(CEACAM) Family Members and Inflammatory Bowel Disease
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Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
Investigation of the Immune Receptors CEACAM3 and CEACAM4
Investigation of the human immune receptors CEACAM3 and CEACAM4 Dissertation Zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) vorgelegt von Julia Delgado Tascón An der Universität Konstanz des Fachbereichs Biologie Konstanz, Oktober 2015 Konstanzer Online-Publikations-System (KOPS) URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-0-306516 Tag der mündlichen Prüfung: 05.11.2015 Vorsitzender und mündlicher Prüfer: Herr Professor Dr. Bürkle 1. Referent und und mündlicher Prüfer: Herr Professor Dr. Hauck 2. Referent und und mündlicher Prüfer: Herr Professor Dr. Tschan, Universität Bern A mi familia Acknowledgements I would like to express my special gratitude to my advisor Prof. Dr. Christof Hauck. His patient guidance and enthusiastic encouragement during these four years of PhD were a crucial aid to my process. I’m very thankful for his willingness and for granting me with his time in search for valuable and constructive suggestions during the planning and development of this research work. This certainly allowed me to grow as a person and as a scientist. I would also like to thank my committee members: to Prof. Dr. Mario Tschan for giving me his academic support at this last phase of my PhD thesis, and to Prof. M.Dr. Alexander Bürkle for his wise advices accompanied with Spanish greetings along this time. My thanks are extended to every member of the AG Hauck as well. To Anne, Susana, Petra and Claudia: thank you very much for your technical and personal guidance during these years. I’m also thankful to my fellow colleagues for countless ‘Kaffeepausen’ full of jokes, nice discussions, and delicious vegan cakes. -
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 -
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. -
Single-Cell RNA Sequencing Demonstrates the Molecular and Cellular Reprogramming of Metastatic Lung Adenocarcinoma
ARTICLE https://doi.org/10.1038/s41467-020-16164-1 OPEN Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma Nayoung Kim 1,2,3,13, Hong Kwan Kim4,13, Kyungjong Lee 5,13, Yourae Hong 1,6, Jong Ho Cho4, Jung Won Choi7, Jung-Il Lee7, Yeon-Lim Suh8,BoMiKu9, Hye Hyeon Eum 1,2,3, Soyean Choi 1, Yoon-La Choi6,10,11, Je-Gun Joung1, Woong-Yang Park 1,2,6, Hyun Ae Jung12, Jong-Mu Sun12, Se-Hoon Lee12, ✉ ✉ Jin Seok Ahn12, Keunchil Park12, Myung-Ju Ahn 12 & Hae-Ock Lee 1,2,3,6 1234567890():,; Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell tran- scriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions. 1 Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea. -
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, -
Product Datasheet
Antibody Technology Product Data Sheet anti-human CEACAM8 monoclonal antibody Product information Catalog Number: GM-0512 Clone: GM2H6 Description: purified monoclonal mouse antibody Specificity: anti-human CEACAM8 (CD66b/NCA-95) Isotype: IgG1 Purification: Protein G Storage: short term: 2°C - 8°C; long term: -20°C (avoid repeated freezing and thawing) Buffer : phosphate buffered saline, pH 7.2 Immunogen: genetic immunisation with cDNA encoding human CEACAM8 Selection: based on recognition of the complete native protein expressed on transfected mammalian cells Working dilutions Flow cytometry: 1.2 µg/106 cells CELISA: 1:200 - 1:400 For each application a titration should be performed to determine the optimal concentration. Specificity testing by flow cytometry CEACAM8 transfectant control transfectant Fig.1: FACS analysis of BOSC23 cells using GM2H6 Cat.# GM- 0512. BOSC23 cells were transiently transfected with an expression vector encoding either CEACAM8 (red curve) or an irrelevant protein (control transfectant). Binding of GM-0512 was detected with a PE -conjugated secondary antibody. A positive signal was obtained only with CEACAM8 transfected cells. For research use only. Not for diagnostic or therapeutic use. Aldevron Freiburg GmbH [email protected] Page 1 / 2 Waltershofener Str. 17 www.aldevron.com 79111 Freiburg, Germany Antibody Technology Antibody cross-reactivity with members of the CEA family CEACAM1 CEACAM3 CEACAM4 CEACAM5 CEACAM6 CEACAM7 CEACAM8 PSG1 Fig. 2: BOSC23 cells were transiently transfected with expression vectors containing either the cDNA of CEACAM1, CEACAM3-8 or PSG. The latter expressed as a membrane bound fusion protein. Expression of the constructs was tested with monoclonal antibodies known to recognize the corresponding proteins (CEACAM1,3,4,5 and 6: D14HD11; CEACAM7: BAC2; CEACAM8: Tet2; PSG: BAP3; green curves). -
Integrating Text Mining, Data Mining, and Network Analysis for Identifying
Jurca et al. BMC Res Notes (2016) 9:236 DOI 10.1186/s13104-016-2023-5 BMC Research Notes RESEARCH ARTICLE Open Access Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends Gabriela Jurca1, Omar Addam1, Alper Aksac1, Shang Gao2, Tansel Özyer3, Douglas Demetrick4 and Reda Alhajj1,5* Abstract Background: Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. Results: We utilized PubMed for the testing. We investigated gene–gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Conclusions: Interesting trends have been identified and discussed, e.g., different genes are highlighted in relation- ship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene–gene relations and gene functions. Keywords: Breast cancer, Data mining, Text mining, Network analysis Background reducing the number of molecules to consider as poten- Introduction tial biomarkers. -
The Regulation of Interleukin 7 Receptor Alpha Internalization, Recycling and Degradation by IL-7
Universidade de Lisboa Faculdade de Medicina Unidade de Biologia do Cancro, Instituto de Medicina Molecular The regulation of Interleukin 7 receptor alpha internalization, recycling and degradation by IL-7 - Possible implications in T-cell homeostasis, migration and leukaemogenesis - Catarina Martins de Oliveira Henriques Doutoramento em Ciências Biomédicas For the degree of Doctor of Philosophy 2009 Universidade de Lisboa Faculdade de Medicina Unidade de Biologia do Cancro, Instituto de Medicina Molecular The regulation of Interleukin 7 receptor alpha internalization, recycling and degradation by IL-7 - Possible implications in T-cell homeostasis, migration and leukaemogenesis - Catarina Martins de Oliveira Henriques (Recipient of a scholarship- SFRH7BD/21940/2005 from Fundação para a Ciência e Tecnologia) Tese orientada pelo Doutor João T. Barata, Prof Doutor.Gerard Graham e Prof. Doutora Leonor Parreira Doutoramento em Ciências Biomédicas, especialidade em Ciências Biopatológicas For the degree of Doctor of Philosophy 2009 As opiniões expressas são da exclusiva responsabilidade do seu autor A impressão desta dissertação foi aprovada pela Comissão Coordenadora do Conselho Científico da Faculdade de Medicina de Lisboa em reunião de 13 de Outubro de 2009. Para a Prof. Filomena Mota. Sem o seu apoio e inspiração há muitos anos atrás, eu não seria hoje uma bióloga nem esta tese teria alguma vez existido… Table of Contents Table of contents……………………………………………………….………………….................. i Aknowledgements……………………………………………………………………………………………. -
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. -
Combination Immune Therapy
Combination Immune Therapy Translational Medicine Plenary SWOG April 17, 2017 San Francisco T‐cell Activation, Proliferation, and Function is Controlled by Multiple Agonist and Antagonist Signals 1. Co‐stimulation via CD28 ligation 2. CTLA‐4 ligation on activated T 3. T cell function in tissue is subject transduces T cell activating signals cells down‐regulates T cell to feedback inhibition responses T cell Functional Block T cell Activation T cell Activation T cell Proliferative Block T cell T cell APC T cell CTLA‐4 T cell CTLA‐4 TCR TCR PD‐1 PD‐1 PD‐1 IFN‐gamma TCR CD28 Cytokines TCR CD4 MHC MHC PD‐‐L1 MHC B7 MHC CD28 B7 PD‐‐L1 Tumor or Tumor or APC APC Immune cell B7 immune cell APC NK Presence of PD-L1 or TILs1 PPD-L1 /TIL+ PD-L1/TIL + + PD-L1 /TIL+ + PD-L1 /TIL PD-L1 /TIL D-L1 /TIL+ Schalper and Rimm, Yale University NSCLC PD-L1/TIL 45% 17% 26% 12% Type 1 Type 2 Type 3 Type 4 45% 41% 13% 1% Melanoma45% 17% 26% 12% Taube et al Type 1 Type 2 Type 3 Type 4 Tumor‐specific T cells are contained in the PD‐1+ TIL population and are functional after in vitro culture Spectrum of PD‐1/PD‐L1 Antagonist Activity Active • Melanoma Minimal to no activity • Renal cancer (clear cell and non‐clear cell) • Prostate cancer • NSCLC – adenocarcinoma and squamous cell • MMR+ (MSS) colon cancer • Small cell lung cancer • Myeloma • Head and neck cancer • Pancreatic cancer • Gastric and gastroesophageal junction • MMR‐repair deficient tumors (colon, cholangiocarcinoma) • Bladder • Triple negative breast cancer • Ovarian Major PD‐1/PD‐L1 antagonists • Hepatocellular -
Table S1| Differential Expression Analysis of the Atopy Transcriptome
Table S1| Differential expression analysis of the atopy transcriptome in CD4+ T-cell responses to allergens in atopic and nonatopic subjects Probe ID S.test Gene Symbol Gene Description Chromosome Statistic Location 7994280 10.32 IL4R Interleukin 4 receptor 16p11.2-12.1 8143383 8.95 --- --- --- 7974689 8.50 DACT1 Dapper, antagonist of beta-catenin, homolog 1 14q23.1 8102415 7.59 CAMK2D Calcium/calmodulin-dependent protein kinase II delta 4q26 7950743 7.58 RAB30 RAB30, member RAS oncogene family 11q12-q14 8136580 7.54 RAB19B GTP-binding protein RAB19B 7q34 8043504 7.45 MAL Mal, T-cell differentiation protein 2cen-q13 8087739 7.27 CISH Cytokine inducible SH2-containing protein 3p21.3 8000413 7.17 NSMCE1 Non-SMC element 1 homolog (S. cerevisiae) 16p12.1 8021301 7.15 RAB27B RAB27B, member RAS oncogene family 18q21.2 8143367 6.83 SLC37A3 Solute carrier family 37 member 3 7q34 8152976 6.65 TMEM71 Transmembrane protein 71 8q24.22 7931914 6.56 IL2R Interleukin 2 receptor, alpha 10p15-p14 8014768 6.43 PLXDC1 Plexin domain containing 1 17q21.1 8056222 6.43 DPP4 Dipeptidyl-peptidase 4 (CD26) 2q24.3 7917697 6.40 GFI1 Growth factor independent 1 1p22 7903507 6.39 FAM102B Family with sequence similarity 102, member B 1p13.3 7968236 5.96 RASL11A RAS-like, family 11, member A --- 7912537 5.95 DHRS3 Dehydrogenase/reductase (SDR family) member 3 1p36.1 7963491 5.83 KRT1 Keratin 1 (epidermolytic hyperkeratosis) 12q12-q13 7903786 5.72 CSF1 Colony stimulating factor 1 (macrophage) 1p21-p13 8019061 5.67 SGSH N-sulfoglucosamine sulfohydrolase (sulfamidase) 17q25.3