PP1-Associated Signaling and − B/AP-1 Κ Inhibition of NF- Tolerance
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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder.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. -
BIOINFORMATICS DOI: 10.1093/Bioinformatics/Btg1030
Vol. 19 Suppl. 1 2003, pages i222–i224 BIOINFORMATICS DOI: 10.1093/bioinformatics/btg1030 GeneLoc: exon-based integration of human genome maps Naomi Rosen, Vered Chalifa-Caspi, Orit Shmueli, Avital Adato, Michal Lapidot, Julie Stampnitzky, Marilyn Safran ∗ and Doron Lancet Weizmann Institute of Science, Rehovot, Israel Received on January 6, 2003; accepted on February 20, 2003 ABSTRACT to provide a comprehensive gene list, NCBI’s LocusLink Motivation: Despite the numerous available whole- contains thousands of model genes, categorized by level genome mapping resources, no comprehensive, inte- and type of support. Even known genes appearing in every grated map of the human genome yet exists. database may have different names in each database. The Results: GeneLoc, software adjunct to GeneCards and biologist must move among databases to figure out which UDB, integrates gene lists by comparing genomic coordi- genes are the same, and which could be a novel gene nates at the exon level and assigns unique and meaningful sought. UCSC’s Genome Browser website maps genes identifiers to each gene. from several sources on the same scale, but the maps are Availability: http://bioinfo.weizmann.ac.il/genecards and not integrated, making it difficult to relate genes from http://genecards.weizmann.ac.il/udb different sources. As stated (Jongeneel, 2000),‘there is an Supplementary information: http://bioinfo.weizmann.ac. urgent need for a human gene index that can be used to il/cards-bin/AboutGCids.cgi, http://genecards.weizmann. identify transcripts unambiguously.’ The author contends ac.il/GeneLocAlg.html that this index should have, among others, the following Contact: [email protected] qualities: comprehensiveness, uniqueness, and stability. -
IL-7 Receptor Blockade Blunts Antigen-Specific Memory T Cell
ARTICLE DOI: 10.1038/s41467-018-06804-y OPEN IL-7 receptor blockade blunts antigen-specific memory T cell responses and chronic inflammation in primates Lyssia Belarif1,2, Caroline Mary1,2, Lola Jacquemont1, Hoa Le Mai1, Richard Danger1, Jeremy Hervouet1, David Minault1, Virginie Thepenier1,2, Veronique Nerrière-Daguin1, Elisabeth Nguyen1, Sabrina Pengam1,2, Eric Largy3,4, Arnaud Delobel3, Bernard Martinet1, Stéphanie Le Bas-Bernardet1,5, Sophie Brouard1,5, Jean-Paul Soulillou1, Nicolas Degauque 1,5, Gilles Blancho1,5, Bernard Vanhove1,2 & Nicolas Poirier1,2 1234567890():,; Targeting the expansion of pathogenic memory immune cells is a promising therapeutic strategy to prevent chronic autoimmune attacks. Here we investigate the therapeutic efficacy and mechanism of new anti-human IL-7Rα monoclonal antibodies (mAb) in non-human primates and show that, depending on the target epitope, a single injection of antagonistic anti-IL-7Rα mAbs induces a long-term control of skin inflammation despite repeated antigen challenges in presensitized monkeys. No modification in T cell numbers, phenotype, function or metabolism is observed in the peripheral blood or in response to polyclonal stimulation ex vivo. However, long-term in vivo hyporesponsiveness is associated with a significant decrease in the frequency of antigen-specific T cells producing IFN-γ upon antigen resti- mulation ex vivo. These findings indicate that chronic antigen-specific memory T cell responses can be controlled by anti-IL-7Rα mAbs, promoting and maintaining remission in T- cell mediated chronic inflammatory diseases. 1 Centre de Recherche en Transplantation et Immunologie (CRTI) UMR1064, INSERM, Université de Nantes, Nantes 44093, France. 2 OSE Immunotherapeutics, Nantes 44200, France. -
Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only. -
Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer Kumardeep Chaudhary1, Olivier B
Published OnlineFirst October 5, 2017; DOI: 10.1158/1078-0432.CCR-17-0853 Statistics in CCR Clinical Cancer Research Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer Kumardeep Chaudhary1, Olivier B. Poirion1, Liangqun Lu1,2, and Lana X. Garmire1,2 Abstract Identifying robust survival subgroups of hepatocellular car- index (C-index) ¼ 0.68]. More aggressive subtype is associated cinoma (HCC) will significantly improve patient care. Current- with frequent TP53 inactivation mutations, higher expression ly, endeavor of integrating multi-omicsdatatoexplicitlypredict of stemness markers (KRT19 and EPCAM)andtumormarker HCC survival from multiple patient cohorts is lacking. To fill BIRC5, and activated Wnt and Akt signaling pathways. We this gap, we present a deep learning (DL)–based model on HCC validated this multi-omics model on five external datasets of that robustly differentiates survival subpopulations of patients various omics types: LIRI-JP cohort (n ¼ 230, C-index ¼ 0.75), in six cohorts. We built the DL-based, survival-sensitive model NCI cohort (n ¼ 221, C-index ¼ 0.67), Chinese cohort (n ¼ on 360 HCC patients' data using RNA sequencing (RNA-Seq), 166, C-index ¼ 0.69), E-TABM-36 cohort (n ¼ 40, C-index ¼ miRNA sequencing (miRNA-Seq), and methylation data from 0.77), and Hawaiian cohort (n ¼ 27, C-index ¼ 0.82). This TheCancerGenomeAtlas(TCGA),whichpredictsprognosis is the first study to employ DL to identify multi-omics features as good as an alternative model where genomics and clinical linked to the differential survival of patients with HCC. Given data are both considered. This DL-based model provides two its robustness over multiple cohorts, we expect this workflow to optimal subgroups of patients with significant survival differ- be useful at predicting HCC prognosis prediction. -
Primary Antibodies Flyer
Primary Antibodies Your choice of size and format Format Concentration Size CF® dye conjugates (13 colors) 0.1 mg/mL 100 or 500 uL Biotin, HRP or AP conjugates 0.1 mg/mL 100 or 500 uL R-PE, APC, or Per-CP conjugates 0.1 mg/mL 250 uL Purified, with BSA 0.1 mg/mL 100 or 500 uL Purified, BSA-free (Mix-n-Stain™ Ready) 1 mg/mL 50 uL Advantages Figure 1. IHC staining of human prostate Figure 2. Flow cytometry analysis of U937 • More than 1000 monoclonal antibodies carcinoma with anti-ODC1 clone cells with anti-CD31/PECAM clone C31.7, • Growing selection of monoclonal rabbit ODC1/485. CF647 conjugate (blue) or isotype control (orange). antibodies • Validated in IHC and other applications Your choice of 13 bright and photostable CF® dyes • Choose from 13 bright and stable CF® dyes CF® dye Ex/Em (nm) Features • Also available with R-PE, APC, PerCP, HRP, AP, CF®405S 404/431 • Better fit for the 450/50 flow cytometer channel than Alexa Fluor® 405 or biotin CF®405M 408/452 • More photostable than Pacific Blue®, with less green spill-over • Purified antibodies available BSA-free, 1 mg/mL, • Compatible with super-resolution imaging by SIM ready to use for Mix-n-Stain™ labeling or other CF®488A 490/515 • Less non-specific binding and spill-over than Alexa Fluor® 488 conjugation • Very photostable and pH-insensitive • Compatible with super-resolution imaging by TIRF • Offered in affordable 100 uL size CF®543 541/560 • Brighter than Alexa Fluor® 546 CF®555 555/565 • Brighter than Cy®3 • Validated in multicolor super-resolution imaging by STORM CF®568 -
Table 2. Significant
Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S. -
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. -
Genome Analysis and Knowledge
Dahary et al. BMC Medical Genomics (2019) 12:200 https://doi.org/10.1186/s12920-019-0647-8 SOFTWARE Open Access Genome analysis and knowledge-driven variant interpretation with TGex Dvir Dahary1*, Yaron Golan1, Yaron Mazor1, Ofer Zelig1, Ruth Barshir2, Michal Twik2, Tsippi Iny Stein2, Guy Rosner3,4, Revital Kariv3,4, Fei Chen5, Qiang Zhang5, Yiping Shen5,6,7, Marilyn Safran2, Doron Lancet2* and Simon Fishilevich2* Abstract Background: The clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient’s phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants. Results: We present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform, with remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex’s main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex’s broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards’ recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters. -
Biological Pathways and in Vivo Antitumor Activity Induced by Atiprimod in Myeloma
Leukemia (2007) 21, 2519–2526 & 2007 Nature Publishing Group All rights reserved 0887-6924/07 $30.00 www.nature.com/leu ORIGINAL ARTICLE Biological pathways and in vivo antitumor activity induced by Atiprimod in myeloma P Neri1,2,3, P Tassone1,2,3, M Shammas1, H Yasui2, E Schipani4, RB Batchu1, S Blotta1,2,3, R Prabhala1, L Catley2, M Hamasaki2, T Hideshima2, D Chauhan2, GS Jacob5, D Picker5, S Venuta3, KC Anderson2 and NC Munshi1,2 1Jerome Lipper Multiple Myeloma Center, Department of Adult Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; 2Boston VA Healthcare System, Department of Medicine, Harvard Medical School, MA, USA; 3Department of Experimental and Clinical Medicine, University of ‘Magna Græcia’ and Cancer Center, Catanzaro, Italy; 4Endocrine Unit, Massachusetts General Hospital, Boston, MA, USA and 5Callisto Pharmaceuticals Inc., New York, NY, USA Atiprimod (Atip) is a novel oral agent with anti-inflammatory tion.7,8 Atip inhibits the inflammatory response and preserves properties. Although its in vitro activity and effects on signaling bone integrity in murine models of rheumatoid arthritis (RA),9–12 in multiple myeloma (MM) have been previously reported, here targets macrophages, inhibits phospholipase A and C in rat we investigated its molecular and in vivo effects in MM. Gene 13,14 expression analysis of MM cells identified downregulation of alveolar macrophages and exhibits antiproliferative and 15–17 genes involved in adhesion, cell-signaling, cell cycle and bone antiangiogenic activities in human cancer models. Impor- morphogenetic protein (BMP) pathways and upregulation of tantly, we have previously reported that Atip inhibits MM cell genes implicated in apoptosis and bone development, follow- growth, induces caspase-mediated apoptosis, blocks the phos- ing Atip treatment. -
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, -
Synthetic Lethal Screen Demonstrates That a JAK2 Inhibitor Suppresses a BCL6 Dependent IL10RA/JAK2/STAT3 Pathway in High Grade B-Cell Lymphoma
BCL6 suppresses an IL10RA/JAK2/STAT3 pathway Synthetic lethal screen demonstrates that a JAK2 inhibitor suppresses a BCL6 dependent IL10RA/JAK2/STAT3 pathway in high grade B-cell lymphoma. Daniel Beck1,6, Jenny Zobel3,6, Ruth Barber1,2,6, Sian Evans1, Larissa Lezina1, Rebecca L. Allchin1, Matthew Blades4, Richard Elliott5, Christopher J. Lord5, Alan Ashworth5, Andrew C.G. Porter3, Simon D. Wagner1 1Department of Cancer Studies, Ernest and Helen Scott Haematology Research Institute and, 2 Leicester Diagnostic and Drug Development (LD3) Centre, University of Leicester, Lancaster Road, Leicester LE1 7HB, UK, 3Department of Haematology, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK. 4Bioinformatics and Biostatistics Analysis Support Hub (B/BASH), University of Leicester, Lancaster Road, Leicester LE1 9HN and 5The Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK. 6The first three authors contributed equally to this work Running title: BCL6 suppresses an IL10RA/JAK2/STAT3 pathway. To whom correspondence should be addressed: Simon D. Wagner, Department of Cancer Studies, Room 104, Hodgkin Building, University of Leicester, Lancaster Road, Leicester LE1 7HB, UK. Tel: 0441162525584, Fax: 0441162525616, Email: [email protected] Keywords: cancer therapy, Janus kinase (JAK), lymphocyte, lymphoma, transcription factor, B-cell lymphoma 6 (BCL-6), synthetic lethal screen. ABSTRACT which shows higher levels of IL10RA, JAK2 and We demonstrate the usefulness of synthetic lethal STAT3 but lower levels of BCL6 than GC- screening of a conditionally BCL6 deficient DLBCL and might be usefully combined with Burkitt lymphoma cell line, DG75-AB7, with a novel approaches such as inhibition of IL10RA.