspondence: [email protected] Simple Summary: The extracellular matrix provides an important scaffold for cells and tissues of multicellular organisms. The scaffold not only provides a secure anchorage point, but also functions as a reservoir for signalling molecules, sequestered and released when necessary. A dysregulated extracellular matrix may therefore modulate cellular behaviour, as seen during cancer progression. The extracellular matrix protein nephronectin was discovered two decades ago and found to regulate important embryonic developmental processes. Loss of either nephronectin or its receptor, integrin α8β1, leads to underdeveloped kidneys. Recent findings show that nephronectin is also dysregulated in breast cancer and plays a role in promoting metastasis. To enable therapeutic intervention, it is important to fully understand the role of nephronectin and its receptors in cancer progression. In Citation: Magnussen, S.N.; this review, we summarise the literature on nephronectin, analyse the structure and domain-related Toraskar, J.; Hadler-Olsen, E.; functions of nephronectin and link these functions to potential roles in cancer progression.
    [Show full text]
  • LOXL2-Mediated H3K4 Oxidation Reduces Chromatin Accessibility in Triple-Negative Breast Cancer Cells
    Oncogene (2020) 39:79–121 https://doi.org/10.1038/s41388-019-0969-1 ARTICLE LOXL2-mediated H3K4 oxidation reduces chromatin accessibility in triple-negative breast cancer cells 1 1 2 1 1 1 J. P. Cebrià-Costa ● L. Pascual-Reguant ● A. Gonzalez-Perez ● G. Serra-Bardenys ● J. Querol ● M. Cosín ● 1,3 4 4 2 5 6 G. Verde ● R. A. Cigliano ● W. Sanseverino ● S. Segura-Bayona ● A. Iturbide ● D. Andreu ● 1 1,7 1 1,7,8,9 10 6,10 P. Nuciforo ● C. Bernado-Morales ● V. Rodilla ● J. Arribas ● J. Yelamos ● A. Garcia de Herreros ● 2 1 T. H. Stracker ● S. Peiró Received: 28 January 2019 / Revised: 8 July 2019 / Accepted: 9 August 2019 / Published online: 28 August 2019 © The Author(s) 2019. This article is published with open access Abstract Oxidation of H3 at lysine 4 (H3K4ox) by lysyl oxidase-like 2 (LOXL2) generates an H3 modification with an unknown physiological function. We find that LOXL2 and H3K4ox are higher in triple-negative breast cancer (TNBC) cell lines and patient-derived xenografts (PDXs) than those from other breast cancer subtypes. ChIP-seq revealed that H3K4ox is located primarily in heterochromatin, where it is involved in chromatin compaction. Knocking down LOXL2 reduces H3K4ox levels 1234567890();,: 1234567890();,: and causes chromatin decompaction, resulting in a sustained activation of the DNA damage response (DDR) and increased susceptibility to anticancer agents. This critical role that LOXL2 and oxidized H3 play in chromatin compaction and DDR suggests that functionally targeting LOXL2 could be a way to sensitize TNBC cells to conventional therapy.
    [Show full text]
  • Supplementary Table S2. List of Genes with Expression That Is Positively Correlated (Pearson Correlation Coefficient P>0.3)
    Supplementary Table S2. List of genes with expression that is positively correlated (Pearson correlation coefficient p>0.3) with HOXA9 expression in the study by Sun et al (1). Most HOXA genes (indicated by bold face) show highly significant positive correlation with HOXA9 expression, except for HOXA6 and HOXA13, similar to our findings in the UCSF and MDA tumor sets. Note that HOXA11, which did not demonstrate a statistically significant positive correlation with HOXA9 expression in both the UCSF and MDA tumor sets, demonstrates a substantially lower correlation coefficient relative to the other HOXA genes that positively correlate with HOXA9 expression in the study by Sun et al. These data were obtained from the online ONCOMINE database (www.oncomine.org) (2) searching for transcripts positively correlated with HOXA9 expression. Gene Name Gene Symbol Reporter ID Correlation (p) homeobox A9 HOXA9 214651_s_at .7682 homeobox A9 HOXA9 209905_at .7682 homeobox A10 HOXA10 213150_at .6058 homeobox A7 HOXA7 235753_at .6058 homeobox A10 HOXA10 213147_at .6058 homeobox A7 HOXA7 206847_s_at .6058 homeobox A4 HOXA4 206289_at .5741 homeobox A2 HOXA2 1557051_s_at .5379 homeobox A1 HOXA1 214639_s_at .5379 homeobox A3 HOXA3 235521_at .5379 homeobox B2 HOXB2 205453_at .5379 homeobox B3 HOXB3 228904_at .5379 EST EST 1555907_at .5379 homeobox A4 HOXA4 230080_at .5379 homeobox A2 HOXA2 228642_at .5379 homeobox A2 HOXA2 1557050_at .5379 homeobox A5 HOXA5 213844_at .5379 homeobox A2 HOXA2 214457_at .5113 homeobox B7 HOXB7 204778_x_at .5042 homeobox C6 HOXC6
    [Show full text]
  • Us 2018 / 0305689 A1
    US 20180305689A1 ( 19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2018 /0305689 A1 Sætrom et al. ( 43 ) Pub . Date: Oct. 25 , 2018 ( 54 ) SARNA COMPOSITIONS AND METHODS OF plication No . 62 /150 , 895 , filed on Apr. 22 , 2015 , USE provisional application No . 62/ 150 ,904 , filed on Apr. 22 , 2015 , provisional application No. 62 / 150 , 908 , (71 ) Applicant: MINA THERAPEUTICS LIMITED , filed on Apr. 22 , 2015 , provisional application No. LONDON (GB ) 62 / 150 , 900 , filed on Apr. 22 , 2015 . (72 ) Inventors : Pål Sætrom , Trondheim (NO ) ; Endre Publication Classification Bakken Stovner , Trondheim (NO ) (51 ) Int . CI. C12N 15 / 113 (2006 .01 ) (21 ) Appl. No. : 15 /568 , 046 (52 ) U . S . CI. (22 ) PCT Filed : Apr. 21 , 2016 CPC .. .. .. C12N 15 / 113 ( 2013 .01 ) ; C12N 2310 / 34 ( 2013. 01 ) ; C12N 2310 /14 (2013 . 01 ) ; C12N ( 86 ) PCT No .: PCT/ GB2016 /051116 2310 / 11 (2013 .01 ) $ 371 ( c ) ( 1 ) , ( 2 ) Date : Oct . 20 , 2017 (57 ) ABSTRACT The invention relates to oligonucleotides , e . g . , saRNAS Related U . S . Application Data useful in upregulating the expression of a target gene and (60 ) Provisional application No . 62 / 150 ,892 , filed on Apr. therapeutic compositions comprising such oligonucleotides . 22 , 2015 , provisional application No . 62 / 150 ,893 , Methods of using the oligonucleotides and the therapeutic filed on Apr. 22 , 2015 , provisional application No . compositions are also provided . 62 / 150 ,897 , filed on Apr. 22 , 2015 , provisional ap Specification includes a Sequence Listing . SARNA sense strand (Fessenger 3 ' SARNA antisense strand (Guide ) Mathew, Si Target antisense RNA transcript, e . g . NAT Target Coding strand Gene Transcription start site ( T55 ) TY{ { ? ? Targeted Target transcript , e .
    [Show full text]
  • Distinct Transcriptomes Define Rostral and Caudal 5Ht Neurons
    DISTINCT TRANSCRIPTOMES DEFINE ROSTRAL AND CAUDAL 5HT NEURONS by CHRISTI JANE WYLIE Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Dissertation Advisor: Dr. Evan S. Deneris Department of Neurosciences CASE WESTERN RESERVE UNIVERSITY May, 2010 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of ______________________________________________________ candidate for the ________________________________degree *. (signed)_______________________________________________ (chair of the committee) ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ (date) _______________________ *We also certify that written approval has been obtained for any proprietary material contained therein. TABLE OF CONTENTS TABLE OF CONTENTS ....................................................................................... iii LIST OF TABLES AND FIGURES ........................................................................ v ABSTRACT ..........................................................................................................vii CHAPTER 1 INTRODUCTION ............................................................................................... 1 I. Serotonin (5-hydroxytryptamine, 5HT) ....................................................... 1 A. Discovery..............................................................................................
    [Show full text]
  • Gene Expression Profiles of Multiple Independent Cadmium- And
    Gene Expression Profiles of Multiple Independent Cadmium‐ and Arsenite‐ Transformed Human Urothelial Cells: Specific Gene Signatures in a Sea of Heterogeneity. Scott Garrett Department of Pathology School of Medicine and Health Sciences University of North Dakota Grand Forks ND Problems in Cancer Research • Environmental causation of cancer – President’s Cancer Panel 2008‐09, Reducing Environmental Cancer Risk, “Environmentally caused cancer are ggyrossly underestimated..” • Desperate need for diagnostic and prognostic markers of cancer. – Screening for common cancers – Prognostic markers for predicting cancer that need aggressive treatments from those that don’t • Also, need to understand carcinogenesis for prevention and treatment. Bladder Cancer Has a High Environmental Etiology • Aromatic amine dyes in bladder cancer was the first environmental etiology in cancer. • Nearly 50% of BC thought to be caused by cigarette smoking • Suscept ibility due to concentration of carcinogens in urine. Arsenite (As+3) & Cadmium (Cd+2) • Arsenite in drinking water linked to BC in South Asia • Confirmed in animal models • Arsenite, 1St in priority as a toxic substance (ATSDR). • Cadmium has also been linked to BC, albeit much weaker, but: – Cigarette smoking is a major source of cadmium Overall Experimental Approach Long‐Term Cultured Cells Transformed Cells Metal Exposure Tranf & assess Discover Induced or tumor behavior Transfect or Repressed Gene knockdown & assess cell behavior Gene X Human Tissues Assess correlation to tumor grade Mechanism of
    [Show full text]